Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Operational analytics overview

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

  • Be the first to comment

  • Be the first to like this

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