©The McGraw-Hill Companies, Inc. 2008McGraw-Hill/Irwin Chapter 10 Demand Forecasting: Building the Foundation for Resource Planning

10-2 Learning Objectives Describe the benefits of effective resource planning. Explain how the planning horizon affects planning tasks. Describe how lead times determine the planning horizon. Explain how product and service life cycles can aid in the planning process. Describe the benefits of collaborative planning, forecasting & replenishment Describe the different types of forecasting methods. Compute a causal forecast using simple linear regression. Recognize the components of a time series and appropriate forecasting techniques for each component. Compute forecasts using averages, exponential smoothing, seasonal indexes, and a multiplicative model. Compute measures of forecast accuracy. Describe how enterprise resource planning (ERP) systems benefit businesses. shot

10-3 Resource Planning - Determining what is needed, and making arrangements to get it, in order to achieve objectives. Contingency Plans – Alternative or back-up plans to be used if an unexpected event makes the normal plans infeasible. Operations Management Framework

10-4 Increasing Alternatives –Management has more options if it plans ahead. Profitability Enhancement –Planning can both reduce costs and increase sales. The further ahead we plan, however, the less we know about future conditions. There is a tradeoff between increasing alternatives and increasing uncertainty. Financial Benefits of Effective Planning

10-5 Planning Horizon –The distance into the future one plans. Looking into the Future: The Planning Horizon

10-6 A business may have many different planning horizons depending on the resources in question –Inventory- Usually very short –Employees - Generally pretty short Temps, new hires, etc. –Equipment - A little longer Purchasing and installation lead times –Facilities - Longest Purchase property, build the building Looking into the Future: The Planning Horizon

10-7 Life Cycle: A pattern of demand growth and decline that occurs from the introduction of a product to its obsolescence. The five stages of a life cycle: Introduction Growth – Demand begins to increase. Maturation – Demand begins to level off. Saturation – Demand shifts to the beginning of its decline. Decline – Final stage as demand disappears. Exhibit 10.2 Product Life Cycle Product and Service Life Cycles

10-8 Market leaders sometimes try to create entry barriers by replacing products and maintaining intentionally short life cycles. Exhibit 10.3 Product Life Cycles Interrupted by New Product Introduction Product and Service Life Cycles

10-9 Demand Forecasting Qualitative Forecasts –Do not use past data. Usually used when such data is not available (such as planning for a new product). –Customer surveys, expert opinions, etc. Quantitative Forecasts –Divided into causal forecasting and time series forecasting techniques.

10-10 Collaborative Planning, Forecasting, and Replenishment (CPFR) A shared process of creation between two or more parties with diverse skills and knowledge delivering a unified approach that provides the optimal framework for customer satisfaction. CPFR requires that data be shared among supply chain partners and that partners collaborate on developing demand forecasts..

10-11 Causal Techniques –Uses external data to predict future demand –Looking for the factors that “cause” demand –Linear regression is often used. Time Series Techniques –Use past demand to predict future demand Demand Forecasting: Quantitative Analysis

10-12 Some external variable is a leading indicator (independent variable) for the demand you want to predict (dependent variable) The example (10.1) uses temperature as the independent variable, but you could use others as well. e.g., exam schedule, promotions, sporting events, day of the week, etc. Causal Models

10-13 Demand Forecasting: Simple Linear Regression Example Predicted High Temp.Beer SalesPredicted High Temp.Beer Sales 624,000636, , , , , , , , , , , , , , , , , , , ,100718, ,200778, ,000

10-14 Demand Forecasting: Simple Linear Regression Example If we believe that fluctuations in demand for beer, Y, are partly due to changes in X, the predicted temperature: Given a particular temperature prediction, what will demand be? Predicted

10-15 Demand Forecasting: Simple Linear Regression Example X 1, Y 1 X 2, Y 2 Regression analysis provides the formula for the line that best fits through the data points. Underlying model: Y = a + bx

10-16 Regression Line: Demand = -23, (Predicted Temperature) Demand Forecasting: Simple Linear Regression Example

10-17 Y = -23, x For an 80-degree day, the demand forecast would be: Y = -23, (80) = 11,540.2 Demand Forecasting: Simple Linear Regression Example

10-18 There are four potential components of a time series: –Cycles A pattern that repeats over a long period of time (such as 20 years). Cycles are less important for demand forecasting, since we rarely have 20 years’ worth of data. –Trend –Seasonality –Randomness Demand Forecasting: Components of a Time Series

10-19 Demand Forecasting: Components of a Time Series Trend – Component of a time series that causes demand to increase or decrease. Exhibit 10.6 Example of a Time Series with Trend

10-20 Seasonality – A pattern in a time series that repeats itself at least once a year. Exhibit 10.7 Time Series with Seasonality Demand Forecasting: Components of a Time Series

10-21 Random Fluctuation – Unpredictable variation in demand that is not due to trend, seasonality, or cycle. Exhibit 10.8 Time Series with Random Fluctuation Demand Forecasting: Components of a Time Series

10-22 Time Series Techniques: Averages Averaging is used to remove random fluctuations in historical data. Various kinds of averages can be used –Differences between them are exploited to create varying degrees of responsiveness. Responsiveness: The degree to which the forecast responds to the most immediate change in demand.

10-23 Time Series Techniques: Averages Averages constructed from bigger data sets (i.e., more history) are less responsive to sudden changes. –An average that uses the eight most recent data points is less responsive than one that uses the past three: Exhibit Three-Period and Eight-Period Moving Average Forecasts

10-24 Time Series Techniques: Averages Moving averages can be weighted to change responsiveness Weights must sum up to 1.0 For more responsiveness, assign heavier weights to more recent data points Period Demand Example 10.2 Use weighted averages and the past four weeks’ demand to predict the next week’s demand. Demands for the past four weeks are:

10-25 Time Series Techniques: Averages

10-26 A variant of moving average (weighted average): –Premise: More recent observations are better indicators of future demand than past observations. –Reduces the need to hold lots of data. Uses a smoothing constant, ‘alpha’ (  ) to weight the previous demand and establish the responsiveness of the forecast. F t+1 =  A t + (1-  )F t Where: F t+1 = The forecast for the next time period  = A smoothing constant, between 0 and 1 A t = The actual demand for the most recent period F t = The forecast for the most recent period Time Series Techniques: Exponential Smoothing

10-27 A higher alpha makes the forecast more responsive to changes: Exhibit Comparison of.1 and.4 Alpha Values for Exponential Smoothing Time Series Techniques: Exponential Smoothing

10-28 Example 10.3: To forecast February’s demand using exponential smoothing with an alpha of.3. (assume an initial January forecast of 90) F t+1 =  A t + (1-  )F t =.3(100) + (1-.3)90 = = 93 Time Series Techniques: Exponential Smoothing Example

10-29 Continue the process until the forecast for July is determined: Time Series Techniques: Exponential Smoothing Example

10-30 Trend-adjusted Exponential Smoothing adds a smoothing constant to account for trend –Also called “forecast including trend” (FIT) FIT t+1 = F t + T t Where F t is the smoothed forecast, T t is the trend estimate and –F t = FIT t +  (A t – FIT t ) –T t = T t-1 +  (FIT t - FIT t-1 - T t-1 ) Time Series Techniques: Trend-Adjusted Exponential Smoothing

10-31 Example 10.4: Using the following data –  = 0.2 –  = 0.9 –Initial trend (F 1 ) = 3 –Initial forecast (T 1 ) = 25 Calculate the demand –For period two –For period three –For subsequent periods Demand (A) Week (t) Time Series Techniques: Trend-Adjusted Exponential Smoothing

10-32 For period 2 FIT t+1 = F t + T t Initial forecast (F 1 ) = 25 Initial Trend (T 1 ) = 3 FIT 2 = F 1 + T 1 = = Demand (A) Week (t) FIT t+1 = F t + T t F t = FIT t +  (A t – FIT t ) T t = T t-1 +  (FIT t - FIT t-1 - T t-1 ) Time Series Techniques: Trend-Adjusted Exponential Smoothing

10-33 For period 3 FIT t+1 = F t + T t FIT 3 = F 2 + T 2 F 2 = FIT 2 +  (A 2 – FIT 2 ) F 2 = (29-28) F 2 = = 28.2 T 2 = T 1 +  (FIT 2 - FIT 1 - T 1 ) T 2 = 3 +.9( ) T 2 = 3 +.9(0) = 3 FIT 3 = F 2 + T 2 FIT 3 = FIT 3 = Demand (A) Week (t) FIT t+1 = F t + T t F t = FIT t +  (A t – FIT t ) T t = T t-1 +  (FIT t - FIT t-1 - T t-1 ) Time Series Techniques: Trend-Adjusted Exponential Smoothing

10-34 For period 4 FIT t+1 = F t + T t FIT 4 = F 3 + T 3 F 3 = FIT 3 +  (A 3 – FIT 3 ) F 3 = ( ) F 3 = (-1.2) = T 3 = T 2 +  (FIT 3 - FIT 2 - T 2 ) T 3 = 3 +.9( ) T 3 = 3 +.9(.2) = 3.18 FIT 4 = F 3 + T 3 FIT 4 = FIT 4 = Demand (A) Week (t) FIT t+1 = F t + T t F t = FIT t +  (A t – FIT t ) T t = T t-1 +  (FIT t - FIT t-1 - T t-1 ) Time Series Techniques: Trend-Adjusted Exponential Smoothing

10-35 Time Series Techniques: Trend-Adjusted Exponential Smoothing

10-36 Time Series Techniques: Using the Linear Trend Equation Identical to using linear regression as a causal technique –Time period is the independent variable –Demand is the dependent variable Example 10.5: –Consider the following 10-month time series with an apparent trend: Month: Demand:

10-37 Time Series Techniques: Using the Linear Trend Equation Exhibit Partial Excel Regression Analysis Output for Backpack Sales for Example 10.5

10-38 Seasonality: a common component in time series –Sell more ski equipment in Fall and Winter Seasonality is described by using a ratio of the average demand for a period to the average demand across all periods –If July has a seasonal index of 1.8, it means that average July demand is 1.8 times greater than overall average monthly demand Time Series Techniques: Including Seasonality

10-39 Calculate average demand for each “season” (period average) –e.g. all Mondays, all January, etc. Compute average of all observations (global average) Divide period averages by global average Time Series Techniques: Including Seasonality

10-40 So if in general I forecast 20 visitors per day, I adjust by the seasonal index to estimate what I expect on a particular day Time Series Techniques: Including Seasonality

10-41 A regression-based approach (Multiplicative model) –Compute seasonal indexes for each period –Remove seasonal component from the time series “Deseasonalize” the data –Model the trend using linear regression on the deseasonalized data –Determine the forecast by using the trend equation and seasonal indexes Time Series Techniques: Dealing with Seasonality and Trend

10-42 Calculate seasonal indexes to deseasonalize data Time Series Techniques: Dealing with Seasonality and Trend

10-43 The regression analysis determines the best-fitting line through the deseasonalized demand. The general equation for that line is: Y = a + bt Where:Y = a point on the trend line a = Y intercept b = slope t = time period Dealing with Seasonality and Trend Using Regression

10-44 Regression analysis result: Y = (period) Forecast (deseasonalized): Y = (17) = = Forecast (seasonal): –Multiply back by the appropriate seasonal index Y=319.58(Q1 index) =319.58(1.67) = Example: Given the trend in demand over the past 4 years and the effects of seasonality, what do we expect demand to be in period 17? Time Series Techniques: Dealing with Seasonality and Trend

10-45 Percentage (79%) of the variation in demand for tax services is explained by the time period Rate of demand growth per period Base-level service demand (period 0) Time Series Techniques: Dealing with Seasonality and Trend

10-46 Forecast Accuracy Forecast error is the actual demand minus the forecast demand. Absolute Error: how far “off” are we, in absolute terms? –Measured by mean absolute deviation (MAD) or mean squared error (MSE) Forecast Bias: Are we consistently high or low? –A forecast should be unbiased (low forecasts are as frequent as high forecasts) –Bias is measured by mean forecast error (MFE) or running sum of forecast error (RSFE)

10-47 The ideal value for both is zero, which would mean there is no forecasting error The larger the MAD or MSE, the less the accurate the model Forecast Accuracy Two similar approaches are used to measure absolute forecast error MAD is the mean of the absolute values of the forecast errors MSE is the mean of the squared values of the forecast errors

10-48 Example 10.8: Calculating MAD

10-49 Example 10.9: Calculating MSE

10-50 Forecast Bias Forecast Bias: Tendency of a forecast to be too high or too low. Mean forecast error (MFE) –The mean of the forecast errors Running sum of forecast errors (RSFE) –The sum of forecast error, updated as each new error is calculated. Ideal measure is zero which indicates no bias. –Positive means forecast tends to low –Negative means forecast tends to high

10-51 Forecast Bias: Calculating MFE and RSFE Mean Forecast Error = 1.00 RSFE (period 8) = 8

10-52 Tracking Signals The size of the cumulative forecast error expressed as MADs TS = RSFE/MAD

10-53 Integrated Resource Planning Systems Enterprise Resource Planning (ERP) Systems –Planning for resources done from common database –Allows decisions to be made from enterprise perspective –Everyone uses the same numbers. –ERP solutions are typically “off the shelf” – not customized to business Major providers: SAP, BAAN, PeopleSoft, Oracle

10-54 Integrated Resource Planning Systems Exhibit Conceptual View of a Generic ERP System