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

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

Qualitative Approaches Quantitative Approaches Forecasting Methods Qualitative Approaches Quantitative Approaches

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

Qualitative Methods Executive committee consensus Delphi method Survey of sales force Survey of customers Historical analogy Market research

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

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. 92-100. Note: More than one response was permitted. 12

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

Components of Time Series What’s going on here? x x Sales 1 2 3 4 Year 6

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

Actual Data & the Regression Line

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 28-31 Week Day 7

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

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

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

Simple Linear Regression Model Yt = a + bx Y 0 1 2 3 4 5 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

Calculating a and b 36

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

Regression Equation Example Slide 18 of 55 38

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 19 of 55 39

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

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)

Mean Absolute Deviation (MAD)

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

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

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

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

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)

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

Simple Moving Average Slide 29 of 55 16

Simple Moving Average Slide 30 of 55 17

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

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

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

Solution 21

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

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

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

Exponential Smoothing Example Slide 38 of 55 26

Effect of  on Forecast 27

Criteria for Selecting a Forecasting Method Cost Accuracy Data available Time span Nature of products and services Impulse response and noise dampening

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

How to Monitor and Control a Forecasting Model Tracking Signal Tracking signal = =

Tracking Signal: What do you notice? 34

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