The document provides an overview of operational analytics, including topics such as descriptive analytics, forecasting, decision-making strategies, and decision trees. It discusses the importance of analyzing past data, optimizing production, and the implications of uncertainty in resource allocation. Additionally, it highlights various decision-making strategies such as risk-averse, risk-seeking, and risk-neutral approaches, along with practical applications of decision trees in complex problem-solving scenarios.

1. Operational
Analytics- An Over
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BY
PRUDHA PALLAVI PENTAPATI

2. Descriptive Analytics
 What is a fundamental Operational Problem?
 Time line of events

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. Forecasting
 What is Forecasting?
 Characteristics of Forecasts
 Probablity Distributions in Forecasting
 Model for future Demand

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. Decision Variables
 What are decision variables?
 Optimizing Production
 Build an Objective Function
 Working around constraints
 How to Identify Key Performance Indicators

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. Decision Trees
 What is a decision tree
 Structure of a decision tree
 Understanding a decision tree

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. 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