Powerpoint Presentation - Operations and Supply Chain
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chap015.pptMcGraw-Hill/Irwin
Copyright © 2011 The McGraw-Hill Companies, All Rights Reserved
Chapter 15
Demand Management and Forecasting
15-*
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.
15-*
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
LO 2
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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
LO 1
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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
LO 1
5
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Components of Demand
Average demand for a period of time
Trend
Seasonal element
Cyclical elements
Random variation
Autocorrelation
LO 3
7
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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
LO 5
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
LO 6
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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
LO 6
15-*
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
LO 5
15-*
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
LO 5
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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
LO 5
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Trend Effects in Exponential Smoothing
- An trend in data causes the exponential forecast to always lag the actual data
Can be corrected by adding in a trend adjustment
- To correct need two constants: alpha and beta
LO 5
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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
LO 5
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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
LO 5
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