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Operational analytics overview

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This Presentation gives an over view of concepts that needs to be studied as a part of Operational Analytics Specialization

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Operational analytics overview

  1. 1. Operational Analytics- An Over View BY PRUDHA PALLAVI PENTAPATI
  2. 2. Descriptive Analytics  What is a fundamental Operational Problem?  Time line of events
  3. 3. Steps before the Solution  Analyse the past data  How much should you order  News Vendor Problem  Time’s Magazine Aprroach  Broader application to the problem
  4. 4. Forecasting  What is Forecasting?  Characteristics of Forecasts  Probablity Distributions in Forecasting  Model for future Demand
  5. 5. Making Best decisions with low uncertainity  A resource allocation Example  Converting a verbal problem application into an algebraic model  Algebraic model to spreadsheet implementation  Matching demand and supply across space
  6. 6. Decision Variables  What are decision variables?  Optimizing Production  Build an Objective Function  Working around constraints  How to Identify Key Performance Indicators
  7. 7. Risk and Evaluation of Alternatives  Making Decisions in Low-Uncertainty vs. High-Uncertainty Settings  Connecting Random Inputs and Random Outputs  Modeling Random Variables using Scenarios
  8. 8. Decision Trees  What is a decision tree  Structure of a decision tree  Understanding a decision tree
  9. 9. Approach to Evaluate Options using Decision Trees  “Maxi-min” strategy  Choose the actions that maximize the minimum outcome  Avoids bad outcomes…and…ignores the possibility of good outcomes  “Risk averse” strategy  “Maxi-max” strategy  Choose the actions that maximize the maximum outcome  Seeks good outcomes...and…ignores the possibility of bad outcomes  “Risk seeking” strategy  Maximize the expected value of the outcomes  Gives equal weight to good and bad outcomes  “Risk neutral” strategy
  10. 10. Decision Trees in Practice  IDEA is a small example designed to convey the essential ideas  In practice, decision trees are used to evaluate a wide range of complex problems. You can find examples in articles published in Interfaces m R&D licensing  Credit Scoring ,Polio Eradication  There exists software to help manage and analyze large decision trees m core-feature, single user, products: e.g., Tree Plan (free trial) massive- feature, enterprise-use products: Decision Tools, Logical Decisions

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