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Considerations In The Development
Of Virtual Factories

Learning from Past Problems

The idea of a virtual factory is not new. But in the past, the development and application of large coherent models of factory operations have been largely unsuccessful, even as more local simulations (e.g., those operating at the equipment level or process level) have succeeded. The models of the past have not had the content to capture the essence of factory operations, yet even these inadequate models were so difficult to produce that after great effort they typically ground down into "analysis paralysis" and were abandoned.

Possible causes for such failure include the following:

The initial models were too poor in detail to provide answers that satisfied factory demands, and so the concepts were abandoned. Even small events may produce large fluctuations in factory operations, and adequate models must be capable of reflecting these subtle influences.

The simulations were too slow to provide timely answers. Timeliness is paramount in factory operations; a piece of equipment that is unexpectedly down, or is being used for another task when required to perform a function, can dis rupt a shift schedule. Data must be provided and acted on very quickly to preserve the integrity of operations.

The representations of the process were inaccurate, leading to wrong answers. Processes may be simple or complex; in any event, all the important characteristics of a process need to be accurately represented so that applying a model will supply useful answers.

The user interfaces were so complicated and/or incomprehensible that they were unusable. The most common user of information is a human being, who can be overwhelmed by either the number or the complexity of the user interfaces required to access or disseminate information. For example, senior factory managers are best able to comprehend results that are explicitly tied to financial metrics of performance; results tied to metrics relevant at lower levels in the hierarchy will be less helpful to them.

There were insufficient skilled personnel to understand and apply the models intelligently. Use of models is not inherently easy. It requires skills that enable using models and simulations, as well as understanding and analyzing the results. Learning these skills requires education and training.

There were sufficient skilled experts on the factory floor to manage operations, so that modeling and simulation were considered unnecessary. When things are going relatively smoothly, new tools such as modeling and simulation are felt to be superfluous; current skill sets are thought to be sufficient to do the job.

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