Network-based approach to modelling uncertainty in a supply chain

J. Blackhurst, Teresa Wu, P. O'Grady

Research output: Contribution to journalArticle

72 Scopus citations

Abstract

Supply chains are interlinked networks of suppliers, manufacturers, distributors and customers that provide a product or service to customers. Typical supply chains can be characterized by their complexity and by the inherent uncertainty in their operations. Therefore, modelling such supply chains is a difficult and challenging research task, particularly given the need to model the stochastic operations of typical supply chains. What is giving added urgency to the need to address this issue are the recent developments in communications, primarily based on Internet technologies, that offer the promise of connecting suppliers, assemblers and customers in a seamless network of information. This offers the promise of substantially improved decision-making and a consequent considerable improvement in operations. However, fulfilment of this promise is dependent on the development of a suitable modelling methodology for supply chains. A network-based methodology to model and analyse supply chain systems is proposed. The methodology represents the operation of a supply chain as an abstracted network. The approach allows for the inclusion of stochastic variables so that uncertainty in the operation of a supply chain can be modelled. The use of the methodology is illustrated using a case study based on company data. The contribution of this paper is threefold. First, an approach is presented that can represent the complex operation of a supply chain as an abstracted network. Second, the use of stochastic variables in this approach is described. The stochastic variables represent the uncertainty present in typical supply chains. Third, a case study is presented that illustrates how this approach can be used to improve the operation of a supply chain.

Original languageEnglish (US)
Pages (from-to)1639-1658
Number of pages20
JournalInternational Journal of Production Research
Volume42
Issue number8
DOIs
StatePublished - Apr 15 2004

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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