8.Demand Forecasting in a Supply Chain

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  • Demand Forecastingin a Supply ChainSupply Chain Management

    2007 Pearson Education

    7-*OutlineThe role of forecasting in a supply chainCharacteristics of forecastsComponents of forecasts and forecasting methodsBasic approach to demand forecastingTime series forecasting methodsMeasures of forecast errorForecasting demand at Tahoe SaltForecasting in practice

    2007 Pearson Education

    7-*Role of Forecasting in a Supply ChainThe basis for all strategic and planning decisions in a supply chainUsed for both push and pull processesExamples:Production: scheduling, inventory, aggregate planningMarketing: sales force allocation, promotions, new production introductionFinance: plant/equipment investment, budgetary planningPersonnel: workforce planning, hiring, layoffsAll of these decisions are interrelated

    2007 Pearson Education

    7-*Characteristics of ForecastsForecasts are always wrong. Should include expected value and measure of error.Long-term forecasts are less accurate than short-term forecasts (forecast horizon is important)Aggregate forecasts are more accurate than disaggregate forecasts

    2007 Pearson Education

    7-*Forecasting MethodsQualitative: primarily subjective; rely on judgment and opinionTime Series: use historical demand onlyStatic AdaptiveCausal: use the relationship between demand and some other factor to develop forecastSimulationImitate consumer choices that give rise to demandCan combine time series and causal methods

    2007 Pearson Education

    7-*Components of an ObservationObserved demand (O) =Systematic component (S) + Random component (R)Level (current deseasonalized demand)Trend (growth or decline in demand)Seasonality (predictable seasonal fluctuation) Systematic component: Expected value of demand Random component: The part of the forecast that deviates from the systematic component Forecast error: difference between forecast and actual demand

    2007 Pearson Education

    7-*Time Series ForecastingForecast demand for thenext four quarters.

    Quarter

    Demand Dt

    II, 1998

    8000

    III, 1998

    13000

    IV, 1998

    23000

    I, 1999

    34000

    II, 1999

    10000

    III, 1999

    18000

    IV, 1999

    23000

    I, 2000

    38000

    II, 2000

    12000

    III, 2000

    13000

    IV, 2000

    32000

    I, 2001

    41000

    2007 Pearson Education

    7-*Time Series Forecasting

    Chart2

    8000

    13000

    23000

    34000

    10000

    18000

    23000

    38000

    12000

    13000

    32000

    41000

    Demand D

    Sheet1

    QuarterPeriod tDemand D

    97,218,000

    97,3213,000

    97,4323,000

    98,1434,000

    98,2510,000

    98,3618,000

    98,4723,000

    99,1838,000

    99,2912,000

    99,31013,000

    99,41132,000

    00,11241,000

    Sheet1

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    Demand D

    movingavg

    Period tDemand DLevel LForecast FError EAbsolute Error AMean Squared ErrorMADBias

    18,000

    213,000

    323,000

    434,00019,500

    510,00020,00019,5009,5009,50090,250,0009,5001.00

    618,00021,25020,0002,0002,00047,125,0005,7501.68

    723,00021,25021,250-1,7501,75032,437,5004,4171.71

    838,00022,25021,250-16,75016,75094,468,7507,500-0.72

    912,00022,75022,25010,25010,25096,587,5008,0500.33

    1013,00021,50022,7509,7509,75096,333,3338,3331.32

    1132,00023,75021,500-10,50010,50098,321,4298,6430.25

    1241,00024,50023,750-17,25017,250123,226,5639,719-1.33

    movingavg

    00

    00

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    Simpleexp

    Period tDemand DLevel LForecast FError EAbsolute Error AMean Squared ErrorMADBias

    22,083

    18,00020,67522,08314,08314,083198,340,27814,0831.00

    213,00019,90820,6757,6757,675128,622,95110,8791.92

    323,00020,21719,908-3,0933,09388,936,4868,2841.98

    434,00021,59520,217-13,78313,783114,196,8609,6590.46

    510,00020,43621,59511,59511,595118,246,64110,0461.52

    618,00020,19220,4362,4362,43699,527,5328,7771.90

    723,00020,47320,192-2,8082,80886,435,7147,9251.73

    838,00022,22620,473-17,52717,527114,031,5509,125-0.13

    912,00021,20322,22610,22610,226112,979,3159,2470.83

    1013,00020,38321,2038,2038,203108,410,2659,1431.63

    1132,00021,54420,383-11,61711,617110,824,0749,3680.51

    1241,00023,49021,544-19,45619,456133,132,06510,208-1.22

    alpha0.1

    12760.5542991397

    Simpleexp

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    hlts-regr

    SUMMARY OUTPUT

    Regression Statistics

    Multiple R0.4813271965

    R Square0.2316758701

    Adjusted R Square0.1548434571

    Standard Error10666.8833748383

    Observations12

    ANOVA

    dfSSMSFSignificance F

    Regression1343092657.342657343092657.3426573.01534028580.1131270224

    Residual101137824009.32401113782400.932401

    Total111480916666.66667

    CoefficientsStandard Errort StatP-valueLower 95%Upper 95%Lower 95.0%Upper 95.0%

    Intercept120156565.01289355751.83017942390.0971472667-2612.611308064326642.9143383673-2612.611308064326642.9143383673

    X Variable 11549892.00959938861.736473520.1131270224-438.57053972593536.472637628-438.57053972593536.472637628

    holts

    Period tDemand DLevel LTrend TForecast FError EAbsolute Error AMean Squared ErrorMADBias

    12,0151,549

    18,00013,0081,43813,5645,5645,56430,958,0965,5641.00

    213,00014,3011,40914,4451,4451,44516,523,5233,5051.72

    323,00016,4391,55515,710-7,2907,29028,732,3184,767-0.05

    434,00019,5941,87517,993-16,00716,00785,603,1467,577-1.76

    510,00020,3221,64521,46911,46911,46994,788,7018,355-0.49

    618,00021,5701,56621,9673,9673,96781,613,7057,624-0.09

    723,00023,1231,56323,13713713769,957,2676,554-0.09

    838,00026,0181,83024,686-13,31413,31483,369,8367,399-1.54

    912,00026,2621,51327,84715,84715,847102,010,0798,3380.18

    1013,00026,2981,21727,77514,77514,775113,639,3488,9811.56

    1132,00027,9631,30727,515-4,4854,485105,137,3958,5731.18

    1241,00030,4431,54129,270-11,73011,730107,841,8648,8360.04

    31,985

    33,526

    alpha0.135,067

    Beta0.236,609

    11044.805388544

    holts

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    Sheet2

    SUMMARY OUTPUT

    1311868

    Regression Statistics1417527

    Multiple R0.95806523661530770

    R Square0.91788899751644794

    Adjusted R Square0.9042038305

    Standard Error414.5033124497

    Observations8

    ANOVA

    dfSSMSFSignificance F

    Regression111523809.523809511523809.523809567.07181522920.0001786086

    Residual61030877.97619048171812.996031746

    Total712554687.5

    CoefficientsStandard Errort StatP-valueLower 95%Upper 95%Lower 95.0%Upper 95.0%

    Intercept18439440.808707877541.82990890540.000000012517360.367255038619517.608935437517360.367255038619517.6089354375

    X Variable 152463.95924968148.18973841030.0001786086367.3067633218680.3122842972367.3067633218680.3122842972

    deseasonalized

    Period tDemand DDeseasonalized ForecastErrorAbsolute Error AMean Squared ErrorMADBias

    18,0008913913913832,6839131.00

    213,00013251251251447,8155821.74

    323,0001975023412412412355,1765252.64

    434,0002062534292292292287,7214673.48

    510,000212509897-103103232,2873943.66

    618,0002175014676-33243,3242,035,451882-1.09

    723,000225002586428642,8642,916,1441,1650.76

    838,0002212537791-2092092,557,0751,0460.69

    912,0002262510882-11181,1182,411,8261,054-0.01

    1013,000241251610031003,1003,131,9021,2591.74

    1132,00028315-36853,6854,081,6291,479-0.30

    1241,000412902902903,748,5141,380-0.16

    8666

    12539

    21574

    30793

    deseasonalized

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    Winters

    Period tDemand DLevel LTrend TSeasonal FactorForecast FError EAbsolute Error AMean Squared ErrorMADBias

    18,439524

    18,00018,8665140.478,913913913832,8579131.00

    213,00019,3675130.6813,179179179432,3675461.66

    323,00019,8695121.1723,260260260310,7204502.42

    434,00020,3805121.6734,0363636233,3643472.87

    510,00020,9215150.479,723-277277202,0363332.47

    618,00021,6895400.6814,558-3,4423,4422,143,255851-1.59

    723,00022,1025271.1725,9812,9812,9813,106,5081,1550.37

    838,00022,6365281.6737,787-2132132,723,8561,0370.26

    912,00023,2915410.4710,810-1,1901,1902,578,6531,054-0.47

    1013,00023,5775150.6916,5443,5443,5443,576,8941,3031.47

    1132,00024,2715331.1627,849-4,1514,1514,818,2581,562-0.62

    1241,00024,7915321.6741,4424424424,432,9871,469-0.44

    0.4711,940

    0.6817,579

    alpha0.051.1730,930

    Beta0.11.6744,928

    Gamma0.1

    1836.1592701453

    Winters

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    2007 Pearson Education

    7-*Forecasting MethodsStatic AdaptiveMoving averageSimple exponential smoothingHolts model (with trend)Winters model (with trend and seasonality)

    2007 Pearson Education

    7-*Basic Approach toDemand ForecastingUnderstand the objectives of forecastingIntegrate demand planning and forecastingIdentify major factors that influence the demand forecastUnderstand and identify customer segmentsDetermine the appropriate forecasting techniqueEstablish performance and error measures for the forecast

    2007 Pearson Education

    7-*Time Series Forecasting MethodsGoal is to predict systematic component of demandMultiplicative: (level)(trend)(seasonal factor)Additive: level + trend + seasonal factorMix