Supply chain simulation Process chain evaluation Internal logistics simulation Supply chain
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Conclusions and recommendations
These pages have made a strong case for the use of simulation to support decision-making in an aerospace manufacturing business. It has been shown how a computer model can represent a variety of systems of logistic control, including some novel or hybrid approaches. The information required to create simulations of this kind was identified, and the kind of results that could be expected as outputs have been reviewed. Best practice has also been discussed, describing how a simulation should be constructed, and validated.
Three case studies have been described, all in general terms such that the results could be applied to many different manufacturing businesses. Key points were as follows:
  1. The ‘best’ system of logistic control will vary, depending upon the circumstances of the business. To determine a suitable strategy, one must take into account the level of variability in supply performance, the incidence of quality problems, breakdowns and other delays within the manufacturing system, and the system of penalties under which deliveries are made.
  2. The simulations described here all exhibit a parametric system of logistic control; the way the model operates can be changed a great deal, without recourse to changing the simulation model itself. This is extremely useful in reducing the time required to carry out an experiment (and validate the results).
  3. Simulation is at its most useful when it is ‘owned’ by the people who perform the tasks it represents. The use of simple Microsoft Excel spreadsheets to define model parameters and scenarios meant that anybody could make use of the models described here; not just a skilled simulation analyst. This significantly improves the usefulness and longevity of the models created.
  4. The results from a simulation do not produce an ‘optimal’ solution for the logistic control of the factory. Our work has demonstrated that optimisation is unwise, in an environment where both upstream supply chain performance and downstream demand can be highly variable.
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recommendations