《第六章供应链中的需求预测》由会员分享，可在线阅读，更多相关《第六章供应链中的需求预测（55页珍藏版）》请在金锄头文库上搜索。
1、第七章Demand Forecastingin a Supply ChainLearning Objectives1. Understand the role of forecasting for both an enterprise and a supply chain.2. Identify the components of a demand forecast.3. Forecast demand in a supply chain given historical demand data using time-series methodologies.4. Analyze demand
2、 forecasts to estimate forecast error.Role of Forecasting in a Supply ChainThe basis for all planning decisions in a supply chainUsed for both push and pull processesProduction scheduling, inventory, aggregate planningSales force allocation, promotions, new production introductionPlant/equipment inv
3、estment, budgetary planningWorkforce planning, hiring, layoffsAll of these decisions are interrelatedCharacteristics of Forecasts1. Forecasts are always inaccurate and should thus include both the expected value of the forecast and a measure of forecast error2. Long-term forecasts are usually less a
4、ccurate than short-term forecasts3. Aggregate forecasts are usually more accurate than disaggregate forecasts4. In general, the farther up the supply chain a company is, the greater is the distortion of information it receivesComponents and MethodsCompanies must identify the factors that influence f
5、uture demand and then ascertain the relationship between these factors and future demandPast demandLead time of product replenishmentPlanned advertising or marketing effortsPlanned price discountsState of the economyActions that competitors have takenComponents and Methods1. QualitativePrimarily sub
6、jectiveRely on judgment2. Time SeriesUse historical demand onlyBest with stable demand3. CausalRelationship between demand and some other factor4. SimulationImitate consumer choices that give rise to demandComponents of an ObservationObserved demand (O) =systematic component (S) + random component (
7、R)Systematic component expected value of demand Level (current deseasonalized demand) Trend (growth or decline in demand) Seasonality (predictable seasonal fluctuation)Random component part of forecast that deviates from systematic componentForecast error difference between forecast and actual deman
8、dBasic Approach1. Understand the objective of forecasting.2. Integrate demand planning and forecasting throughout the supply chain.3. Identify the major factors that influence the demand forecast.4. Forecast at the appropriate level of aggregation.5. Establish performance and error measures for the
9、forecast.Time-Series Forecasting MethodsThree ways to calculate the systematic componentMultiplicativeS = level x trend x seasonal factorAdditiveS = level + trend + seasonal factorMixedS = (level + trend) x seasonal factorStatic MethodswhereL= estimate of level at t = 0 T= estimate of trendSt= estim
10、ate of seasonal factor for Period tDt= actual demand observed in Period tFt= forecast of demand for Period tTahoe SaltYearQuarterPeriod, tDemand, Dt1218,00013213,00014323,00021434,00022510,00023618,00024723,00031838,00032912,000331013,000341132,000411241,000Table 7-1Tahoe SaltFigure 7-1Estimate Leve
11、l and TrendPeriodicity p = 4, t = 3Tahoe SaltFigure 7-2Tahoe SaltFigure 7-3A linear relationship exists between the deseasonalized demand and time based on the change in demand over timeEstimating Seasonal FactorsFigure 7-4Estimating Seasonal FactorsAdaptive ForecastingThe estimates of level, trend,
12、 and seasonality are adjusted after each demand observationEstimates incorporate all new data that are observedAdaptive ForecastingwhereLt=estimate of level at the end of Period t Tt=estimate of trend at the end of Period t St=estimate of seasonal factor for Period t Ft=forecast of demand for Period
13、 t (made Period t 1 or earlier)Dt=actual demand observed in Period t Et=Ft Dt = forecast error in Period tSteps in Adaptive ForecastingInitializeCompute initial estimates of level (L0), trend (T0), and seasonal factors (S1,Sp)ForecastForecast demand for period t + 1 Estimate errorCompute error Et+1
14、= Ft+1 Dt+1 Modify estimatesModify the estimates of level (Lt+1), trend (Tt+1), and seasonal factor (St+p+1), given the error Et+1Moving AverageUsed when demand has no observable trend or seasonalitySystematic component of demand = levelThe level in period t is the average demand over the last N per
15、iods Lt = (Dt + Dt-1 + + DtN+1) / NFt+1 = Lt and Ft+n = Lt After observing the demand for period t + 1, revise the estimatesLt+1 = (Dt+1 + Dt + + Dt-N+2) / N, Ft+2 = Lt+1Moving Average ExampleA supermarket has experienced weekly demand of milk of D1 = 120, D2 = 127, D3 = 114, and D4 = 122 gallons ov
16、er the past four weeksForecast demand for Period 5 using a four-period moving averageWhat is the forecast error if demand in Period 5 turns out to be 125 gallons?Moving Average ExampleL4= (D4 + D3 + D2 + D1)/4 = (122 + 114 + 127 + 120)/4 = 120.75Forecast demand for Period 5F5 = L4 = 120.75 gallonsError if demand in Period 5 = 125 gallonsE5 = F5 D5 = 125 120.75 = 4.25Revised demandL5 = (D5 + D4 + D3 + D2)/4= (125 + 122 + 114 + 127)/4 = 122Simple Exponential SmoothingUsed when demand has no observ
