1. Demand Management and Forecasting
Chapter 15
Demand Management and Forecasting

2. Learning Objectives
Understand the role of forecasting as a basis for supply chain planning.
Compare the differences between independent and dependent demand.
Identify the basic components of independent demand: average, trend, seasonal, and random variation.
Describe the common qualitative forecasting techniques such as the Delphi method and Collaborative Forecasting.
Show how to make a time series forecast using regression, moving averages, and exponential smoothing.
Use decomposition to forecast when trend and seasonality is present.

3. Demand Management
Strategic forecasts: forecasts used to help set the strategy of how demand will be met
Tactical forecasts: forecasted needed for how a firm operates processes on a day-to-day basis
The purpose of demand management is to coordinate and control all sources of demand
Two basic sources of demand
Dependent demand: the demand for a product or service caused by the demand for other products or services
Independent demand: the demand for a product or service that cannot be derived directly from that of other products
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4. Demand Management Continued
Not much a firm can do about dependent demand
It is demand that must be met
There is a lot a firm can do about independent demand
Take an active role to influence demand
Take a passive role and respond to demand
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5. Types of Forecasts
Four basic types
Qualitative
Time series analysis
Causal relationships
Simulation
Time series analysis is based on the idea that data relating to past demand can be used to predict future demand
Primary focus of this chapter
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6. Average demand for a period of time Trend Seasonal element
Components of Demand
Average demand for a period of time
Trend
Seasonal element
Cyclical elements
Random variation
Autocorrelation
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7. Common Types of Trends
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8. Short term: forecast under three months
Time Series Analysis
Short term: forecast under three months
Tactical decisions
Medium term: three months to two years
Capturing seasonal effects
Long term: forecast longer than two years
Detecting general trends
Identifying major turning points
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9. A Guide to Selecting an Appropriate Forecasting Method
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10. Pick Forecasting Model Based On
Time horizon to forecast
Data availability
Accuracy required
Size of forecasting budget
Availability of qualified personnel
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11. Linear Regression Analysis
Regression: functional relationship between two or more correlated variables
It is used to predict one variable given the other
Y = a + bX
Y is the value of the dependent variable
a is the Y intercept
b is the slope
X is the independent variable
Assumes data falls in a straight line
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12. Example 15.1: The Data and Least Squares Regression Line
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13. Example 15.1: Equations and Calculating Totals
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14. Example 15.1: Calculating the Forecast
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15. Decomposition of a Time Series
Time series: chronologically ordered data that may contain one or more components of demand
Decomposition: identifying and separating the time series data into these components
Seasonal variation
Additive: the seasonal amount is constant
Multiplicative: the seasonal variation is a percentage of demand
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16. Additive and Multiplicative Seasonal Variation Superimposed on Changing Trend
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17. Example 15.3: The Data and Hand Fitting
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18. Example 15.3: Computing Seasonal Factors and Computing Forecast
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19. Decomposition Using Least Squares Regression
Determine the seasonal factor
Deseasonalize the original data
Develop a least squares regression line for the deseasonalized data
Project the regression line through the period of the forecast
Create the final forecast by adjusting the regression line by the seasonal factor
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20. Steps 1-3 Deseasonalized Demand
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21. Steps 4 – 5
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22. Important to select the best period
Simple Moving Average
Useful when demand is neither growing nor declining rapidly and does not have seasonal characteristics
Moving averages can be centered or used to predict the following period
Important to select the best period
Longer gives more smoothing
Shorter reacts quicker to trends
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23. Simple Moving Average Formula
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24. Forecast Demand Based on a Three- and a Nine-Week Simple Moving Average
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25. Weighted Moving Average
The moving average formula implies an equal weight being placed on each value that is being averaged
The weighted moving average permits an unequal weighting on prior time periods
All the weights must sum to one
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26. Experience and trial-and-error are the simplest ways
Choosing Weights
Experience and trial-and-error are the simplest ways
Generally, the most recent past is the best indicator
When data are seasonal, weights should be established accordingly
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27. Exponential Smoothing
Most used of all forecasting techniques
Integral part of all computerized forecasting programs
Widely used in retail and service
Widely accepted because…
Exponential models are surprisingly accurate
Formulating an exponential model is relatively easy
The user can understand how the model works
Little computation is required to use the model
Computer storage requirements are small
Tests for accuracy are easy to compute
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28. Exponential Smoothing Model
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29. Exponential Smoothing Example (=0.20)
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30. Trend Effects in Exponential Smoothing
An trend in data causes the exponential forecast to always lag the actual data
Can be corrected somewhat by adding in a trend adjustment
To correct the trend, we need two smoothing constants
Smoothing constant alpha ()
Trend smoothing constant delta (δ)
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31. Exponential Forecasts versus Actual Demand over Time Showing the Forecast Lag
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32. Trend Effects Equations
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33. Bias errors: when a consistent mistake is made
Forecast Error
Bias errors: when a consistent mistake is made
Random errors: errors that cannot be explained by the forecast model being used
Measures of error
Mean absolute deviation (MAD)
Mean absolute percent error (MAPE)
Tracking signal
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34. The MAD Statistic to Determine Forecasting Error
The ideal MAD is zero which would mean there is no forecasting error
The larger the MAD, the less the accurate the resulting model
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35. Tracking Signal
The tracking signal (TS) is a measure that indicates whether the forecast average is keeping pace with any genuine upward or downward changes in demand
Depending on the number of MAD’s selected, the TS can be used like a quality control chart indicating when the model is generating too much error in its forecasts
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36. Computing the MAD, the RSFE, and the TS from Forecast and Actual Data
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37. Causal Relationship Forecasting
Causal relationship forecasting: using independent variables other than time to predict future demand
The independent variable must be a leading indicator
Must find those occurrences that are really the causes
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38. Qualitative Techniques in Forecasting
Qualitative forecasting techniques take advantage of the knowledge of experts
Most useful when the product is new or there is little experience with selling into a new region
The following are samples of qualitative forecasting techniques
Market research
Panel consensus
Historical analogy
Delphi method
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39. Web-Based Forecasting: (CPFR)
Collaborative planning, forecasting, and replenishment (CPFR): a Web-based tool used to coordinate demand forecasting, production and purchase planning, and inventory replenishment between supply chain trading partners
Used to integrate the multi-tier or n-Tier supply chain
Objective is to exchange selected internal information to provide for a reliable, longer term future views of demand
CPFR uses a cyclic and iterative approach to derive consensus forecasts
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40. Web-Based Forecasting: Steps in CPFR
Creation of a front-end partnership agreement
Joint business planning
Development of demand forecasts
Sharing forecasts
Inventory replenishment
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