A supply network-modelling system for a small- to medium-sized manufacturing company

  • Authors:
  • Stephen Smith;David Petty;David Trustrum;Ashraf Labib;Ali Khan

  • Affiliations:
  • School of Mechanical, Aerospace and Civil Engineering, University of Manchester, Sackville Building, Sackville Street, Manchester M60 1QD, UK;Manchester Metropolitan University Business School, Aytoun Building, Aytoun Street, Manchester M1 3GH, UK;Strix Innovative Technologies Ltd., Isle of Man, Forrest House, Ronaldsway, Isle of Man IM9 2RG, British Isles;Portsmouth Business School, Richmond Building, Portland Street, Portsmouth PO1 3DE, UK;School of Mechanical, Aerospace and Civil Engineering, University of Manchester, Sackville Building, Sackville Street, Manchester M60 1QD, UK

  • Venue:
  • Robotics and Computer-Integrated Manufacturing
  • Year:
  • 2008

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Abstract

During the late 1990s and early 2000s, the profile of global manufacturing has experienced many changes. There is anecdotal evidence that many western manufacturing companies have chosen to expand their manufacturing base across geographical boundaries. The common reasons sited for these ventures are to exploit less expensive labour markets, to establish a presence in expanding markets and in response to the threat of new competition. Whilst a global manufacturing base can prove to have many cost and sales benefits, there are also many disadvantages. Logistics operations can often increase in complexity leading to higher reliance on planning and effective interpretation of demand data. In response, systems modelling has remerged as a fertile research area after many years. Many modelling and simulation techniques have been developed, but these have had very limited practical success. The authors have identified that majority of these simulation techniques rely upon a detailed market structure being known, when this is rarely the case. This paper describes the outcome of a research project to develop of a pragmatic set of tools to gather, assess and verify supply chain structure data. A hybrid collection of technologies are utilised to assist these operations and to build a dynamic supply network model.