Planning Information Processing along the Supply-Chain: A Socio-Technical View

  • Bernard Grabot
  • Stefan Marsina
  • Anne Mayère
  • Ralph Riedel
  • Peter Williams
Chapter
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Abstract

The increasing focus of manufacturing companies on their core business results in the development of larger and more complex supply chains which are made up of both large companies and small companies. From the perspective of large companies, the quality of coordination in Supply Chains is mainly dependent upon the competence of the human planners involved at each level of the Supply Chain in information processing methods and tools. In this interpretation, coordination problems are commonly considered to be the consequence of poor competence in the SMEs regarding information processing, and especially in operations planning and control. As a consequence, most large companies have launched programs disseminating “best practice”, standardised business processes, and software tools aimed at developing efficient planning procedures throughout their supply chains, and so convergence of operational objectives. However, this is picture does not take into account other inherent aspects of the planning problem within a supply chain, such as variety in organisational culture and ways of gathering and interpreting information. The aim of this Chapter is to illustrate the problematic situation outlined above through real industrial examples, and to suggest a framework allowing better representation and understanding of these coordination problems, to inform future system design and improvement activity. It will be emphasised that critical non-technical issues have to be taken into account in the planning process across a Supply Chain. We link these to justified practice and cultural concerns of particular interest to SMEs, a voice not well represented in the literature.

Keywords

Supply Chain Large Company Small Company Automotive Sector Aeronautical Industry 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer Berlin Heidelberg 2010

Authors and Affiliations

  • Bernard Grabot
    • 1
  • Stefan Marsina
    • 2
  • Anne Mayère
    • 3
  • Ralph Riedel
    • 4
  • Peter Williams
    • 5
  1. 1.ENITUniversité de ToulouseToulouseFrance
  2. 2.University of Economics in BratislavaBratislavaSlovakia
  3. 3.University of ToulouseToulouseFrance
  4. 4.Department of Factory Planning and Factory ManagementChemnitz University of TechnologyChemnitzGermany
  5. 5.University of LimerickLimerickIreland

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