Chapter 4, HeizerRender, 5th and 7th edition

Click to edit Master text styles,Second level,Third level,Fourth level,Fifth level, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,*,Click to edit Master title style, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,1,Operations Management,ForecastingChapter 4, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,2,What is Forecasting?,Process of predicting a future event,Underlying basis of all business decisions,Production,Inventory,Personnel,Facilities,Sales will be $200 Million!, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,3,Types of Forecasts,Economic forecasts,Address business cycle, e.g., inflation rate, money supply etc.,Technological forecasts,Predict rate of technological progress,Predict acceptance of,new,product,Demand forecasts,Predict sales of,existing,product, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,4,Short-range forecast,Up to 1 year; usually less than 3 months,Job scheduling, worker assignments,Medium-range forecast,3 months to 3 years,Sales & production planning, purchasing, budgeting,Long-range forecast,3,+,years,New product planning, facility location,Types of Forecasts by Time Horizon, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,5,What Do We Forecast - Aggregation,Clustering goods or services that have similar demand requirements and common processing, labor, and materials requirements:,Red shirts,White shirts,Blue shirts,Big Mac,Quarter Pounder,Regular Hamburger,Shirts,Pounds of Beef,$,$,Why do we aggregate?,What about units of measurement?, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,6,Realities of Forecasting,Forecasts are seldom perfect,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, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,7,Persistent, overall upward or downward pattern,Due to population, technology etc.,Several years duration,Trend Component,Time,Response, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,8,Regular pattern of up & down fluctuations,Due to weather, customs etc.,Time,Response,Seasonal Component,Summer, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,9,Repeating up & down movements,Due to interactions of factors influencing economy,Usually 2-10 years duration,Time,Response,Cyclical Component,Cycle, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,10,Product Demand,Year,1,Year,2,Year,3,Year,4,Actual demand line,Demand for product or service,Seasonal peaks,Trend component,Average demand over four years,Random variation, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,11,Forecasting Approaches,Used when situation is stable & historical data exist,Existing products,Current technology,Involves mathematical techniques,e.g., forecasting sales of color televisions,Quantitative Methods,Used when situation is vague & little data exist,New products,New technology,Involves intuition, experience,e.g., forecasting sales on Internet,Qualitative Methods, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,12,Overview of Qualitative Methods,Jury of executive opinion,Pool opinions of high-level executives, sometimes augmented by statistical models,Delphi method,Panel of experts, queried iteratively,Sales force composite,Estimates from individual salespersons are reviewed for reasonableness, then aggregated,Consumer Market Survey,Ask the customer, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,13,Overview of Quantitative Approaches,Nave approach,Moving averages,Exponential smoothing,Trend projection,Seasonal variation,Linear regression,Time-series Models,Associative Models, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,14,Set of evenly spaced numerical data,Observing the response variable at regular time intervals,Forecast based only on past values,Assumes that factors influencing the past and present will continue to influence the future,Example,Year:19992000200120022003,What is a Time Series?, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,15,Naive 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 48,Sometimes cost effective & efficient, 1995 Corel Corp., 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,16,Forecast,n,n,Demand in Previous,Periods,Simple Moving Average,F = A + A + A + A,F = A + A + A,t t1 t2 t3 t4,t t1 t2 t3,4,3, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,17,Forecast,=,(Weight for period n) (Demand in period n),Weights,Weighted Moving Average,F =,.4,A +,.3,A +,.2,A +,.1,A,F =,.7,A +,.2,A +,.1,A,t t1 t2 t3 t4,t t1 t2 t3, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,18,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 Method, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,19,Exponential Smoothing,F =,A + (1,) (F ),=,A + F,F,= F +,(A,F ),t t1 t1,t1 t1 t1,t1 t1 t1,Forecast =,(Demand last period),+,(1 ,),( Last forecast),Forecast =,Last forecast +,(Last demand,Last forecast), 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,20,T,t,=, (Forecast this period Forecast last period),+ (1-) (Trend estimate last period),=,(F,t,- F,t-1,) + (1- )T,t-1,Exponential Smoothing with Trend Adjustment,Forecast = Exponentially smoothed forecast (,F,) + Exponentially smoothed trend (,T,),t,t, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,21,Seasonal Variation,QuarterYear 1Year 2Year 3Year 4,14570100100,2335370585725,35205908301160,4100170285215,Total1000120018002200,Average250300450550,Seasonal Index =,Actual Demand,Average Demand,= = 0.18,45,250,Forecast for Year 5 = 2600, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,22,Quarter Year 1 Year 2 Year 3 Year 4,145/250 = 0.1870/300 = 0.23100/450 = 0.22100/550 = 0.18,2335/250 = 1.34370/300 = 1.23585/450 = 1.30725/550 = 1.32,3520/250 = 2.08590/300 = 1.97830/450 = 1.841160/550 = 2.11,4100/250 = 0.40170/300 = 0.57285/450 = 0.63215/550 = 0.39,QuarterAverage Seasonal Index,1(0.18 + 0.23 + 0.22 + 0.18)/4 = 0.20,2(1.34 + 1.23 + 1.30 + 1.32)/4 = 1.30,3(2.08 + 1.97 + 1.84 + 2.11)/4 = 2.00,4(0.40 + 0.57 + 0.63 + 0.39)/4 = 0.50,Seasonal Variation,Forecast,650(0.20) = 130,650(1.30) = 845,650(2.00) = 1300,650(0.50) = 325, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,23,Overview of Quantitative Approaches,Nave approach,Moving averages,Exponential smoothing,Trend projection,Seasonal variation,Linear regression,Time-series Models,Associative Models, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,24,Linear Regression,Independent Dependent Variables Variable,Factors Associated with Our Sales,Advertising,Pricing,Competitors,Economy,Weather,Sales, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,25,Scatter Diagram,Sales vs. Payroll,0,1,2,3,4,0,1,2,3,4,5,6,7,8,Area Payroll (in $ hundreds of millions),Sales (in $ hundreds of thousands),Regression Line,Now What?, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,26,Short-range forecast,Up to 1 year; usually less than 3 months,Job scheduling, worker assignments,Medium-range forecast,3 months to 3 years,Sales & production planning, budgeting,Long-range forecast,3,+,years,New product planning, facility location,Types of Forecasts by Time Horizon,Time Series,Associative,Qualitative, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,27,Forecast Error, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,28,Forecast Error,+ 5, 3,E = A F,t t t, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,29,Forecast Error - CFE,CFE =,E,t,CFE, Cumulative sum of Forecast Errors,Positive errors offset negative errors,Useful in assessing,bias,in a forecast, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,30,Forecast Error - MSE,MSE, Mean Squared Error,Accentuates large deviations,MSE =,E,t,n,2, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,31,Forecast Error - MAD,|,E,t,|,n,MAD =,MAD,Mean Absolute Deviation,Widely used, well understood measurement of forecast error, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,32,Forecast Error - MAPE,MAPE,=,100,|,E,t,|,/ A,t,n,MAPE, Mean Absolute Percent Error,Relates forecast error to the level of demand, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,33,Forecast Error,E,t,=,A,t,F,t,CFE =,E,t,100,|,E,t,|,/,A,t,n,MAPE =,|,E,t,|,n,MAD =,MSE =,E,t,n,2, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,34,Monitoring & Controlling Forecasts,We need a,TRACKING SIGNAL,to measure how well the forecast is predicting actual values,TS = Running sum of forecast errors (,CFE,) Mean Absolute Deviation (,MAD,),=,E,|,E,|,/ n,t,t, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,35,Plot of a Tracking Signal,Time,Lower control limit,Upper control limit,Signal exceeded limit,Tracking signal CFE / MAD,Acceptable range,+,0,-, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,36,Forecasting in the Service Sector,Presents unusual challenges,special need for short term records,needs differ greatly as function of industry and product,issues of holidays and calendar,unusual events, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,37,Forecast of Sales by Hour for Fast Food Restaurant,11-12 12-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 10-11, 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458,4-,38,Summary,Demand forecasts drive a firms plans,- Production- Capacity- Scheduling,Need to find the forecasting method(s) that best fit our pattern of demand no one right tool,- Qualitative methods e.g. customer surveys- Time series methods (quantitative) rely on historical demand to predict future demand - Associative models (quantitative) use historical data on independent variables to predict demand e.g. promotional campaign,Track forecast error to determine if forecasting model requires change,