A Multi-criteria Decision Making Approach and its Applications in Business

We live in a world of information where decision takes a very important role. Human are considered one of the best species since we like to think and quantitatively analysis our decision process based on the available information. But the question may arise:


1. How can we take a right decision or mathematically, optimal solution among the multiple alternatives?


In academic world, this knowledge is known as Multiple criteria decision making (MCDM), that involves to making decisions in the presence of multiple, usually conflicting criteria and alternatives. Multi-Criteria Decision Aid (MCDA) or Multi-Criteria Decision Making (MCDM) methods have successfully utilized by researchers and practitioners in evaluating, assessing and ranking alternatives across diverse business problem. As we know, every decision involves trade-offs between risk and opportunity. So, the next question may arise:


2. How to minimize the risk and define in mathematically for a given business problem?


Many MCDA/MCDM methods developed to solve real-world decision problems, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) continues to work satisfactorily across different real business application areas. So, the next comes:


3. How to apply TOPSIS methods to design and solve any practical business problem?


In this session, we will try to understand the theory behind decision science and discuss step by step implementation of business cases in open source environment

The overall objective of the session is how to design decision based on knowledge and planning in business.

 
 

Outline/Structure of the Tutorial

1. Introduction
2. Decision Theory
3. Mathematics of Risk
4. Multi-Criteria Decision Analysis and application in business
5. Implemetation strtegy in open source enviroment
6. Step to step implementation of two Business case study

Learning Outcome

1. Enter to the world of decision science.

2. Can Apply Multi-Criteria Decision Analysis to any business decision problem.

3. Can Implement in open source environment.

4. Able to understand concept of risk by implementation of business problem

Target Audience

Decision maker/ Researcher/ Student

Prerequisites for Attendees

Basic math

schedule Submitted 1 year ago

Public Feedback

comment Suggest improvements to the Speaker
  • Vishal Gokhale
    By Vishal Gokhale  ~  1 year ago
    reply Reply

    Thanks for the proposal, Saibal!
    This is an interesting topic. 

    1. Can you cite some examples of the real-world business problems solved using MCDA?
    2. It may be helpful to include a comparison of results (accuracy and performance) between the solution using MCDA and other machine learning techniques. The audience needs to know the advantages of the said technique.
    3. Also, I think you should include caveats when using MCDA. 
    4. For the program committee to get an idea of your presentation style, can you please share links to your prior talks on this topic?
      If you would be presenting on this topic for the first time, you may share videos of other technical talks you may have delivered earlier. If those are not available, we request you to record a short trailer of this talk and share a link.
    • Naresh Jain
      By Naresh Jain  ~  1 year ago
      reply Reply

      Thanks for the proposal, Saibal.

      This is a very interesting topic. I'm curious to know how you've applied MCDA and if you can pivot the presentation around your experience so that the audience can relate much better to the concept at hand. Generally, people shy away from a session that sounds theoretical.


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