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Incorporating demand uncertainty and forecasting in supply chain planning models

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Incorporating demand uncertainty and forecasting in supply chain planning models. / Fildes, R A; Kingsman, B G.

In: Journal of the Operational Research Society, Vol. 62, 2011, p. 483-500.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Fildes, RA & Kingsman, BG 2011, 'Incorporating demand uncertainty and forecasting in supply chain planning models', Journal of the Operational Research Society, vol. 62, pp. 483-500. https://doi.org/10.1057/jors.2010.40

APA

Fildes, R. A., & Kingsman, B. G. (2011). Incorporating demand uncertainty and forecasting in supply chain planning models. Journal of the Operational Research Society, 62, 483-500. https://doi.org/10.1057/jors.2010.40

Vancouver

Author

Fildes, R A ; Kingsman, B G. / Incorporating demand uncertainty and forecasting in supply chain planning models. In: Journal of the Operational Research Society. 2011 ; Vol. 62. pp. 483-500.

Bibtex

@article{f487532b498a4ff39da5ba2e63e56900,
title = "Incorporating demand uncertainty and forecasting in supply chain planning models",
abstract = "This paper develops a framework for examining the effect of demand uncertainty and forecast error on unit costs and customer service levels in the supply chain, including Material Requirements Planning (MRP) type manufacturing systems. The aim is to overcome the methodological limitations and confusion that has arisen in much earlier research. To illustrate the issues, the problem of estimating the value of improving forecasting accuracy for a manufacturer was simulated. The topic is of practical importance because manufacturers spend large sums of money in purchasing and staffing forecasting support systems to achieve more accurate forecasts. In order to estimate the value a two-level MRP system with lot sizing where the product is manufactured for stock was simulated. Final product demand was generated by two commonly occurring stochastic processes and with different variances. Different levels of forecasting error were then introduced to arrive at corresponding values for improving forecasting accuracy. The quantitative estimates of improved accuracy were found to depend on both the demand generating process and the forecasting method. Within this more complete framework, the substantive results confirm earlier research that the best lot sizing rules for the deterministic situation are the worst whenever there is uncertainty in demand. However, size matters, both in the demand uncertainty and forecasting errors. The quantitative differences depend on service level and also the form of demand uncertainty. Unit costs for a given service level increase exponentially as the uncertainty in the demand data increases. The paper also estimates the effects of mis-specification of different sizes of forecast error in addition to demand uncertainty. In those manufacturing problems with high demand uncertainty and high forecast error, improved forecast accuracy should lead to substantial percentage improvements in unit costs. Methodologically, the results demonstrate the need to simulate demand uncertainty and the forecasting process separately.",
author = "Fildes, {R A} and Kingsman, {B G}",
year = "2011",
doi = "10.1057/jors.2010.40",
language = "English",
volume = "62",
pages = "483--500",
journal = "Journal of the Operational Research Society",
issn = "0160-5682",
publisher = "Taylor and Francis Ltd.",

}

RIS

TY - JOUR

T1 - Incorporating demand uncertainty and forecasting in supply chain planning models

AU - Fildes, R A

AU - Kingsman, B G

PY - 2011

Y1 - 2011

N2 - This paper develops a framework for examining the effect of demand uncertainty and forecast error on unit costs and customer service levels in the supply chain, including Material Requirements Planning (MRP) type manufacturing systems. The aim is to overcome the methodological limitations and confusion that has arisen in much earlier research. To illustrate the issues, the problem of estimating the value of improving forecasting accuracy for a manufacturer was simulated. The topic is of practical importance because manufacturers spend large sums of money in purchasing and staffing forecasting support systems to achieve more accurate forecasts. In order to estimate the value a two-level MRP system with lot sizing where the product is manufactured for stock was simulated. Final product demand was generated by two commonly occurring stochastic processes and with different variances. Different levels of forecasting error were then introduced to arrive at corresponding values for improving forecasting accuracy. The quantitative estimates of improved accuracy were found to depend on both the demand generating process and the forecasting method. Within this more complete framework, the substantive results confirm earlier research that the best lot sizing rules for the deterministic situation are the worst whenever there is uncertainty in demand. However, size matters, both in the demand uncertainty and forecasting errors. The quantitative differences depend on service level and also the form of demand uncertainty. Unit costs for a given service level increase exponentially as the uncertainty in the demand data increases. The paper also estimates the effects of mis-specification of different sizes of forecast error in addition to demand uncertainty. In those manufacturing problems with high demand uncertainty and high forecast error, improved forecast accuracy should lead to substantial percentage improvements in unit costs. Methodologically, the results demonstrate the need to simulate demand uncertainty and the forecasting process separately.

AB - This paper develops a framework for examining the effect of demand uncertainty and forecast error on unit costs and customer service levels in the supply chain, including Material Requirements Planning (MRP) type manufacturing systems. The aim is to overcome the methodological limitations and confusion that has arisen in much earlier research. To illustrate the issues, the problem of estimating the value of improving forecasting accuracy for a manufacturer was simulated. The topic is of practical importance because manufacturers spend large sums of money in purchasing and staffing forecasting support systems to achieve more accurate forecasts. In order to estimate the value a two-level MRP system with lot sizing where the product is manufactured for stock was simulated. Final product demand was generated by two commonly occurring stochastic processes and with different variances. Different levels of forecasting error were then introduced to arrive at corresponding values for improving forecasting accuracy. The quantitative estimates of improved accuracy were found to depend on both the demand generating process and the forecasting method. Within this more complete framework, the substantive results confirm earlier research that the best lot sizing rules for the deterministic situation are the worst whenever there is uncertainty in demand. However, size matters, both in the demand uncertainty and forecasting errors. The quantitative differences depend on service level and also the form of demand uncertainty. Unit costs for a given service level increase exponentially as the uncertainty in the demand data increases. The paper also estimates the effects of mis-specification of different sizes of forecast error in addition to demand uncertainty. In those manufacturing problems with high demand uncertainty and high forecast error, improved forecast accuracy should lead to substantial percentage improvements in unit costs. Methodologically, the results demonstrate the need to simulate demand uncertainty and the forecasting process separately.

U2 - 10.1057/jors.2010.40

DO - 10.1057/jors.2010.40

M3 - Journal article

VL - 62

SP - 483

EP - 500

JO - Journal of the Operational Research Society

JF - Journal of the Operational Research Society

SN - 0160-5682

ER -