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.

  • Liked Srijak Bhaumik

    Srijak Bhaumik - Let the Machine THINK for You

    20 Mins

    Every organization is now focused on the business or customer data and trying hard to get actionable insights out of it. Most of them are either hiring data scientists or up-skilling their existing developers. However, they do understand the domain or business, relevant data and the impact, but, not essentially excellent in data science programming or cognitive computing. To bridge this gap, IBM brings Watson Machine Learning (WML), which is a service for creating, deploying, scoring and managing machine learning models. WML’s machine learning model creation, deployment, and management capabilities are key components of cognitive applications. The essential feature is the “self-learning” capabilities, personalized and customized for specific persona - may it be the executive or business leader, project manager, financial expert or sales advisor. WML makes the need of cognitive prediction easy with model flow capabilities, where machine learning and prediction can be applied easily with just a few clicks, and to work seamlessly without bunch of coding - for different personas to mark boundaries between developers, data scientists or business analysts. In this session, WML's capabilities would be demonstrated by taking a specific case study to solve real world business problem, along with challenges faced. To align with the developers' community, the architecture of this smart platform would be highlighted to help aspiring developers be aware of the design of a large-scale product.

  • Liked Dr. Savita Angadi

    Dr. Savita Angadi - Connected Vehicle – is far more than just the car…

    45 Mins

    For many IoT use cases there is a real challenge in streaming large amounts of data in real time, and the connected vehicle is no exception. Cars and trucks have the ability to generate TB of data daily, and connectivity can be spotty, especially in remote areas. To address this issue companies will want to move the analysis to the edge, on to the device where the data is generated. Will walk through the case in which there is an installed streaming engine on a gateway on a commercial vehicle. Data is analyzed locally on the vehicle, as it is generated, and alerts are communicated via cell connection. Models can be downloaded when a vehicle comes in for service, or over the air. Idea is to use data from the vehicle, like model, horsepower, oil temp, etc, to buid a decision tree to predict our target, turbo fault. Decision trees are nice in that that lay out the rules for you model clearly. In this case the model was predictive for certain engine horsepower ratings, time in service, model, and oil temps. Once this model generated acceptable accuracy with a 30 day window, plenty of time to act on the alert. Now in order to capture the value of this insight, we need to know immediately when a signal is detected, so this model will run natively on the vehicle, in our on board analytics engine.

  • Liked Dr. Savita Angadi

    Dr. Savita Angadi - What Chaos and Fractals has to do with Machine Learning?

    45 Mins

    The talk will cover how Chaos and Fractals are connected to machine learning. Artificial Intelligence is an attempt to model the characteristics of human brain. This has lead to model that can use connected elements essentially neurons. Most of the biological systems or simulation related developments in neural networks have practical results from computer science point of view. Chaos Theory has a good chance of being one of these developments. Brain itself is an good example of chaos system. Several attempts have been made to take an advantage of chaos in artificial neural systems to reproduce the benefits that have met quite a bit success.