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decision levels of the operational hierarchy are fully integrated through the shared use of consistent, robust models. Models serve as the central repository of process knowledge. Information flows from the lower levels to the higher levels to ensure that decisions fully consistent with the status and capacity of the production resources are made.
The third area of need is in the development of tools to support the overall business decision processes. The objective is to expand the envelope beyond the process itself and to encompass the business processes that are essential to driving manufacturing and the entire supply chain. The tools include improved sales and market forecasting methodologies, supply and logistics planning techniques, methodologies for quantitative risk assessment, optimization-based plant scheduling methods, business modeling frameworks, and approaches to dynamic supply chain optimization. Optimization-based scheduling requires the solution of very high dimensionality models expressed in terms of discrete 0-1 variables. The key need is to be able to solve scheduling problems with hundreds of thousands of such variables reliably and quickly. Such capabilities need to be extended to allow treatment of models that encompass the entire supply chain and to quantitatively address business issues such as resource and capital planning associated with the supply chain, siting of new products, and the impact of mergers and acquisition on the supply chain.
Finally, in order to realize the benefits of the developments in the other three areas, it is necessary, indeed essential, to create training methodologies for the work force. These computer-based training methodologies must make efficient use of students' time, recognize differences in levels of expertise, and employ extensive visualization tools, including virtual reality components. Methods must also be developed to aid process staff in the understanding of models and the meaning of the solutions resulting from the various decision support tools that are based on these models. Such understanding is critical both to the initial adoption of such models and to the continuous improvement process, as it is only from understanding the constraints of the existing operation and their implications that cost-effective improvements can be systematically generated.
In conclusion, the process-operations-specific information systems and technical computing developments outlined above are essential to the realization of the goal of the dynamically optimized supply chain. Continuing increases in computing power, network bandwidth, and availability of faster and cheaper memory will no doubt facilitate achievement of this goal. However, the scope and complexity of the underlying decision problems require methodological developments that offer effective gains orders of magnitude beyond the likely increases in raw computing power and communication bandwidth. Process-oriented technical computing really does play the pivotal role in the future of process operations.