Managing Uncertainty in Multilevel Manufacturing Systems

Vargas, Gustavo; Dear, Roger G.

doi:10.1108/eum0000000002081

Cited by 11 publications

(10 citation statements)

References 12 publications

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“…Failure to include uncertainty in an analysis severely limits its practical implications. The earlier research suffered from limitations that include a lack of clarity in distinguishing between demand uncertainty and forecast error (De Bodt and Van Wassenhove, 1983a;Lee and Adam, 1986) and the trade off between cost and service level, which itself depends on the cost structures assumed in the problem (Vargas and Dear, 1991).…”

Section: Demand Uncertainty and Forecast Errors In Supply Chain Systemsmentioning

confidence: 99%

“…Although much of the research analysed a simple production system (eg De Bodt and Van Wassenhove, 1983b;Lee and Adam, 1986;Ritzman and King, 1993;Chen et al, 2000a) a few researchers have considered manufacturing systems closer to the complexity observed in practice (eg Vargas and Dear, 1991;Jeunet, 2006). The problem with more complex studies is that the quantitative results are likely to be specific to the distinctive features of the chosen production system.…”

Section: Demand Uncertainty and Forecast Errors In Supply Chain Systemsmentioning

confidence: 99%

“…The scheduled production will either be insufficient to meet the realised actual demand, thus producing shortages, or exceed the realised demand resulting in unanticipated stocks. As several authors have pointed out, for example Vargas and Dear (1991), the effect of demand uncertainty on manufacturing systems that rely on deterministic lot-sizing techniques, such as MRP applications, is an important problem. Although all researchers seem to agree with the early conclusions of De Bodt and Van Wassenhove (1983a) and others that demand uncertainty dramatically increases costs, the simple questions, of how those costs increase with forecasting error, their relationship to demand uncertainty and whether the form of demand pattern and the forecasting model used are important, remain unresolved.…”

Section: Introductionmentioning

confidence: 99%

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Incorporating demand uncertainty and forecast error in supply chain planning models

Fildes

Kingsman

2011

Journal of the Operational Research Society

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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.

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Section: Demand Uncertainty and Forecast Errors In Supply Chain Systemsmentioning

confidence: 99%

Section: Demand Uncertainty and Forecast Errors In Supply Chain Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Incorporating demand uncertainty and forecast error in supply chain planning models

Fildes

Kingsman

2011

Journal of the Operational Research Society

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“…Quelques écrits récents illustrent la diversité des justifications avancées expliquant comment les stocks de sécurité permettent de contrer différentes sources d'incertitude (au sens large). Par exemple, l'incertitude en production est traitée par Ho et al (1995) qui s'intéressent à l'incertitude affectant les processus de production (pannes, délais d'exécution variables…) ou par Vargas & Dear (1991) qui se penchent sur les effets de l'incertitude concernant les temps de lancement et de production, les pannes des machines et les temps de réparation nécessaires. L'incertitude sur les approvisionnements est traitée, par exemple, par Chiu & Huang (2003) pour l'incertitude portant sur les délais de livraison et par Koh et al (2002) pour l'incertitude sur les composants (qualité, problèmes liés au fournisseur).…”

Section: Travaux Antérieursunclassified

Détermination des stocks de sécurité dans une chaîne logistique-amont dédiée à une production de masse de produits très diversifiés

Camisullis¹,

Giard²

2010

ournal Européen des Systèmes Automatisés

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le cas des politiques calendaires de réapprovisionnement, chaque maillon dispose de deux types de stocks de sécurité lui permettant de faire face à ces différentes sources de fluctuations : des stocks de sécurité de composants produits à destination des maillons aval et des stocks de sécurité de composants approvisionnés en provenance des maillons amont. Les procédures mobilisées doivent tenir compte du cadre organisationnel des échanges de flux d'informations, la pertinence des règles de réapprovisionnement ou de lancement en production reposant sur celle des informations structurelles propagées le long de la chaîne logistique. ABSTRACT. In an upstream supply chain dedicated to the mass production of customized products, many sources of production and supply instability can be identified: the level and structure of the production of the final assembly line, the variability of lead times, the quality issues, packaging and loading constraints in transport, the anticipation of the demand, the synchronization of the flows of the components sent, received and produced. In the case of periodic replenishment policies, two different types of safety stocks are needed for every plant of the supply chain, which prevent from these various sources of fluctuations: safety stocks of produced components to face up to the demand of its downstream links and safety stocks of supplied components to face up to its production. The mobilized procedures must take into account the organizational framework of the exchanges of information and products. The relevance of the supply and production rules is based on the relevance of structural information broadcast along the supply chain.

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“…In the simulation study carried by Vargas and 2409 Uncertainty under MRP-planned manufacture Dear (1991), it was identi®ed that safety stock should be used robustly to bu er external demand and internal supply uncertainties, which comprises variability in set-up and run time and variability in resource supply caused by machine breakdown and variation in repair time. They also identi®ed that variability in set-up and run time causes the most systems disruption, whereas external demand uncertainty is di cult to eliminate.…”

Section: Internal Supply and External Demandmentioning

confidence: 99%

Uncertainty under MRP-planned manufacture: Review and categorization

Koh¹,

Saad²,

Jones³

2002

International Journal of Production Research

103

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Extensive research can be found in coping with uncertainty under Material Requirements Planning (MRP)-planned manufacture, but the under-performance of many manufacturing enterprises is still reported. To examine the reasons, a review of the literature on uncertainty under MRP-planned manufacture has been carried out. This paper presents a comprehensive review and categorisation of such research. It aims to provide a structure within which directions can be given for future research and the results of past research work are summarized to give an indication to practitioners of how to cope with uncertainty. An uncertainty categorization structure has been developed using systems theory to categorize uncertainty into input and process, and simultaneously to highlight the uncertainty that occurs in the supply and demand chain of the manufacturing process. Bu ering and dampening (BAD) approaches are proposed and the experimental methods used are incorporated in the structure. A range of research gaps is identi®ed: the lack of a detailed structure to enable the signi®cant uncertainty to be diagnosed optimally, as most of the past research studied uncertainty discretely and only some in speci®c combinations; the sub-optimal approach in coping with uncertainty; and little research has examined the interactions between uncertainties. Several directions for future research are proposed: the development of a cause-and-e ect structure for diagnosing uncertainty, a holistic approach to deduce to the underlying causes of uncertainty, generalization of the cause-and-e ect structure, examination of the BAD approaches applied in industry and consideration of interactions between uncertainties by ®rst diagnosing the signi®cant uncertainty. It is found that safety stock, the use of appropriate lot-sizing rules and rescheduling are the most robust approaches to cope with uncertainty, while safety capacity is not used at all for bu ering process uncertainty. Hedging/over-planning is useful for dampening process uncertainty. It can be concluded that input uncertainty has been maturely researched with external demand uncertainty dominating others, but with a range of suggested BAD approaches available. Scheduling heuristics, simulation modelling and mathematical modelling are the most applicable experimental methods in this research area. This review and categorization have identi®ed that a structured and systematic approach is required to cope with uncertainty holistically within MRP-planned manufacture.

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Managing Uncertainty in Multilevel Manufacturing Systems

Cited by 11 publications

References 12 publications

Incorporating demand uncertainty and forecast error in supply chain planning models

Incorporating demand uncertainty and forecast error in supply chain planning models

Détermination des stocks de sécurité dans une chaîne logistique-amont dédiée à une production de masse de produits très diversifiés

Uncertainty under MRP-planned manufacture: Review and categorization

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