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**The Starting Point for All Planning**

Chapter 3 Forecasting in POM: The Starting Point for All Planning

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**Overview Introduction Qualitative Forecasting Methods**

Quantitative Forecasting Models How to Have a Successful Forecasting System Computer Software for Forecasting Forecasting in Small Businesses and Start-Up Ventures Wrap-Up: What World-Class Producers Do

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Demand Management Independent demand items are the only items demand for which needs to be forecast These items include: Finished goods and Spare parts

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**Demand Management Independent Demand (finished goods and spare parts)**

B(4) C(2) D(2) E(1) D(3) F(2) Dependent Demand (components) 3

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Introduction Demand estimates for independent demand products and services are the starting point for all the other forecasts in POM. Management teams develop sales forecasts based in part on demand estimates. Sales forecasts become inputs to both business strategy and production resource forecasts.

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**Forecasting is an Integral Part of Business Planning**

Inputs: Market, Economic, Other Demand Estimates Forecast Method(s) Sales Forecast Management Team Business Strategy Production Resource Forecasts

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**Examples of Production Resource Forecasts**

Forecast Horizon Time Span Item Being Forecast Units of Measure Long-Range Years Product lines Factory capacities Planning for new products Capital expenditures Facility location or expansion R&D Dollars, tons, etc. Medium-Range Months Product groups Department capacities Sales planning Production planning and budgeting Short-Range Weeks Specific product quantities Machine capacities Planning Purchasing Scheduling Workforce levels Production levels Job assignments Physical units of products 12

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**Qualitative Approaches Quantitative Approaches**

Forecasting Methods Qualitative Approaches Quantitative Approaches

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**Qualitative Forecasting Applications Small and Large Firms**

Technique Low Sales (less than $100M) High Sales (more than $500M) Manager’s Opinion 40.7% 39.6% Executive’s Opinion 41.6% Sales Force Composite 29.6% 35.4% Number of Firms 27 48 Source: Nada Sanders and Karl Mandrodt (1994) “Practitioners Continue to Rely on Judgmental Forecasting Methods Instead of Quantitative Methods,” Interfaces, vol. 24, no. 2, pp Note: More than one response was permitted. 12

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**Qualitative Approaches**

Usually based on judgments about causal factors that underlie the demand of particular products or services Do not require a demand history for the product or service, therefore are useful for new products/services Approaches vary in sophistication from scientifically conducted surveys to intuitive hunches about future events

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**Qualitative Methods Executive committee consensus Delphi method**

Survey of sales force Survey of customers Historical analogy Market research

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**Quantitative Forecasting Approaches**

Based on the assumption that the “forces” that generated the past demand will generate the future demand, i.e., history will tend to repeat itself Analysis of the past demand pattern provides a good basis for forecasting future demand Majority of quantitative approaches fall in the category of time series analysis

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**Quantitative Forecasting Applications Small and Large Firms**

Technique Low Sales (less than $100M) High Sales (more than $500M) Moving Average 29.6% 29.2 Simple Linear Regression 14.8% 14.6 Naive 18.5% Single Exponential Smoothing 20.8 Multiple Regression 22.2% 27.1 Simulation 3.7% 10.4 Classical Decomposition 8.3 Box-Jenkins 6.3 Number of Firms 27 48 Source: Nada Sanders and Karl Mandrodt (1994) “Practitioners Continue to Rely on Judgmental Forecasting Methods Instead of Quantitative Methods,” Interfaces, vol. 24, no. 2, pp Note: More than one response was permitted. 12

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Time Series Analysis A time series is a set of numbers where the order or sequence of the numbers is important, e.g., historical demand Analysis of the time series identifies patterns Once the patterns are identified, they can be used to develop a forecast

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**Components of Time Series**

What’s going on here? x x Sales 1 2 3 4 Year 6

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**Components of Time Series**

Trends are noted by an upward or downward sloping line Seasonality is a data pattern that repeats itself over the period of one year or less Cycle is a data pattern that repeats itself... may take years Irregular variations are jumps in the level of the series due to extraordinary events Random fluctuation from random variation or unexplained causes

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**Actual Data & the Regression Line**

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**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

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**Eight Steps to Forecasting**

Determining the use of the forecast--what are the objectives? Select the items to be forecast Determine the time horizon of the forecast Select the forecasting model(s) Collect the data Validate the forecasting model Make the forecast Implement the results

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**Quantitative Forecasting Approaches**

Linear Regression Simple Moving Average Weighted Moving Average Exponential Smoothing (exponentially weighted moving average) Exponential Smoothing with Trend (double smoothing)

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**Simple Linear Regression**

Relationship between one independent variable, X, and a dependent variable, Y. Assumed to be linear (a straight line) Form: Y = a + bX Y = dependent variable X = independent variable a = y-axis intercept b = slope of regression line

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**Simple Linear Regression Model**

Yt = a + bx Y x (weeks) b is similar to the slope. 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

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Calculating a and b 36

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**Regression Equation Example**

Develop a regression equation to predict sales based on these five points. 37

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**Regression Equation Example**

Slide 24 of 55 38

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**y = 143.5 + 6.3t Regression Equation Example 180 175 170 165 160 Sales**

155 Sales Forecast 150 145 140 135 Period 1 2 3 4 5 Slide 25 of 55 39

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Forecast Accuracy Accuracy is the typical criterion for judging the performance of a forecasting approach Accuracy is how well the forecasted values match the actual values

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Monitoring Accuracy Accuracy of a forecasting approach needs to be monitored to assess the confidence you can have in its forecasts and changes in the market may require reevaluation of the approach Accuracy can be measured in several ways Mean absolute deviation (MAD) Mean squared error (MSE)

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**Mean Absolute Deviation (MAD)**

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**Mean Squared Error (MSE)**

MSE = (Syx)2 Small value for Syx means data points tightly grouped around the line and error range is small. The smaller the standard error the more accurate the forecast. MSE = 1.25(MAD) When the forecast errors are normally distributed

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**Example--MAD Month Sales Forecast 1 220 n/a 2 250 255 3 210 205 4 300**

320 5 325 315 Determine the MAD for the four forecast periods 31

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**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 32

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**Simple Moving Average An averaging period (AP) is given or selected**

The forecast for the next period is the arithmetic average of the AP most recent actual demands It is called a “simple” average because each period used to compute the average is equally weighted . . . more

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Simple Moving Average It is called “moving” because as new demand data becomes available, the oldest data is not used By increasing the AP, the forecast is less responsive to fluctuations in demand (low impulse response) By decreasing the AP, the forecast is more responsive to fluctuations in demand (high impulse response)

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Simple Moving Average Let’s develop 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

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Simple Moving Average Slide 35 of 55 16

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Simple Moving Average Slide 36 of 55 17

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**Weighted Moving Average**

This is a variation on the simple moving average where instead of the weights used to compute the average being equal, they are not equal This allows more recent demand data to have a greater effect on the moving average, therefore the forecast . . . more

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**Weighted Moving Average**

The weights must add to 1.0 and generally decrease in value with the age of the data The distribution of the weights determine impulse response of the forecast

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**Weighted Moving Average**

Determine the 3-period weighted moving average forecast for period 4 Weights (adding up to 1.0): t-1: .5 t-2: .3 t-3: .2 20

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Solution 21

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**Exponential Smoothing**

The weights used to compute the forecast (moving average) are exponentially distributed The forecast is the sum of the old forecast and a portion of the forecast error Ft = Ft-1 + a(At-1 - Ft-1) . . . more

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**Exponential Smoothing**

The smoothing constant, , must be between 0.0 and 1.0 (excluding 0.0 and 1.0) A large provides a high impulse response forecast A small provides a low impulse response forecast

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**Exponential Smoothing Example**

Determine exponential smoothing forecasts for periods 2 through 10 using =.10 and =.60. Let F1=D1 25

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**Exponential Smoothing Example**

Slide 44 of 55 26

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Effect of on Forecast 27

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**Criteria for Selecting a Forecasting Method**

Cost Accuracy Data available Time span Nature of products and services Impulse response and noise dampening

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**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 (think in terms of interval rather than point forecasts) Not forecasting the right things (forecast independent demand only) Not selecting an appropriate forecasting method (use MAD to evaluate goodness of fit) Not tracking the accuracy of the forecasting models

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**How to Monitor and Control a Forecasting Model**

Tracking Signal Tracking signal = =

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**Tracking Signal: What do you notice?**

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**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

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**Wrap-Up: World-Class Practice**

Predisposed to have effective methods of forecasting because they have exceptional long-range business planning Formal forecasting effort Develop methods to monitor the performance of their forecasting models Use forecasting software with automated model fitting features, which is readily available today Do not overlook the short run.... excellent short range forecasts as well

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