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McGraw-Hill/Irwin Copyright © 2009 by The McGraw-Hill Companies, Inc. All rights reserved.

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Presentation on theme: "McGraw-Hill/Irwin Copyright © 2009 by The McGraw-Hill Companies, Inc. All rights reserved."— Presentation transcript:

1 McGraw-Hill/Irwin Copyright © 2009 by The McGraw-Hill Companies, Inc. All rights reserved.

2 Demand Management and Forecasting
Chapter 15 Demand Management and Forecasting

3 Qualitative Forecasting Methods
15-3 OBJECTIVES Demand Management Qualitative Forecasting Methods Simple & Weighted Moving Average Forecasts Exponential Smoothing Simple Linear Regression Web-Based Forecasting 2

4 Independent Demand: Finished Goods Dependent Demand: Raw Materials,
15-4 Demand Management Independent Demand: Finished Goods Dependent Demand: Raw Materials, Component parts, Sub-assemblies, etc. A B(4) C(2) D(2) E(1) D(3) F(2) 3

5 Independent Demand: What a firm can do to manage it?
15-5 Independent Demand: What a firm can do to manage it? Can take an active role to influence demand Can take a passive role and simply respond to demand 4

6 Qualitative (Judgmental)
15-6 Types of Forecasts Qualitative (Judgmental) Quantitative Time Series Analysis Causal Relationships Simulation 5

7 Average demand for a period of time Trend Seasonal element
15-7 Components of Demand Average demand for a period of time Trend Seasonal element Cyclical elements Random variation Autocorrelation 7

8 Finding Components of Demand
15-8 Finding Components of Demand Seasonal variation 1 2 3 4 x Year Linear Trend Sales 6

9 Grass Roots Qualitative Methods Market Research Historical analogy
15-9 Qualitative Methods Executive Judgment Grass Roots Qualitative Methods Market Research Historical analogy Delphi Method Panel Consensus

10 15-10 Delphi Method l. Choose the experts to participate representing a variety of knowledgeable people in different areas 2. Through a questionnaire (or ), obtain forecasts (and any premises or qualifications for the forecasts) from all participants 3. Summarize the results and redistribute them to the participants along with appropriate new questions 4. Summarize again, refining forecasts and conditions, and again develop new questions 5. Repeat Step 4 as necessary and distribute the final results to all participants 10

11 You can pick models based on: 1. Time horizon to forecast
15-11 Time Series Analysis Time series forecasting models try to predict the future based on past data You can pick models based on: 1. Time horizon to forecast 2. Data availability 3. Accuracy required 4. Size of forecasting budget 5. Availability of qualified personnel 14

12 Simple Moving Average Formula
15-12 Simple Moving Average Formula The simple moving average model assumes an average is a good estimator of future behavior The formula for the simple moving average is: Ft = Forecast for the coming period N = Number of periods to be averaged A t-1 = Actual occurrence in the past period for up to “n” periods 15

13 Simple Moving Average Problem (1)
15-13 Simple Moving Average Problem (1) Question: What are the 3-week and 6-week moving average forecasts for demand? Assume you only have 3 weeks and 6 weeks of actual demand data for the respective forecasts 15

14 Calculating the moving averages gives us:
15-14 Calculating the moving averages gives us: F4=( )/3 =682.67 F7=( )/6 =768.67 The McGraw-Hill Companies, Inc., 2004 16

15 15-15 Plotting the moving averages and comparing them shows how the lines smooth out to reveal the overall upward trend in this example Note how the 3-Week is smoother than the Demand, and 6-Week is even smoother 17

16 Simple Moving Average Problem (2) Data
15-16 Simple Moving Average Problem (2) Data Question: What is the 3 week moving average forecast for this data? Assume you only have 3 weeks and 5 weeks of actual demand data for the respective forecasts 18

17 Simple Moving Average Problem (2) Solution
15-17 Simple Moving Average Problem (2) Solution F4=( )/3 =758.33 F6=( )/5 =710.00 19

18 Weighted Moving Average Formula
15-18 Weighted Moving Average Formula While 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 The formula for the moving average is: wt = weight given to time period “t” occurrence (weights must add to one) 20

19 Weighted Moving Average Problem (1) Data
15-19 Weighted Moving Average Problem (1) Data Question: Given the weekly demand and weights, what is the forecast for the 4th period or Week 4? Weights: t-1 .5 t-2 .3 t-3 .2 Note that the weights place more emphasis on the most recent data, that is time period “t-1” 20

20 Weighted Moving Average Problem (1) Solution
15-20 Weighted Moving Average Problem (1) Solution F4 = 0.5(720)+0.3(678)+0.2(650)=693.4 21

21 Weighted Moving Average Problem (2) Data
15-21 Weighted Moving Average Problem (2) Data Question: Given the weekly demand information and weights, what is the weighted moving average forecast of the 5th period or week? Weights: t-1 .7 t-2 .2 t-3 .1 22

22 Weighted Moving Average Problem (2) Solution
15-22 Weighted Moving Average Problem (2) Solution F5 = (0.1)(755)+(0.2)(680)+(0.7)(655)= 672 23

23 Exponential Smoothing Model
15-23 Exponential Smoothing Model Ft = Ft-1 + a(At-1 - Ft-1) Premise: The most recent observations might have the highest predictive value Therefore, we should give more weight to the more recent time periods when forecasting 24

24 Exponential Smoothing Problem (1) Data
15-24 Exponential Smoothing Problem (1) Data Question: Given the weekly demand data, what are the exponential smoothing forecasts for periods 2-10 using a=0.10 and a=0.60? Assume F1=D1 25

25 15-25 Answer: The respective alphas columns denote the forecast values. Note that you can only forecast one time period into the future. 26

26 Exponential Smoothing Problem (1) Plotting
15-26 Exponential Smoothing Problem (1) Plotting Note how that the smaller alpha results in a smoother line in this example 27

27 Exponential Smoothing Problem (2) Data
15-27 Exponential Smoothing Problem (2) Data Question: What are the exponential smoothing forecasts for periods 2-5 using a =0.5? Assume F1=D1 28

28 Exponential Smoothing Problem (2) Solution
15-28 Exponential Smoothing Problem (2) Solution F1=820+(0.5)( )=820 F3=820+(0.5)( )=797.75 29

29 The MAD Statistic to Determine Forecasting Error
15-29 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 30

30 15-30 MAD Problem Data Question: What is the MAD value given the forecast values in the table below? Month Sales Forecast 1 220 n/a 2 250 255 3 210 205 4 300 320 5 325 315 31

31 15-31 MAD Problem Solution Month Sales Forecast Abs Error 1 220 n/a 2 250 255 5 3 210 205 4 300 320 20 325 315 10 40 Note that by itself, the MAD only lets us know the mean error in a set of forecasts 32

32 Tracking Signal Formula
15-32 Tracking Signal Formula The Tracking Signal or 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. The TS formula is: 33

33 Simple Linear Regression Model
15-33 Simple Linear Regression Model The simple linear regression model seeks to fit a line through various data over time Y a x (Time) Yt = a + bx Is the linear regression model Yt is the regressed forecast value or dependent variable in the model, a is the intercept value of the the regression line, and b is similar to the slope of the regression line. However, since it is calculated with the variability of the data in mind, its formulation is not as straight forward as our usual notion of slope. 35

34 Simple Linear Regression Formulas for Calculating “a” and “b”
15-34 Simple Linear Regression Formulas for Calculating “a” and “b” 36

35 Simple Linear Regression Problem Data
15-35 Simple Linear Regression Problem Data Question: Given the data below, what is the simple linear regression model that can be used to predict sales in future weeks? 37

36 15-36 Answer: First, using the linear regression formulas, we can compute “a” and “b” 37

37 15-37 The resulting regression model is: Yt = x Now if we plot the regression generated forecasts against the actual sales we obtain the following chart: 180 Period 135 140 145 150 155 160 165 170 175 1 2 3 4 5 Sales Forecast 37

38 Web-Based Forecasting: CPFR
15-38 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, including manufacturers, distributors and retailers. CPFR’s objective is to exchange selected internal information to provide for a reliable, longer term future views of demand in the supply chain. CPFR uses a cyclic and iterative approach to derive consensus forecasts. 33

39 Web-Based Forecasting: Steps in CPFR
15-39 Web-Based Forecasting: Steps in CPFR Creation of a front-end partnership agreement. Joint business planning Development of demand forecasts Sharing forecasts Inventory replenishment 33

40 Transportation method Simulation Linear programming All of the above
15-40 Question Bowl Which of the following is a classification of a basic type of forecasting? Transportation method Simulation Linear programming All of the above None of the above Answer: b. Simulation (There are four types including Qualitative, Time Series Analysis, Causal Relationships, and Simulation.) 7

41 15-41 Question Bowl Which of the following is an example of a “Qualitative” type of forecasting technique or model? Grass roots Market research Panel consensus All of the above None of the above Answer: d. All of the above (Also includes Historical Analogy and Delphi Method.) 7

42 Exponential smoothing Panel consensus All of the above
15-42 Question Bowl Which of the following is an example of a “Time Series Analysis” type of forecasting technique or model? Simulation Exponential smoothing Panel consensus All of the above None of the above Answer: b. Exponential smoothing (Also includes Simple Moving Average, Weighted Moving Average, Regression Analysis, Box Jenkins, Shiskin Time Series, and Trend Projections.) 7

43 Time horizon to forecast Data availability Accuracy required
15-43 Question Bowl Which of the following is a reason why a firm should choose a particular forecasting model? Time horizon to forecast Data availability Accuracy required Size of forecasting budget All of the above Answer: e. All of the above (Also should include “availability of qualified personnel” .) 7

44 Answer: d. Only b and c above
15-44 Question Bowl Which of the following are ways to choose weights in a Weighted Moving Average forecasting model? Cost Experience Trial and error Only b and c above None of the above Answer: d. Only b and c above 7

45 Answer: d. All of the above
15-45 Question Bowl Which of the following are reasons why the Exponential Smoothing model has been a well accepted forecasting methodology? It is accurate It is easy to use Computer storage requirements are small All of the above None of the above Answer: d. All of the above 7

46 15-46 Question Bowl The value for alpha or α must be between which of the following when used in an Exponential Smoothing model? 1 to 10 1 to 2 0 to 1 -1 to 1 Any number at all Answer: c. 0 to 1 7

47 Which of the following are sources of error in forecasts? Bias Random
15-47 Question Bowl Which of the following are sources of error in forecasts? Bias Random Employing the wrong trend line All of the above None of the above Answer: d. All of the above 7

48 15-48 Question Bowl Which of the following would be the “best” MAD values in an analysis of the accuracy of a forecasting model? 1000 100 10 1 Answer: e. 0 7

49 15-49 Question Bowl If a Least Squares model is: Y=25+5x, and x is equal to 10, what is the forecast value using this model? 100 75 50 25 None of the above Answer: b. 75 (Y=25+5(10)=75) 7

50 Which of the following are examples of seasonal variation?
15-50 Question Bowl Which of the following are examples of seasonal variation? Additive Least squares Standard error of the estimate Decomposition None of the above Answer: a. Additive (The other type is of seasonal variation is Multiplicative.) 7

51 15-51 End of Chapter 15

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