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.
| Original language | English |
|---|---|
| Pages (from-to) | 579-584 |
| Number of pages | 6 |
| Journal | Robotics and Computer-Integrated Manufacturing |
| Volume | 24 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - Aug 2008 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 8 Decent Work and Economic Growth
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SDG 9 Industry, Innovation, and Infrastructure
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