Manufacturing Planning and ControlCopyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved.
McGraw-Hill/Irwin
3-*
Forecasting
The forecasting process involves much more than just the estimation of future demand. The forecast also needs to take into consideration the intended use of the forecast, the methodology for aggregating and disaggregating the forecast, and assumptions about future conditions.
3-*
Agenda
3-*
The forecast information and technique must match the intended application
For strategic decisions such as capacity or market expansion highly aggregated estimates of general trends are necessary
Sales and operations planning activities require more detailed forecasts in terms of product families and time periods
Master production scheduling and control demand highly detailed forecasts, which only need to cover a short period of time
3-*
Forecast is presented in general terms (sales dollars, tons, hours)
Aggregation level may be related to broad indicators (gross national product, income)
Causal models and regression/correlation analysis are typical tools
Managerial insight is critical and top management involvement is intense
Forecast is generally prepared annually and covers a period of years
3-*
Forecast is presented in aggregate measures (dollars, units)
Aggregation level is related to product families (common family measurement)
Forecast is typically generated by summing forecasts for individual products
Managerial involvement is moderate and limited to adjustment of aggregate values
Forecast is generally prepared several times each year and covers a period of several months to a year
3-*
Forecast is presented in terms of individual products (units)
Forecast is typically generated by mathematical procedures, often using software
Projection techniques are common
Assumption is that the past is a valid predictor of the future
Managerial involvement is minimal
Forecast is updated almost constantly and covers a period of days or weeks
3-*
Regression identifies a relationship between two or more correlated variables
Linear regression is a special case where the relationship is defined by a straight line, used for both time series and causal forecasting
Y = a + bX
Y is value of dependent variable, a is the y-intercept of the line, b is the slope, and X is the value of the independent variable
3-*
Least Squares Method
Objective–find the line that minimizes the sum of the squares of the vertical distance between each data point and the line
Y – calculated dependent variable value
yi – actual dependent variable point
a – y intercept
x – time period
Y = a + bx
Least Squares Regression Line
Regression errors are the vertical distance from the point to the line
3-*
Least Squares Example
Standard Error of Estimate (Syx) – how well the line fits the data
Quarter
Calculation
Forecast
13
3-*
Seasonality
Seasonality may (or may not) be relative to the general demand trend
Additive seasonal variation is constant regardless of changes in average demand
Multiplicative seasonal variation maintains a consistent relationship to the average demand (this is the more common case)
3-*
Seasonal Factor
To account for seasonality within the forecast, the seasonal factor is calculated
The amount of correction needed in a time series to adjust for the season of the year
Season
Seasonal Factor
Seasonal Factor
If we expect (forecast) next year’s sales to be 1,100 units, the seasonal forecast is calculated using the seasonal factors
Season
ExpectedSales
Seasonal Factor
Trend = 170 +55t
Estimate of trend, use linear regression software to obtain more precise results
Quarter
Amount
Seasonality–Trend and Season
Seasonal factors are calculated for each season, then averaged for similar seasons
Seasonal Factor = Actual/Trend
Seasonality–Trend and Season
Forecasts are calculated by extending the linear regression and then adjusting by the appropriate seasonal factor
FITS–Forecast Including Trend and Seasonal Factors
3-*
Decompose the time series into its components
Find seasonal component
Deseasonalize the demand
Find trend component
Project trend component into future
Multiply trend component by seasonal component
3-*
Calculate average of same period values
Period
Quarter
Seasonal Factor
Period
Quarter
Seasonal Factor
Calculate seasonal factor for each period
Period
Quarter
Seasonal Factor
Period
Quarter
Seasonal Factor
Calculate deseasonalized demand for each period
Period
Quarter
Y= 555.0 + 342.2x
Use linear regression to fit trend line to deseasonalized data
Period
Project Linear Trend
Short-Term Forecasting Techniques
Statistical Forecasting Models
Moving Average–Unweighted average of a given number of past periods is used to forecast the future
Exponential Smoothing–Weighted average of all past periods is used to forecast the future
Both assume that there is an underlying pattern of demand that is consistent over some period of time
3-*
Moving Average Forecasting
i – period number
t – current period
n - number of periods in moving average (smaller n makes forecast more responsive to recent values
3-*
Exponential Smoothing Forecasting
α – smoothing constant (0≤α≤1) (higher α makes forecast more responsive to recent values)
t – current period
3-*
Mean Error (bias)
Mean Absolute Deviation (MAD)
Standard Deviation of forecast error = 1.25*MAD
Measuring both bias and MAD is critical to understanding the quality of the forecast
3-*
The SOP process reconciles differences in forecasts from various sources
Customer/product knowledge
Sum of individual product detailed forecasts (by product family, for example)
SOP result is an aggregate demand forecast
Long-term and/or aggregate forecasts are more accurate than short-term, detailed forecasts
3-*
One means of aggregating and disaggregating forecasts is pyramid forecasting
Ensures consistency as the forecast sources are integrated
Provides a logical framework for summing lower level forecasts and distributing higher level forecast changes to individual products
3-*
External Information
Activities or conditions that may invalidate the assumption that history is a good predictor must be accounted for in the forecasting process
Special promotions, product changes, advertising, competitors’ actions
Changes to forecasting process may be needed
Change exponential smoothing parameter to place more (or less) emphasis on recent history
Forecast more frequently to identify conditions that result in higher forecast errors
3-*
Principles
Forecast models should be as simple as possible. Simple models often outperform more complicated approaches.
Inputs (data) and outputs (forecasts) must be monitored for quality and appropriateness.
Information on the sources of variation (seasonality, market trends, company policies) should be incorporated into the forecasting system.
Forecasts from different sources must be reconciled and made consistent with company plans and constraints.
3-*
Quiz – Chapter 3
A forecast used for Master Production Scheduling and Control is likely to cover a period of _____________.
Regression analysis where the relationship between variables is a straight line is called _______ _______.
In a time series analysis, time is the _________ variable.
An exponential smoothing forecast considers all past data (T/F).
In an exponential smoothing forecast, a higher level of alpha (α) will place more emphasis on recent history (T/F).
Mean error of a forecast provides information concerning the forecast’s ________.
2
2
2
2
2
1
1