Module

Issues + Resources

1

Fundamentals of Decision Making & Problem Solving

 Words of the Wise

 What is a Decision? What does it mean to solve a problem?

 Understanding Limitation – every problem does not have a solution

 What is a Good/ Bad Decision?

 Why are so many decisions faulty? Where things go wrong...

 Why Process is Important

 Laziness

 Predictably Irrational – Self Deception

 The wrong people doing the analysis, making the decision

 No or Poor Objectives

 Faulty Decision Criteria

 Weak, Irrelevant Assumptions

 Selective Perceptions & Data Contamination

 Foregone Conclusions

2

Fundamentals of Logical Processes

 Examples of Logical Fallacies

 The Limits of Probabilities Built on Past Experience

 Inductive, Deductive Reasoning

 The Roles of Creative Thinking in Decision and Problem Solving

 Tautologies

3

How Quantitative Methods Can Help with Decisions and Problems

 Comparing Quantitative and Other Approaches

 Practical Limits on Quantitative Decision Analysis

 Principles of Optimization

 Introduction to Optimization Software

 Statistics & Probabilities

4

Overview & Introduction to Tools and Methods

 Recognizing the Expertise of the User Limits Effectiveness

 Spreadsheets & Data Bases

 Linear Programming & Simplex

 Decision Analysis

 Decision Trees and Implications

 Probabilistic Statistics and Decision Making

5

Linear Programming Applications

 Reducing Bias in Qualitative Analysis

 Applications of Linear Programming That Have Been Encountered in Practice

 An Appreciation for the Problems That Can be Modelled as Linear Programs

 Practice and Experience in Formulating Realistic Linear Programming Models

 Make or Buy Decisions

 Production Scheduling Decisions

 Work Force Allocation Decisions

6

Marketing and Financial Applications

 Basic and Feasible Solutions to Systems of Linear Equations

 Simplex Method for Solving Linear Programming Problems

 Marketing Decisions

 Financial Decisions

 Operations Decisions

7

Distribution / Network Decisions

 Special Features of Transportation, Assignment and Transhipment Problems

 Network and Linear Programming Models of the Transportation, Assignment and Transhipment problems

 The Shortest Route Problem

 The Maximal Flow Problem

 The Workforce Assignment Problem

 Inventory as a Transhipment Problem

 Additional Software and Tools

8

Inventory Decisions

 Where Inventory Costs Occur and Why Inventory Policy Decision are Important

 Economic Order Quantity (EOQ) Model

 Total Cost Model for Specific Inventory Systems

 Total Cost Model for How-Much-To-Order and When- To- Order Decisions

 Inventory Systems Involving Production Lot Size, Planned Shortages, and Quantity Discounts

 Single –Period Inventory Models

 Quantity and Reorder Point Decisions When Demand Must be Described by a Probability Distribution

 Lead Time Demand Distributions Used to Meet Acceptable Service Levels

 Order Quantity Decisions for Periodic Review Inventory Systems

9

Waiting Lines Decisions

 Where Waiting Line Problems Occur and Why These Problems are Important

 Single-Channel and Multiple- Channel Waiting Lines

 Poisson Distribution Describes Arrivals; Exponential Distribution Describes Service Times

 Formulas Identifying Operating Characteristics of the Following Waiting Line Models:

o Single- Channel Model with Poisson Arrivals and Exponential Service Times

o Multiple-Channel Model with Poisson Arrivals and Exponential Service Times

o Single-Channel Model with Poisson Arrivals and Arbitrary Service Times, and No Waiting

o Single-Channel Model with Poisson Arrivals, Exponential Service Times, and a Finite Calling Populations

 Economic Considerations in Decisions Concerning the Operation of a Waiting Line

10

Forecasting & Simulation

 Why Forecasting

 Basic Forecasting Categories and Methods

 Principles and Tools for Time Series Methods

 Principles and Tools for Regression Methods

 Markov Analysis and Other Methods

 Simulations as a Forecasting Method

 Relative Advantages and Disadvantages of Simulation Models