INCIDENCE MANAGEMENT

Bhawik Kumar RajaPrashant KumarK.Venkataraghavan

Department of Management Studies, IIT Madras

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Agenda Introduction Literature Review Model Development Implementation Implications Conclusion

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Introduction

What is Incidence Management ?

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Backlogs in Incidence Management

Common Issues in Incidence Management

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Literature Analysis

Examining Capability of the IS Desk

(Simulation environment for IT service support processes) Tickets are considered as discrete events occuring with a certain probability Tickets are of different nature like SAP, Networking etc Tickets are assigned to various consultants with varying capabilities Time taken to solve tickets vary with consultants IS is SLA bound Helps to optimize help desk resources

Auction based Models for Ticket allocation in IT Service Delivery Industry Online scheduling of tickets Inefficiencies in the standard model are overcome Ticket is allocated to the member who bids the least time

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Model Development

Gaps in Literature

• The Papers were online scheduling models

• Objectives were in broad are of Incidence Management

• Our Model is focused on Decision Making in allocating Backlog tickets

Backlog Situation

Back Log tickets

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DSS Model Optimization of problems with few alternatives – use decision tables,

decision trees Optimization via algorithm – use linear and other mathematical

programming models, network models Optimization via an analytic formula – example Inventory models Simulation Heuristics – Use Heuristics Programming and expert systems What if Analysis – Financial Modeling, Waiting Lines Predictive Models – Forecasting models. Markov analysis.

OBJECTIVE Function

N N N N

Minimize ∑ AmiTai + ∑ BmiTbi + ∑ CmiTci + ∑DmiTdi

i =1 i =1 i =1 i =1

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DSS ModelConstraints Set 1

N N N N

∑AmiTai + ∑BmiTbi + ∑CmiTci + ∑DmiTdi <= Hi

i = 1 i = 1 i = 1 i =1

Hi is available time for a consultant i.

Constraints Set 2

N N N N

∑Ami <= A, ∑Bmi <= B, ∑Cmi <= C, ∑Dmi <= D

i = 1 i =1 i =1 i =1

Constraints Set 3

A mi , Bmi , Cmi , Dmi > 0 and integers

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Implementation – Prototype TestingPrototype implemented using Excel Solver

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Implementation – user Interface

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Implementation - Lingo

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Implications, Limitations and Future Scope

IMPLICATIONS

• Aides judgment in allocation of Backlog tickets

• Optimize resource usage

• Helpful in Identifying marginal utilization

LIMITATIONS

• Only few variables are considered in this model

• Tests were done with synthetic data

• Soft Variables like Knowledge Management, Resource Related risks have not been included

FUTURE SCOPE

• Constraints on Ticket type i.e. certain tickets cannot be assigned to certain users

• Apart from time constraint include a schedule constraint

• Incorporate existing workload of the consultants

• Incorporate ticket dependencies

• Look at knowledge optimization Issues

• Incorporate Prediction Capabilities.

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Thank you