Experimental Investigation of the Implications of Model Granularity for Design Process Simulation

Abstract

Determining a suitable level of description, or granularity, for a product or process model is not straightforward, especially since granularity can manifest in multiple ways, but it is important to capture important elements in the model without building models that are too large to understand. This article investigates the implications of model granularity choices by simulating the design process of a diesel engine on different levels of detail, comparing the results and exploring ways to account for the differences. It uses two Design Structure Matrix (DSM) models for change prediction in a diesel engine at different levels of granularity to run simulations of the design process. Changes are a major source of rework and lead to frequent rescheduling of design tasks. The incremental nature of product development as well as design changes and their propagation complicate design process planning further. Process simulation may provide support in such contexts when it is based on an appropriate description of the product. The article shows that while coarse models can give an indication of likely process behavior, they miss potentially significant iteration loops.