To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Chapter 9 Demand Forecasting

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Learning Objectives Be able to apply concepts listed in learning goals Be able to use formulas listed in the equation summary of chapter

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Learning Objectives Demonstrate proficiency in use of linear regression

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Learning Objectives Demonstrate proficiency in use of simple moving average

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Learning Objectives Demonstrate proficiency in use of weighted moving average

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Learning Objectives Demonstrate proficiency in use of exponential smoothing

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Learning Objectives Demonstrate proficiency in use of measures of forecast accuracy

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Introduction Demand estimates for products & services are starting point for all other forecasts in OM. Management teams develop sales forecasts based in part on demand estimates. Sales forecasts become inputs to both business strategy & production resource forecasts.

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Forecasting is an Integral Part of Business Planning ForecastMethod(s) DemandEstimates SalesForecastManagementTeam Inputs:Market,Economic,Other BusinessStrategy Production Resource Forecasts

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Examples of Production Resource Forecasts LongRange MediumRange ShortRange Years Months Weeks Product Lines, Factory Capacities ForecastHorizonTimeSpan Item Being Forecasted Unit of Measure Product Groups, Depart. Capacities Specific Products, Machine Capacities Dollars,Pounds Dollars,Pounds Prod. Units, Units

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Forecasting Methods Qualitative approaches Quantitative approaches

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Qualitative Approaches Usually based on judgments about causal factors that underlie demand of particular products or services Do not require demand history for product or service, therefore are useful for new products/services Approaches vary in sophistication from scientifically conducted surveys to intuitive hunches about future events Approach/method that is appropriate depends on product’s life cycle stage

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Qualitative Methods Executive opinionintuitive hunches Executive committee consensus Delphi method Survey of sales force Survey of customers Historical analogy Market research Scientifically Conducted Surveys

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Quantitative Forecasting Approaches Based on assumption that “forces” that generated past demand will generate future demand, i.e., History will tend to repeat itself Analysis of past demand pattern provides a good basis for forecasting future demand Majority of quantitative approaches fall in category of time series analysis

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Time series is repeated observations of demand for product or service in order of occurrence Analysis of time series identifies patterns Once patterns are identified, they can be used to develop forecast Time Series Analysis

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Data Patterns Horizontal is fluctuation of data around mean Trend is noted by an upward or downward sloping line Cycle is a data pattern that repeats itself... May take years Seasonality is a data pattern that repeats itself over period of one year or less Random fluctuation (noise) from random variation or unexplained causes

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Seasonality Length of time Number of before pattern Length of seasons is repeated season in pattern Year Quarter 4 Year Month 12 Year Week 52 Month Week 4 Month Day Week Day 7

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Time Span of Forecasts Long-range –Time spans usually greater than one year –Necessary to support strategic decisions about planning products, processes, & facilities Short-range –Time spans ranging from a few days to a few weeks –Cycles, seasonality, & trend may have little effect –Random fluctuation is main data pattern

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Quantitative Forecasting Approaches  Causal--linear regression  Time series  Naïve  Simple moving average  Weighted moving average  Exponential smoothing (exponentially weighted moving average)  Exponential smoothing with trend (double smoothing)  Learn how to calculate  Learn concept only

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Simple Linear Regression Relationship between one independent variable, X, and a dependent variable, Y. Assumed to be linear (straight line) Form: Y = a + bX –Y = Dependent variable –X = Independent variable – a = y-axis intercept – b = Slope of regression line Coefficient of correlation—r Coefficient of determination—r 2

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Causal Linear Regression

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Causal Methods Linear Regression Dependent variable Independent variable X Y

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Causal Methods Linear Regression Dependent variable Independent variable X Y

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Causal Methods Linear Regression Dependent variable Independent variable X Y Regression equation: Y = a + bX

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Causal Methods Linear Regression Dependent variable Independent variable X Y Actual value of Y Value of X used to estimate Y Regression equation: Y = a + bX

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Causal Methods Linear Regression Dependent variable Independent variable X Y Actual value of Y Estimate of Y from regression equation Value of X used to estimate Y Regression equation: Y = a + bX

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Causal Methods Linear Regression Dependent variable Independent variable X Y Actual value of Y Estimate of Y from regression equation Value of X used to estimate Y Deviation, or error { Regression equation: Y = a + bX

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Causal Methods Linear Regression SalesAdvertising Month(000 units)(000 $)

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Causal Methods Linear Regression SalesAdvertising Month(000 units)(000 $) a = b = X r = r 2 = s yx =

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Causal Methods Linear Regression SalesAdvertising Month(000 units)(000 $) a = b = X r = r 2 = s yx = |||| Advertising (thousands of dollars) 300 — 250 — 200 — 150 — 100 — 50 Sales (thousands of units)

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Causal Methods Linear Regression SalesAdvertising Month(000 units)(000 $) a = b = X r = r 2 = s yx = |||| Advertising (thousands of dollars) 300 — 250 — 200 — 150 — 100 — 50 Sales (thousands of units)

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Causal Methods Linear Regression SalesAdvertising Month(000 units)(000 $) a = b = X r = r 2 = s yx = |||| Advertising (thousands of dollars) 300 — 250 — 200 — 150 — 100 — 50 Y = X Sales (thousands of units)

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Causal Methods Linear Regression SalesAdvertising Month(000 units)(000 $) a = b = X r = r 2 = s yx = |||| Advertising (thousands of dollars) 300 — 250 — 200 — 150 — 100 — 50 Y = X Sales (thousands of units)

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Causal Methods Linear Regression SalesAdvertising Month(000 units)(000 $) a = b = X r = r 2 = s yx = |||| Advertising (thousands of dollars) 300 — 250 — 200 — 150 — 100 — 50 Y = X Sales (thousands of units) Forecast for Month 6 X = $1750, Y = (1.75)

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Causal Methods Linear Regression SalesAdvertising Month(000 units)(000 $) a = b = X r = r 2 = s yx = |||| Advertising (thousands of dollars) 300 — 250 — 200 — 150 — 100 — 50 Y = X Sales (thousands of units) Forecast for Month 6 X = $1750, Y = or 183,015 units

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Causal Methods Linear Regression SalesAdvertising Month(000 units)(000 $) a = b = X r = r 2 = s yx = |||| Advertising (thousands of dollars) 300 — 250 — 200 — 150 — 100 — 50 Y = X Sales (thousands of units) If current stock = 62,500 units, Production = 183, ,500 = 120,515 units

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Causal Methods Linear Regression SalesAdvertising Month(000 units)(000 $)

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Causal Methods Linear Regression Advertising, XSales, Y Month(000 units)(000 $)X 2 XYY , , , , , ,259

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Causal Methods Linear Regression Advertising, XSales, Y Month(000 units)(000 $)X 2 XYY , , , , , ,259

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Causal Methods Linear Regression Advertising, XSales, Y Month(000 units)(000 $)X 2 XYY , , , , , ,259

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Causal Methods Linear Regression Advertising, XSales, Y Month(000 units)(000 $)X 2 XYY , , , , , ,259

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Causal Methods Linear Regression Advertising, XSales, Y Month(000 units)(000 $)X 2 XYY , , , , , ,259

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Causal Methods Linear Regression Advertising, XSales, Y Month(000 units)(000 $)X 2 XYY , , , , , ,259

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Causal Methods Linear Regression Advertising, XSales, Y Month(000 units)(000 $)X 2 XYY , , , , , ,259

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Causal Methods Linear Regression Advertising, XSales, Y Month(000 units)(000 $)X 2 XYY , , , , , ,259

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Whittenberg’s Linear Regression Worksheet

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Simple Moving Average An averaging period (AP) is given or selected Forecast for next period is arithmetic average of AP most recent actual demands It is called a “simple” average because each period used to compute average is equally weighted... more

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Simple Moving Average It is called “moving” because as new demand data becomes available, oldest data is not used By increasing AP, forecast is less responsive to fluctuations in demand (low impulse response) By decreasing AP, forecast is more responsive to fluctuations in demand (high impulse response)

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Simple Moving Average

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Time Series Methods Simple Moving Averages Week 450 — 430 — 410 — 390 — 370 — Patient arrivals |||||| Patient arrivals

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Time Series Methods Simple Moving Averages Week 450 — 430 — 410 — 390 — 370 — |||||| Actual patient arrivals Patient arrivals

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Time Series Methods Simple Moving Averages Actual patient arrivals Actual patient arrivals 450 — 430 — 410 — 390 — 370 — Week |||||| Patient WeekArrivals Patient arrivals

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Time Series Methods Simple Moving Averages Actual patient arrivals Actual patient arrivals 450 — 430 — 410 — 390 — 370 — Week |||||| Patient WeekArrivals Patient arrivals

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Time Series Methods Simple Moving Averages Actual patient arrivals Week 450 — 430 — 410 — 390 — 370 — |||||| Patient WeekArrivals F 4 = Patient arrivals

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Time Series Methods Simple Moving Averages Actual patient arrivals 450 — 430 — 410 — 390 — 370 — Week |||||| Patient WeekArrivals F 4 = Patient arrivals

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Time Series Methods Simple Moving Averages Actual patient arrivals 450 — 430 — 410 — 390 — 370 — Week |||||| Patient WeekArrivals F 4 = Patient arrivals

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Time Series Methods Simple Moving Averages Actual patient arrivals Week 450 — 430 — 410 — 390 — 370 — |||||| Patient WeekArrivals F 5 = Patient arrivals

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Time Series Methods Simple Moving Averages Actual patient arrivals 450 — 430 — 410 — 390 — 370 — Week |||||| Patient WeekArrivals F 5 = Patient arrivals

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Time Series Methods Simple Moving Averages 450 — 430 — 410 — 390 — 370 — Week |||||| Actual patient arrivals Patient arrivals

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Time Series Methods Simple Moving Averages 450 — 430 — 410 — 390 — 370 — Week |||||| Actual patient arrivals 3-week MA forecast Patient arrivals

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Weighted Moving Average This is a variation on simple moving average where instead of weights used to compute average being equal, they are not equal This allows more recent demand data to have greater effect on moving average, & therefore the forecast... more

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Weighted Moving Average Weights must add to 1.0 & generally decrease in value with age of data Distribution of weights determine impulse response of the forecast

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Exponential Smoothing Weights used to compute forecast (moving average) are exponentially distributed Forecast is sum of old forecast and a portion of forecast error

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Exponential Smoothing Smoothing constant, , must be between 0.0 & 1.0 A large  provides a high impulse response forecast A small  provides a low impulse response forecast

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Time Series Methods Exponential Smoothing 450 — 430 — 410 — 390 — 370 — Week |||||| Patient arrivals

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Time Series Methods Exponential Smoothing 450 — 430 — 410 — 390 — 370 — Week |||||| Exponential Smoothing  = 0.10 F t +1 = F t +  (D t - F t ) Patient arrivals

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Time Series Methods Exponential Smoothing 450 — 430 — 410 — 390 — 370 — Week |||||| Exponential Smoothing  = 0.10 F 4 = (21) F 3 = 390 D 3 = 411 F t +1 = F t +  (D t - F t ) Patient arrivals

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Time Series Methods Exponential Smoothing 450 — 430 — 410 — 390 — 370 — Week |||||| F 4 = Exponential Smoothing  = 0.10 F 4 = F t +1 = F t +  (D t - F t ) Patient arrivals

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Time Series Methods Exponential Smoothing Week 450 — 430 — 410 — 390 — 370 — |||||| F 4 = D 4 = 415 Exponential Smoothing  = 0.10 F 4 = F 5 = F t +1 = F t +  (D t - F t ) Patient arrivals

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Time Series Methods Exponential Smoothing Week 450 — 430 — 410 — 390 — 370 — |||||| Patient arrivals

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Time Series Methods Exponential Smoothing 450 — 430 — 410 — 390 — 370 — Patient arrivals Week |||||| Exponential smoothing  = 0.10

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Forecast Accuracy Accuracy is typical criterion for judging performance of a forecasting approach Accuracy is how well forecasted values match actual values

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Forecast Accuracy

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Forecast Accuracy Forecast error Cumulative sum of forecast errors Mean absolute deviation Mean squared error

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Forecast Accuracy Tracking signal Standard deviation Mean absolute percent error

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Measures of Forecast Error E t = D t - F t Choosing a Method Forecast Error

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Choosing a Method Forecast Error Measures of Forecast Error E t = D t - F t  |E t | n Et2nEt2n CFE =  E t  = MSE = MAD = MAPE =  [ |E t | (100) ] / D t n  (E t - E ) 2 n - 1

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Absolute Error AbsolutePercent Month,Demand,Forecast,Error,Squared,Error,Error, tD t F t E t E t 2 |E t |(|E t |/D t )(100) % Total % Choosing a Method Forecast Error

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Choosing a Method Forecast Error Absolute Error AbsolutePercent Month,Demand,Forecast,Error,Squared,Error,Error, tD t F t E t E t 2 |E t |(|E t |/D t )(100) % Total % Measures of Error

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Choosing a Method Forecast Error Absolute Error AbsolutePercent Month,Demand,Forecast,Error,Squared,Error,Error, tD t F t E t E t 2 |E t |(|E t |/D t )(100) % Total % CFE = - 15 Measures of Error

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Choosing a Method Forecast Error Absolute Error AbsolutePercent Month,Demand,Forecast,Error,Squared,Error,Error, tD t F t E t E t 2 |E t |(|E t |/D t )(100) % Total % CFE = - 15 Measures of Error E = =

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Choosing a Method Forecast Error Absolute Error AbsolutePercent Month,Demand,Forecast,Error,Squared,Error,Error, tD t F t E t E t 2 |E t |(|E t |/D t )(100) % Total % MSE = = CFE = - 15 Measures of Error E = =

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Choosing a Method Forecast Error Absolute Error AbsolutePercent Month,Demand,Forecast,Error,Squared,Error,Error, tD t F t E t E t 2 |E t |(|E t |/D t )(100) % Total % MSE = = CFE = - 15 Measures of Error E = =  = 27.4

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Choosing a Method Forecast Error Absolute Error AbsolutePercent Month,Demand,Forecast,Error,Squared,Error,Error, tD t F t E t E t 2 |E t |(|E t |/D t )(100) % Total % MSE = = CFE = - 15 Measures of Error MAD = = E = =  = 27.4

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Choosing a Method Forecast Error Absolute Error AbsolutePercent Month,Demand,Forecast,Error,Squared,Error,Error, tD t F t E t E t 2 |E t |(|E t |/D t )(100) % Total % MSE = = CFE = - 15 Measures of Error MAD = = MAPE = = 10.2% 81.3% 8 E = =  = 27.4

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Choosing a Method Forecast Error Absolute Error AbsolutePercent Month,Demand,Forecast,Error,Squared,Error,Error, tD t F t E t E t 2 |E t |(|E t |/D t )(100) % Total % MSE = = CFE = - 15 Measures of Error MAD = = MAPE = = 10.2% 81.3% 8 E = =  = 27.4

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Choosing a Method Tracking Signals Percentage of the Area of the Normal Probability Distribution Within the Control Limits of the Tracking Signal Control Limit SpreadEquivalentPercentage of Area (number of MAD)Number of  2 Within Control Limits ± 1.0 ± 1.5 ± 2.0 ± 2.5 ± 3.0 ± 3.5 ± 4.0

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Choosing a Method Tracking Signals Percentage of the Area of the Normal Probability Distribution Within the Control Limits of the Tracking Signal Control Limit SpreadEquivalentPercentage of Area (number of MAD)Number of  2 Within Control Limits ± 1.0± 0.80 ± 1.5± 1.20 ± 2.0± 1.60 ± 2.5± 2.00 ± 3.0± 2.40 ± 3.5± 2.80 ± 4.0± 3.20

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Choosing a Method Tracking Signals Percentage of the Area of the Normal Probability Distribution Within the Control Limits of the Tracking Signal Control Limit SpreadEquivalentPercentage of Area (number of MAD)Number of  2 Within Control Limits ± 1.0± ± 1.5± ± 2.0± ± 2.5± ± 3.0± ± 3.5± ± 4.0±

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Choosing a Method Tracking Signals Tracking signal = CFE MAD +2.0 — +1.5 — +1.0 — +0.5 — 0 — — — — ||||| Observation number Tracking signal Control limit

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Choosing a Method Tracking Signals Tracking signal = CFE MAD +2.0 — +1.5 — +1.0 — +0.5 — 0 — — — — ||||| Observation number Tracking signal Control limit Out of control

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Criteria for Selecting a Forecasting Method Cost Accuracy Data available Time span Nature of products & services Impulse response & noise dampening

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Reasons for Ineffective Forecasting Not involving a broad cross section of people Not recognizing that forecasting is integral to business planning Not recognizing that forecasts will always be wrong Not forecasting right things Not selecting an appropriate forecasting method Not tracking accuracy of forecasting models

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Sources of Forecasting Data Consumer confidence index Consumer price index Housing starts Index of leading economic indicators Personal income and consumption Producer price index Purchasing manager’s index Retail sales