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The performance of most operations systems is significantly affected by the interaction of human decision-makers. A methodology, based on the use of visual interactive simulation (VIS) and artificial intelligence (AI), is described that aims to identify and improve human decision-making in operations systems. The methodology, known as 'knowledge based improvement' (KBI), elicits knowledge from a decision-maker via a VIS and then uses AI methods to represent the decision-making. By linking the VIS and AI representation it is possible to predict the performance of the operations system under different decision-making strategies and to search for improved strategies. The KBI methodology is applied to the decision-making surrounding unplanned maintenance operations at a Ford Motor Company engine assembly plant.

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The work reported in this paper is part of a project simulating maintenance operations in an automotive engine production facility. The decisions made by the people in charge of these operations form a crucial element of this simulation. Eliciting this knowledge is problematic. One approach is to use the simulation model as part of the knowledge elicitation process. This paper reports on the experience so far with using of a simulation model to support knowledge management in this way. Issues are discussed regarding the data available, the use of the model, and the elicitation process itself.

Increasingly, modular housing manufacturers choose to compete by becoming system integrators. The transition to this new business model requires companies to develop system-level knowledge that 'they know more than they produce' in managing supply networks. Engineering system design tools, through visualizing supply network design and make processes, can help companies to overcome many uncertainties when they choose to become system integrators within the supply network. This paper illustrates how simulation models can be derived from design characteristics, experiential data, and a pragmatic system engineering design 'vee' framework. This helps modular housing managers to observe and compare the risks of different products and supply network configurations, and see how behavior changes of individual organizations impact system-level performance. A modular housing case study illustrates the implementation and benefits of our approach.