1st iima international conference on advanced data analysis, business analytics and intelligence 6-7...

ICADABAI 2009 – Abstracts

2

TABLE OF CONTENTS

SCHEDULE –ICADABAI 2009 .................................................................................................................. 7

BUSINESS ANALYTICS – A TIME FOR INTROSPECTION ............................................................ 16

GEOMETRIC CONVERGENCE OF THE HAAR PX-DA ALGORITHM FOR THE BAYESIAN

MULTIVARIATE REGRESSION MODEL WITH STUDENT T ERRORS ...................................... 18

MULTI-TREATMENT LOCATION-INVARIANT OPTIMAL RESPONSE-ADAPTIVE DESIGNS

FOR CONTINUOUS RESPONSES ......................................................................................................... 19

STATISTICAL ISSUES WITH SURROGATE ENDPOINTS TO ESTIMATE THE DIFFERENCE

OF TREATMENT EFFECTS................................................................................................................... 20

CONDITIONAL INFERENCES AND LARGE SAMPLE TESTS FOR INTENSITY

PARAMETERS IN POWER LAW PROCESS....................................................................................... 21

STOCK PRICE AND MACROECONOMIC INDICATORS IN INDIA: EVIDENCE FROM

CAUSALITY AND COINTEGRATION ANALYSIS ............................................................................ 22

STOCK PRICE RETURN DISTRIBUTION: NON-GAUSSIAN VS. GAUSSIAN- AN EMPIRICAL

EXAMINATION ........................................................................................................................................ 23

SKEW-ELLIPTICALITY IN HEDGE FUND RETURNS: WHICH IS THE BEST FIT

DISTRIBUTION? ...................................................................................................................................... 24

A CASE STUDY - TO PRIORITIZE THE INFORMATION MANAGEMENT REGISTER (IMR)

ISSUES USES ∆RWA (RISK WEIGHTED ASSETS) APPROACH .................................................... 25

CLOSENESS BETWEEN HEURISTIC AND OPTIMUM SELECTIONS OF PORTFOLIO: AN

EMPIRICAL ANALYSIS.......................................................................................................................... 26

DECISION ANALYTICS: THE CHALLENGE OF LEVERAGING THE TRANSDUCTION OF

PROCESSES............................................................................................................................................... 27

CLUSTERING OF INFLAMMATORY SKIN DISEASE PATIENTS USING LATENT CLASS

ANALYSIS.................................................................................................................................................. 28

IMPROVING MAXIMUM MARGIN CLUSTERING THROUGH SPAN OF SUPPORT VECTORS

MINIMIZATION ....................................................................................................................................... 29

PROBABILISTIC IDENTIFICATION OF DEFECTS IN AN INDUSTRIAL PROCESS USING

LEVEL CROSSING TECHNIQUES ....................................................................................................... 30

ON BUILDING INFORMATION WAREHOUSES............................................................................... 31

A GENERALIZED FRAMEWORK FOR ESTIMATING CUSTOMER LIFETIME VALUE WHEN

CUSTOMER LIFETIMES ARE NOT OBSERVED .............................................................................. 32

A SEGMENTATION APPROACH USING CUSTOMER LIFETIME VALUE: INSIGHTS FOR

CUSTOMER RELATIONSHIP MANAGEMENT ................................................................................ 33

DOUBLE JEOPARDY DIAGNOSTICS: A TOOL TO UNDERSTAND MARKET DYNAMICS ... 34

COMPELLING SIGNALS: COMPETITIVE POSITIONING RESPONSES TO SERVICE MARK

FILINGS ..................................................................................................................................................... 35

USING LISREL FOR STRUCTURAL EQUATION SUB-MODELS .................................................. 36


3

COVERING BASED ROUGH SET APPROACH TO UNCERTAINTY MANAGEMENT IN

DATABASES .............................................................................................................................................. 37

REAL TIME SPIKE DETECTION FROM MICRO ELECTRODE ARRAY RECORDINGS USING

WAVELET DENOISING AND THRESHOLDING............................................................................... 38

MOTIF FINDING USING DNA DATA COMPRESSION .................................................................... 39

AN APPROACH OF SUMMARIZATION OF HINDI TEXT BY EXTRACTION ............................ 40

FORMAL MODELING OF DIGITAL RIGHTS MANAGEMENT FOR SUSTAINABLE

DEVELOPMENT OF E-COMMERCE................................................................................................... 41

RECOVERY RATE MODELING FOR CONSUMER LOAN PORTFOLIO..................................... 42

THE PROACTIVE PRICING MODEL- USING FORECASTED PRICE ESCALATION

FUNCTION................................................................................................................................................. 43

BEHAVIOURAL SEGMENTATION OF CREDIT CARD CUSTOMERS ........................................ 44

PRECISION TARGETING MODELS FOR IMPROVING ROI OF DIRECT MARKETING

INTERVENTIONS..................................................................................................................................... 45

CUSTOMER PURCHASE BEHAVIOUR PREDICTION APPROACH FOR MANAGING THE

CUSTOMER FAVOURITES LIST ON A GROCERY E-COMMERCE PORTAL........................... 46

PRODUCT INVENTORY MANAGEMENT AT BPCL & EFFECTIVE AND EFFICIENT

DISTRIBUTION OF PRODUCTS TO DEMAND CENTERS.............................................................. 47

INDIAN MUTUAL FUNDS PERFORMANCE: 1999-2008................................................................... 48

HOUSEHOLD MEAT DEMAND IN INDIA – A SYSTEMS APPROACH USING MICRO LEVEL

DATA........................................................................................................................................................... 49

THE LEAD-LAG RELATIONSHIP BETWEEN NIFTY SPOT AND NIFTY FUTURES: AN

INTRADAY ANALYSIS ........................................................................................................................... 50

CAN ETF ARBITRAGE BE EXTENDED TO SECTOR TRADING? ................................................ 51

DEVELOPMENT OF EMOTIONAL LABOUR SCALE IN INDIAN CONTEXT ............................ 52

WOMEN IN SMALL BUSINESSES: A STUDY OF ENTREPRENEURIAL ISSUES ...................... 53

EMPLOYEES PERCEPTION OF THE FACTORS INFLUENCING TRAINING

EFFECTIVENESS ..................................................................................................................................... 54

ONE SHOE DOESN’T FIT ALL: AN INVESTIGATION INTO THE PROCESSES THAT LEAD

TO SUCCESS IN DIFFERENT TYPES OF ENTREPRENEURS........................................................ 55

USE OF ANALYTICS IN INDIAN ENTERPRISES: A SURVEY ...................................................... 56

USING DATA TO MAKE GOOD MANAGEMENT DECISIONS ...................................................... 57

ENHANCING BUSINESS DECISIONS THROUGH DATA ANALYTICS AND USE OF GIS ....... 58

A BUSINESS APPLICATION.................................................................................................................. 58

TRENDS IN TECHNICAL PROGRESS IN INDIA, 1968 TO 2003 ..................................................... 59

TERRORIST ATTACK & CHANGES IN THE PRICE OF THE UNDERLYING OF INDIAN

DEPOSITORIES ........................................................................................................................................ 60

CO-INTEGRATION OF US & INDIAN STOCK INDEXES................................................................ 61

A COMMON FINANCIAL PERFORMANCE APPRAISAL MODEL FOR EVALUATING

DISTRICT CENTRAL COOPERATIVE BANKS ................................................................................. 62


4

ANALYSIS OF RENDERING TECHNIQUES FOR THE PERCEPTION OF 3D SHAPES ............ 63

MMER: AN ALGORITHM FOR CLUSTERING CATEGORICAL DATA USING ROUGH SET

THEORY..................................................................................................................................................... 64

ROLE OF FORECASTING IN DECISION MAKING SCIENCE ....................................................... 65

BULLWHIP DIMINUTION USING CONTROL ENGINEERING ..................................................... 66

AUTOMATIC DETECTION OF CLUSTERS ....................................................................................... 67

REVENUE MANAGEMENT ................................................................................................................... 68

DATA ANALYSIS USING SAS IN RETAIL SECTOR......................................................................... 69

SEGMENTING THE APPAREL CONSUMERS IN THE ORGANIZED RETAIL MARKET........ 70

THE IMPACT OF PSYCHOGRAPHICS ON THE FOOTWEAR PURCHASE OF YOUTH:

IMPLICATIONS FOR THE MANUFACTURERS TO REPOSITION THEIR PRODUCTS. ......... 71

FACTOR ANALYTICAL APPROACH FOR SITE SELECTION OF RETAIL OUTLET - A CASE

STUDY ........................................................................................................................................................ 72

A STATISTICAL ANALYSIS FOR UNDERSTANDING MOBILE PHONE USAGE PATTERN

AMONG COLLEGE-GOERS IN THE DISTRICT OF KACHCHH, GUJARAT.............................. 73

EXPLORING THE FACTORS AFFECTING THE MIGRATION FROM TRADITIONAL

BANKING CHANNELS TO ALTERNATE BANKING CHANNELS (INTERNET BANKING, ATM) ........................................................................................................................................................... 74

WEATHER BUSINESS IN INDIA – POTENTIAL & CHALLENGES............................................... 75

UNDERSTANDING OF HAPPINESS AMONG INDIAN YOUTH: A QUALITATIVE APPROACH

...................................................................................................................................................................... 76

ANALYTICAL APPROACH FOR CREDIT ASSESSMENT OF MICROFINANCE BORROWERS

...................................................................................................................................................................... 77

DATA MINING & BUSINESS INTELLIGENCE IN HEALTHCARE ............................................... 78

BUSINESS INTELLIGENCE IN CUSTOMER RELATIONSHIP MANAGEMENT, A SYNERGY

FOR THE RETAIL BANKING INDUSTRY .......................................................................................... 79

‘COMPETITIVE INTELLIGENCE’ IN PRICING ANALYTICS....................................................... 81

RETAIL ANALYTICS AND ‘LIFESTYLE NEEDS’ SEGMENTATIONS ........................................ 82

REVENUE/PROFIT MANAGEMENT IN POWER STATIONS BY MERIT ORDER OPERATION

...................................................................................................................................................................... 83

HOW TO HANDLE MULTIPLE UNSYSTEMATIC SHOCKS TO A TIME SERIES

FORECASTING SYSTEM - AN APPLICATION TO RETAIL SALES FORECASTING ............... 84

A MODEL USING SCIENTIFIC METHOD TO CUT DOWN COSTS BY EFFICIENT DESIGN OF

SUPPLY CHAIN IN POWER SECTOR ................................................................................................. 85

CLUSTERING AS A BUSINESS INTELLIGENCE TOOL ................................................................. 86

VALIDATING SERVICE CONVENIENCE SCALE AND PROFILING CUSTOMERS: A STUDY

IN THE INDIAN RETAIL CONTEXT.................................................................................................... 87

A MODEL FOR CLASSIFICATION AND PRIORITIZATION OF CUSTOMER

REQUIREMENTS IN THE VALUE CHAIN OF INSURANCE INDUSTRY..................................... 88


5

ON THE FOLLY OF REWARDING WITHOUT MEASURING: A CASE STUDY ON

PERFORMANCE APPRAISAL OF SALES OFFICERS AND SALES MANAGERS IN A

PHARMACEUTICAL COMPANY ......................................................................................................... 89

THE FORMAT OR THE STORE. HOW BUYERS MAKE THEIR CHOICE?................................. 90

CONSUMER INVOLVEMENT FOR DURABLE AND NON DURABLE PRODUCT: KEY

INDICATORS AND IT’S IMPACT ......................................................................................................... 91

DEVELOPMENT OF UTILITY FUNCTION FOR LIFE INSURANCE BUYERS IN THE INDIAN

MARKET.................................................................................................................................................... 92

A RIDIT APPROACH TO EVALUATE THE VENDOR PERCEPTION TOWARDS BIDDING

PROCESS IN A VENDOR-VENDEE RELATIONSHIP....................................................................... 93

LINEAR PROBABILISTIC APPROACH TO FLEET SIZE OPTIMISATION ................................ 94

OPTIMISATION OF MANUFACTURING LEAD TIME IN AN ENGINE VALVE

MANUFACTURING COMPANY USING ECRS TECHNIQUE ......................................................... 95

EFFICIENT DECISIONS USING CREDIT SCORING MODELS...................................................... 96

IMPROVING PREDICTIVE POWER OF BINARY RESPONCE MODEL USING MULTI STEP

LOGISTIC APPROACH........................................................................................................................... 97

NET OPINION IN A BOX ........................................................................................................................ 98

USING INVESTIGATIVE ANALYTICS & MARKET-MIX MODELS FOR BUSINESS RULE &

STRATEGY FORMULATION – A CPG CASE STUDY ...................................................................... 99

IMPROVE DISPATCH CAPACITY OF CENTRAL PHARMACY.................................................. 100

APPLICATION OF NEURAL NETWORKS IN STATISTICAL CONTROL CHARTS FOR

PROCESS QUALITY CONTROL......................................................................................................... 101

MEASUREMENT OF RISK AND IPO UNDERPRICE...................................................................... 102

EFFICIENCY OF MICROFINANCE INSTITUTIONS IN INDIA.................................................... 103

MEASURING EFFICIENCY OF INDIAN RURAL BANKS USING DATA ENVELOPMENT

ANALYSIS................................................................................................................................................ 104

RANKING R&D INSTITUTIONS: A DEA STUDY IN THE INDIAN CONTEXT ......................... 105

A NEW FILTERING APPROACH TO CREDIT RISK...................................................................... 106

VOLATILITY OF EURODOLLAR FUTURES AND GAUSSIAN HJM TERM STRUCTURE

MODELS................................................................................................................................................... 107

WAVELET BASED VOLATILITY CLUSTERING ESTIMATION OF FOREIGN EXCHANGE

RATES....................................................................................................................................................... 108

MODELLING MULTIVARIATE GARCH MODELS WITH R: THE CCGARCH PACKAGE... 109

WIND ENERGY: MODELS AND INFERENCE ................................................................................. 110

FIELD DATA ANALYSIS - A DRIVER FOR BUSINESS INTELLIGENCE AND PROACTIVE

CUSTOMER ORIENTED APPROACH ............................................................................................... 111

SIMPLE ALGORITHMS FOR PEAK DETECTION IN TIME-SERIES ......................................... 112

USING THE DECISION TREE APPROACH FOR SEGMENTATION ANALYSIS – AN

ANALYTICAL OVERVIEW.................................................................................................................. 113

NOVEL BUSINESS APPLICATION - BUSINESS ANALYTICS..................................................... 114


6

SERVICE QUALITY EVALUATION ON OCCUPATIONAL HEALTH IN FISHING SECTOR

USING GREY RELATIONAL ANALYSIS TO LIKERT SCALE SURVEYS ................................. 115

AN EMPIRICAL STUDY ON PERCEPTION OF CONSUMER IN INSURANCE SECTOR........ 116

TWO COMPONENT CUSTOMER RELATIONSHIP MANAGEMENT MODEL FOR HEALTH

CARE SERVICES.................................................................................................................................... 118

AN ANALYTICAL STUDY OF THE EFFECT OF ADVERTISEMENT ON THE CONSUMERS

OF MIDDLE SIZE TOWN ..................................................................................................................... 119

EMPIRICAL FRAMEWORK OF BAYESIAN APPROACH TO PURCHASE INCIDENCE

MODEL..................................................................................................................................................... 121

EXPLORING TEMPORAL ASSOCIATIVE CLASSIFIERS FOR BUSINESS ANALYTICS....... 122

APPLICATION OF ANALYTICAL PROCESS FRAMEWORK FOR OPTIMIZATION OF NEW

PRODUCT LAUNCHES IN CONSUMER PACKAGED GOODS AND RETAIL INDUSTRY ..... 124

THE PREDICTIVE ANALYTICS USING INNOVATIVE DATA MINING APPROACH ............ 125

ON ROUGH APPROXIMATIONS OF CLASSIFICATIONS, REPRESENTATION OF

KNOWLEDGE AND MULTIVALUED LOGIC.................................................................................. 126

SB-ROBUST ESTIMATION OF PARAMETERS OF CIRCULAR NORMAL DISTRIBUTION. 127

BAYESIAN ANALYSIS OF RANK DATA WITH COVARIATES ................................................... 128

SELECTING A STROKE RISK MODEL USING PARALLEL GENETIC ALGORITHM........... 129

LINKING PSYCHOLOGICAL EMPOWERMENT TO WORK-OUTCOMES .............................. 130

TO IDENTIFY THE EMPLOYABILITY SKILLS FOR MANAGERS THROUGH THE CONTENT

ANALYSIS OF THE SELECTED JOB ADVERTISEMENTS........................................................... 131

PERFORMANCE MEASUREMENT IN RELIEF CHAIN: AN INDIAN PERSPECTIVE ............ 132

MACHINE LEARNING APPROACH FOR PREDICTING QUALITY OF COTTON USING

SUPPORT VECTOR MACHINE........................................................................................................... 133

MACHINE LEARNING TECHNIQUES: APPROACH FOR MAPPING OF MHC CLASS

BINDING NONAMERS .......................................................................................................................... 134

THE CLICK CLICK AGREEMENTS –THE LEGAL PERSPECTIVES ......................................... 135


7

Schedule –ICADABAI 2009

1st IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence

6-7, June 2009, Ahmedabad, India

Schedule

6th June 2009

8:00-9:00 Registration

9:00-9:45 Inauguration

9:45-10:30

Dr. Siddhartha Roy, Chief Economist, TATA Group

Key Note Address

11:00 - 13:00 Session 1-1

Vivekananda Roy, James P. Hobert (ic031)

Geometric convergence of the Haar PX-DA algorithm for the Bayesian multivariate regression model with Student t errors

Atanu Biswas, Saumen Mandal (ic230)

Multi-treatment location-invariant optimal response-adaptive designs for continuous responses

Buddhananda Banerjee (ic232) Statistical Issues with Surrogate Endpoints to Estimate the Difference of Treatment Effects

K. Muralidharan (ic233) Conditional inferences on large sample tests for intensity parameters in power law process

11:00 - 13:00 Session 1-2

Rudra P Pradhan (ic143) Stock Price and Macroeconomic Indicators in India: Evidence from Causality and Cointegration Analysis

Kousik Guhathakurta, Santo Bannerjee, Basabi Bhattacharya, A. Roy Chowdhury (ic035)

Stock Price Return distribution: Non-Gaussian vs. Gaussian- an empirical examination


8

Shankar Prawesh, Martin Eling, Debasis Kundu, Luisa Tibiletti (ic025)

Skew Ellipticality in Hedge Fund Returns: Which is the Best Fit Distribution?

Ashif Tadvi, Rakesh D. Raut (ic002)

A Case Study - To Prioritize the Information Management Register (IMR) Issues uses ∆RWA (Risk Weighted Assets) Approach

Dilip Roy, Goutam Mitra, Soma Panja (ic023)

Closeness between Heuristic and Optimum Selections of Portfolio: An Empirical Analysis

11:00 - 13:00 Session 1-3

Vijay Chandru, Nimisha Gupta, Ramesh Hariharan, Anand Janakiraman, R. Prabhakar, Vamsi Veeramachaneni (ic218)

Decision Analytics:The Challenge of Leveraging the Transduction of Processes

Rupesh Khare, Gauri Gupta (ic153)

Clustering of Inflammatory Skin Disease Patients Using Latent Class Analysis

V. Vijaya Saradhi, Girish K. Palshikar (ic060)

Improving Maximum Margin Clustering Through Span of Support Vectors Minimization

Anand Natarajan(ic038) Probabilistic identification of defects in an industrial process using level crossing techniques

Arijit Laha (ic217) On building Information Warehouses

14:00-16:00 Session 2-1

Siddharth S. Singh, Sharad Borle, Dipak C. Jain (ic202)

A Generalized Framework For Estimating Customer Lifetime Value When Customer Lifetimes Are Not Observed

Siddharth S. Singh,P. B. Seetharaman, Dipak C. Jain (ic216)

A Segmentation Approach Using Customer Lifetime Value: Insights for Customer Relationship Management

Cullen Habel, Larry Lockshin (ic214)

Double Jeopardy Diagnostics: A Diagnostic Tool for Market Dynamics

Alka Varma Citrin, Matthew Semadeni (ic212)

Compelling Signals: Competitive Positioning Responses to Service Mark Filings

Pradip Sadarangani, Sridhar Parthasarathy (ic175)

Using LISREL for Structural Equation Sub-Models

14:00-16:00 Session 2-2

B.K.Tripathy, V.M.Patro (ic128)

Covering based Rough set Approach to Uncertainty Management in Databases


9

Nisseem S. Nabar, K. Rajgopal (ic129)

Real time spike detection from Micro Electrode Array Recordings using Wavelet Denoising and Thresholding

Anjali Mohapatra, P.M.Mishra, S.Padhy (ic152)

Motif Finding using DNA Data Compression

Swapnali Pote, L.G.Mallik (ic191)

An Approach of Summarization of Hindi text by Extraction

Shefalika Ghosh Samaddar (ic015)

Formal Modeling of Digital Rights Management for Sustainable Development of e-Commerce

14:00-16:00 Session 2-3

Gyanendra Singh, Tamal Krishna Kuila (ic146)

Recovery Rate Modeling for Consumer Loan Portfolio

Satavisha Mukherjee, Sourabh Datta (ic207)

The Proactive Pricing Model using Forecasted Price Escalation Function

Jyoti Ramakrishnan, Ramasubramanian Sundararajan, Pameet Singh (ic201)

Behavioural Segmentation Of Credit Card Customers

Santhanakrishnan R, Sivakumar R, Harish Akella, Bimal Horo (ic099)

Precision Targeting Models for improving ROI of Direct Marketing Interventions

Sunit Pahwa, Prasanna Janardhanam, Rajan Manickavasagam (ic070)

Customer Purchase Behaviour Prediction Approach for Managing the Customer Favourites List on a Grocery E-Commerce Portal

V. Ramachandran (ic249)

Product Inventory Management at BPCL & Effective and Efficient Distribution of Products to Demand Centers

16:30-18:00 Session 3-1

Rajkumari Soni (ic148)

Indian mutual Funds Performance: 1999-2008

Astha Agarwalla, Amir Bashir Bazaz, Vinod Ahuja (ic098)

Household Meat Demand in India: A Systems Approach Using Micro Level Data

Priyanka Singh, Brajesh Kumar (ic237)

The Lead-Lag Relationship between Nifty Spot and Nifty Futures: An Intraday Analysis

Ramnik Arora, Utkarsh Upadhyay (ic119)

Can ETF Arbitrage be extended to Sector Trading?

16:30-18:00 Session 3-2

Niharika Gaan (ic177)

Development of emotional labour Scale in Indian context

Anil Kumar (ic062)

Women in Small Businesses: A Study of Entrepreneurial Issues


10

Amitabh Deo Kodwani, Manisha K. (ic226)

Employees Perception of the Factors Influencing Training Effectiveness

Anurag Pant, Sanjay Mishra (ic225)

One Shoe Doesn’t Fit All: An Investigation into the Processes that Lead to Success in Different Types of Entrepreneurs

16:30-18:00 Session 3-3

M. J. Xavier, Anil Srinivasan, Arun Thamizhvanan (ic048)

Use of Analytics in Indian Enterprises – A Survey’

A. Hayter (ic227)

Using Data to Make Good Management Decisions

Abhinandan Jain, Uma V (ic205) Enhancing Business Decisions through Data Analytics and Use of GIS A Business Application

R. Dholakia, Amir B. Bazaz, Prasoon Agrawal, Astha Govil (ic234)

Trends in Technical Progress in India, 1968 – 2003

18:30-20:00 Poster Sessions

18:30-20:00 P-I

Gaurav Agrawal (ic026)

Terrorist Attack & Changes in the Price of the underlying of Indian Depositories

Ankit Goyal, Gunjan Malhotra (ic107)

Co-integration of US & Indian Stock Indexes

A. Oliver Bright (ic163) A common Financial Performance Appraisal Model for Evaluating District Central Cooperative Banks

Vishal Dahiya(ic050)

Analysis of Rendering Techniques for the Perception of 3D shapes

M S Prakash Ch., B. K. Tripathy (ic080)

MMeR: An algorithm for clustering categorical data using Rough Set Theory

Jyoti Verma, Sujata Verma (ic108)

Role of Forecasting in Decision Making Science

Mohit Salviya, Sunil Agrawal (ic131)

Bullwhip Diminution using control engineering

Goyal L.M., Mamta Mittal, Kaushal V.P.Singh, Johari Rahul (ic238)

Automatic Detection of Clusters

Patita Paban Pradhan (ic168) Revenue Management

18:30-20:00 P-II

Shyamal Tanna, Sanjay Shah (ic032)

Data Analysis using SAS in Retail Sector

Bikramjit Rishi(ic042)

Segmenting the Apparel Consumers in the Organized Retail Market


11

V R Uma (ic135)

The Impact of Psychographics on the Footwear Purchase of Youth: Implications for the manufacturers to reposition their products.

Anita Sukhwal, Hamendra Kumar Dangi (ic141)

Factor analytical approach for site selection of retail outlet- A Case Study

Fareed F Khoja, Surbhi Kangad (ic183)

A statistical analysis for Understanding Mobile Phone Usage Pattern among College-Goers in the district of Kachchh, Gujarat.

Amol G, Aswin T, Basant P, Deepak S, Harish D (ic190)

Exploring the Factors Affecting the Migration from Traditional Banking Channels to Alternate Banking Channels (Internet Banking, ATM)

Pratap Sikdar (ic082)

Weather Business in India – Potential & Challenges

Mandeep Dhillon (ic095)

Understanding of Happiness among Indian Youth: A qualitative approach

18:30-20:00 P-III

Keerthi Kumar, M.Pratima (ic086)

Analytical Approach for Credit Assessment of Microfinance Borrowers

Sorabh Sarupria (ic174)

Data Mining & Business Intelligence in Healthcare

Chiranjibi Dipti Ranjan Panda (ic186)

Business Intelligence in Customer Relationship Management, A Synergy for the Retail Banking Industry

Chetna Gupta, Abhishek Ranjan (ic193)

‘Competitive Intelligence’ in Pricing Analytics

Sagar J Kadam, Biren Pandya (ic220)

Retail Analytics and ‘Lifestyle Needs’ Segmentations

E. Nanda Kishore (ic016)

Revenue/Profit Management in Power Stations by Merit Order Operation

Anindo Chakraborty (ic185)

How to handle Multiple Unsystematic Shocks to a Time Series Forecasting System - an application to Retail Sales Forecasting

U.K.Panda, GBRK Prasad, A.R Aryasri (ic200)

A Model using scientific method to cut down costs by efficient design of supply chain in Power Sector

Suresh Veluchamy, Andrew Cardno, Ashok K Singh (ic219)

Clustering as a Business Intelligence Tool

7th June 2009

9:00-11:00 Session 5-1


12

Jayesh P Aagja, Toby Mammen, Amit Saraswat (ic140)

Validating Service Convenience scale and Profiling Customers- a study of Indian retail context

Saroj Datta, Shivani Anand, Sadhan K De (ic188)

A model for Classification and Prioritization of customer requirements in the value chain of Insurance industry

Ramendra Singh, Bhavin Shah (ic244)

On the Folly of Rewarding Without Measuring: A Case Study on Performance Appraisal of Sales Officers and Sales Managers in a Pharmaceutical Company

Sanjeev Tripathi, P. K. Sinha (ic242)

The format or the store? How buyers make their choice.

Sapna Solanki (ic145) Consumer Involvement for Durable and Non Durable Product: Key Indicators and It’s Impact

9:00-11:00 Session 5-2

Goutam Dutta, Sankarshan Basu, Jose John (ic221)

Development of Utility Function for Life Insurance Buyers in the Indian Market

Sreekumar, Ranjit Kumar Das, Rama Krishna Padhi, S.S. Mahapatra (ic081)

A RIDIT Approach to Evaluate the Vendor Perception towards Bidding Process in a Vendor-Vendee Relationship

Ashif Tadvi, Rakesh D. Raut, Prashant Singh (ic013)

Linear Probabilistic Approach to Fleet Size Optimization

T Manikandan, Senthil Kumaran S (ic184)

Optimization of Manufacturing Lead Time in an Engine Valve Manufacturing Company Using ECRS Technique

Srinivas Prakhya, Jayaram Holla, Shrikant Kolhar (ic240)

Competitive Intelligence’ in Pricing Analytics

9:00-11:00 Session 5-3

Sandeep Das, (ic208) Improving predictive power of Binary Response model using Multi Step Logistic Approach

Nimisha Gupta, Vamsi Veeramachaneni, O.M.V. Sucharitha, Ramesh Hariharan, V. Ravichandar, Saroj Sridhar, T. Balaji (ic115)

Net Opinion in a box

Mitul Shah, Jayalakshmi Subramanian, Suyashi Shrivastava, Kunal Krishnan (ic134)

Using Investigative Analytics & Market-Mix Models for Business Rule & Strategy Formulation – A CPG Case Study

Ruhi Khanna, Atik Gupta, Devarati Majumdar, Shubhra Verma (ic064)

Improve Dispatch Capacity of Central Pharmacy


13

Chetan Mahajan, Prakash G. Awate (ic103)

Application of Neural Networks in Statistical Control Charts for Process Quality Control

11:30 - 13:00 Session 6-1

Seshadev Sahoo, Prabina Rajib (ic125)

Measurement of Risk and IPO Underprice

Debdatta Pal (ic239)

Efficiency of Microfinance Institutions in India

Gunjan M Sanjeev(ic187)

Measuring efficiency of Indian Rural Banks using Data envelopment analysis

Santanu Roy(ic228)

Ranking R&D institutions: A DEA study in the Indian context

11:30 - 13:00 Session 6-2

M. K. Ghosh, Vivek S. Borkar, Govindan Rangarajan (ic204)

A New Approach to Credit Risk

Balaji Raman, Vladimir Pozdnyakov (ic077)

Volatility of Eurodollar futures and Gaussian HJM term structure models

A.N. Sekar Iyengar (ic053) Wavelet based volatility clustering estimation of foreign exchange rates

Tomoaki Nakatani (ic066)

Modelling Multivariate GARCH Models with R: The ccgarch Package

11:30 - 13:00 Session 6-3

Abhinanda Sarkar (ic213) Wind energy: models and inference

Prakash Subramonian, Sandeep Baliga, Amarnath Subrahmanya (ic165)

Field data analysis - a driver for business intelligence and proactive customer oriented approach

Girish Keshav Palshikar (ic041)

Simple Algorithms for Peak Detection in Time-Series

Rudra Sarkar(ic206) Using the Decision Tree approach for Segmentation analysis – an analytical overview

Sanjay Bhargava

Novel Business Application- Business Analytics

14:00-16:00 Session 7-1

Gouri Sankar Beriha, B.Patnaik, S.S.Mahapatra (ic106)

Service Quality Evaluation on Occupational Health in Fishing Sector using Grey Relational Analysis to Likert Scale Surveys

Binod Kumar Singh(ic007)

An Empirical study on perception of consumer in insurance sector


14

Hardeep Chahal(ic012) Two Component Customer Relationship Management Model for Health Care Services

Uma V. P. Shrivastava (ic091) An analytical study of the effect of Advertisement on the consumers of middle size towns

Sadia Samar Ali, R. K. Bharadwaj, A. G. Jayakumari (ic222)

Empirical Framework of Bayesian Approach to Purchase Incidence Model

14:00-16:00 Session 7-2

O.P. Vyas, Ranjana Vyas, Vivek Ranga, Anne Gutschmidt (ic022)

Exploring Temporal Associative Classifiers for Business Analytics

Ganeshan Kannabiran, Derick Jose, Shriharsha Imrapur (ic150)

Application of analytical process framework for Optimization of new product Launches in CPG & Retail industry

Jyothi Pillai, Sunita Soni, O.P. Vyas (ic118)

The Predictive Analytics using Innovative Data Mining approach

D Mohanty, B.K.Tripathy, J.Ojha (ic176)

On Rough Approximations of Classifications, Representations of Knowledge and Multivalued Logic

14:00-16:00 Session 7-3

K. C. Mahesh, Arnab K Laha (ic078)

SB-robust estimation of parameters for circular normal distribution

Somak Dutta, Arnab K Laha (ic247)

Bayesian Analysis of Rank Data with Covariates

Ritu Gupta, Siuli Mukhopadhyay (ic055)

Selecting a Stroke Risk Model Using Parallel Genetic Algorithm

16:30-17:30 Session 8-1

Anita Sarkar, Manjari Singh(ic243)

Linking Psychological Empowerment to Work-Outcomes

Mandeep Dhillon(ic093) To Identify the Employability Skills for Managers through the content analysis of the selected Job advertisements

Hamendra Dangi, A.S Narag, Amit Bardhan (ic037)

Performance Measurement in relief chain: An Indian perspective

16:30-17:30 Session 8-2


15

M. Selvanayaki, Vijaya MS (ic127) Machine Learning Approach for Predicting Quality of Cotton using Support Vector Machine

V. S. Gomase, Yash Parekh, Subin Koshy, Siddhesh Lakhan, Archana Khade(ic209)

Machine learning techniques: approach for mapping of MHC class binding nonamers

Rashmi Kumar Agrawal, Sanjeev Prashar (ic059)

The Click Click Agreements- legal perspectives

17:45-18:15 Closing Session


16

Business Analytics – A Time for Introspection

Siddhartha Roy Economic Advisor – Tata Group

At the outset, let me thank the organizers of the 1st IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence for inviting me to deliver the keynote address; one feels both privileged and honoured. These are turbulent times, for someone like me who is usually an indolent user of established quantitative methods, the recent events provide a wakeup call. Discontinuity in the behavioural reaction when income growth, consumption expenditure growth and corporate earnings come hurtling down make us question the adequacy of methodologies in predicting the future. Nothing seems incongruous, housing, durables, FMCG off take collapse yet chocolate sales merrily climb up. For nearly three decades one has been associated with the application of decision analytics in business. Both as a practitioner and a user one has marveled at the developments like the surge in computing power, the progress from simple multivariate techniques to sophisticated data mining, the increasing use of Neural Nets and GA in addressing financial, marketing and advertising response issues, extensive use of simulation for scenario studies. All these are intellectually fascinating and this conference provides a veritable feast of such papers. Yet more often than not one has been dismayed by the incapacitating predictive failures at the major turning points of the economy or asset markets. Our business cycle research and understanding of lead and lag indicators have progressed a lot – yet we are not quite there! We did not predict the timing when we slipped into the current meltdown; nor do we know when we’ll manage to get out of it. Someone said in retrospect everything is obvious, in fact our grand children will seriously question the intellectual sanity of a set of risk management experts who could not predict the last snivel of investment bankers in 2008. For a moment one is not suggesting that when quantitative methods succeed in predicting the outcome it is pure serendipity; nor is one saying that our failings put us on par with a Voodoo practitioner, or an astrologer. However, there is a need for serious introspection. In order to maintain our credibility it is better to avoid the temptation of competing with an aphrodisiac or snake-oil seller. Moving ahead, one possibly has to focus a lot more on the context and the behavioural information captured in the data. For example, in the same product group, the consumer’s sensitivity (elasticity) to a pricing change is very different when the economy gets into disequilibrium. How consumer and investor confidence or the lack of it keep reinforcing each other in the formation of demand cycles often escapes the attention of


17

researcher focused on the micro issue. Then there could be asymmetry in micro behaviour related to pricing and advertising as well as demand ratchets. Their linkage with the changing macro context appears to be more visible in a meltdown phase. Cars, durables, revenue per mobile unit even certain FMCG items seem to get affected. The next question is how do we generalize a research result; there are significant cross cultural and cross country differences in behavioral response functions. A forum like this can certainly be helpful for exchanging research results and experience. However, this also requires a contextual understanding of the socio-economic stages of development, cultural alignments, etc. In other words, the quantitative specialists have to broad base their thinking and welcome experts from other disciplines. Many a times, lack of connectivity with other disciplines become quite brazen. We have excellent simulation models for minimizing enterprise value at risk, but do we really understand how risk and greed react with each other possibly nonlinearly. Similarly, there are other questions, is past a good indicator of the future? How do we incorporate structural discontinuity in our understanding? Retrofitting dummy variable may not be the smartest solution. In physical sciences the development of a cogent theory is at a fairly advanced stage; but knowledge about a behavioural response function is still evolving. Advanced statistical methods and AI can possibly help in this journey which seems to have just begun. However, there is a need to ring-fence inductive logic and hypothesis building from crass empiricism. In a meltdown phase, in volatile markets, the limits of our knowledge tend to get seriously exposed. Finally, there is a career related question, would you rather be an adviser to a gambler rolling a six-faced die or even picking a card from a standard packet or join a day trader facing new outcomes everyday. The meltdown has one good effect. It has exposed the limits of our understanding in delineating the outcomes. May be this softer side of business analytics calls for greater creativity and needs some focus.


18

Geometric convergence of the Haar PX-DA algorithm for the Bayesian multivariate regression model with Student t errors

Vivekananda Roy James P. Hobert Department of Statistics Department of Statistics Iowa State University University of Florida

We consider Bayesian analysis of data from multivariate linear regression models whose errors have a distribution that is a scale mixture of normals. Such models are used to analyze data on financial returns, which are notoriously heavy-tailed. Let π denote the intractable posterior density that results when this regression model is combined with the standard non-informative prior on the unknown regression coefficients and scale matrix of the errors. Roughly speaking, the posterior is proper if and only if n ≥ d + k, where n is the sample size, d is the dimension of the response, and k is number of covariates. We provide a method of making exact draws from π in the special case where n = d + k, and we study Markov chain Monte Carlo (MCMC) algorithms that can be used to explore π when n > d + k. In particular, we show how the Haar PX-DA technology of Hobert and Marchev (2008) can be used to improve upon Liu’s (1996) data augmentation (DA) algorithm. Indeed, the new algorithm that we introduce is theoretically superior to the DA algorithm, yet equivalent to DA in terms of computational complexity. Moreover, we analyze the convergence rates of these MCMC algorithms in the important special case where the regression errors have a Student’s t distribution. We prove that, under conditions on n, d, k, and the degrees of freedom of the t distribution, both algorithms converge at a geometric rate. These convergence rate results are important from a practical standpoint because geometric ergodicity guarantees the existence of central limit theorems which are essential for the calculation of valid asymptotic standard errors for MCMC based estimates. Key words and phrases: Data augmentation algorithm, Drift condition, Markov chain, Minorization condition, Monte Carlo, Robust multivariate regression


19

Multi-treatment location-invariant optimal response-adaptive designs for continuous responses

Atanu Biswas

Applied Statistics Unit, Indian Statistical Institute, [email protected]

Saumen Mandal

Department of Statistics, University of Manitoba, Canada [email protected]

Optimal response-adaptive designs in phase III clinical trial, involving two or more treatments at hand, is of growing interest. Optimal response-adaptive designs were provided by Rosenberger et al. (2001) and Biswas and Mandal (2004) [BM] for binary responses and continuous responses respectively. Zhang and Rosenberger (2006) [ZR] provided another design for normal responses. Biswas, Bhattacharya and Zhang (2007) [BBZ] pointed out some serious drawback of the ZR design. Moreover, all the earlier works of BM, ZR and BBZ suffer seriously if there is any common shift in location to observe the responses. The present paper provides a location invariant design for that purpose, then extends the present approach for more than two treatments. The proposed methods are illustrated using some real data sets.

Key words: Constraints, Ethical allocation, Minimization, Truncated normal distribution, Two parameter exponential family.


20

Statistical Issues with Surrogate Endpoints to Estimate the Difference of Treatment Effects

Buddhananda Banerjee Indian Statistical Institute, Kolkata

[email protected]

Surrogate endpoint is defined as a measure or indicator of a biological process that is obtained sooner, at less cost than a true endpoint of health outcome and is used to make conclusions about the effect of intervention on the true endpoint. Instead of assuming well-known Prentice criterion(1989) here we introduce a new assumption for binary as well as continuous endpoints that only the true endpoint absorbs entire information and so given the true endpoint surrogate is independent of or less influenced by treatment. We have established Principle1 by Begg and Leung (2000) for two-treatment binary response problem. We studied the nature of deviation when surrogate endpoints along with few true endpoints are used to estimate the difference of success probabilities between two treatments and addressed the problem where estimation through surrogate endpoint is not consistent, rather underestimate it. The surrogate end points having very low ``concordance'' probability with true endpoint is also addressed here. For continuous end points we assume that the conditional expectation of surrogate endpoint given the true one is same as the given value of true endpoint and suggest an optimal use of both data to minimize the standard error in estimation. Key words: surrogate endpoint, true endpoint, Prentice criterion, Concordance probability


21

Conditional Inferences and Large Sample Tests for Intensity Parameters in Power Law Process

K. Muralidharan Department of Statistics,

The M. S. University of Baroda

[email protected]

Power law process (PLP) or Weibull process is the simplest point process model applied to repairable systems and reliability growth situations. A repairable systems sometimes called a maintained system is usually characterized by the intensity function )(xλ usually

a time dependant function. Therefore, a test for 00 )(: λλ =xH , a constant intensity

against an increasing or decreasing intensity is very important to assess the presence of trend in the process. The test for trend is also essential for a maintained system working under different environmental conditions as many often the repair policy is decided on the basis of the type of trend present in the model. We investigate some conditional inferences for constructing test statistics for testing trend and study their practical importance from the repair policy point of view. Some numerical computations and example are also studied.

Keywords: Power law process, Reliability growth, Repairable systems, Repair policy


22

Stock Price and Macroeconomic Indicators in India: Evidence from Causality and Cointegration Analysis

Rudra P. Pradhan Vinod Gupta School of Management Indian Institute of Technology Kharagpur [email protected]

The behaviour of stock price has been a recurrent topic in the financial jargon. Stock price is time varying and depends upon its past information, market news and various macroeconomic factors. The paper, however, aims at examining the impact of macroeconomic factors on the stock price by using Bombay Stock Exchange as a case study. The cointegration and vector Error Correction Model (VECM) has been used to ascertain both short run and long run relationships. Monthly data over the period 1994-2005, especially during the globalization era of 1990s, has been taken for the empirical investigation. The findings reveal that stock price and macroeconomic variables (such as stock price, index of industrial production, money supply, inflation and exchange rate) are integrated of order one and an existence of long run equilibrium relationship between them. The VECM finally confirms that the possibility of both short run and long run dynamics between the stock price and macro economic variables. The policy implication of this study is that macroeconomic variables are considered as the policy variable to forecast the stock price in the economy.

Keywords: Stock Price, Macroeconomics variables, VECM


23

Stock Price Return distribution: Non-Gaussian vs. Gaussian- an empirical examination

Kousik Guhathakurta1, Santo Bannerjee2, Basabi Bhattacharya3 and

A. Roy Chowdhury4

1,2Army Institute of Management, Kolkata, 3Department of Economics, Jadavpur University, 4High Energy Physics Division, Department of Physics, Jadavpur University

[email protected];

[email protected];

[email protected];

[email protected]

It has long been challenged that the distributions of empirical returns do not follow the lognormal distribution upon which many celebrated results of finance are based including the Black Scholes Option Pricing model. There have been many alternative approaches to it. To our knowledge, none result in manageable closed form solutions, which is a useful result of the Black and Scholes approach. However, Borland (2002) succeed in obtaining closed form solutions for European options. Their approach is based on a new class of stochastic processes, recently developed within the very active field of Tsallis non-extensive thermo statistics, which allow for statistical feedback as a model of the underlying stock returns. Motivated by this, we simulate two distinct time series based on initial data from NIFTY daily close values. One is based on the classical Gaussian model where stock price follows Geometric Brownian Motion. The other is based on the Non-Gaussian model based on Tsallis distribution as proposed by Borland. Using techniques of Non-linear dynamics we examine the underlying dynamic characteristics of both the simulated time series and compare them with the characteristics of actual data. Our findings give a definite edge to the Non Gaussian Model over the Gaussian one. Keywords: Stock Price Movement, Brownian Motion, Tsallis Distribution, Non Linear Analysis


24

Skew-Ellipticality in Hedge Fund Returns: Which is the Best fit Distribution?

Martin Eling1, Debasis Kundu2, Luisa Tibiletti3, Shankar Prawesh4 1University of St. Gallen, Switzerland : [email protected] 2Indian Institute of Technology, Kanpur : [email protected] 3University of Torino, Italy : [email protected] 4Indian Institute of Technology, Kanpur : [email protected]

To study the nature of financial products it is necessary to model the empirical return data with a proper statistical distribution. In view of deviations from the normal distribution and the heavy tails of empirical densities, studying statistical distributions of financial returns have become imperative. Due to the use of options and leverage, hedge funds are especially prone to non-normality. The aim of this present paper is to model heavy tailed hedge fund returns with the skew elliptical distributions. Specifically, we focus on the skew-normal, skew-t and skew-logistic.

Keywords: Heavy tail distribution, Skew-Elliptical distribution, Goodness of fit.


25

A Case Study - To Prioritize the Information Management Register (IMR) Issues uses ∆RWA (Risk Weighted Assets) Approach

Ashif J. Tadvi1 and Rakesh D. Raut2

NITIE, Mumbai,

[email protected], [email protected]

Strategic Information Management (SIM) department processes to store financial and non-financial data for regulatory and business performance reporting and providing extracted and crucial information to Top Management for making Strategic Decisions related to Wholesale Banking business. The case is mainly based on the Credit Data Warehouse from where the extracted data is used by Basel Capital & Reporting Solution (BCRS) to calculate the Risk Weighted Assets (RWA). To assure the Data Quality in the Data Ware House Basel Capital Reporting System (BCRS) Metrics is used which contain 94 attributes based on 3 principles:-Accuracy, Completeness, and Appropriateness. Complying with BASELII-AIRB (advanced internal rating-based approach) Standards, for Capital Adequacy Ratio, Bank has to keep aside a certain % of its Risk Weighted Assets (RWA) as capital. So the need is to calculate the RWA as accurately as possible. But due to some Data Quality Issues in Ware House which are also logged in Information Management Register(IMR), BCRS metrics is using assumptions to calculate the RWA using RWA Calculation Engine due to which RWA is not calculated accurately. IMR has more than 190 issues. To deal with each and every issue simultaneously is almost impossible due to following reasons: - Low available Resources, Large Number (greater than 190) of issues, Cost Constraint, Time Constraint. Key words: - Strategic Information Management; Information Management Register; Basel Capital & Reporting Solution (BCRS); BASELII-AIRB


26

Closeness between Heuristic and Optimum Selections of Portfolio: An Empirical Analysis

Dilip Roy1, Goutam Mitra2 and Soma Panja3

1Centre for Management Studies, University of Burdwan, West Bengal:

[email protected]

2Department of Business Administration, University of Burdwan, West Bengal:

[email protected]

3Management Institute of Durgapur, Durgapur, West Bengal:

[email protected]

Selection of the optimum portfolio is difficult task to the investors as choice of optimum weight is very difficult. In this paper, we have selected heuristic portfolios based on the investors’ propensity to take risk. For this purpose, two extreme situations have been chosen – risk taker and risk aversive investors. To construct heuristic portfolios, we have calculated portfolio weights heuristically and tried to see whether there is any closeness exists between the optimum portfolio constructed on the basis of traditional method and portfolio constructed on the basis of heuristic method. For demonstration purpose, we have taken Nifty data of 2006 and 2007 and selected a portfolio of 10 securities. After detailed discussion, we have obtained that closeness exists between the optimum portfolio selected traditionally and portfolio selected heuristically. Key words: portfolio return, portfolio risk, optimum portfolio, heuristic portfolio, City Block Distance and Euclidian Distance.


27

Decision Analytics: The Challenge of Leveraging the Transduction of Processes

Vijay Chandru1, Nimisha Gupta, Ramesh Hariharan, Anand Janakiraman, R. Prabhakar, and Vamsi Veeramachaneni Strand Analytics, Bengaluru, India 1Hon. Professor, National Institute for Advanced Studies, Bengaluru: [email protected]

The time has come for India to leverage information technology to accelerate its internal development on several fronts. One important aspect of improved efficiency in the economy, that is within immediate grasp for rapid implementation, is to move towards empirically based decision support by leveraging the databases that are emerging from digitization or “transduction” of complex processes in the economy. The decision sciences have given us a plethora of modeling paradigms. However, advanced training is required for skilled use of these methodologies and the scale at which analytics needs to be effectively applied leaves us with a massive challenge. We need a semi-automated and high content software platform that assembles and homogenizes data pulled from huge repositories of raw, partial and fragmented data, aids the “semi-skilled” user in performing deep analysis with ease in interaction and helps him/her discover preliminary hypotheses that can be handed off to the specialists for deeper modeling and decision support. There is an insufficient pool of trained analysts to cope with the scale and complexity of data spewing out. It is this need that has been called “De-Skilled Decision Analytics” and for which a solution is described in this paper along with a number of case studies. Keywords: Decision support, De-skilling, Software platform, Training


28

Clustering of Inflammatory Skin Disease Patients Using Latent Class Analysis

Rupesh K Khare1 and Gauri Gupta2

1Hewitt Associates, Gurgaon, India: [email protected] 2MICA, Ahmedabad, India: [email protected]

This paper highlights the concept, advantages and application of Latent Class Analysis (LCA). The first section presents a preview of LCA, a clustering technique, to underline the method’s suitability for various research and analytics work. The second section highlights the relevance of LCA in light of the limitations encountered by other frequently used clustering techniques such as K Means and hierarchical clustering. Subsequently, the third section underscores the application of LCA by presenting a real life project executed by the authors while they were working with marketRx, a consulting company in pharmaceutical analytics. Key Words: Clustering, Latent Class Analysis, Latent Gold, Consumer Behavior and Attitudes


29

Improving Maximum Margin Clustering Through Span of Support Vectors Minimization

V. Vijaya Saradhi, Girish K. Palshikar TRDDC, Tata Consultancy Services, Pune, Maharashtra E-mail: [email protected], [email protected]

Maximum margin based clustering has shown to be a promising method. The central idea in the maximum margin clustering (MMC) method is to assign labels (belonging to the set {-1, +1}) to all the N data points such that the resulting label assignment has maximum margin. This convex integer programming problem is cast as a semi definite programming (SDP) formulation by introducing a few relaxations Linli, X., James, N., Bryce L., and Dale S., (2005). Experiments show the superiority of MMC over spectral kernel clustering method, and other clustering methods. In the present work, we aim at improving further the MMC formulation. Our idea is to assign labels to all the N data points such that margin is maximized and the generalization error bound on the support vector machine (SVM) (given in terms of span of support vectors) is minimized simultaneously. Minimizing the span of support vectors is formulated as SDP formulation and is combined with the original MMC formulation which aims at maximizing the margin. The resulting formulation is shown to perform better compared to original MMC on UCI data sets. Key words: Kernel methods, maximum margin, span of support vectors, clustering, unsupervised learning


30

Probabilistic Identification of Defects in an Industrial Process using Level Crossing Techniques

Anand Natarajan Caterpillar India Pvt. Ltd., Engineering Design Center Chennai, India [email protected]

The focus of this paper is to delineate the importance of minimizing variations in process rate as opposed to attempting to minimize process variation alone. Normal distributions are used extensively in industrial settings to derive the probability of defects occurring in processes, using sampled data and applying the central limit theorem of statistics. In this paper, a different approach is taken, whereby defects are described by the number of up-crossings of a prescribed level, set as the specification limit for the output of a process. The number of level crossings is modeled as a Poisson process, over a constant or time varying barrier and an exceedance probability is computed. Modeling defect occurrences using a level crossing approach is shown to be inclusive of deterministic events and tracking of time dependent factors that impact the processes. The paper expands on the principle of level crossings to emphasize that the achievement of 6-Sigma quality levels should be focused on minimizing the variation of the process rate and not the process by itself, as done conventionally. An algorithm based on linear algebra to connect the process rate with the process is developed to enable direct integration into minimization procedures, which provides optimal statistical process control. Key Words: Level crossings, Poisson processes, mean crossing rate, 6-Sigma, probability of exceedance


31

On building Information Warehouses

Arijit Laha Center for Knowledge-driven Information Systems Software Engineering and Technology Labs Infosys Technologies Ltd. Hyderabad [email protected]

One of the most important goals of information management (IM) is supporting the

knowledge workers in performing their works. In this paper we examine issues of relevance, linkage and provenance of information, as accessed and used by the knowledge workers. These are usually not adequately addressed in most of the IT based solutions for IM. Here we propose a non-conventional approach for building information systems for supporting the knowledge workers which addresses these issues. The approach leads to the ideas of building Information Warehouses (IW) and Knowledge work Support Systems (KwSS). Such systems can open up potential for building innovative applications of significant impact, including those capable of helping organizations in implementing processes for double-loop learning.

Keywords: information system, knowledge management, relevance, linkage,

provenance, knowledge work support systems


32

A Generalized Framework for Estimating Customer Lifetime Value When Customer Lifetimes Are Not Observed

Siddharth S. Singh1, Sharad Borle2, and Dipak C. Jain3

1, 2 Jesse H. Jones Graduate School of Management, Rice University, Texas 3 J. L. Kellogg School of Management, Northwestern University, Illinois E-mail: [email protected], [email protected], [email protected]

Measuring customer lifetime value (CLV) in contexts where customer defections are not observed, i.e. noncontractual contexts, has been very challenging for firms. This paper proposes a flexible Markov Chain Monte Carlo (MCMC) based data augmentation framework for forecasting lifetimes and estimating customer lifetime value (CLV) in such contexts. The framework can be used to estimate many different types of CLV models—both existing and new. Models proposed so far for estimating CLV in noncontractual contexts have built-in stringent assumptions with respect to the underlying customer lifetime and purchase behavior. For example, two existing state-of-the-art models for lifetime value estimation in a noncontractual context are the Pareto/NBD and the BG/NBD models. Both of these models are based on fixed underlying assumptions about drivers of CLV that cannot be changed even in situations where the firm believes that these assumptions are violated. The proposed simulation framework—not being a model but an estimation framework—allows the user to use any of the commonly available statistical distributions for the drivers of CLV, and thus the multitude of models that can be estimated using the proposed framework (the Pareto/NBD and the BG/NBD models included) is limited only by the availability of statistical distributions. In addition, the proposed framework allows users to incorporate covariates and correlations across all the drivers of CLV in estimating lifetime values of customers. Key Words: Customer Lifetime Value; Forecasting; Simulation; Data Augmentation; MCMC.


33

A Segmentation Approach Using Customer Lifetime Value: Insights for Customer Relationship Management

Siddharth S. Singh1, P. B. Seetharaman2, Dipak C. Jain3

1, 2 Jesse H. Jones Graduate School of Management, Rice University, Texas 3 J. L. Kellogg School of Management, Northwestern University, Illinois E-mail: [email protected], [email protected], [email protected]

A valuable metric used in Customer Relationship Management (CRM) is Customer Lifetime Value (CLV). We propose a latent class methodology to recover CLV segments in a unique contractual context where customer lifetimes are observed. To our knowledge, this is the first paper that uses the popular latent class segmentation methodology to segment customers based on their lifetime value by jointly considering the key drivers of CLV. Using customer-level data from a membership-based direct marketing company, we estimate a statistical model of three simultaneous behavioral drivers of CLV, i.e., (1) customer lifetime, (2) customer inter-purchase time, and (2) dollar spending, while allowing the model parameters to be heterogeneous across customers along observed and unobserved dimensions. The estimated segment-specific model parameters are used to obtain segment-specific CLV estimates for the firm. We uncover three CLV segments. We find that longer (shorter) lifetime customers have lower (higher) CLV which is contrary to popular wisdom regarding contractual contexts. Further, longer (shorter) lifetime customers also have longer (shorter) inter-purchase times with the company. Lastly, the average dollar spending per purchase is a non-monotonic function of customer lifetimes. Finally, we compare our results to those obtained from another segmentation method used in the extant literature. Key-Words: Customer Lifetime Value (CLV), Customer Relationship Management (CRM), Latent Class Segmentation.


34

Double Jeopardy Diagnostics: A Tool to Understand Market Dynamics

Cullen Habel The University of Adelaide, South Australia [email protected] Larry Lockshin University of South Australia, South Australia [email protected] This paper extends a well established normative model of market behaviour to the analysis of dynamics in repeat purchase (FMCG) markets. Whilst the NBD-Dirichlet is a stochastic market model most commonly associated with stationary markets, we argue that it may also be used to develop sequential snapshots of a market that changes over time. A broad range of observed changes in a market environment can be parsimoniously represented by the changes in just three category parameters of the NBD-Dirichlet. Many of the changes in brand performance are also represented by changes in each brand specific (alpha) parameter from the NBD-Dirichlet. We also harness the double jeopardy (DJ) line as a method of describing these dynamics. A DJ line is an x-y plot of a penetration measure against average purchase frequency for brands in a market. The position and shape of a DJ line can be expected to change as market conditions change. In drawing the theoretical double jeopardy lines for consecutive periods we use an NBD-Dirichlet based functional form. This allows for the meanings of the NBD-Dirichlet parameters to be given a visual dimension and used to develop predictions for brand growth. The NBD-Dirichlet parameters can be interpreted as parameters of category acceptance (K), weight of category purchasing (A), category competition (S) and each brand’s strength - αj for each brand j. From the analysis in this paper, we establish that there are three types of brand growth – balanced, expansive and reductive – and that these correlate to different patterns in parameter changes. We conclude that the infinite array of nonstationary market behaviours can be given some structure through a sound understanding of NBD-Dirichlet parameters, viewed through the lens of the DJ line. Keywords: Dynamics, NBD-Dirichlet, Double Jeopardy, Stochastic Models, DJ Line


35

Compelling Signals: Competitive Positioning Responses to Service Mark Filings

Alka Varma Citrin Georgia Institute of Technology,College of Management Georgia Institute of Technology, Atlanta alka.citrin@mgt,gatech.edu

Matthew Semadeni Department of Management & Entrepreneurship, Kelley School of Business, Indiana University, Bloomington [email protected]

This research examines the characteristics of firm signals that influence whether competitor firms move toward or away from a predecessor firm’s market position. Specifically, we examine if competing firms in the professional service industry follow (or stay away from) the market position defined by a market predecessor’s trademark linked to a service, referred to as a service mark. We predict differential effects for the market-positioning responses of follower firms depending on the interaction of two factors signaled in a firm’s service mark application: (1) existing firm capability to address a market space opportunity and (2) firm commitment to pursue that opportunity. Using a novel combination of text and network analysis, we examine all service mark filings by the top 50 professional service firms from 1989 to 1999. Results indicate that when existing capabilities are signaled as being low, firms attract greater competitive overlap compared to when signaled capabilities are high. The level of commitment signaled by the predecessor firm moderates this relationship. Key Words: market signaling, competitive positioning, service marks, time series analysis


36

Using LISREL for Structural Equation Sub-Models

Pradip Sadarangani IIMB, Bangalore, Karnataka [email protected] Sridhar Parthasarathy IIMB, Bangalore, Karnataka, [email protected] LISREL is a package that is used to perform analysis of covariance structures, also known as Structural Equation Modelling. There are other programs that also perform this type of analyses, of which the best known is EQS. Today however, LISREL is no longer confined to SEM. The latest LISREL for Windows includes other modules for applications like data manipulations, basic statistical analyses, hierarchical linear and non-linear modelling and generalized linear modelling. We address the concerns of a beginner to LISREL and provide normative guidelines for modelling various multivariate techniques like Exploratory Factor Analysis, Confirmatory Factor Analysis, multiple regression, ANOVA/ MANOVA and multiple group analysis. Keywords - Linear Structural Relations, Structural Equations Model, Causal Models


37

Covering Based Rough Set Approach to Uncertainty Management in Databases

B.K.Tripathy V.M.Patro School of Computing Sciences Biju Pattanaik Computer VIT University, Vellore Centre, Berhampur Tamilnadu University,Berhampur, Orissa [email protected] [email protected]

Relational databases were extended by Beaubouef and Petry to introduce rough relational databases, fuzzy rough relational databases and intuitionistic rough relational databases. The introduction of these concepts into the realm of databases, enhanced capabilities of databases by allowing for the management of uncertainty in them. Rough set, due to its versatility can be integrated into an underlying database model, relational or object oriented, and also used in the design and querying of databases. Covering based rough sets provide generality as well as better modeling power to basic rough sets. Also, this new model unifies many other extensions of the basic rough set model. In this article, we introduce the concept of covering based rough relational databases and define basic operations on them. Besides comparison with previous approaches, it is our objective to illustrate the usefulness and versatility of covering based rough sets for uncertainty management in databases. Key words: CB-rough sets, CB-fuzzy rough sets, CB-intuitionistic fuzzy rough sets, CB-rough relational operators, CB-intuitionistic fuzzy rough relational operators.


38

Real Time Spike Detection from Micro Electrode Array Recordings using Wavelet Denoising and Thresholding.

Nisseem S. Nabar* and K. Rajgopal Department of Electrical Engineering, Indian Institute of Science, Bangalore. E-mail: [email protected], [email protected]

Brain Machine Interfaces can be used to restore functions lost through injury or disease. Micro Electrode Arrays are an invasive method of acquiring neural signals which can then be used as control signals. The first requirement for such a use is to extract time-stamped spike trains from the MEA recordings. For use in BMI applications this extraction needs to be real time and computationally less expensive. We propose an algorithm based on wavelet denoising and thresholding of the denoised signal. Wavelets provide localization in both time and frequency domains and the ability to analyze signals at different resolutions. Appropriate thresholding of wavelet coefficients followed by reconstruction provides a less noisy version of the input signal. The algorithm proposed is tested on simulated data whose parameters have been decided from actual MEA recordings. It is found to be real-time and has variable memory requirements which make it ideal for BMI applications. Detection accuracy of 90% with false positives of less than 5% are achieved as compared to detection accuracy of 80% with false positives of 10% shown in literature (Kim and Kim, 2003).

Key words: Time series analysis, signal processing, analysis of biological data.

* Nisseem Nabar is currently pursuing a Post Graduate Diploma in Business Management at the Indian Institute of Management, Ahmedabad.


39

Motif Finding Using DNA Data Compression

Anjali Mohapatra P.M.Mishra S.Padhy IIIT, Bhubaneswar EIC Electricity, Orissa Utkal University [email protected]

The problem of finding motifs in biological sequences has been studied extensively due to its paramount importance. Researchers have taken many different approaches and the progress made in this area is very encouraging. As we move to higher organisms with more complex genomes, more sensitive methods are needed. Despite extensive studies over the past decade, this problem is far from being satisfactorily solved. DNA specific compression algorithms exploit the repetitiveness of bases in DNA sequences. However, compression of DNA sequences is recognized as a tough task and needs much more improvement. In this paper we exploit a compression method based on the fact that the variation of sequences in the same organism is small and finite. We use and extend Generalized Suffix Tree (GST) based compression approach with a proposed scoring method for motif finding problems.

Key Words: DNA, compression, suffix tree, motif, GST.


40

An Approach of Summarization of Hindi text by Extraction

Swapnali Pote1, L.G.Mallik G. H. Raisoni College of Engineering, Nagpur, Maharashtra [email protected]

This paper proposes a method to generate the summary of Hindi textual data by extracting the most relevant sentences from the text. The method is based on the combination of Statistical & Linguistic approach. The growth of the Internet has lead to the ample of the digitally stored information, so it must be filtered and extracted in order to avoid drowning in it. With the growth in Indian economy, very soon the broadband will reach most parts of India and then the non-English (Hindi) speaking user base will outgrow the English-speaking user base in India. Therefore in the coming years the text summarizer in Hindi becomes essential. The summary generated from the text will help readers to learn new facts without reading the whole text. Keywords: Text summarization, Text extraction, Sentence weight, Hindi


41

Formal Modeling of Digital Rights Management for Sustainable Development of e-Commerce Shefalika Ghosh Samaddar Motilal Nehru National Institute of Technology Allahabad [email protected], [email protected], [email protected]

Structured approaches for intellectual property rights management system provide a prescription and guidelines for the process of development; typically requirements are written using natural language without having a formal foundation. Our approach is to provide the advantages of structured approaches and formal methods by capturing requirements using structured approaches and its subsequent transformation into a formal description. This creates a sample Z specification from an Object Role-Rank Model (ORRM) schema. An object role-rank model schema is the end product of a transformation procedure from ORRM to Z by choosing suitable types and variables for a Z specification and predicates that express all the constraints required to model ORRM. The representation in Z preserves ORRM’s concepts in a way that aids validation. An ORRM schema successfully differentiates between object oriented concepts and role-centric dynamic objects. The approach is illustrated by using the modeling the management of copyright in the Internet, known as Digital Rights Management (DRM) - a mechanism to enforce access control over a resource without considering its location. The DRM framework, that lies behind and the whole value chain from creators to end-users based on different roles assumed at different point of time, is achieved transforming the core concepts for creations, rights, actions. The set of actions operating on content using and assuming various roles at different juncture of application domain are the building blocks of the complex copyright domain ensuring interoperability. Rights and action patterns are modeled as role-ranks of actions, and concrete actions are modeled as instances of these role-ranks. If some right or license requires an action, it is required to check for role-rank it assumes and dynamic instance classification through roles they assume. The resulting copyright model framework is flexible enough to model the moral exploitation of content. Keywords: Formal Model, Object Role-Rank Model, IPR Management, Digital Rights Management (DRM), Z Specification of DRM, Object Z.


42

Recovery Rate Modeling for Consumer Loan Portfolio

Tamal Krishna Kuila1 and Gyanendra Singh2

ICICI Bank

Email: [email protected], [email protected] , [email protected]

Basel Accord II allows bank to estimate their risk determinants under the IRB approach. The present study tries to provide an empirical framework for estimating Loss Given Default (LGD) for a retail consumer loan portfolio. LGD is modeled using Hurdle regression model and family of censored regression model. Results indicate ability of the model to estimate LGD with bimodal distribution. Key determinants which affect LGD are total outstanding as proportion of loan size along with loan size and historical payment performance of the consumer. Key words: Credit risk, Hurdle model, Recovery rate, LGD


43

The Proactive Pricing Model- Using Forecasted Price Escalation Function

Satavisha Mukherjee & Sourabh Datta

Analytics,

Genpact India Kolkata

E-mail: [email protected] / [email protected]

Pricing is always a dynamic decision in a market economy. In a not so competitive market, where the price can be set or adjusted within a limit, the pricing decisions are taken not only to protect margin but also to set a strategic position in the market. Proactive pricing moves are always expected to reap better results than following others’ price. In such situations with multiple and fluctuating cost heads, maintaining a steady margin is a challenge in absence of hedging. Again any ad-hoc change in price to account for increase in cost can be a threat to market share from other competitors. This paper proposes a method for proactive price adjustment that addresses, both, the increase in cost and retaining the strategic share in the market and most importantly with a targeted profit. The time series forecasting technique is used to predict the input cost increase. After the price function is designed with the forecasted cost, the concept of elasticity is used to capture market sensitivity of the pricing moves. Then the final adjustment to align the pricing with business profit target gives us the price function.

Key Words: Producer Price Index (PPI), Time Series Modeling, Autoregressive Integrated Moving Average (ARIMA), Arc Elasticity, Sales Segment, Contribution Margin (CM).


44

Behavioural Segmentation of Credit Card Customers

Jyoti Ramakrishnan Ramasubramanian Sundararajan and Pameet Singh Computing & Decision Sciences Lab GE Global Research John F. Welch Technology Centre Bangalore Email: {jyoti.ramakrishnan, ramasubramanian.sundararajan, pameet.singh}@ge.com

Many financial companies consider segmenting their customers based on the way these customers transact with the company, to help them design customized marketing programs for each segment which would help improve customer satisfaction & eventually increase customer profitability. In this paper we describe a way of segmenting credit card customers based on their transactional behaviour with the card company. The segmentation solution has been obtained using a combination of factor analysis, k-means clustering & transition analysis models. Factor analysis was used to obtain key factors from the available set of transactional variables. The factors were found to encapsulate the following characteristics: monetary value, utilization, spending frequency, speed of activation, preference for POS/ATM transactions and propensity to “revolve” (i.e., pay interest on borrowings). The k-means clustering algorithm was used on these factor scores to arrive at the segments. The customers from the study were segmented into 6 groups based on their behavior. Segment dynamics were analyzed and used to come up with recommendations for differentiable marketing treatment to each of these 6 segments to enhance profitability. Keywords: CRM, Data Analysis in Banking and Financial Services, Cluster Analysis


45

Precision Targeting Models for improving ROI of Direct Marketing Interventions

Santhanakrishnan R1, Sivakumar R2, Harish Akella3, Bimal Horo4 Infosys Technologies Limited E-mail: [email protected] , [email protected], [email protected], [email protected]

Activities across the Customer Relationship Management cycle involve evaluating customers / prospects on multiple behavioral dimensions, or on shades of a single dimension. Being able to prioritize shoppers that would not only respond to promotional mailers but deliver higher incremental sales through repeat purchases, over those that just walk into a store once; or mailing prospects that not only sign-up for a credit card but activate and spend, over those that just sign-up, are examples of situations that test the ability to differentiate between seemingly relevant behavior and truly relevant behavior. Conventional approach used by CRM Analytics practitioners to address such situations involves using multiple models to score the customer / prospect base. In testing the conventional multi-model approach and a lesser known alternative that uses a single model with carefully chosen weighting schemes, we find that the alternative approach has the potential to address a wide range of business objectives, virtually eliminate the need to build & maintain multiple models and to have complex selection strategies based on them, and also maintain the business impact & ease of implementation. Keywords: Customer Relationship Management (CRM) Analytics, Statistical Scoring Models, Response/Activation & Incremental Sales Models, Direct Marketing Campaigns


46

Customer Purchase Behaviour Prediction Approach for Managing the Customer Favourites List on a Grocery E-Commerce Portal

Sunit Pahwa Prasanna Janardhanam Rajan Manickavasagam

The approach discussed in this paper uses a machine learning technique to capture the buying patterns of the products bought by a customer over a period of time. It then predicts and generates a list of items which are most likely to be bought by the customer on his next visit to the grocery e-commerce portal of the retailer. Since the prediction is done at a product level and have just two options: (a) Customer will purchase the product, (b) Customer will not purchase the product. This problem is more like classifying a product for a customer in either a likely purchase or a likely non purchase. Being a typical classification problem, we used the Naïve Bayes Classifier to generate a likelihood score of purchase for each item previously purchased by a customer (and for each customer). This likelihood score is then used to rank all the items and generate the favourites list for each customer. This approach was accurate enough to predict about two-third of the baskets of about three-fourth of the total customers. The immediate result of this approach is enhanced online experience for the customers who feel their needs are better understood by the online retailer. Keywords: Data Mining, Predictive Analytics, Bayesian Methods, Data Analysis in Retailing


47

Product Inventory Management at BPCL & Effective and Efficient Distribution of Products to Demand Centers

V. Ramachandran BPCL

The paper highlights the process adopted by BPCL in managing its system inventories of petrol and diesel at about 101 locations (22 terminals, 9 tap off points and 70 depots/demand points)

The inventory management process becomes more challenging especially when product supplies to 101 demand centers are mainly catered to by 3 own refineries, 10 PSU refineries and 2 private refineries, 16 pipeline tap off points by means of different modes of transport like pipelines, rail, road and ship.


48

Indian Mutual Funds Performance: 1999-2008

Rajkumari Soni Department of Accounting and Financial Management, Faculty of Commerce, The Maharaja Sayajirao University of Baroda, Vadodara, Gujarat E-mail: [email protected] Mutual fund is the prominent investment institution today. Mutual fund performance is one of the most frequently studied topics in investment area in many countries. The reason for this popularity is availability of data and the importance of mutual funds as vehicles for investment by for both individuals and institutions. Since mutual funds have become popular, there is a growing importance of research by institutions and academician. The present study examines the past performance of mutual funds as a criterion for investors’ future choices. The study started the analysis by the fund attributes influenced the return. In this paper, hypotheses are based on the fund characteristics i.e. beta, standard deviation, fund size, NAV, fund age, management tenure and expense ratio. The study covers 47 equity mutual fund schemes (with equity option) for which the data is available for the entire study period i.e. from Jan 1999 to Dec 2008. The results indicate that the hypothesized relationship between mutual funds performance and the explanatory variables are generally upheld. The study provides a comprehensive examination of recent Indian mutual funds performance by analyzing the fund returns and fund attributes affecting the funds performance and an effort to link performance to funds specific characteristics. Key Words: Mutual funds Performance, Correlation, Regression analysis, Mutual Funds characteristics


49

Household Meat Demand in India – A Systems Approach Using Micro Level Data

Amir Bashir Bazaz1, Astha Agarwalla2 and Vinod Ahuja3

Indian Institute of Management, Ahmedabad, Gujarat

E-mail: [email protected], [email protected], [email protected]

This study presents the results of estimation of a linear approximate almost ideal demand system for Indian meat products, using cross-sectional household level data collected by National Sample Statistics Organization in India as part of the 60th survey in 2004. The paper uses a censored regression method for the system of equations to analyze the consumption patterns for meat products. The Heckman’s two-step procedure was used to estimate the demand system. In the first step, Inverse Mills Ratio(IMR) was estimated using a Probit model. In the second step, IMR was included in the LA/AIDS model as an independent variable, while estimating the system of equations using the Seemingly Unrelated Regression model. The objective of this study is to provide econometric estimates of price and expenditure elasticity estimates for meat demand in India. Some other demographic variables influencing the demand for meat products are identified, as Sector (Rural/Urban), Religion, Land Ownership and Size of the Household. The results revealed that the demand for Beef, Pork and Fish is elastic while that for egg and chicken is inelastic. The cross-price elasticity estimates indicated that mutton and beef are substitutes to chicken, whereas, egg and fish are substitutes to each-other. Keywords: Price elasticity, Expenditure elasticity, Meat demand, Censored regression, Consumption pattern


50

The Lead-Lag Relationship between Nifty Spot and Nifty Futures: An Intraday Analysis

Priyanka Singh1 and Brajesh Kumar2 Indian Institute of Management Ahmedabad Ahmedabad, India Email: 1 [email protected], 2 [email protected]

This paper focuses on the price discovery in the Indian stock market by taking the case of Nifty Spot and Futures using five minute prices. The data considered is of two periods: bull and bear market. Vector Error Correction Model is used to examine the lead lag relationship between Nifty spot and futures return. It is found that futures return leads spot return by as much as ten minutes in bull and thirty minutes in bear market. Spot return leads futures return by five minute in bull market and thirty minute in recent bear market. Vector Autoregressive model is used for finding the price discovery in spot and futures volatilities. Futures market lead spot market by as much as twenty minutes in bull market and twenty five in bear market. In conclusion, there is no significant role that Nifty futures is playing in price discovery. Keywords: Granger Causality, Impulse Response, Weak Exogeneity


51

Can ETF Arbitrage be Extended to Sector Trading?

Ramnik Arora, Utkarsh Upadhyay Indian Institute of Technology, Kanpur We design and deploy a trading strategy that mirrors the Exchange Traded Fund (ETF) arbitrage technique for sector trading. Artificial Neural Networks (ANNs) are used to capture pricing relationships within a sector using intra-day trade data. The fair price of a target security is learnt by the ANN. Significant deviations of the true price from the computed price (ANN predicted price) are exploited. To facilitate arbitrage, output function of the trained ANN is locally linearly approximated. The strategy has been backtested on intra-day data from September 2005. Results are very promising, with a high percentage of profitable trades. With low average trade durations and ease of computation, this strategy is well suited for algorithmic trading systems. Keywords: ETF Arbitrage; Neural Networks; Sector Trading; Statistical Arbitrage


52

Development of emotional labour Scale in Indian context

Niharika Gaan

IMIS, Bhubaneshwar.

E-mail: [email protected], [email protected]

This study describes the development and validation of emotional labour scale (ELS) as tested on samples of 491 respondents from B-schools of India. The ELS is a 12-item self- reporting questionnaire that measures 4 facets of emotional labour in the work place, which includes variety in emotional display, deep acting, surface acting and emotional regulation. Estimates of internal consistency for the subscales ranged from .67 to .89. Confirmatory factor analysis results provided support to the 4 facets of unidimensional subscales emotional labour scale, which contradicts the six facets of existing emotional labour scale. Evidence was also provided for convergent and discriminant validity. Key Words: Deep acting, Surface acting, Automatic regulation, Variety in emotional display, Emotional Labour


53

Women in Small Businesses: A Study of Entrepreneurial Issues

Anil Kumar Haryana School of Business, Guru Jambheshwar University of Science & Technology, Hisar, Haryana E-mail: [email protected] This paper examines entrepreneurial issues of women in small businesses by taking a sample of 120 respondents from the state of Haryana. 26 statements have been administered to women involved in small businesses. Opinion of women on these issues has been taken on five degree likert scale. The factor analytical model has clubbed different entrepreneurial issues of women into nine factors. Motivation related issues can be tackled by imparting training in the management of small enterprises. Problem of handling of finance and marketing of product will also be solved during the training process. Requirements of separate support agencies can be tackled by creating special cells under the charge of women officials within different departments. Infrastructure assisting women in business should be further strengthened. Policy relating to entrepreneurship development should be made more liberal for existing and potential women entrepreneurs. There is a need to redesign the course curriculum to make it more self- employment oriented. Key words: Entrepreneurship, Motivation, Training, Finance, Marketing


54

Employees Perception of the Factors Influencing Training Effectiveness

Amitabh Deo Kodwani Manisha K.

Institute of Management Technology, Ghaziabad. Consultant

Email: [email protected]

Indian Public Sector Enterprises are passing through massive changes due to rapid technological change on one hand and competition from the private sector (especially MNCs) on the other hand. In order to compete in such a liberalized and globalized economy, PSEs are required to improve their organisational effectiveness. These changes necessitate the need of training and development in PSEs for the optimal use of manpower, which will benefit both employees and organization. The need for systematic training and development has also increased because of the rapid technological change, competition that creates new kind of jobs and eliminates old ones. New jobs require some sort of special skills which may be developed in existing work force by providing them necessary training, otherwise employee’s train themselves by trial and error or by observing others if no formal training programme exist in the organization. In this way the employees will take much longer time to learn new skills. Systematic training and development not only increases the skill levels but also increases the versatility and adaptability of employees. With the changing time there is also need for reexamining the existing system of training and development and to look at the training and development policies and practices from new perspective. Organizations need to rethink and modify these training and development policies and practices to get maximum benefit, which is essential for improving organisational effectiveness. Success of training not only depends upon instructor, content, input, training method, but also depends upon the perception of the participants/employees about the training, training awareness, motivation to learn and transfer, learning efforts, training participation & involvement, training transfer climate, and training evaluation. In order to make it more effective, perception of the employees towards training and development need to be made positive. This can be done by involving them in training and development activities, by creating good learning environment and by helping and encouraging them to learn and then practice those learning’s on the job.

Keywords: Learning environment, Organisational effectiveness, Training and development.


55

One Shoe Doesn’t Fit All: An Investigation into the Processes that Lead to Success in Different Types of Entrepreneurs

Anurag Pant School of Business and Economics Indiana University South Bend Email: [email protected] Sanjay Mishra School of Business University of Kansas Email: [email protected]

Some entrepreneurs, who lack the cognitive ability to elaborate on issues, can still be successful. Such ‘naive’ entrepreneurs have a lower need for cognition, a lower recall, and a higher feeling of knowing about a topic than sophisticated entrepreneurs. Consequently, we expect them to be lower risk takers than sophisticated entrepreneurs. On the other hand, naïve entrepreneurs induce higher empathetic support from key business associates and employees. This lets them get more “resources” than sophisticated ones. A better understanding of naïve entrepreneurs could help to reduce new venture failure rates. This paper uses content analysis to measure the constructs and structural equation modeling to show their interrelations. Keywords: Need for Cognition, Risk-taking, Empathy, Successful Entrepreneurs.


56

Use of Analytics in Indian Enterprises: A Survey

M.J.Xavier Kotler-Srinivasan Center for Research in Marketing Great Lakes Institute of Management, Chennai [email protected] Anil Srinivasan Kotler-Srinivasan Center for Research in Marketing Arun Thamizhvanan Great Lakes Institute of Management, Chennai

In 2007, India accounted for one-third of the total $17-billion global market for analytics. However, the rate of adoption of analytics for decision making and enhancing the customer experience has been slow on the uptake. While the term ‘analytics’ has found universal usage in almost all business platforms, what it refers to and the specific contexts in which it ought to be used is still ambiguous among senior managers in the Indian corporate milieu. To uncover the antecedents of these observations, at least in part, we conducted a survey among 84 senior managers across domains, company profiles and regions across the country. We find that an effective understanding of analytics as a decision craft tool grows with time and experience for most individuals, and the prevalence of more heuristic-based decisionmaking is still in vogue. Further, only companies of a certain size (turnover of Rs 500 Crore or more) make a concerted effort to maintain and update data necessary for efficacious use of analytics, and place this high on their priorities. Further, many ambiguities regarding the definition and scope of analytics were observed. The paper discusses these findings in detail and concludes with a brief discussion on the steps ahead. Keywords: Rate of Adoption, Corporate Milieu, Ambiguities


57

Using Data to Make Good Management Decisions

Anthony Hayter, Department of Statistics and Operations Technology University of Denver Denver, Colorado, USA

Some thoughts and perspectives are provided on quantitative courses in the business school curriculum and the challenges of motivating and equipping managers with appropriate statistical techniques and skills. Experiences gained from teaching in the business school environment and from consulting with companies will be presented. Some case studies from the USA and Asia will be provided that illustrate how data analysis has been employed to better understand business situations, and to provide the basis for better decision making. Are businesses today making efficient use of the data they have available, and would they be surprised by their own data? Generally, the curriculum of a quantitative course in a business school would address the following goals.

• Develop an understanding of the basic concepts of probability and statistics, and how they relate to managerial type problems and decision making.

• Develop experience performing and interpreting standard data analysis methodologies.

• Obtain familiarity with a statistical software package.

However, a crucial aspect of this education is the motivation of students of the value of this material to their businesses. Some Golden Rules will be discussed which provide the foundation for this motivation. Additionally, the pitfalls and dangers of a lack of appreciation of the complexities of probability theory are presented. Case studies are a wonderful tool for motivating students. Case studies from various countries and industrial sectors are presented that illustrate how data analysis techniques can be applied to real problems and how they can have an impact on the company’s financial bottom line.

Keywords: Case studies, Decision making, Quantitative courses in business schools,


58

Enhancing Business Decisions through Data Analytics and Use of GIS

A Business Application

Uma V

Datafix Technologies Pvt. Ltd., Mumbai

Abhinandan Jain,

Indian Institute of Management, Ahmedabad

This paper presents the application of data analytics and spatial analysis (GIS) in one circle of a leading Indian telecom service organisation (Bharti Airtel: BA). BA was facing the problem of increasing bad debts and collection costs in one of the circles. BA turned to a data analytics organisation (Datafix Technologies Pvt Ltd1 ) for resolving the issues by using a data based approach. The available data included filled up customer application forms and company’s collection points. The customer data, like name, address, etc., was of poor quality. The methodology included (i) identifying relevant variables, (ii) splitting/ exploding the data fields and deriving new variables (iii) deriving linkages to identify unique customers and their relationships, (iv) using spatial analysis to study and link customers and collection centers. Paper uses, and shares the rationale of choosing the, specific tools in the Indian context. The application helped BA in consolidating billing, reducing billing costs, identifying spatial pockets of higher defaults, identifying corporate clients for building relationships, and possibility of optimizing the location of collection centers. The paper shares efforts to generate emotional touch points from text data in Indian context.

Key words: data parsing, data enrichment, data linkages, spatial analysis


59

Trends in Technical Progress in India, 1968 to 2003

Ravindra H. Dholakia1, Astha Agarwalla2, Amir Bashir Bazaz3 & Prasoon Agarwal4 Indian Institute of Management, Ahmedabad, Gujarat.

E-mail: [email protected], [email protected], [email protected], [email protected]

The paper is based on the Input – Output (I-O) tables for the Indian economy for the eight years covering a period of 36 years from 1968-69 to 2003-04. The technical progress (TP) in the context of the I-O tables is based on the concept of the production function defining the relationship between gross output and material inputs as well as value added. Moreover, it is also at the disaggregated sectoral level. The paper empirically verifies the following hypotheses: (i) Indian economy experienced substantial TP continuously through out the period; (ii) The rate of TP during the inward looking and outward looking growth strategy phases of the Indian economy remained the same; (iii) The rate of TP at the disaggregated sectoral level is almost uniform over time; and (iv) Liberalization and globalization have not impacted sectoral rates of TP differentially. In order to measure the rate of TP, the available eight national I-O tables in India are first made compatible for the number, scope and definitions of sectors as well as for prices by converting them at constant 1993-94 prices. Simple measures are also used for converting changes in technical coefficients into the aggregate rate of TP for a sector and for the economy. Keywords: Input-Output (I-O), Technical Progress, Technical coefficients, Indian economy, Liberalization, Globalization


60

Terrorist Attack & Changes in the Price of the underlying of Indian Depositories

Gaurav Agrawal Atal Bihari Vajpayee - Indian Institute of Information Technology & Management (ABV - IIITM), Gwalior

This research paper empirically examine the impact on the stock returns of the underlying domestic shares of the Indian companies’ listed ADRs / GDRs issues in NYSE, NASDAQ and LSE of the terrorist attack at London’s Public Transport System on 7th July 2005. An event study was conducted on the stock returns of the underlying domestic shares of the 08 Indian ADRs listed in NYSE/NASDAQ and 07 GDRs listed in LSE. For the study 07th July 2005 was considered the event day. The Abnormal Returns (ARs), Average Abnormal Returns (AARs) and Cumulative Average Abnormal Returns (CAARs) were computed based on the Market model using daily closing price data of the underlying companies and S&P CNX Nifty. The behavior of these variables was examined for 15 days before and 15 days after the event day. The study found that the impact of the announcement on the event day was insignificant for the all baskets of underlying domestic shares of Indian ADRs/GDRs listed in NYSE/NASDAQ/LSE. However during the event window of 31 days (i.e. -15 to +15) AARs and CAARs were negative on most of the days for all the baskets of ADRs / GDRs, that clearly indicated that announcements possess important information which leads changes in the underlying stock prices. Therefore study concluded that the terrorist attack hold important information to the baskets of underlying domestic shares of Indian ADRs / GDRs. Further the trend of CAARs that declined continuously even several days after the event day indicated slow assimilation of information to the stock prices that concluded that Indian stock market was inefficient in the semi strong form of Efficient Market Hypothesis (EMH) during the study period. Key Words: Terrorist Attack, Event Study, Average Abnormal Returns (AARs), Efficient Market Hypothesis (EMH), ADRs/GDRs


61

Co-integration of US & Indian Stock Indexes

Gunjan Malhotra & Ankit Goyal Institute of Management Technology , Ghaziabad

One of the most profound phenomenon prevailing in the present financial markets is the increase in international financial transactions across the world. With the advent of liberalization, globalization and advances in information technology, this process has gained much momentum resulting in a progressive integration of the emerging markets with the developed markets. In line with the global trend, the present paper empirically investigates the long-run equilibrium relationship between the US and Indian stock market indexes. Econometric tests like test of cointegration, Augmented Dickey-Fuller test for unit roots, and Granger causality test have been employed in the analysis. We conclude that BSE Sensex is highly influenced by Nasdaq Composite Index which reinforces the long run relationship between the two stock markets. Keywords: Interrelationship between Indian and US stock markets, cointegration, unit root test, Granger causality test.


62

A common Financial Performance Appraisal Model for Evaluating District Central Cooperative Banks

A. Oliver Bright, Dept. of MBA, Infant Jesus College of Engineering, Tuticorin, Tamil Nadu. Email: [email protected]

In India there are 31 State Cooperative Banks (SCBs) and 372 District Cooperative Banks (DCCBs) functioning under respective SCB and 97,224 Primary Agricultural Cooperative Banks (PACBs) are functioning under respective DCCB. The DCCBs are formed in each district with a prime objective of uplifting the economically weaker sections, poor agriculturists and to foster savings among them. The Government of India is allotting a huge amount in this sector every year. Among the DCCBs in India 262 DCCBs are operating in profit. Most of them are able to earn only a marginal amount of profit. Only 85 DCCBs struggled to get profit and could declare dividend. Many DCCBs are sustaining loss year after year. Every year the performance of the DCCBs are assessed by awarding marks on 18 selected parameters with a maximum of 800 marks. The State Cooperative Banks have circulated this format to the DCCBs for its evaluation. The current performances appraisal system is incomplete and it does not cover all the essential factors for assessment. Moreover there is no standard format which is universally applicable for all the DCCDs in India to evaluate. The Economic Value Addition (EVA) to the development of people of the respective region such as fostering of savings, generation of direct and indirect employment, self employment, economic growth of economically weaker sections and the poor agriculturists are not included in it. The profit earned and the dividend declared are not given due consideration. The collection, reduction of NPA and deposit mobilization are not given due weightage Considering all the factors an intensive study is made and a fair model for assessing the performance of the DCCBs in India is developed. This model will help for assessing the performance of the DCCBs. This system can be used as a tool for evaluating the relative performance level of DCCBs also. This “BRIGHT” model can be used for evaluating the performance and for assessing the relative position of DCCBs which may be further extended to countries having similar cooperative banking or credit system. Key words: Non-performing Assets (NPA), Economic Value Addition (EVA)


63

Analysis of Rendering Techniques for the Perception of 3D shapes

Vishal Dahiya

IBMR, Ahmedabad

Human vision system starts with just the shower of photons that hit the retina of each eye and proceeds to construct a 2D contours and 3D shape by consulting various sources of information such as shading, texture, motion, occlusion, binocular disparity. In this process it uses many law which is based on reflectance, geometry, projection and lighting. An image is perceptually realistic if a viewer of the image synthesizes a mental image similar to that synthesized by the virtual viewer. The human visual process synthesizes many different signals into an internal mental image. Normally the input to this process is the light coming from the various surfaces in the scene. 3D shape visualization is usually done on the 2D screens. The algorithm and techniques involved in the process of generating 2D image from a 3D world coordinate system is basically known as Rendering. Lighting Models, Shading techniques, the presence of textures and the properties of 3D shape material provide very different rendering quality. Other important factors that influence the visual quality of a 3D model are Line Of Detail (LOD). The perception of different LODs strongly depends on the selected rendering technique In this research paper, I will explore the factors that influence the perception of a rendered image and also the analysis of rendering technique that are used in this area and their limitation. Key Words- Rendering, perception, techniques, 3D shapes.


64

MMeR: An algorithm for clustering categorical data using Rough Set Theory

B.K.Tripathy and M S Prakash Kumar.Ch School of Computing Sciences VIT University, Vellore, Tamilnadu E-mail: [email protected], [email protected]

So far, in the field of clustering various techniques have been introduced in dealing with categorical data. These algorithms are not able to deal with uncertainty and stability. Our algorithm is using Rough Set Theory (RST), which handles uncertainty from its very basic definition. Though an algorithm named MMR has already introduced RST it needs a few consistency measures to improve the results, in which we worked on. The areas, selection of the splitting attribute and selection of a cluster for re-clustering are improved. In case of re-clustering the cluster with highest average distance is chosen for re-clustering rather than the cluster with highest number of objects, which is done by introducing a criterion for finding the distance between any two objects. This is basically derived from Hamming distance. The results thus obtained are found out by calculating the purity of the clusters. For example, clustering the ZOO data set using MMeR resulted in a purity of 96.5% and 78% is the highest ever achieved till now, by MMR. This algorithm can still be developed by introducing Fuzzy properties. Rough-Fuzzy properties are already defined. Keywords: Data Mining, Clustering, Rough Set Theory, MMR, Hamming Distance


65

Role of Forecasting in Decision Making Science

Jyoti Verma & Sujata Verma

ISB&M Pune

Forecasting comprises of the techniques that predict the future on the basis of probability. This paper is about forecasting for a new product development project; of Hyundai Construction Equipment India Private Ltd (HCEIPL), which ultimately grounded the completion of the feasibility study of the company setting up the plant for the production of excavators. HCEIPL is the wholly owned subsidiary of Hyundai Heavy Industries (HHI) (Korea). The main objectives for this study are, First to explore the best forecasting technique for predicting the total cost of HCEIPL and secondly to check the financial feasibility of HCEIPL over the period of five years. Hypothesis of creating a role model has been selected on the basis of cross-sectional analysis, cash flow analysis, and ratio analysis of the competitors in India with HHI (Construction Division).L&T-Komatsu is the role model on the basis of similar financial risk, growth, cash flow characteristics. Estimation of L&T-Komatsu is done for the next five years after that the ratio of forecasted value of expenditure to forecasted value of sales of L&T for the 5 year and with an approximation of 1 or 2 % in the ratio estimate for HCEIPL from 2008 to 2012 has been taken for further calculation. Computation of Net Present Value (NPV) of the project is required to check the feasibility of the project. Quadratic regression is the best forecasting technique for deterministic model. Validation of the model is done by residual analysis (accuracy measures being MAPE, MSD, MAD), & F- statistic (deciding upon the model).Quadratic regression gives us the exact value. As future prediction is not always exact value. So there is the need of confidence level. Double exponential can create the prediction interval. Since in double exponential MSD was coming higher than quadratic regression. Differencing with lag one was required. Double Exponential Smoothing is the best forecasting technique on the basis of residual analysis, f-statistic, and t-statistic for prediction interval. Decision-making is an essential part of the management process. The Net Present value of the project of 230 crores is positive. NPV comes to around 6.38 crores. Project should be accepted. Keywords: Net Present value, double exponential, Quadratic regression, role model.


66

Bullwhip Diminution using control engineering

Sunil Agrawal & Mohit Salviya PDPM IIITDM Jabalpur Email: [email protected], [email protected]

The bullwhip effect is a well known instability phenomenon in supply chains, related to volatility amplification of demand profiles in the upper nodes of the chain. This paper proposes a novel control engineering approach for analyzing the bullwhip effect using an exponential smoothening forecasting model with a simple type 0, 1 and 2 systems representing constant, linear and quadratic demand input respectively. Analyses of bullwhip effect with different demand trends are done using both the statistical control engineering approaches. Using control engineering approach, various techniques are studied for calculating explicitly the associated noise form under the bullwhip effect. Therefore an analysis for minimizing the error for different smoothening factors under constant, linear and quadratic demand input in the studied inventory policies on the bullwhip effect can be studied. Stability analysis of the output signals is done by using Bode plot and root locus plot. Also, the representation of bullwhip in terms of noise transmission and its reduction via matching the bandwidth, with the “Control Engineering Perspective” is done and results are analyzed. Keywords: Bullwhip Effect; Exponential Smoothening; Inventory Policy; Forecasting; Ordering Decisions.


67

Automatic Detection of Clusters

Goyal L.M. Johari Rahul Apeejay College of Engineering Sohna Gurgaon GGSIPU USIT Delhi Mamta Mittal Kaushal V.P.Singh CDED TU Patiala CDED TU Patiala Email: [email protected]

Knowledge discovery is primary goal of data warehousing. Data Mining is one of the steps in knowledge discovery process It is a technique of extracting meaningful information from large databases or data warehouse. Mining can be done by different techniques. Clustering is one of the techniques, which partitions the database in various groups. Its use in data mining is growing very fast. There are different clustering methods but the major focus here is on partitioning based clustering which requires prior information from the outside world of the number of clusters into which the database is to be divided. But today there is requirement of such algorithms that can generate different clusters automatically. The objective here is to propose a new partitioning based clustering algorithm that can generate clusters automatically without any previous knowledge on the user side. Keywords--- KDD, Data mining, partitioning based clustering


68

Revenue Management

Patita Paban Pradhan NIMT Ghaziabad, Delhi [email protected] Revenue Management (RM) is a relatively new field currently receiving much attention of researchers and practitioners and essentially means setting and adjusting prices on a tactical level in order to maximize profit. Clearly, traditional well-known pricing techniques are closely related, however, the new twist is that RM avails itself of sophisticated demand forecasting and pricing that is based on research in many areas such as management science, economics, mathematics and others. Due the availability of a vast amount of data through customer relationship management systems that can be used to calibrate the models, these techniques had a tremendous impact on the airline industry where RM first was applied, and subsequently in other industries such as car rentals, cargo or hotels, . As part of ongoing changes in the industry, companies throughout the entire hospitality spectrum are placing a strong emphasis on implementing major operational changes. Beyond recognizing that meaningful cost reductions must be achieved without compromising safety, capacity and service levels, they are also looking at reducing costs by increasing flexibility and improving asset utilization through an RM strategy. In doing so, they continue to reassess their true core. Keywords: RM, RM & Pricing, RM in Hotel Industry, RM vs. MIS, Problem in Future Research


69

Data Analysis using SAS in Retail Sector

Shyamal Tanna Sanjay Shah Globsyn Business School S V Insti. Comp. Studies & Research

Ahmedabad Kadi

[email protected] [email protected]

This research paper is mainly focused on the analysis of the available data in the area of the retail sector. In this experiment, a store wants to examine its customer base and to understand which of its products tend to be purchased together. It has chosen to conduct a market basket analysis of a sample of its customer base. After this analysis the store can put those items that the customers buy, together. Then there will be more chances that customers buy both of those products. Association rules are used for the market basket analysis. Process flow for this experiment involves; firstly, selecting the input data source node, this data set contains the grocery products purchased by 1,001 customers and from this, twenty possible items are represented in this data set. The next phase of the experiment concentrates on defining the role of the different variables such as customer-id, product-nm etc, from here the associations nodes are configured and then lastly we will attempt to run the model. It is hoped that the resultant data will be in percentage of support that will provide conclusive evidence that certain products should be put together. Key Words: Association node, Market basket analysis, Support


70

Segmenting the Apparel Consumers in the Organized Retail Market

Bikramjit Rishi Institute of Management Technology (IMT) Raj Nagar, Ghaziabad – U.P. Email: [email protected]

Retailing in India has emerged as one of the most dynamic and fast paced industries with several players entering the market. Apparel Retailing in India is gradually inching its way to becoming major contributor in the retailing growth in India. The whole concept of shopping in apparel category has undergone change in terms of format and consumer buying behavior, ushering in a revolution in shopping. This study makes an effort to understand the Indian apparel buyer so that the Indian retailers can devise strategies to fulfill the needs of the buyers in a better way. The study highlights the four segments i.e Modern & Professional, Orthodox, Incautious and perfectionist. The study further entices the researchers in this field to go for more in depth analysis for the better understanding of the Indian apparel buyer. Keywords : Apparel buying, Indian consumer, Cluster analysis.


71

The Impact of Psychographics on the Footwear Purchase of Youth: Implications for the manufacturers to reposition their products.

V.R.UMA Christ University, Bangalore This paper focuses on the influence of psychographics on the footwear purchase of the Indian youth. For the purpose of the study 401 males and 401 females between the age group of 19 to 26 from Bangalore were considered. Cluster analysis revealed that 62% of the male population comprised of the Fashionables, 15% were Economicals and 23% were Independents. In the case of females 6 clusters were formed wherein 6% were Traditionals, 38% were Economicals, 12% were Independents, 3% were Health conscious, 38% were Fashionables and 3% were Economic Fashionables. Separate analysis was done for casuals and formal footwear. The major attributes that were used to measure the preferences include - Footwear should go with the colour of the dress, Standard Colours, Warranty, Durability, Price, Quality, Variety, Elegance, Bargain preferred than fixed price, periodicity of shopping, Convenient location, Amenities, Ambience of the store and courteousness of salesmen. These attributes were listed by the respondents. Data regarding Income, such as monthly income and spendable income was also collected. The study revealed that people belonging to different lifestyles have different preferences irrespective of the income class they were in. Key Words: Cluster analysis, Fashionables, Economical, Formal, Casual


72

Factor analytical approach for site selection of retail outlet - A Case Study

Anita Sukhwal Hamendra Kumar Dangi

Pacific Institute of Technology, Udaipur Faculty of management, Delhi

Rajasthan

[email protected] [email protected]

The growing affluence of India’s consuming class, emergence of new breed of retail entrepreneurs & flood of imported products in the grocery space, has driven the current retail boom. Against this backdrop the purpose of the present paper is to –

• Evaluate the factors affecting the choice of Retail outlets by consumers. • Existing literature • Seek opinion of Retail Customers • Provide useful suggestions for Improvement • Use of statistical tools of Factor and Regression Analysis .

Research & Methodology

1.1 Area under the study - Mumbai, Delhi 1.2 Research Design - Exploratory

Age range -20-30

Data Sources - Primary, Secondary

1.3 Sampling frame - Convenience

Sample units -120.

2.0 Analysis of Results

1. Respondents profile, Market in Retail sectors, Products. 2. Review of literature 3. Analysis of 20 variables 4. Regression Analysis 5. Recommendations

3.0 Findings

1. Companies should try and improvise on the services 2. Location of the outlet should be strategically designed 3. Engagement of Consumers to curb billing time 4. Loyalty schemes to be emphasized 5. Exclusive brands corner to be displayed In nutshell the retail outlets should focus on this complete experience so as build a strong customer base.


73

A Statistical Analysis for Understanding Mobile Phone Usage Pattern among College-Goers in the District of Kachchh, Gujarat.

Fareed F Khoja & Surbhi Kangad

SRK INSTITUTE OF MANAGEMENT AND COMPUTER EDUCATION, GANDHIDHAM

Kachh, Gujarat

E-mail: [email protected]

India is one of the fastest growing telecommunication markets In the world. It is the youth which is the real growth driver of the telecom industry in India. Considering this fact, the present paper is an attempt to give a snapshot of how frequently young people use their mobile phones for several embodied functions of the cell phones. Data was collected from a sample of 208 mobile phone owners, aged between 20 and 29. The study sheds light on how gender, monthly voucher amount and years of owning mobile phones Influence the usage pattern of this device. The findings show that there is a significant difference in the usage pattern of mobile phones because of these three variables. Findings of the study would be helpful for the telecom service providers and handset manufacturers to formulate a marketing strategy for different market segments. This paper of ours tries to use the statistical tools for understanding the consumer behavior for formulating the marketing strategy.

The paper throws light on mobile phone consumption pattern among college-goers.

Understanding youngsters as one of the market provides a competitive advantage to them. The study reveals how gender, monthly voucher amount and years of owning mobile phones influence the usage pattern of this device. Findings of the study would be helpful for the telecom service providers and handset manufacturers to design a mix of product and promotion, for different market segments. Also, research undertaken in this area helps researchers and scholars understand the individual usage pattern of a new media

Keywords:- Consumption pattern, Statistical Inferences, Statistical Tests – T – Test, F- Test, and Statistical Parameters.


74

Exploring the Factors Affecting the Migration from Traditional Banking Channels to Alternate Banking Channels (Internet Banking, ATM)

Amol G, Aswin T, Basant P, Deepak S & Harish D


In today’s competitive world everyone wants a greater pie in the value derived from providing the consumer needs. The banks have evolved over a period of time from being just a product provider to a becoming a combination of service and product provider. One way of deriving maximum value is by reducing the cost of offering the service. This has lead to the birth of alternate mode of channels for banking. This report tries to looks at some of the factors, which enable and deter the adoption of these alternate channels in the Indian context. A literature survey was done to get an idea of various factors which may affect the adoption of alternate channels. Then in-depth interviews were conducted to get insights regarding the attitude, motivational, and behaviour aspects of the adoption of alternate channels. Factor analysis followed by multi-variate regression lead to the results that factors of benefit awareness, easy accessibility, self-image motivation, ease of instructions, time saving, perception of future substitution and perception of human element have been seen to be very important.

Key words: Factor Analysis, Multi-variate Regression, Consumer Needs


75

Weather Business in India – Potential & Challenges

Pratap Sikdar Express Advisory Services Private Limited (Express Weather) Salt Lake City, Kolkata, West Bengal www.expressgrp.com

In this Business case we have discussed the potential and challenges of Weather business in India. It is evident that even though the usefulness is understood for the different market segments but the effort in improving the service has not reached the satisfactory level because of quality weather data. This is where we emphasized highly in developing quality weather forecasts. Developing quality weather data is a part of the total initiative taken by Express, the other important facet of the initiative lies in the proper packaging and dissemination of the data and service which could be easily got implemented in the existing system of the clients. The Indian market is in its fledging existence, and the perception which the target segments have is not matured upto the desired extent. It’s a cumbersome task for Express to make the target segments understand the benefits of such use of location specific weather forecast information in their operational areas and its application in the decision support system of the client. We have discussed one such successful application case of weather service in the value chain of an agrochemical company which has reaped immense benefits in its marketing value chain.

Keywords: Weather forecasting, Energy weather, Agrochemical, Weather business


76

Understanding of Happiness among Indian Youth: A qualitative approach

Mandeep Dhillon ICFAI National College, Chandigarh

This qualitative study explored what Indian Youth think about Happiness. Eight hundred (800) students wrote free-format essays in response to a simple open ended question, “what is happiness”? All these essays were coded using thematic analysis. Using thematic analysis main themes were found, (1) Happiness is a state of satisfaction, positive feelings and contentment. (2) Happiness is goal achievement and sense of accomplishment. (3)Social capital (i.e. family and friends) is more instrumental in happiness than financial capital. (4)Happiness comes from spiritual enrichment, freedom from ill-being i.e. being healthy. These themes were discussed in the context of Indian philosophical and spiritual views of happiness. Keywords: Subjective well being, Life satisfaction, Indian philosophy


77

Analytical Approach for Credit Assessment of Microfinance Borrowers

Keerthi Kumar and M.Pratima

ICICI Bank

Traditional models of Microfinance which include Group Lending offer substantial scope for one-to-one customer interaction leading to customer credit assessment. However, with larger banks venturing into the sector - a greater focus on sustainable finance was sought and consequently the need for statistical tools for credit assessment was felt. In this direction, this study has been conducted by ICICI Bank with one of its key MFI partners.

The objective of this study was to identify borrower characteristics which distinguish good customers who are bankable.

Cross-sectional data of One Lakh borrowers was collected by the MFI. Multivariate analysis was conducted which led to useful insights. Clients residing in better housing conditions showed lesser probability of default. Older borrowers (by age) were observed to have a higher credit quality. Moreover when the sons of borrowers were in their working age, the repayment performance was superior. Another interesting result was clients residing near Primary Health Centers showed better repayment performance.

However it may be noted that the results hold for the MFI in question and may not be extended to microfinance lending in general.

Keywords: Micro Finance; Group Lending; Credit Assessment; Loan Repayment


78

Data Mining & Business Intelligence in Healthcare

Sorabh Sarupria

Product Practice team, Healthcare and Lifesciences,

Syntel Inc.

Topics Discussed

This paper (poster) discusses data mining and its applications within healthcare in major areas such as the evaluation of treatment effectiveness, management of healthcare, customer relationship management, and the detection of fraud and abuse. The paper (poster) highlights the limitations of data mining and discusses some future directions.

Major conclusions

Treatment effectiveness: Data mining applications can be developed to evaluate the effectiveness of medical treatments. By comparing and contrasting causes, symptoms, and courses of treatments, data mining can deliver an analysis of which courses of action prove effective. Healthcare management: To aid healthcare management, data mining applications can be developed to better identify and track chronic disease states and high-risk patients, design appropriate interventions, and reduce the number of hospital admissions and claims. Customer relationship management: As in the case of commercial organizations, data mining applications can be developed in the healthcare industry to determine the preferences, usage patterns, and current and future needs of individuals to improve their level of satisfaction. Fraud and abuse: Data mining applications that attempt to detect fraud and abuse often establish norms and then identify unusual or abnormal patterns of claims by physicians, laboratories, clinics, or others. Among other things, these applications can highlight inappropriate prescriptions or referrals and fraudulent insurance and medical claims. Keywords: Data mining methodology and techniques, Data mining applications, Predictive modeling


79

Business Intelligence in Customer Relationship Management, A Synergy for the Retail Banking Industry

Chiranjibi Dipti Ranjan Panda

ICICI Bank Ltd. Business Intelligence Unit (BIU)Mumbai

Incorporating customer value strategies into a product-driven business model has always been a predicament for the Retail Banking industry, as has been proven by recent SAS (Statistical Analytical Software) studies. Even ICICI Bank, the second largest private sector bank in India & the pioneer in implementing Business Intelligence & Analytics, has struggled to filter the huge volume of data at hand for their interpretation in the lights of modern day flatter & leaner management to cope up with the ever growing competition. Organizing, enhancing interpretation & communication of the data acquired through Business Intelligence (BI), therefore, become critical for sustaining competitive advantage in a long run. The success of BI initiatives can only be attained by ensuring adequate user involvement, sufficient funding, management support & right choice of technology. Incremental improvement of the existing business models is consequently necessary & for attaining so, three BI models are henceforth suggested for realistic implementation:- BI 4A model:-

� Approach: Monitoring, analytical & predictive intelligence. � Acumen: BI investment alignment with strategic goals. � Assumption: Willingness to identify & solve business problems. � Activation: Pre- & post-launch strategy for successful implementation of BI

process.

Cause & effect model (Imperatives & implications):- Steps Cause (Imperatives) Effect (Implications)

1 Creation of a Customer focused model

Establishment of data structure for customer single view.

2 Having a clear image of customer category

Implementing analytics to support customer segmentation.

3 Accessing the life time value of the customer

Analysis & prediction of risk & profitability

4 Maintaining profitability of each customer relationship

Maximizing cross sell & up sell initiatives

5 Understanding how to attract & retain the customer

Preparation of customer retention model

6 Maximizing ROI on marketing campaigns

Integrated campaign management


80

Process & technology flow model:-

1. Customer information 2. Segmentation 3. The Game plan 4. Identifying the high-end customers 5. Tracking 6. Reaching to the customer 7. Alternate channels 8. Delivery of the product/services 9. Feedback 10. Product/service innovation

Key words: BI Analytics, BI 4A Structure, The Cause & Effect Model


81

‘Competitive Intelligence’ in Pricing Analytics

Chetna Gupta1 and Abhishek Ranjan2

Dell Global Analytics

[email protected], [email protected]

Pricing Analytics is a core activity within any organization. It becomes even more critical in a commodity market where customers are extremely price sensitive. Right pricing gets Market share, Increased Revenue and Profitability to a business. ‘Competitive Intelligence’ (CI) is a critical process by which management assesses the capabilities and behavior of its current and potential competitors to assist in maintaining or developing a “competitive advantage”. Pricing decisions in any Company are based on Competitive Intelligence besides Cost Analytics, Technology Transitions, Product Positioning and Business Strategy. It provides actionable insights on competing products for pricing decisions besides supporting defensive Competitive Intelligence. There are four stages in monitoring competitors - the four "C"s:

• Collecting the information, • Converting information into intelligence by Collating, • Cataloguing, Interpreting and Analyzing it by data visualization, • Communicating the intelligence and countering any adverse competitor actions

Competitive indices often triangulated with P&L line items give valuable insights to improve profitability. They are used in several industry verticals like Retail, Telecom, Airlines etc. helping them in demand shaping, understanding their own product portfolio, competitors’ response to their pricing actions and the implications of change in competitive environment on the different products which the industry offers.

Key Words: Pricing Analytics, Data Visualization, Computational Intelligence


82

Retail Analytics and ‘Lifestyle Needs’ Segmentations

Sagar J Kadam1* and Biren Pandya2 Clear Cell Group, Sampatti, Sardar baug Lane, Alkapuri, Vadodara

1 [email protected] 2 [email protected]

Customer Segmentation is a well known tool used by retailers globally to understand their customers. Clear Cell develop segmentations with retailers in order to create a deep understanding of their customers and then use this to improve decision making focused around their Value Levers. Value levers are the areas within a business which are positively impacted by customer insight: Pricing, Promotions, Store location & Layouts, Assortment and Communication. The basis of the Lifestyle Needs segmentation is that a customer’s lifestyle needs can be defined by their grocery purchases, effectively “you are what you eat.” Even the simplest approach to create needs segmentation requires both sound statistical analysis along with regular inputs from business users. In this paper, we have described the Analytical approach to create a Lifestyle Needs Segmentation. Product association methodology was used to identify trends in the contents of the grocery baskets. Final segments were obtained by using cluster analysis to group segments with similar lifestyle needs but potentially different products purchased. Stability and robustness of final segments were established through different period rollout and observing how customers move between segments from period to period. Keywords: Retail Analytics, Value Levers, ‘Lifestyle Needs’ Segmentation, Cluster analysis, Triangulation process


83

Revenue/Profit Management in Power Stations by Merit Order Operation

E.Nanda Kishore NTPC Ramagundam, Employee Development Center

[email protected]

Application of Classical Linear Regression model in Power station Operations Management is discussed. The structural change in regression (with coal consumption as independent variable and power generation as dependant Variable) is checked. This experiment helped in deciding generation levels during backing down and increasing load to full level. Results:

1. There is structural change in regression at 450mw. The slope of equation is significantly different in both regions (<450mw and >450mw).

2. Hence Coal consumption pattern is significantly different in one region than that of other.

Topics Discussed: � Classical Linear regression model. � Dummy variables Regression model. � Checking for Structural change in Regression. � Other aspects like MAPE etc.

Main Conclusions: Above experiment proved that there is change in behavior of unit at 450mw. This unit load is to be reduced upto 450mw more rapidly during backing down. Other units load can be reduced for further load reduction had they also show similar behavior. If unit load were to be reduced to 425mw and then to be raised with load demand this unit load has to be increased immediately upto 450mw as it can be done with less amount of coal. Key Words: Coal Consumption, Backing down, Structural change, Classical Linear Regression Model.


84

How to handle Multiple Unsystematic Shocks to a Time Series Forecasting System - an application to Retail

Sales Forecasting

Anindo Chakraborty

Target Corporation, India – Bangalore

[email protected]

Ordinarily, time series methodologies like Box-Jenkins or Smoothening methods do a good job when it comes to forecasting sales for a retailer. However for retailers the future sales cannot only be predicted using historical sales in time series. It is hugely affected by various critical factors like a competitor opening a store, cannibalization from sister stores, re-model or relocation of stores just to name a few. These “shocks” to the system are not seasonal and neither do they similarly affect the sales for every store. Market research has revealed that the competitor density, demographics, store type and maturity are the most important factors determining how such “shocks” affect retail sales. The effect of shocks can be estimated using a simple objective segmentation technique like CART. Using CART, with year-over-year sales change as the dependent variable and other factors as independent variables, would create different segments of stores with varying impacts. These impacts measure % change in year over year sales. Some segments would show a highly positive impact whereas some highly negative. When these impacts when applied over the time series forecasts can improve (reduce) the MAPE by 3 to 4 percentage points. Key words: Retail sales forecasting, Classification & Regression Trees, Multiple shocks to time series, Segmentation as a tool to reduce MAPE


85

A Model using scientific method to cut down costs by efficient design of supply chain in Power Sector

U.K.Panda GBRK Prasad A.R Aryasri, APERC, Hyderabad Dr. Reddy Labs, Hyderabad JNTU,Hyderabad

[email protected] [email protected] [email protected]

This paper gives an overview of Supply Chain Operations Reference Model (SCOR), Different Cost Components of tariff and Models that are required to arrive at Tariff Formulation and identify potential areas of the energy utilities companies (Genco, Transco, Discoms) to optimize Costs in Power Sector. Key Words: Tariff, Power purchase model, Sales Module, Revenue Model, Tariff Schedule


86

Clustering as a Business Intelligence Tool

Suresh Veluchamy1, Andrew Cardno2, Ashok K Singh3 1Einksoft Technologies Private Limited, India

2 BIS2 Solutions, San Diego, USA 3UNLV, USA

Large store chains deploy sophisticated forecasting and planning systems based on a clustering or grouping of their stores. Large stores with customer loyalty cards also use customer transaction data for segmentation of customers in order to improve their marketing and increase membership into their loyalty programs. The groupings of stores or customer segments, quite often, are formed by simplistic methods, with clusters formed by all stores (or customers) in a geographic location such as a zip code, or by a ranking of stores by total sales. These clustering approaches typically ignore a large amount of data collected by the store chains. This data is multivariate in nature, with variables representing, for example, amounts sold by individual stores for various product categories. Cluster analysis is a data mining tool that uses the correlation among different variables in a database to form store clusters or customer clusters. Forecasting or planning systems that utilize this added information obtained from statistical clustering will lead to increased potential for growth. In this paper, we describe a method of clustering stores or customers based upon transactions data. We illustrate the method using a simulated example. Keywords: Cluster Analysis, Non-hierarchical, K-means clustering


87

Validating Service Convenience Scale and Profiling Customers: A Study in the Indian Retail Context

Jayesh P Aagja Institute of Management,

Nirma University [email protected]

Toby Mammen Amit Saraswat Marketing Area Marketing Area ICFAI Business School, ICFAI Business School, Ahmedabad Ahmedabad [email protected] [email protected]

Post Liberalization, the Indian economy has seen intense competition due to the entry of new players in most sectors. But in the current recessionary phase which set in 2007, most service organizations were trying to hold their market share than increasing it. The objective of this study is to validate a service convenience scale in the Indian organized food & grocery retail context, and develop linkage between service convenience on one side, and satisfaction / behaviourial intentions on the other. A convenience sample was drawn from SEC A & SEC B from various parts of Ahmedabad city, with experience of shopping from organized retail food & grocery outlets. The samples, drawn in two phases – the first during Jan-March 2008, the second between Feb-March 2009, had 270 and 326 respondents respectively. As in the original scale, through the scale validation process five dimensions emerged though with 15 items instead of the original 17 items (Seiders et al, 2007). Neural networks used for nomological model testing indicated a good model fit. Subsequently, an attempt was made to segment respondents based on their service convenience scores which resulted in four groups for both the datasets. Statistically insignificant differences were observed amongst these clusters based on demographics. Key words: Service Convenience, Validation, Cluster Analysis, Artificial Neural

Networks.


88

A model for Classification and Prioritization of customer requirements in the value chain of Insurance industry

Shivani Anand1, Sadhan K De2, Saroj Datta3

1, 2 Indian Institute of Technology, Kharagpur

3Faculty of Management Studies, Mody Institute of Technology and Science

E-mail: [email protected], [email protected], [email protected]

The purpose of this paper is to study the different enhancements/requirements in the value chain of the Insurance Industry as suggested by the customers and to find a model to classify and prioritize them in order to maximize customer satisfaction. There were three main conclusions drawn from the study. First, there exists a distinctive trend in the requirements which can be differentiated and recognized by careful observation of certain parameters. Second, certain requirements which were highly correlated as these requirements were found to complement each other in their implementations which lead to an exponential increase in customer satisfaction. On similar lines, we also found pairs of negatively related requirements as they would diminish the implementation impact of each other on customer satisfaction. Third, we perceived that similarly classified requirements produced similar customer satisfaction. Primarily, the study elucidated four main categories of requirements. These were process gaps, efficiency, regulatory and breakthrough requirements. This particular work will allow an organization to have better understanding of the demands of their customers and enable it to satisfy user needs within organizational constraints by way of a model to classify and prioritize requirements suggested by customers to maximize customer satisfaction. Keywords: classification and prioritization of requirements, process gaps, K-means clustering, customer satisfaction.


89

On the Folly of Rewarding Without Measuring: A Case Study on Performance Appraisal of Sales Officers and

Sales Managers in a Pharmaceutical Company

Bhavin Shah, B. K. Majumdar Institute of Business Administration

H. L. College Campus, Navrangpura Ahmedabad, Gujarat


Ramendra Singh IIM Ahmedabad

Ahmedabad, Gujarat Email: [email protected]

This case study highlights the performance appraisal measurement issues and challenges for the sales force of a pharmaceutical company, Pharmex (name disguised). We found that Pharmex was measuring too many (41) qualitative (e.g. competencies, skills and job knowledge) aspects for Business Officers (BOs) and Area Business Managers (ABMs), using inconsistent measures. ABMs had a tendency to score their supervisee BOs higher on qualitative aspects, since such skills and competencies were difficult to measure accurately, compared to more specific measures on numerical quotas. Due to this measurement dichotomy between Part A (qualitative) and Part B (numerical/quantitative) little correlation was found between these two parts of the performance appraisal. Multiple regression analysis also suggested that little variance in performance is explained by efforts, activities or qualitative aspects of BOs and ABMs. Such measures which varied with appraisal aspects led to inconsistencies, and therefore are likely to be faulty. Like Pharmex, other organizations too may be measuring performance appraisal of their sales force using unreliable and invalid measures, leading to erroneous managerial decisions about its salesforce’s performance, and its consequent rewards. Efforts should be made by organizations on making performance appraisal measures more scientific, and robust.

Key Words: Correlations, Factor Analysis, Multiple Regressions, Performance Appraisal.


90

The Format or the Store. How Buyers Make their Choice?

Sanjeev Tripathi, P. K. Sinha Indian Institute of Management, Ahmedabad

Gujarat


The literature on store choice has mainly studied the store attributes, and ignored the consumer attributes in store choice. Even when, the consumer attributes have been incorporated the strength of relationship has been weak. Also, the literature on store choice has completely ignored format choice, when studying store choice. The paper argues for incorporating both the shopper attributes in store choice, and the store formats. Shopper attributes can be captured through the demographic variables, as they can be objectively measured, and these also capture a considerable amount of attitudinal and behavioural variables. The paper proposes to link store choice, format choice and consumer demographic variables, through a hierarchical logistic choice model in which the consumers first choose a store format and then a particular store within that format. A nested logit model is developed, and the variables predicting the choice probabilities are identified. The requirement of data for the empirical analysis is specified, the model has not been verified in the absence of empirical data but the operationalization of variables is done. Keywords: Format choice, hierarchical choice model, nested logit, shopper attributes, store attributes, store choice.


91

Consumer Involvement for Durable and Non Durable Product: Key Indicators and It’s Impact

Sapna Solanki Sanghvi Institute of Management & Science

Indore .

E-mail : sapna.solanki594@gmail .com, sapnaa_solanki@rediffmail .com

Involvement refers to how much time, attention, energy and other resources people devote for purchasing or learning about the product or it is one of the fundamental concepts used to explain the consumer buying process. Study finds that how level of consumer involvement for durable and non durable product influence by financial risk, performance risk, physical risk, social risk, time risk, uncertainty in selection, psychological risk, previous Shopping experiences, product attribute, situation, brand personality, hedonic value, motivation, level of learning, utility of the product , price, durability, gift (for whom a product is purchased), life style, store, frequency of use, additional benefits, packaging and endorsement. A self design opinionnaire was framed to find out the various dimensions influencing on consumer involvement. For dependent variable (consumer involvement) Zaichkowsky’s (1985) unidimensional a 20 items bipolar likert scale called Personal- Involvement-Inventory (PII) was adapted. Stepwise regression was used for both durable and non durable product categories. This regression method suggested four models for each product. The best model suggests that level of consumer involvement while purchasing garments is influenced by Previous Shopping Experiences Hedonic Value, Special Offer and Uncertainty and for Laptop models explains Brand Personality, Hedonic Value, Frequency of Use and Durability are the core predictors. Key Word: Consumer Involvement, Level of Learning, Social Risk and Experience.


92

Development of Utility Function for Life Insurance Buyers in the Indian Market

Goutam Dutta Indian Institute of Management

Ahmedabad, Gujarat E-mail : [email protected]

Sankarshan Basu

Indian Institute of Management Bangalore

Jose John

Indian Institute of Management Ahmedabad, Gujarat

Insurance as a financial instrument has been used for a long time. The dramatic increase in competition within the insurance sector (in terms of providers coupled with awareness for the need for insurance) has concomitantly resulted in more policy options being available in the market. The insurance seller needs to know the buyer’s preference for an insurance product accurately. Based on such multi-criterion decision-making, we use a logarithmic goal programming method to develop a linear utility model. The model is then used to develop a ready reckoner for policies that will aid investors in comparing them across various attributes.

Keywords: Goal programming, Multi-criterion decision making, Utility function


93

A RIDIT Approach to Evaluate the Vendor Perception towards Bidding Process in a Vendor-Vendee

Relationship

Sreekumar

Rourkela Institute of Management Studies Rourkela


Ranjit Kumar Das

College of Engineering and Technology Bhubaneswar

Rama Krishna Padhi

National Productivity Council Bhubaneswar

S.S. Mahapatra

Department of Mechanical Engineering National Institute of Technology

Rourkela E-mail: [email protected] ; [email protected]

In today’s competitive business environment where the organisations are competing on effectiveness of their supply chain, a better vendor-vendee relationship can provide an edge to one over other. Critical review of literature shows that many studies are made on vendor evaluation and selection but little study has been devoted to vendor perception and satisfaction. This study aims at evaluating vendor perception towards the vendor-vendee relationship. A ridit approach is used to rank the parameters under one of the dimension viz. bidding process. Organisation can focus more on the top ranked parameters to have the better relationship with the vendor. Key Words: Bidding process, ridit, technical specifications, vendor perception.


94

Linear Probabilistic Approach to Fleet Size Optimisation

Rakesh D. Raut1, Ashif J. Tadvi2, Prashant Singh3

NITIE Mumbai

Email: [email protected], 2 [email protected], [email protected]

As a well-structured and costly activity that pervades industries in both the public and private sector, vehicle fleet management would appear to be an excellent candidate for model-based planning and optimization. And yet, until recently the combinatorial intricacies of vehicle routing and of vehicle scheduling have precluded the widespread use of optimization (exact) methods for this problem class. The objective of paper minimising freight on a day t subjected to mathematical & existing business constraints gives a fleet requirement on a day t. Thus we have fleet requirement for 6 days. Now turnaround time analysis has been done. The weightage turnaround time of quantities send to different destinations comes about to be 2.89 days which has been rounded to three days that means truck dispatched from hub on Monday would be again available on Thursday. Using this analysis, Initial solution is reached which gives the initial fleet mix. Now, existing clubbing zones are identified & a search is made on the last three months dispatch data where clubbing would have been possible & is not being taken care of in the initial model. The initial model would have selected a two smaller vehicles where as the clubbed locations could be served by a single large vehicle. Thus initial solution is modified to attain final fleet mix. The results obtained are validated by examining the dispatch pattern of the last three months by considering the average breakeven volumes (for all destinations) & maximum dispatches occur for the type of truck which is required in maximum quantity & vice-versa. Key words: Minimising freight; Optimization methods; Vehicle Routing; Vehicle Scheduling


95

Optimisation of Manufacturing Lead Time in an Engine Valve Manufacturing Company Using ECRS Technique

Manikandan T.1, Senthil Kumaran S.2

College of Engineering Guindy, Anna University Chennai, Chennai, Tamilnadu.

E-mail: [email protected], 2 [email protected]

There has been a constant need for an efficient and robust scheduling technique to reduce the lead time and increase the productivity. This paper focuses on reducing the Manufacturing Lead Time in a production layout of an engine valve manufacturing company. The layout experiences problems like high inventory, higher setup time and more part travel, etc. A six sigma tool, DMAIC is used to approach the problem. Details with regard to Product mix, Volume of production, Work – In – Progress (WIP), and Sequence of operations were collected. And a simple heuristic technique was proposed to decrease the Manufacturing Lead Time and the approach was validated using WITNESS, a simulation software. The simulation of the proposed heuristic technique in WITNESS software show considerable reduction in Work – In – Progress (WIP) and thereby resulted in monetary benefits. When this proposed heuristic technique was implemented, the situation demanded a lean tool, Eliminate – Combine – Rearrange – Simplify (ECRS). In total, the setup time for a particular process step reduced from the existing 20 hours per month to 6 hours per month.

Keywords: Batch size, Six Sigma tool, WITNESS software, Inventory reduction.


96

Efficient Decisions Using Credit Scoring Models

Jayaram Holla Shrikant Kolhar Srinivas Prakhya

Indian Institute of Management, Bangalore

Information-based strategies have proved to be extremely successful in the credit industry. The first step in deploying such a strategy is the development of a robust credit-scoring model. Scoring approaches provide two primary advantages. The first advantage is in improving profitability by using the predictive power of the model. The second advantage lies in the highly consistent, objective and efficient manner in which credit decisions are made. Modeling is used to accept or reject applications and usually not in the post- disbursement phase. Credit-scoring models can also be used post disbursement to enhance efficiency in allocation of collection resources. Literature has not paid much attention to this aspect. How can posterior beliefs about consumers propensity to default be used to develop a collection strategy? In this paper, an ordered model that classifies applicants into good, marginal and bad categories is developed. A model of repayment behavior is proposed where variation in willingness and ability to repay is explained by individual specific factors and heterogeneity. Category probabilities are derived assuming that the categories are ordinal. The model explains more variation than standard two category models used in the industry. The model estimates are robust as evidenced by results when deployed on a validation sample. The model has the additional benefit of being useful in allocation of collection resources post-disbursement. The model can also be used in conjunction with other information to design cross-selling programs. The final model could be the core for assessing value-at-risk and moving to risk-based pricing. Information-based strategies are knowledge intensive. Firms deploying such strategies are in a learning-by-doing mode, resulting in the accumulation of tacit knowledge that is an inimitable resource. Development of inimitable resources is a key factor in obtaining sustainable competitive advantage. Keywords: Credit Decisions, cross-selling, risk-based pricing.


97

Improving Predictive Power of Binary Responce Model Using Multi Step Logistic Approach

Sandeep Das Analytics, Genpact India

Rajarhat, Kolkata


This paper discusses a methodology called “Multi Step Logistic Regression” to improve the predictive power of the binary logistic regression model in terms of a higher Hit/Miss ratio. A ‘Hit’ is defined as right classification/tagging and a ‘Miss’ is defined as wrong classification obtained from cross tabulation between actual vs. predicted tagging. In this approach, after choosing the final cut logistic model, the model building population is segregated into two parts – predicted 1 and predicted 0 by selecting a cut off on predicted probability distribution. For predicted 1 group, parameter estimates are re-estimated keeping the same variables came significant for initial model. User may choose to introduce new variables in each iteration and keep them in the model as per significance. These steps are iteratively repeated till we get a good cost-benefit cause to stop. The conventional logistic method (single step) doesn’t help to tackle a situation where the proportion of 1 & 0 distinctly different or cost of misallocation is high. To tackle such a situation, we will discuss this alternative approach. This paper targets to improve the concentration in Hit cells with (without) tolerable/regulated (alarming) increase in concentration of misclassification compared to the Single step approach. KEY WORDS: Multistep Logistic, Binary Response Model, Improving predictive power of Probability of Default (PD) model.


98

Net Opinion in a box

Nimisha Gupta*1, Vamsi Veeramachaneni*, O.M.V. Sucharitha*, Ramesh

Hariharan*, V. Ravichandar+, Saroj Sridhar+, T. Balaji+

*Strand Life Sciences

Bangalore

+Feedback Consulting Bangalore


Market research plays an integral role in the product development lifecycle. One of the goals of market research is to understand the likes and dislikes of customers and identify the features that need to be added or enhanced. Traditional market research involving survey design, focus groups and quantitative research can be very expensive. In this paper, we describe, Net Opinion in a Box (NOB), an opinion mining platform that can aid the market research process. Using Natural Language Processing (NLP) technologies, NOB extracts opinions expressed on Web 2.0 platforms like blogs, product forums, and social networking sites. An optional curation module can be used to manually improve the precision of NLP results. The opinions about specific features of products are stored along with relevant meta-data like publication date, author location, product brand, model etc. These sentiments are presented to the end user in an intuitive web based visualization dashboard. The dashboard allows users to apply filtering criteria to examine all aspects of a product, perform a side-by-side comparative analysis of different brands, and study how the opinions about a brand change with time. The interface allows users to drill-down to the actual sentences and provides links to the source site. The entire pipeline from product definition to publishing can be configured and monitored via simple web-based user interfaces. The platform is currently being used to power opinion research for 17 products at http://www.feedbackstrands.com/.

Keywords: Sentiment analysis, Opinion extraction, Natural Language Processing, Dashboard Visualization, Manual curation, Data Retrieval


99

Using Investigative Analytics & Market-Mix Models for Business Rule & Strategy Formulation – A CPG Case

Study

Mitul Shah*, Infosys Consulting, Bangalore. E-mail: [email protected]

* Corresponding author

Jayalakshmi Subramanian, Infosys Consulting, Bangalore.


Suyashi Shrivastava,

Infosys Technologies Limited, Bangalore E-mail: [email protected]

Kunal Krishnan,

Infosys Technologies Limited, Bangalore .


FMCG (or CPG) companies spend anywhere between 12 to 25% of their revenue in various marketing activities which drives 40-60% of the sales volume. Annual budgeting in this industry is of paramount importance and sets the tone of marketing initiatives for the rest of the year. Plethora of metrics and models are deployed in understanding the effectiveness of campaigns and promotions enabled by powerful IT tools. Yet, more often than not, category managers are left with open questions – which are not explained by any metrics or regular market mix models. CPG companies are increasingly looking for investigative analytics in understanding market drivers and how they are influenced by various promotional activities. It influences annual budgeting activity and effective allocation of Trade Funds. The paper discusses how to use investigative analytics in aiding such strategic decisions with the help of a case study. The study highlights a process which aims to answer a simple question – What is the extent to which market drivers influence the sales of two top brands and where organization can focus its marketing spend for each brand in next one year. The study used live data from a leading Consumer packaged goods (CPG) company. The study also aimed at preparing the hierarchy of the key factors that influence sales of each brands. The study was aided by statistical tools SAS 9.2, SPSS 17.0 and iCAT platform developed by Infosys Product Incubation & Engineering Group. Key Words: CPG Industry, Strategy Formulation, Understanding Market Drivers, Marketing Spend Allocation, Analytics & Market Mix Models


100

Improve Dispatch Capacity of Central Pharmacy

Ruhi Khanna, Atik Gupta, Devarati Majumdar, Shubhra Verma Max Healthcare Institute Ltd

New Delhi


To increase the new patient enrollments and to retain existing patients, healthcare organizations are increasingly becoming patient-centric, as word-of-mouth publicity affects the market-share of a healthcare set-up, most largely. It has thus become of equal importance to optimize the performance with respect to quality. One of the means to assess patient expectations is Feedback forms. Through these feed back forms the Voice of Customer revealed high dissatisfaction amongst patients on Max, Chemist Experience The CTQ drill down suggested that the ‘Low Dispatch Capacity of Central Pharmacy’ was directly impacting Customer Satisfaction at the different Pharmacies. With the help of LEAN Six Sigma we achieved increase in dispatch capacity of Central Pharmacy to satellite pharmacies by 37% against the target of 32 %. The post project analysis of Patient feedback revealed an immediate impact of 12% increase in Customer satisfaction and reduced negative VOCs.

Keywords: CTQ, Healthcare, LEAN Six Sigma


101

Application of Neural Networks in Statistical Control Charts for Process Quality Control

Chetan Mahajan

Business Analytics & Research, Fidelity Investments, Bangalore Bangalore, Karnataka

Email: cvmahajan[at]gmail.com, cvmahajan[at]yahoo.com

Prakash G. Awate

Mechanical Engg. Department, IIT BOMBAY, Mumbai Industrial Engg. & Operations Research Group, I.I.T. BOMBAY, Mumbai

Emai:l awatepg[at]iitb.ac.in

The use of neural networks for pattern classification/recognition in statistical

quality control charts is considered in the context of computerized systems having automated inspection and on-line decision making capabilities in real time.

The objective of the study is to obtain the best network configurations to detect

the different out-of-control patterns present in x bar control charts after investigating in detail several aspects and issues concerning use of multi-layer feed forward networks.

Our experimentations indicated that for preventing neural networks from

mistaking one type of pattern for another, it was crucially important to employ two hidden layers in the feed forward neural network. Further it was found that the sudden shift (upward or downward) pattern is relatively more difficult to learn for the neural networks.

The multi layer feed forward network with standard back-propagation algorithm

training was employed to detect the non-random patterns in x bar control chart. The structures as well as neurons’ parameters in the network were obtained through extensive simulations of learning and testing.

Keywords: Pattern Recognition, Multi-layer Feed Forward Network, Quality

Management, Statistical Quality Control, Artificial Intelligence


102

Measurement of Risk and IPO Underprice

Seshadev Sahoo1 & Prabina Rajib 2

1 Vinod Gupta School of Management (VGSOM) IIT Kharagpur Institute of Management & Information Science, Bhubaneswar, Orissa.

Email: [email protected] [email protected]

2Corresponding Author, Vinod Gupta School of Management, Indian Institue of

Technology, Kharagpur, West Bengal.

Email: [email protected] [email protected]

Empirical studies on IPO underprice anomaly in recent years have closely examined use of various proxies for risks, but none of which seems to explain significant portion of underprice. The paper seeks to shed light on this controversy by taking a sample of 92 IPOs issued in India during 2002-2006. We examine suitability of high price deflated to low price (H/L) as risk surrogate to explain underprice. The sample displays some evidence that H/L is a better proxy for ex-ante risk than other risk surrogates. The H/L ratio is estimated as average high price to low price for initial one month of trading has superior predictive ability for underprice. Besides H/L, other risk proxies proves statistically significant include investment bank prestige and inverse of offer proceeds. Further, we studied variation in predictive behavior of risk proxies across manufacturing and non-manufacturing sectors. We found no significant difference in average H/L value for manufacturing and non-manufacturing firms. We also document, after market H/L, investment bank prestige, and age of issue firm are suitable risk proxies for manufacturing sector IPOs, while risk for non-manufacturing sector IPOs is better represented by H/L, investment bank prestige, inverse of offer proceeds, and after market price volatility. Key Words: High Price to Low Price, Risk Proxy, Investment Bank Prestige, Initial Day Return.


103

Efficiency of Microfinance Institutions in India

Debdatta Pal Indian Institute of Management, Ahmedabad


In this study data envelopment analysis approach to efficiency has been used on a sample of thirty six Indian Microfinance Institutions (MFIs), taking each institution as a Decision Making Unit. The analysis considers portfolio outstanding as on March 31, 2008 as output variable while on the input side number of personnel involved in the organization and cost per borrower been considered as a proxy for labour and expenditures respectively. MFIs that remain efficient under both constant returns to scale and variable returns to scale assumption are Sanghamithra Rural Financial Services, Spandana Sphoorty Financial Limited and Pusthikar. The study also attempted to identify and analyze the possible determinants of efficiency of MFIs in India and variables are grouped under four wide categories namely location, governance, presence & outreach and financial management & performance. The results indicate that value of total assets, level of operational self sufficiency, returns on assets, returns on equity, age and borrower per staff of MFI are positively correlated with all efficiency measures, while, portfolio at risk (PAR 30 days) is positively correlated with only Technical Efficiency (TE) and Pure Technical Efficiency (PTE). As expected debt equity ratio is negatively related with TE and PTE. In case of location only the MFIs from southern Indian states have positive correlation with all three measures of efficiency.

Key words: Data Envelopment Analysis, Rural Financial Services, Pure Technical Efficiency


104

Measuring Efficiency of Indian Rural Banks Using Data Envelopment Analysis

Gunjan M. Sanjeev

Indus World School of Business (IWSB),

Greater Noida, Uttar Pradesh


Plethora of literature is available in the context of measurement of efficiency of financial institutions using various parametric and non parametric methods. Western world have witnessed many studies being out in this area. Emerging countries too, including India, have shown an encouraging contribution in the recent past in this area. Though in the Indian context many studies have been carried out to evaluate the efficiency of the public sector, private sector and the foreign banks, no significant focus gas been given to the Regional Rural Banks. This study has made an exploratory attempt to measure the efficiency of the 96 Regional Rural Bank (RRS) using a mathematical programming approach, Data Envelopment analysis (DEA). It is found that seven RRBs emerge fully efficient out of the 96 total studied. The mean efficiency score is 0.764. Few banks need immediate attention as their efficiency scores are very low. A preliminary effort has been made to see i) if there is any link between the efficiency of a RRB and its association with a respective sponsor bank; ii) if the efficiency of the RRB has any link with the geographical location. It is found that there are a few sponsor banks who emerge winners- all RRBs operating under them are efficient. Also, there are a few states in India where all the RRBs are efficient. Keywords: Non parametric method, technical efficiency, Indian banks


105

Ranking R&D institutions: A DEA study in the Indian context

Santanu Roy

Institute of Management Technology Ghaziabad

Email: [email protected], [email protected]

One major problem in evaluating the efficiencies of public institutions as pointed out by many researchers is the lack of a good estimate of the production function. The study reported in the paper adopts the methodology of data envelopment analysis (DEA) and measures the relative efficiencies of public-funded research and development laboratories in India (each laboratory being considered as a decision making unit) with data drawn from 12 such laboratories functioning under the Council of Scientific and Industrial Research (CSIR). The laboratories considered are spread over different regions of the country and work on diverse fields of science, engineering and technology. The input data for the study consist of the total number of scientific personnel and the total number of technical personnel working in each laboratory and the output data consist of the number of papers published in Indian journals, the number of papers published in foreign journals, the number of patents filed by these laboratories. Both the global efficiency scores and the different local efficiency scores (with specific inputs and outputs) were evaluated and potential improvements were ascertained. The implications of the study results have been analyzed and discussed. Keywords: Data envelopment analysis, efficiency scores, potential improvement, public institutions, relative efficiency, research and development laboratory.


106

A New Filtering Approach to Credit risk

Vivek S. Borkar School of Technology & Computer Science, TATA Institute of Fundamental Research,

Mumbai Email: [email protected]

Mrinal K. Ghosh

Department of Mathematics, Indian Institute of Science,

Bangalore Email: [email protected]

Govindan Rangarajan

Department of Mathematics, Indian Institute of Science,

Bangalore. Email: [email protected]

The celebrated Merton's model for equity of a firm views equity as a long call of a European call option on the assets of a firm. This allows one to treat the assets as a partially observed process observed through the `observation' process of equity. This is the standard framework for nonlinear filtering, which in particular allows us to write an explicit expression for the likelihood ratio for underlying parameters in terms of the nonlinear filter. As the evolution of the filter itself depends on the parameters in question, this does not permit direct maximum likelihood estimation, but does pave way for the `Expectation-Maximization' (EM) method for estimating parameters.

Key words: Merton's model, assets, equity, nonlinear filter, EM algorithm


107

Volatility of Eurodollar futures and Gaussian HJM term structure models

Vladimir Pozdnyakov1 and Balaji Raman2 Department of Statistics, University of Connecticut, CT

Email: 1 [email protected], 2 [email protected].

One of the standard tools for the theoretical analysis of fixed income securities and their associated derivatives is the term structure model of Heath, Jarrow and Morton. In this paper we suggest a simple criteria based on realized volatility that tells which Gaussian HJM model is consistent with observed Eurodollar futures. We also address the question of estimation of parameters of these models by two different methods - method of realized volatility and method of maximum likelihood.

Keywords: Gaussian HJM; Eurodollar futures; Realized volatility; Maximum

likelihood


108

Wavelet Based Volatility Clustering Estimation of Foreign Exchange Rates

A.N.Sekar Iyengar, Saha Institute of Nuclear Physics, 1/AF Bidhan Nagar, Kolkata


We have presented a novel technique of detecting intermittencies in a financial time series of the foreign exchange rate data of U.S.- Euro dollar( US/EUR) using a combination of both statistical and spectral techniques. This has been possible due to Continuous Wavelet Transform (CWT) analysis which has been popularly applied to fluctuating data in various fields science and engineering and is also being tried out in finance and economics. We have also been able to qualitatively identify the presence of nonlinearity and chaos in the time series Key words: Time-Scale analysis, Intermittency, Nonlinearity and Chaos


109

Modelling Multivariate GARCH Models with R: The ccgarch Package

Tomoaki Nakatani Department of Agricultural Economics

Hokkaido University Sapporo, Japan

and Department of Economic Statistics Stockholm School of Economics

Stockholm, Sweden


A preliminary version. Please do not cite without a permission from the author. This paper contains a brief introduction to the package ccgarch that is developed for use in the open source statistical environment R. ccgarch can estimate certain types of multivariate GARCH models with explicit modelling of conditional correlations (the CC-GARCH models). The package is also capable of simulating data from major types of the CC-GARCH models with multivariate normal or Student’s t innovations. Small Monte Carlo simulations are conducted to see how the choice of the initial values a.ects the parameter estimates in estimation. The usefulness of the package is illustrated by .tting a trivariate Dynamic Conditional Correlation GARCH model to stock returns data. Keywords: Dynamic conditional correlations, multivariate generalised autoregressive conditional heteroskedasticity, .nancial econometrics


110

Wind energy: Models and Inference

Abhinanda Sarkar

GE John F Welch Technology Center

EPIP, Whitefield

Bangalore


Wind turbines are sources of electrical power and convert a random source – the wind – to electricity at a steady frequency. The wind velocity can be considered as a time series with a marginal distribution that permits extreme winds. This mechanical energy relates to electrical energy via a power curve that also depends on other characteristics. The modeling and estimation challenge is to model and estimate a non-normal time series, together with implications for aspects such as turbulence parameters and the effects of averaging. The uncertainty in the wind can be converted to risk measures for power generated. Key words: Weibull distribution, autoregressive time series, power curve, turbulence intensity, value at risk


111

Field Data Analysis - A Driver for Business Intelligence and Proactive Customer Oriented Approach

Prakash Subramonian, Sandeep Baliga & Amarnath Subrahmanya

Reliability Engineering Department, Honeywell Technology Solutions, Bangalore

Today, industries across the world are driven to be innovative, globalized and cost effective due to a vigilant globalized consumer (customer). Consumers are looking for innovative, reliable and safe products with extended warranties, sales and service, and clauses for liabilities and penalties for product non-function. There is also the cost factor that the consumer looks for in addition to the above stated needs. In order to cater to the consumer’s needs, industries must be innovative and cost effective, not only in terms of their product design but also to proactively address product reliability, and reduce warranty claims. This paper deals with product performance, life modeling and simulation to make business decisions. The technique of data collection and computation through a concept called Early Indicators Product Tracking which dynamically alerts product performance is dealt in detail. The use of Weibull analysis for product life modeling and risk forecasting is explained. The advantage of time dependent modeling over the traditional “Take-away” constant failure rate model is discussed. The concept of business simulation to help plan operations and anticipate bottle necks along with its benefits is addressed in the paper. The above techniques if applied in a systematic way can help manage the business by taking right decisions and being proactive in addressing customer issues Key words: Reliability, risk forecasting, Weibull, Early indicator product tracking, Simulation, Mean Time between Failures (MTBF).


112

Simple Algorithms for Peak Detection in Time-Series

Girish Keshav Palshikar Tata Research Development and Design Centre (TRDDC)

Pune


Identifying and analyzing peaks (or spikes) in a given time-series is important in many applications. Peaks indicate significant events such as sudden increase in price/volume, sharp rise in demand, bursts in data traffic etc. While it is easy to visually identify peaks in a small univariate time-series, there is a need to formalize the notion of a peak to avoid subjectivity and to devise algorithms to automatically detect peaks in any given time-series. The latter is important in applications such as data center monitoring where thousands of large time-series indicating CPU/memory utilization need to be analyzed in real-time. A data point in a time-series is a local peak if (a) it is a large and locally maximum value within a window, which is not necessarily large nor globally maximum in the entire time-series; and (b) it is isolated i.e., not too many points in the window have similar values. Not all local peaks are true peaks; a local peak is a true peak if it is a reasonably large value even in the global context. We offer different formalizations of the notion of a peak and propose corresponding algorithms to detect peaks in the given time-series. We experimentally compare the effectiveness of these algorithms. Keywords: Time-series, Peak detection, Burst detection, Spike detection


113

Using the Decision Tree approach for Segmentation analysis – an analytical overview

Rudra Sarkar Genpact Analytics, Genpact India

Rajarhat, Kolkata


Very frequently we want to find ‘proper’ segments within our customer base to meet various business challenges. These segments can be aligned to various business verticals like marketing, risk or collections. In this paper we will review one such method of doing segmentation analysis, which is relevant for the analytics support of the business. The ‘decision tree’ helps us do the CHI-Square Automatic Interaction Detector (CHAID) segmentation analysis and eventually drill down to the target segments. The tree can be produced using different software applications. One of the most popular amongst them is the Knowledge Seeker Studio application from Angoss. We have also talked about some basic concepts on Segmentation with relevant examples from business. And then we have discussed how we typically deal with such a segmentation analysis using a decision tree and eventually how do we come up with recommendations for the business. Through proper segmentation and right targeting a business can add a lot to the bottom-line. Moreover, segmentation analysis does not demand a huge investment. Gathering data points, doing segmentation and coming up with specific business implementations are the actionable. Keywords: Segmentation, CHAID, Decision Tree, Bad Rate as dependant variable


114

Novel Business Application - Business Analytics

Sanjay Bhargava Bharat Petroleum Corporation Limited

Crude oil procurement is one of the most critical activities in an oil refinery. Crude oil constitutes 95% of total refining cost. After procurement the refining of crude oil and the product slate obtained thereby needs to be planned. There is a conventional method of crude oil evaluation and arriving at product slate which was being used by oil industry when the refineries complexity was less, used to process one type of crude oil and products quality requirement was not stringent. However with passage of time crude oils evaluation and preparing product slate posed greater challenges as refineries complexity increased to improve value addition from processing of crude oils, products specifications became more environment friendly, meeting pollution control norms. The presentation deals with transformation from simple yield based calculations to Linear Programming (LP) based model for refineries crude procurement and production planning. In LP model in addition to yields of crude oils and various processing units of refinery, products demand, processing units capacity, crude oils and products price, make or buy decisions etc. is considered. The output in addition to crude processing, products slate and quality, also indicates the marginal value for each crude oil and product. Scenario analysis like production of various products, external streams can be carried out. We at BPCL are using Process Industry Modeling System (PIMS), LP model from M/s Aspen Tech., USA for term and spot crude procurement, yearly and monthly planning. The quarterly planning is broken into period say 4 each of 7/8 days for immediate month to arrive at realistic production slate considering crude arrivals schedule by using multi-period PIMS. The subsequent months are run on fortnight / monthly basis which helps to arrive at decision for crude oils transportation schedule. Keywords: Linear Programming, Process Industry Modeling Systems, Supply Chain


115

Service Quality Evaluation on Occupational Health in Fishing Sector using Grey Relational Analysis to Likert

Scale Surveys

G.S.Beriha 1, B.Patnaik 2 & S.S.Mahapatra 3

1, 2 Department of Humanities and Social sciences National Institute of Technology (NIT), Rourkela, Orissa

3 Department of Mechanical Engineering

National Institute of Technology (NIT), Rourkela, Orissa

Email: 1 [email protected], 2 [email protected], 3 [email protected]

The study assesses the service quality of occupational health and safety of fishermen. Occupational hazards are a major concern in fishing, particularly in sea water fishing. The productivity of fishing companies is greatly affected due to occupational health related problems. Occupational health related problems caused to fishing personnel impacts on economy and social well being of the community in addition to loss of economic and goodwill of companies. The aim of this study is to assess occupational health care system prevailing in the sector and propose some remedial measure to control such hazards in future. To this end, a specially designed questionnaire has been prepared and distributed to respondents. A Grey relational analysis has been adapted to the responses derived in Likert type scale for prioritization of remedial action needed for improving quality of occupational health care system in the sector. Keywords: Occupational health hazards, service quality evaluation, Grey relational analysis, Likert Scale


116

An Empirical Study on Perception of Consumer in Insurance Sector

Binod Kumar Singh

Alphia Institute of Business Management (ICFAI)

Ranchi, Jharkhand.

E-mail: [email protected], [email protected]

Consumer behavior studies the behavior of individual or a group of people. The study of consumer behavior provides marketers to understand and predict the future market behavior. In this paper, role of IRDA, role of Indian banks, role of private insurance companies, function of insurance company, various factors influencing consumer behavior, factors influencing buying decision and model of consumer decisions making process have been considered. Also, the types of insurance policy taken by consumer, the total sum assured of life insurance, the total sum assured of life insurance for the spouse, the share of public insurance in insurance sector, share of LIC in life insurance in insurance sector and the reasons for invested in life insurance have been studied. The survey was conducted across 334 cities/towns in all the states and union territories. A sample of 1947 individuals has been selected by setting questionnaire. The online response system has self-checking and its validation system vetted the quality and veracity of the responses. Indicus Analytics then cross-checked and inputs with its databases on investors and their habits. The majorities of the respondents were from the top five metros and 10 major cities and had at least 30 participants. The profile of the target respondents is typically matched. The target respondents are well educated, familiar with English, spread over major urban centers having a higher socio-economic and income profile and spread across a range of occupations, professions and different age groups.

Insurance sector provides some security to the consumer for any type of mishappening.In this sector, IRDA plays an important role and time to time gives important guide lines to various companies. Still, LIC plays an important role and has maximum share in this sector. Recently, banking sector has also moved towards insurance sector since they would get better dividends than the commission they would get by entering into partnerships with other major insurance market players. Union Bank, Federal Bank, Allahabad Bank, Bank of India, Karnataka Bank, Indian Overseas Bank, Bank of Maharashtra, Bank of Baroda, Punjab National Bank, and Dena Bank are planning to enter in this sector. Among private sectors Max New York insurance company plays a vital role. There are various factors that affect the consumer buying decision and also influence consumer thinking when they are planning to invest in insurance scheme .Major respondents generally prefer insurance like vehicle insurance, term cover insurance, medical/health insurance and they also prefer sum assured of life insurance less than Rs


117

10 lakh. Most of the respondents have shown their interest in life insurance having higher risk coverage and also for tax saving purpose.

Key words: Consumer behavior, Buying decision and Consumer decisions making process


118

Two Component Customer Relationship Management Model for Health Care Services

Hardeep Chahal Department of Commerce University of Jammu

Jammu


Although customer relationship management (CRM) is a recent concept but, its tenets have been around for long some time. To sustain competitive advantage it is necessary to understand what customers require and equip employees to deliver to customers more than they expect i.e.. customer value, while constantly refining value propositions to ensure customer loyalty and retention. .But at the same time developing and maintaining CRM is not an easy task. There is need to have objective mechanism to operationalise CRM in the organization. The paper has made an maiden attempt to conceptualise and operationalise CRM through Two Component Model (Operational CRM ( OCRM) and Analytical CRM ( ACRM) ), particularly in healthcare sector. Relationship between OCRM, based on three patient-staff constructs ( physicians, nurses and supportive staff) and ACRM four constructs ( satisfaction, repatronization, recommend and organizational performance) with service quality as an antecedent to OCRM rather than as a moderator between two CRM components – OCRM and ACRM was analysed using confirmatory factor analysis ( AMOS). The data for the model were collected from three large hospitals from 306 patients who have been associated with the hospital for atleast last five years. The validity and reliability of the varied multi-dimensional OCRM and ACRM scales were duly assessed. Dimensions primarily caring attitude, friendliness, helpfulness, response to queries, expertise and effective treatment are found to be significant for OCRM from physicians, nurses and supportive staff perspectives that can impact four ACRM dimensions - satisfaction, repatronization, recommend and organizational performance. The paper concludes with implications (managerial, theoretical and patient) and limitations and future research. Key Words: Operational Customer Relationship Management, Analytical Customer Relationship Management, Total Customer Relationship Management and Service Quality.


119

An Analytical study of the effect of Advertisement on the consumers of middle size town

Uma V.P.Shrivastava Department of Business Administration,

Hitkarini College of Engineering and Technology, Jabalpur, Madhya Pradesh

Advertisement as understood is a medium of communication and expression for manufacturers, traders and marketers. It is a medium of expression of image and personality of a product or service. It is a medium of communication of product features and utility for the consumers. It is a medium which ensures that the required information regarding a product / service should be rightly communicated to the right target consumers at the right time in the right manner.

In the middle size towns the impact of advertising is very strong, and more than what it is on the residents of metros and big towns. The effect on the consumers of advertising is very high and the consumers are very highly influenced. Studies have proved that 37% consumers tend to buy a product for the first time because they have liked the advertisement of the product and it has aroused a curiosity about the product / service. Moreover, as now-a-days the advertisements show, the celebrities endorse one or the other products. Consumers usually identify themselves with such celebrities and thus end up using or purchasing the endorsed product. Thus, there are different reasons why advertisements have such a huge impact; it can be imagery, self identification, glamour or just like that feeling. But the crux is that “advertising have a strong impact on the consumers.” The hypothesis which was adhered to working on this study was that, “the advertisement does have a very strong impact on the consumers of middle size towns.” The reasons why this topic was considered was that as the secondary data support; the consumers of middle size town mostly have a defined disposable income and are majorly away from the world of glamour. They aspire to become “someone” out of “no-one”. The advertisements are the most glamorous ways of selling a product / service and these consumers thus fall for them. The core objectives which were focused consist of (a) What type of an impact do the advertisements create on the consumers of middle size towns?; (b) What percentage of the income do consumers of middle income group divert as the disposable income? (c) Do the consumers make purchases out of requirement of the product or out of fancy for advertisement? This study was conducted in six small and semi-small towns of Madhya Pradesh and followed the basic research tools of random sampling, customer and consumer interviews and FGD’s. Its sample size was more than one thousand respondents including respondents in the age group of 9 yrs to 66 yrs. The respondents included males and females of A1, A2 and B1, B2 category. This was cautiously done because it is normally people of this category who have the buying capacity as an influence of and


120

reaction to advertisement. The respondents were interviewed at various locations of the cities. Data both qualitative and quantitative was collected to analyze. The research data was analyzed and the key findings were a list: (a) the advertisements do make a strong impact on the consumers in various ways – they either end up buying the product / service for themselves or they recommend it to other fellow consumers with immense confidence proving themselves a loyal consumer to the product.(b) The middle income group and the higher income groups of middle size town both have a defined percentage range of their house-hold income which they are willing to use as disposable income; but this percentage varies amongst the various age groups of consumers. (c) The products / services are broadly categorized as impulse purchase products and non-impulse or thought over purchase products as per their own life-style and living requirements. The impulse purchase products are more governed by fancy but the non-impulse is governed more by deliberate purchase due to requirement of a particular product / services. Apart from this a lot of information and insight was received related to the consumers, their buying behaviour and patterns, their thought process and the reasons of their ways and manners of buying products / services. The study also helps to an extent in understanding the reasons why some particular products / services do better than other in any middle size town as against some others. This paper would at length discuss the various aspects of the influence and / or impact, advertisement makes on the consumers and their buying behaviour and patterns; also the other aspects of the same supported with both qualitative and quantitative data. Keywords: Advertisement Impact; Consumer Purchase; Disposable Incomes; Impulse Product Advertising.


121

Empirical Framework of Bayesian Approach to Purchase Incidence Model

Sadia Samar Ali 1, R. K. Bharadwaj 2 & A. G. Jayakumari 3

1, 2 Institute of Management Studies, Ghaziabad 3 SSR Institute of Management & Research, Silvassa

Email: 1 [email protected], 2 [email protected],

[email protected]

The most favorable alternative for any company is to satisfy consumers’ demand, which has always been a key consideration of any product demand and supply system. The nature of related decisions usually is considered to be single dimensional retailer’s information or consumer’s purchase data. Bayesian decision methodology provides an alternative framework to handle the problem of over-stocking and under-stocking and is used to determine the decision strategies for best alternative selection for efficient supply-chain management. Designing a purchase incidence model for the same requires data of purchase either obtained from retailers or from consumers’ survey. In this paper, we propose a Bayesian Criteria Purchase-Incidence Model (BCPIM). The proposed model can help in designing effective and efficient policy depending on the information available from both. Further, companies can use this analysis as a strategic decision-making tool to develop efficient and sufficient supply chain management. Finally, an example has been illustrated to highlight the procedural implementation of the proposed model.

Key Words: Marketing, Consumer behaviour, purchase, purchase-incidence,

Poisson distribution, Gamma distribution, Bayesian criteria model


122

Exploring Temporal Associative Classifiers for Business Analytics

O.P. Vyas1, Ranjana Vyas1, Vivek Ranga2, Anne Gutschmidt3

1 Pt. Ravishankar Shukla University, Raipur 2 ICFAI Business School – Ahmedabad

3University of Rostock, Rostock (Germany)

Email : [email protected], vivekranga @rediffmail.com, [email protected], [email protected]

Many crucial business decisions are being taken without having systematic study

of the existing scenario and also without applying suitable data analytics. The availability of large volume of data on customers, made possible by new information technology tools, has created opportunities as well as challenges for businesses to leverage the data and gain competitive advantage. On the one hand, many organizations have realized that the knowledge in these huge databases are key for supporting the various organizational decisions. Particularly, the knowledge about customers from these databases is critical for the marketing function. But, much of this useful knowledge is hidden and untapped.

In Germany one such effort for Supermarket retailing is being discussed by

academia and Industry both. This approach aims to increase the customer loyalty through applying technologies like position tracking and Radio Frequency Identification (RFID) scanning. The Metro Group, a large German retailer, introduced the so-called Future Stores where several technologies, especially RFID and WLAN (Wireless Local Area Network), enable new services for the supermarket customers. Data is retrieved from loyalty cards combined with RFID tags on products, palettes and cases. The Supermarket transactional data coupled with the data generated in above Recommender System is proposed to be harnessed for exploring hidden sales-patterns through efficient data mining techniques by suitably modifying MBA (Market Basket Analysis) techniques.

The Associationships, synonym for MBA is now an important component of data

mining and with a series of algorithmic techniques; it can handle the different categories of business data. Experiments report that Associative Classification systems achieve competitive classification results with traditional classification approaches such as C4.5 and is also more effective than Association rule mining approaches. This paper investigates the temporal data mining as it was observed that that transactional data is


123

time-sensitive in nature. VVaalluuaabbllee ppaatttteerrnnss cannot be discovered by traditional non-temporal data mining approaches that treat all the data as one large segment, with no attention paid to utilizing the time information of the transactions.

We have extensively studied the performance issues of significant Associative Classifiers CBA, CMAR and CPAR, with and without temporal dimension. The experimentation done for ten significant bench marking data sets was concluded to see that TACs (Temporal Associative Classifiers) perform better in terms of classifier accuracy as compared to their non-temporal counterparts. This assumes significance because although the transactional data have shown definite time-sensibilities and many data mining approaches do not include the temporal aspect into the mining process considering that this may further slow down or complicate the process of Knowledge generation.

Key words: Market Basket Analysis, Temporal Associative Classification, CBA, CMAR, CPAR.


124

Application of Analytical Process Framework for Optimization of New Product Launches in Consumer

Packaged Goods and Retail Industry

Derick Jose MindTree Consulting, Bangalore

Ganesan Kannabiran

National Institute of Technology, Tiruchirappalli

Shriharsha Imrapur MindTree Consulting, Bangalore

Consumer Packaged Goods (CPG)/ Retail Organizations are looking at new product development (NPD) process as a critical component to deliver breakthroughs in the market space. With every launch organizations spend millions of dollars in researching new products; test marketing it and releasing it to the broader market. This paper attempts to introduce rigorous analytical techniques and processes along with new sources of data which would help optimize the critical decisions which are undertaken when a new product is launched. We use an analytical framework to optimize six crucial New Product Development (NPD) decisions which consist of a pre fabricated industry specific data model and a 10- step process using advanced statistical techniques. We then attempt to implement the framework using a commercial tool. Preliminary outputs show significant insights can be leveraged by the product development and the marketing groups to align decisions regarding product features, packaging and messaging to their target market. Key words: New product development- analytical framework-text mining- engagement analytics- product configuration


125

The Predictive Analytics Using Innovative Data Mining Approach

Sunita Soni 1, Jyothi Pillai 2 & O.P. Vyas 3

Bhilai Institute of Technology, Durg, Chhattisgarh

SOS in Computer Science

Pt. Ravi Shankar University, Raipur, Chhattisgarh

E-Mail: 1 [email protected], 2 [email protected], 3 [email protected]

The availability of huge amount of information does not mean wealth of information. Filtering the data using various mining techniques gives the essence of valuable information called knowledgeable information. Data Mining is the exploration and analysis of large sets, in order to discover meaningful patterns and rules. Almost every business process today involves some form of data mining. Most of today's structured business data is stored in relational databases. Existing data mining algorithms (including those for classification, clustering, association analysis, and outlier detection) work on single tables or single files. Unfortunately, information in the world can hardly be represented by such independent tables. One of the main tasks in data mining is supervised classification, whose goal is to induce a predictive model from a set of training data. Multi-relational classification is a very important research area because of the popularity of relational database. It can be widely used in many disciplines, such as financial decision-making, medical research, and geographical applications. In this paper we are analyzing the performance of Multi-relational Classifiers an innovative approach, to generate predictive model used to build business intelligence solution, which is helpful for strategic decision-making. Key Words: Data Mining, supervised classification, Multi-relational classifiers

.


126

On Rough Approximations of Classifications, Representation of Knowledge and Multivalued Logic

B.K.Tripathy 1, J.Ojha 2 & D.Mohanty 3

1 Department of Mathematics School of Computing Science,

VIT University, Vellore, Tamil Nadu Email: [email protected]

2 Khallikote College, Berhampur, Orissa


3 Department of Mathematics

Simanta Mahavidyalaya, Jharpokharia, Orissa Email: [email protected]

Several approaches have been introduced to deal with impreciseness in data. The concept of fuzzy sets put forth by Zadeh (1965), is one of the earliest among them. The other major and perhaps a better approach is the concept of rough sets due to Pawlak (1982). Classification of universes is the core concept in defining basic rough sets. Approximations of classifications are of great interest due to the fact that, in the process of learning from examples, the rules are derived from classifications generated by single decisions (Busse 1988 and Tripathy et al. 2009). These rules can be used in multivalued logic. Four propositions were established by Busse (1988) which characterize properties of approximations of classifications. These results are instrumental in defining types of classifications, which are used to generate rules from information systems. In this article, we extend these propositions to obtain necessary and sufficient type results. From these theorems, several results are derived, in addition to the above four results. We shall provide interpretations to each of these results and also illustrate them through examples, to determine the kind of knowledge one can infer from the information systems, which satisfy the conditions of these propositions. Key words: Rough set, classification, R-definable, R-lower approximation, R-upper approximation.


127

SB-Robust Estimation of Parameters of Circular Normal Distribution

Arnab Kumar Laha and Mahesh K. C


The Circular Normal distribution (a.k.a von Mises distribution), is the most widely used probability model for circular data. The maximum likelihood estimators (m.l.e) of the location parameter (µ) and the concentration parameter (κ) of the distribution are known to be not SB-robust at F = {CN(µ,κ) : κ>0}. In this paper, we define a natural measure of dispersion (S) and show that the directional mean is not SB-robust at F with respect to S. Next, we show that the directional mean is SB-robust at F for the following families (1) mixture of normal and circular normal distributions, (2) mixture of two circular normal distributions, and (3) mixture of wrapped normal and circular normal distributions with respect to S. Subsequently we define a γ-circular trimmed mean with trimming proportion γ and show that it is an SB-robust estimator for µ at F with respect to S. Next, we study the SB-robustness of the m.l.e. of the concentration parameter of circular normal distribution and show that the m.l.e. of the concentration parameter is not SB-robust at F with respect S. Next, we define the γ-trimmed dispersion measure (Sγ) and γ-trimmed estimator for concentration parameter and show that this new estimator is SB-robust at F with respect to Sγ. Keywords: Circular Data, Circular Trimmed Mean, Mixture distributions, Robust estimation, SB-robust estimators.


128

Bayesian Analysis of Rank Data with Covariates

Arnab K. Laha


Somak Dutta Indian Statistical Institute, Kolkata

Rank data arise quite frequently in many areas of management like marketing, finance, organizational behaviour and psychology. Such data arises when a group of randomly chosen respondents are asked to rank a set of k items according to their order of preference. The resultant data is a set of permutations of {1, …, k}– one permutation for each respondent. Analysis of rank data becomes difficult because the permutation groups do not have rich structure like the real line or the real space and the dimension of the data increases very rapidly with increase in the number of items to be ranked. In this paper we propose a model that assumes the observed ranks to be a random permutation of the true rank where each permutation has some specific probability of appearing. We consider the general case where covariates are present and the true rank is a function of the covariate values. A Bayesian approach is taken to estimate the model parameters. The Gibbs Sampler and the Population Monte Carlo methods are used to sample from the posteriors. Two real life data sets are analyzed using the model to indicate their usefulness. Keywords: Gibbs Sampling, Permutation Group, Population Monte Carlo, Ranking Data


129

Selecting a Stroke Risk Model Using Parallel Genetic Algorithm

Ritu Gupta and Siuli Mukhopadhyay* Department of Mathematics, Indian Institute of Technology Bombay, Powai,

Mumbai *Corresponding Author


Increased transcranial doppler ultrasound (TCD) velocity is an indicator of cerebral infarction (stroke) in children with sickle cell disease (SCD). In this paper we use parallel genetic algorithm (PGA) to select a stroke risk model with TCD velocity as the response variable. Development of such a stroke risk model leads to the identification of children with SCD who are at a higher risk of stroke and treating them in the early stages. Using blood velocity data from SCD patients we show that PGA is an easy-to-use-computationally variable selection tool. Model selection results obtained from using PGA shows that PGA performs well when compared with stepwise selection and best subset selection techniques. Keywords: Transcranial doppler ultrasound velocity; sickle cell disease; stepwise selection; model validation


130

Linking Psychological Empowerment to Work-Outcomes

Anita Sarkar & Manjari Singh Indian Institute of Management, Ahmedabad

Based on the concept put forth by researchers (Conger & Kanungo, 1988; Thomas & Velthouse, 1990; Spreitzer, 1995, 1996) we studied the concept of empowerment in the context of women primary school teachers of India. Relationship of two important work-outcomes of empowerment, job involvement and innovative behavior are studied. While individual’s empowerment is based on self report, the data for work-outcomes have been collected from self, superiors and colleagues. Total 113 teachers, 8 superiors and 303 colleagues from three schools of Gujarat, India have participated in the study. All the latent constructs under study were tested for both convergent and discriminant validity. We performed both single rater and multi rater confirmatory factor analysis, as appropriate for multi-rater research. Before aggregating the data for colleagues, rwg, average deviation, and intra-class correlation were calculated. Structural equation modeling has been used to test the model fit. Results show that empowerment lead to job involvement and innovative behavior. The study supports earlier rating researchers’ perspective that different types of raters have different perspectives for the same dimension and that influence their ratings (Landy & Farr, 1980; Harris, & Schaubroeck, 1988). Overall the research indicates importance of psychological empowerment in the workplace. Key words: Empowerment, multi-rater, job involvement, innovative behavior, teacher


131

To Identify the Employability Skills for Managers through the Content Analysis of the Selected Job

Advertisements

Mandeep Dhillon ICFAI National College, Chandigarh


The purpose of this paper was to outline the basic employability skills and or competencies required by employees in business organizations to perform and compete. For the purpose of the present study employability skills were, “a set of attributes, skills and knowledge that all labor market participants should possess to ensure that they have the capability of being effective in the workplace-to the benefit of themselves, their employer and the wider economy”. A content analysis was conducted of selected job advertisements during August to December 2008. Three Hundred and Ten advertisements in print (Newspapers) and online (Naukri, Monster) were selected for the study. Two judges independently coded and assessed the coding of the advertisements. This study has identified that the employability skills can be categorized as Basic Academic skills, Technical skills and Generic skills. Generic skills were further grouped as Operational, Behavioral skills and other soft skills. Communication skills, analytic skill and leadership skills were found to be most important generic skills. Keywords: Generic Skills, Operational Skills, Behavioral Skills, Textual Analysis


132

Performance Measurement in Relief Chain: An Indian Perspective

A.S Narag, Amit Bardhan and Hamendra Dangi

Faculty of Management Studies, University of Delhi, Delhi

Email: [email protected], [email protected], [email protected]

When disasters strike, government agencies, military, paramilitary forces, and relief organizations responds by delivering aid to those in need. The distribution chain needs to be both fast and agile in responding sudden onset of disasters. Given the stake and size of relief industry, the study of relief chain is an important domain of supply chain management. The distribution chain needs to be both fast and agile in responding sudden onset of disasters. Performance measurement is critical to relief operations accountability. The ultimate goal of performance measurement in supply chain systems is to establish relationships between decision variables and performance outputs, leading to the creation and maintenance of high-performance systems. A performance measure (or performance measurement system), describes the effectiveness and/or efficiency of a system. The aim of this paper was to define, compare and contrast the commercial supply chain and the relief chain, discuss an approach to performance measurement in the domain of humanitarian relief, and identify the challenges faced by relief chain logisticians in practice and research. Key Words: Supply Chain, Disaster Management, Performance Evaluation System


133

Machine Learning Approach For Predicting Quality Of Cotton Using Support Vector Machine

Selvanayaki M PSGR Krishnammal College for Women

Coimbatore E-mail: [email protected]

Vijaya MS

GR Govindarajulu School of Applied Computer Technology PSGR Krishnammal College for Women

Coimbatore E-mail: [email protected]

Yarn strength depends extremely on the quality of cotton. The physical characteristics such as fiber length, length distribution, trash value, color grade, strength, shape, tenacity, density, moisture absorption, dimensional stability, resistance, thermal reaction, count, etc., contributes to the quality of cotton. The quality of cotton mainly depends on the major factor such as the fibre length, strength, maturity, fineness, tenacity, color, uniformity ratio, lint etc.. Three important issues considered during cotton data collection are unlabeled samples, imbalance of the cotton samples due to high proportion and cotton samples of different quality. So, the prediction of cotton quality is a challenging and important task in manufacturing the quality yarn. Hence there is a need to generate an efficient classification model for predicting the quality of cotton with high predictive accuracy. This paper presents an implementation of a machine learning algorithm, Support Vector Machine for cotton quality prediction. Support vector machine is a training algorithm for learning classification and regression rules from data. SVM is a supervised pattern classification technique suitable for working accurately and efficiently with high dimensionality feature spaces. SVM has an extremely well developed statistical learning theory. SVM is based on strong mathematical foundations and results in simple yet very powerful algorithm. In this paper the support vector machine is trained using the data collected from a spinning unit. The dataset is trained using SVM with linear, polynomial and RBF kernel and with different parameter settings for d, gamma and C - regularization parameter. The performance of the trained model is evaluated using 10 – fold cross validation for its predictive accuracy. Prediction accuracy is measured as the ratio of number of correctly classified instances in the test dataset and the total number of instances. Prediction accuracy is measured as the ratio of number of correctly classified instances in the test dataset and the total number of instances. It is found that the predictive accuracy shown by SVM with Radial Basis Function kernel is higher than the other two models. Keywords: Classification, Prediction, Support Vector Machine, Machine learning.


134

Machine Learning Techniques: Approach for Mapping of MHC Class Binding Nonamers

Gomase V.S.*, Yash Parekh, Subin Koshy, Siddhesh Lakhan and Archana Khade

*Department of Bioinformatics, Padmashree Dr. D.Y. Patil University, Navi Mumbai

Email- [email protected]

The machine learning techniques are playing a major role in the field of immunoinformatics for DNA-binding domain analysis. Functional analysis of the binding ability of DNA-binding domain protein antigen peptides to major histocompatibility complex (MHC) class molecules is important in vaccine development. The variable length of each binding peptide complicates this prediction. Such predictions can be used to select epitopes for use in rational vaccine design and to increase the understanding of roles of the immune system in infectious diseases. Antigenic epitopes of DNA-binding domain protein form Human papilloma virus-31 are important determinant for protection of many host form viral infection. This study shows active part in host immune reactions and involvement of MHC class-I and MHC II in response to almost all antigens. We used PSSM and SVM algorithms for antigen design, which represented predicted binders as MHCII-IAb, MHCII-IAd, MHCII-IAg7, and MHCII- RT1.B nonamers from viral DNA-binding domain crystal structure. These peptide nonamers are from a set of aligned peptides known to bind to a given MHC molecule as the predictor of MHC-peptide binding. Analysis shows potential drug targets to identify active sites against diseases. Keywords: DNA-binding domain crystal structure, Position Specific Scoring Matrices (PSSM), Support Vector Machine (SVM), major histocompatibility complex (MHC), epitope, peptide vaccine


135

The Click Click Agreements –The Legal Perspectives

Rashmi Kumar Agrawal 1, Sanjeev Prashar 2

Institute of Management Technology, Ghaziabad

Email: 1 [email protected], 2 [email protected]

The world post WTO -1995 has opened new dimensions of trade and commerce both nationally and internationally. This got further facilitated by the advent of technology. This research paper attempts to understand the legal implications of different types of contracts envisaged through internet and the statutory provisions pertaining to the same. The main contentions for E Contracts are the legal sanctity of E commerce per se and electronic governance as to writing and signature for legal recognition. The first step in this direction was the enactment of The Information Technology Act, 1999 which provided for equal legal treatment to users of electronic communication as the paper base communication followed by amendments in the Indian Penal Code, 1890, The Indian Evidence Act, 1872, the Reserve Bank of India Act, 1934 and the Bankers Books Evidence Act, 1891. The three basic forms of "E-Contracts"; the Click Wrap, the Shrink-Wrap agreements and the Electronic Data Interchange, and their legal sanctity have been analysed by content analysis of Information Technology Act, 2002 and Indian Contract Act, 1872 supported by relevant judicial pronouncements both from India and developed countries.

Keywords: E-Contracts, Click- Wrap, Shrink- Wrap, Electronic Data Interchange agreements.


136

1st iima international conference on advanced data analysis, business analytics and intelligence 6-7...

Documents

segmentation approach

customer lifetimes

uncertainty management

approach of summarization

empirical analysis

rough set approach

cointegration analysis

latent class analysis