Production Planning. Chapter 4 Forecasting. Overview. Overview. Chapter 04 Forecasting 1. 7 Steps to a Forecast. What is forecasting?

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1 Chapter 4 Forecasting Production Planning MRP Purchasing Sales Forecast Aggregate Planning Master Production Schedule Production Scheduling Production What is forecasting? Types of forecasts 7 steps of forecasting Qualitative forecasting Overview Quantitative forecasting Time-series forecasting Naïve Moving average Exponential smoothing Seasonal variations Associative methods Monitoring and Controlling Forecasts Overview Could be a prediction based on historical data and mathematical models What is forecasting? Forecasting - Is the art and science of predicting the future Sales will be $200 Million! Could be a prediction based on expertise and intuition Could be a prediction based on both a model and a manager s expertise 7 Steps to a Forecast Determine the use of the forecast Select the items to be forecast Determine the time horizon of the forecast Select the forecasting model(s) Gather the data Make the forecast Validate and implement results Chapter 04 Forecasting 1

2 Quantity Quantity Realities of Forecasting Forecasts never perfect and seldom correct. Most forecasting methods assume that there is some underlying stability in the system Both product family and aggregated product forecasts are more accurate than individual product forecasts Demand Forecasts OM manager is primarily interested in demand forecasts (as opposed to economic forecasts and technological forecasts) Underlying basis of all business decisions Production Inventory Personnel Facilities Demand Forecast Applications Time Horizon Medium Term Long Term Short Term (3 months (more than Application (0 3 months) 2 years) 2 years) Forecast quantity Individual Total sales Total sales products or Groups or families services of products or services Decision area Inventory Staff planning Facility location management Production Capacity Final assembly planning planning scheduling Master production Process Workforce scheduling management scheduling Purchasing Master production Distribution scheduling Forecasting Time series Associative Associative technique Associative Overview of Qualitative Methods Jury of executive opinion READ in TEXT (p th edition) Pool opinions of high-level executives, sometimes augment by statistical models Sales force composite Estimates from individual salespersons are reviewed for reasonableness, then aggregated Delphi method Panel of experts, queried iteratively Consumer Market Survey Ask the customer Patterns of Demand Patterns of Demand Time Time Chapter 04 Forecasting 2

3 Quantity Quantity Patterns of Demand Patterns of Demand Year 1 Year 2 J F M A M J J A S O N D Months Years Overview of Quantitative Methods Naïve approach Moving averages Exponential smoothing Linear regression Time-series Models no trend, seasonal, or cyclical fluctuations Associative models What is a Time Series? Set of evenly spaced numerical data Obtained by observing response variable at regular time periods Forecast based only on past values Assumes that factors influencing past and present will continue influence in future Example Year: Sales: Naïve Approach Assumes demand in next period is the same as demand in most recent period e.g., If May sales were 48, then June sales will be Sometimes cost effective & efficient Moving Average Approach MA is a series of arithmetic means Used if little or no trend Used often for smoothing Provides overall impression of data over time MA Demand in Previous n Periods n Chapter 04 Forecasting 3

4 Patient arrivals Patient arrivals Patient arrivals Simple Moving Averages Patient arrivals have been recorded at a medical clinic over the past 28 weeks. Want to predict the number of patient arrivals for the 29 th week. Simple Moving Averages Actual patient arrivals Simple Moving Averages Simple Moving Averages 3-week MA forecast 3-week MA forecast 6-week MA forecast Actual patient arrivals Actual patient arrivals SKIP WEIGHTED Moving Averages Disadvantages of Moving Average Methods Increasing n makes forecast less sensitive to changes Do not forecast trend well Require much historical data T/Maker Co. Chapter 04 Forecasting 4

5 Number of customers Patient arrivals Patient arrivals Exponential Smoothing Method Form of weighted moving average Weights decline exponentially Most recent data weighted most Requires smoothing constant ( ) Ranges from 0 to 1 Subjectively chosen Involves little record keeping of past data Exponential Smoothing Exponential Smoothing = 0.10 F t +1 = F t + (D t F t ) Exponential Smoothing 3-week MA forecast Exponential smoothing with trend adjustment SKIP Exponential smoothing = 0.10 Trend projection (p ) Regression analysis Total Average Stanley Steamer Carpet Cleaners Quarter Year 1 Year 2 Year 3 Year 4 Chapter 04 Forecasting 5

6 Total Average Total Average Projected Annual Demand = Average Quarterly Demand = Seasonal Index = Actual Demand Average Demand Total Average /250 = /300 = /450 = /550 = /250 = /300 = /450 = /550 = /250 = /300 = /450 = /550 = /250 = /300 = /450 = /550 = 0.39 Seasonal Index = 1 45/250 = /300 = /450 = /550 = /250 = /300 = /450 = /550 = /250 = /300 = /450 = /550 = /250 = /300 = /450 = /550 = /250 = /300 = /450 = /550 = /250 = /300 = /450 = /550 = /250 = /300 = /450 = /550 = /250 = /300 = /450 = /550 = 0.39 Quarter Average Seasonal Index 1 ( )/4 = ( )/4 = ( )/4 = ( )/4 = 0.50 Quarter Average Seasonal Index Forecast 1 ( )/4 = (0.20) = ( )/4 = (1.30) = ( )/4 = (2.00) = ( )/4 = (0.50) = 325 Chapter 04 Forecasting 6

7 Annual Sales ($1000s) Dependent variable Remember Regression Analysis? Deviation, Y Estimate of or error Y from regression equation { Actual value of Y Value of X used to estimate Y Independent variable Regression equation: Y = a + bx X Regression analysis in forecasting Two applications of regressions analysis in forecasting Time-series data Independent variable is time Dependent variable is the variable that you want to forecast (i.e. demand) Data is not time-series Independent variable is a known variable that can be used to predict (i.e. advertising dollars, customer population) Dependent variable is the variable that you want to forecast (i.e. demand) Regression analysis is the same in both applications Armand, Inc.: Regression Analysis Armand, Inc. is a chain of Italian restaurants located in a five-state area. The most successful locations have been near college campuses. Prior to opening a new restaurant, management requires a forecast of the yearly sales revenues. Such an estimate is used in planning the restaurant capacity, personnel requirements, and to see if the operations costs are smaller than the predicted revenue. Armand, Inc. Student population Annual sales Restaurant (1000s) ($1000s) Armand, Inc. Armand, Inc. Annual sales and Student Population Intercept Y a bx Coefficient for Student Population 0 Student Population (1000s) Chapter 04 Forecasting 7

8 Armand, Inc. Armand, Inc. Forecast the Annual Sales if the student population is 20,000. Forecast the Annual Sales if the student population is 20,000. Forecast is : Forecasting accuracy I think there is a world market for about FIVE computers. Thomas J. Watson, chairman of IBM, 1943 Forecast accuracy IBM 1994 $700 million inventory of OBSOLETE PCs that took 6 months to unload. Reaction: too conservative when releasing the new Aptiva home PCs. New models sold out before the holiday season had begun. Measuring the quality of forecasting MAD mean absolute deviation ForecastEr rors n MAD MSE mean square error n MSE n ForecastEr ror n 2 Your Turn Demand for April-September is given. Determine the exponential smoothing forecasts for those April. Forecast for Mar was 58 Demand for Mar was 60. Determine the regression equation forecasts for those April. X is the number of months in the future (for April, X = 1) Chapter 04 Forecasting 8

9 Tracking signal Your Turn Demand Exponential Smoothing alpha = 0.2 April 60 May 55 June 75 July 60 August 80 September 75 Regression Y = X Third Wave Research Group - offers marketing software and databases - Forecasts sales for specific -Market areas -Products -segments Tracking Signals Control limit Control limit Tracking signal = RSFE MAD Out of control Observation number Tracking Signal Computation Mo Fcst Act Error RSFE Abs Cum MAD TS Error Error Chapter 04 Forecasting 9