1. Forecasting

2. For Next Week
Read five articles under the Additional Required Readings/Supply Chain Management folder on CULearn
Quiz 7 on Wednesday will cover these readings

3. It's tough to make predictions, especially about the future.
Forecasting Basics
It's tough to make predictions, especially about the future.
Yogi Berra

4. Forecasts are usually wrong Forecasts should contain error measure
Forecasting Basics
Forecasts are usually wrong
Forecasts should contain error measure
Aggregate forecasts are more accurate
The longer the horizon, the lower the accuracy

5. Costs of Forecasting Errors
IBM sells out new PC. Shortage may cost millions
Dell stock plunges, sharply off in its forecast of demand
Liz Claiborne said earnings decline is a consequence of excess inventories
Toyota believes it can save $100M with accurate ordering and inventory management

6. Qualitative - Subjective
Forecasting Basics
Qualitative - Subjective
Expertise Based
The “Sage”
Sales Force
Customer Surveys

7. Quantitative – Objective
Forecasting Basics
Quantitative – Objective
Causal Models
Time Series Models

8. Principal Tool: Regression Analysis
Causal Models
Use when historical data are available and when the relationship between the dependent (Y) and independent variable(s) (X) can be identified
Causal methods are good at predicting turning points in demand and for longer range forecasting
Principal Tool: Regression Analysis

9. Regression Analysis
Example: Forecasted value is calculated as follows:
Car Value (Y) = Car Price + (-Annual Depreciation * Age)
The difference between the actual value and the forecasted value is the residual

10. Causal Models
The goal of regression analysis is to determine the values of the parameters that minimize the sum of the squared residual values for the set of observations. This is known as a “least squares” regression fit.

11. Prediction based exclusively on previously observed values
Time Series Models
Prediction based exclusively on previously observed values
General Idea: Detect Patterns!
Short Term Demand Prediction
Prevalent Tool In Operations
Understand the players

12. Random Trend Seasonality Cyclic Time Series Patterns No Pattern
Linear (default) or Nonlinear
Seasonality
Repetition at Fixed Intervals
Cyclic
Long Term Economy
Understand the players

13. Random

14. Increasing Linear Trend

15. Curve Linear Trend

16. Seasonal & Increasing Trend

17. The Basic Concept of Time Series
The forecast for period t+1 can be calculated at the end of period t as a simple moving average as follows:
Ft+1 = (Sum of last n demands/n)
= (Dt + Dt-1 +…+Dt-n+1)/n
Understand the players
Ft+1: Forecast for the next period t+1, made in the current period t
D : Actual Demand
n : Number of periods

18. How to Choose the Right Technique
Understand the players
Demand predictions are dependent on life cycle

19. How to Choose the Right Technique
Understand the players
Product Development
Expert
Historical comparisons
Competitive comparisons

20. How to Choose the Right Technique
Understand the players
Product Introduction
Market tests
Consumer surveys

21. How to Choose the Right Technique
Understand the players
Growth
Causal models

22. How to Choose the Right Technique
Understand the players
Steady State
Time series
Causal models

23. How to Choose the Right Technique
Understand the players
Decline
Time series
Causal models