[mpkd1] introduction to business analytics and simulation

INTRODUCTION TO BUSINESS ANALYTICS & SIMULATION

The rise in demand for

Analytics and Data Science

talent

Source: LinkedIn’s data indicates

Google trends graph of searches on the term AnalyticsSource: http://www.google.com.vn/trends/explore#q=analytics

Source: Rainer et al. (2014)

Information technology inside the organization

Business Analytics

Source: http://www.gartner.com/technology/cio/

PwC 18th Annual Global CEO Survey (2015)

How strategically important are the following

categories of digital technologies for your

organisation?

The strategic importance

of key technologies

Business Analytics

Source: www.pwc.com/ceosurvey

The strategic importance

of key technologies

Evolution of Business Analytics

Source: Delen (2015)

Source: Asllani (2015)

The Institute for Operations

Research and the

Management Sciences

(INFORMS) is the largest

society in the world for

professionals in the field of

operations research (O.R.),

management science,

and analytics.

https://www.informs.org

(Business) Analytics:

• Scientific process of transforming data into

insight for making better decisions.

• Used for fact-driven decision making, which is

often seen as more objective than other

alternatives for decision making.

Camm et al. (2015)

WHAT IS BUSINESS ANALYTICS?


Analytics vs Analysis

• Analysis refers to the process of separating a

whole problem into its parts so that the parts

can be critically examined at the granular level.

• Analytics is a variety of methods, technologies,

and associated tools for creating new

knowledge/insight to solve complex problems

and make better and faster decisions.

Delen (2015)


• Analytics = The science of analysis Data Science

• People who conduct analyses and develop analytic

applications = data analysts data scientists (have a

deeper knowledge of algorithms)

• Analytics includes any type of computer-supported

analysis used to support fact-based decisions.

• Analytics may be input for human decisions or drive

fully automated decisions.

Power (2013)

TYPES OF BUSINESS ANALYTICS

• Decision making: A process of choosing among two or more alternative courses of action for the purpose of attaining a goal(s)

• Managerial decision making is synonymous with the entire management process (Simon, 1977)

• All management is prediction (Deming, 1993)

• Humans consciously or subconsciously follow a systematic decision-making process: (Simon, 1977)

1. Intelligence

2. Design

3. Choice

4. ImplementationDe

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Descriptive analytics

Prescriptive analytics

Predictive analytics

BA process

Descriptive analytics(reporting analytics, business intelligence) the use of data to understand past and current business

performance and make informed decisions

Predictive analytics predict the future by examining historical data, detecting

patterns or relationships in these data, and then extrapolating these relationships forward in time

Prescriptive analytics(decision analytics, normative analytics) identify the best alternatives to minimize or maximize some

objective


Ad

van

ced

an

aly

tics

Proposed by INFORMS (the Institute of Operations

Research and Management Sciences)

Example: Retail Markdown Decisions

Most department stores clear seasonal inventory by

reducing prices.

The question is: When to reduce the price and by how

much?

Descriptive analytics: examine historical data for similar

products (prices, units sold, advertising, …)

Predictive analytics: predict sales based on price

Prescriptive analytics: find the best sets of pricing and

advertising to maximize sales revenue


Source: Gartner (2013)



Categories of business analytics modeling techniques

Ragsdale (2015)


The Spectrum of Business Analytics

Source: Kiron et al. (2013)

Data vs Information

Data: numerical or textual facts and figures that are

collected through some type of measurement process.

Information: result of analyzing data; that is, extracting

meaning from data to support evaluation and decision

making.

DATA FOR BUSINESS ANALYTICS

• Data set - a collection of data.

• Examples: Marketing survey responses, a table of historical stock

prices, and a collection of measurements of dimensions of a

manufactured item.

• Database - a collection of related files containing

records on people, places, or things.

• A database file is usually organized in a two-dimensional table,

where the columns correspond to each individual element of data

(called fields, or attributes), and the rows represent records of

related data elements.

• Data warehouse - a collection of databases used for

reporting and data analysis.

• Data mart: A departmental data warehouse that stores only

relevant data.


Four Types Data Based on Measurement Scale:

Nominal data

Ordinal data

Interval data

Ratio data


Categorical data

Scale/numerical/quantitative dada

Data

Categorical Numerical

Nominal Ordinal Interval Ratio

StructuredUnstructured or

Semi-Structured

MultimediaTextual HTML/XML

Model:

An abstraction or representation of a real system, idea,

or object

Captures the most important features

Can be a written or verbal description, a visual display, a

mathematical formula, or a spreadsheet representation

Example: The sales of a new product, such as a first-

generation iPad or 3D television, often follow a common

pattern.

MODELS IN BUSINESS ANALYTICS

S = aebect

A decision model is a logical or mathematical

representation of a problem or business situation that

can be used to understand, analyze, or facilitate making

a decision.

Building decision models is more of an art than a

science. Creating good decision models requires: solid understanding of business functional areas

knowledge of business practice and research

logical skills

It is best to start simple and enrich models as necessary.


• Three types of input:

• Data (or parameters), which are assumed to be constant for

purposes of the model.

• Uncontrollable variables, which are quantities that can change but

cannot be directly controlled by the decision maker.

• Decision variables (or controllable variables), which are

controllable and can be selected at the discretion of the decision

maker.


Nature of Decision Models

dependent variables

independent variables

f(X1, X2,…, Xn) = Y

Strategies for Modeling

• Logic-Driven Models: based on experience, knowledge,

and logical relationships of variables and constants

connected to the desired business performance outcome

situation

• Data-Driven Models: use data collected from many

sources to quantitatively establish model relationships

Influence Diagrams visually show how various model

elements relate to one another


Example: A Profit Model

• Develop a decision model for predicting profit in face of

uncertain demand.

MODELS IN BA: LOGIC-DRIVEN MODELS

P = profit

R = revenue

C = cost

p = unit price

c = unit cost

F = fixed cost

S = quantity sold

D = demand

Q = quantity produced

Influence Diagram

Example: A Profit Model

• Cost = fixed cost + variable cost

C = F + c*Q

• Revenue = price * quantity sold

R = p*S

• Quantity sold =

Minimum{demand, quantity produced}

S = min{D, Q}

• Profit = Revenue − Cost

P = p*min{D, Q} − (F + cQ)

MODELS IN BA: LOGIC-DRIVEN MODELS

Example: A Sales-Promotion Model

In the grocery industry, managers typically need to know

how best to use pricing, coupons and advertising

strategies to influence sales.

Using Business Analytics, a grocer can develop a model

that predicts sales using price, coupons and advertising.

MODELS IN BA: DATA-DRIVEN MODELS

Price

Coupons

Advertising

Sales

Influence Diagram

Example: A Sales-Promotion Model


Sales = 500 – 0.05(price) + 30(coupons)

+0.08(advertising) + 0.25(price)(advertising)

Example: Predicting Crude Oil Prices

• Line chart of historical crude oil prices



Logarithmic y = 13 ln(x) + 39 R2 = 0.382

Power y = 45.96x0.0169 R2 = 0.397

Exponential y = 50.5e0.021x R2 = 0.664

Polynomial 2° y = 0.13x2 − 2.4x + 68 R2 = 0.905

Polynomial 3° y = 0.005x3 − 0.111x2

+ 0.648x + 59.5 R2 = 0.928


R-squared values closer

to 1 indicate better fit of

the trendline to the data.


• Third Order Polynomial Trendline fit to the data


MONTE CARLO SIMULATION

• Monte Carlo simulation

is the process of

generating random values

for uncertain inputs in a

model, computing the

output variables of

interest, and repeating

this process for many

trials to understand the

distribution of the output

results.

SYSTEM SIMULATION

• Discrete-Event System Simulation

• Continuous Simulation (System Dynamics)

SYSTEM SIMULATION

WINTERSIM.ORG

• A very useful resource is the Winter Simulation

Conference (wintersim.org) to track the

developments in the field of simulation.

• The Winter Simulation Conference provides the

central meeting place for simulation

practitioners, researchers, and vendors working

in all disciplines and in the industrial,

governmental, military, and academic sectors.

Source: WinterSim.org