In many engineering design contexts models are indispensable. They offer decision support and help tackle complex and interconnected design projects, capturing the underlying structure of development processes or resulting products. Because managers and engineers base many decisions on models, it is crucial to understand their properties and how these might influence their behaviour. The level of detail, or granularity, of a model is a key attribute that results from how reality is abstracted in the modelling process. Despite the direct impact granularity has on the use of a model, the general topic has so far only received limited attention and is therefore not well understood or documented. This article provides background on model theory, explores relevant terminology from a range of fields and discusses the implications for engineering design. Based on this, a classification framework is synthesised, which outlines the main manifestations of model granularity. This research contributes to theory by scrutinising the nature of model granularity. It also illustrates how this may manifest in engineering design models, using Design Structure Matrices as an example, and discusses associated challenges to provide a resource for modellers navigating decisions regarding granularity.

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Models of products and design processes are key to interacting with engineering designs and managing the processes by which they are developed. In practice, companies maintain networks of many interrelated models which need to be synthesised in the minds of their users when considering issues that cut across them. This article considers how information from product and design process models can be integrated with a view to help manage these complex interrelationships. A framework highlighting key issues surrounding model integration is introduced and terminology for describing these issues is developed. To illustrate the framework and terminology, selected modelling approaches that integrate product and process information are discussed and organised according to their levels and forms of integration. Opportunities for further work to advance integrated modelling in engineering design research and practice are discussed.

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The design of many products is incremental, based on a prior architecture and may be thought of as a series of changes to an existing design. Nonetheless, design changes and their propagation complicate design process planning. They are a major source of rework and lead to frequent rescheduling and reprioritisation of design tasks. Few methods exist to estimate the impact of such planning decisions with regard to both the design process and the product. Process simulation may provide support in this context when it is based on an appropriate description of the product. However, it can be hard to determine a suitable level of description, or granularity, for the product model. This article explores, by reference to a diesel engine, the implications of product model granularity for simulating design change propagation, and design process planning.

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The development of models, especially simulation models of both products and processes, has increased in industry and now offer substantial competitive advantages in decision support across many fields. Even so, little is known about the structures of applied modelling processes as the focus so far has primarily been on improving modelling tools and software, methodologies, and modelling outcomes. In this paper, we gain insights into the value creation activities in modelling practice through the analysis of activity structures from 12 different modelling processes across two large UK companies. The results show that modelling process structures can be divided into three distinct process types; ad-hoc modelling for decision support, new model development, and model change management. Existing research mainly considers new model development and therefore it is suggested that the other two types are also part of modelling practice, and therefore should be included in modelling process management. The process types are categorized from a modelling management perspective and a tentative modelling process management toolbox is suggested for further research.

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While product models and process models have a long standing transiting, there are few models that integrate the two type of models. Those that exist are research systems, which even if validated in industry do not have a broad uptake to date. This paper develops an integrated framework for product and process models based on the purpose the models are put it building on a model of Browning and Ramasesh. Selected integrated model are classified according to the framework. This revealed the no model to date gives equal weight to product and process models.

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Although modelling tools are intensively used within companies, the modelling process itself is still scarcely researched. The few related works focus on the steps encompassed when developing a model, without taking into consideration the context surrounding it. Nevertheless understanding this context is crucial since this influences the modelling process in terms of objectives, available data and tools. A survey conducted among expert modellers in 1994 provided insights into this context by establishing a profile of the modeller and highlighting the qualities needed to improve modelling practice. However software, technology and businesses have evolved over twenty years, which may have impacted the modelling practice. Twenty years later, we conduct a similar survey. Comparing the results enables studying the evolution of modelling practice over time. The findings are discussed in the light of potentially impacting technological progress and provide insight for future research concerned with improving the modelling process.

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Design tasks need to be rescheduled and reprioritised frequently during product development. Inappropriate priority decisions generate rework; thus, the policy used to guide such decisions may have a significant effect on design cost and lead time. Generic priority rules provide easily implementable guidelines for task prioritisation and are theoretically effective for many planning problems. But can they be used in design processes, which include iteration, rework and changes? In this article, a discrete-event simulation model is developed to investigate priority policies in design. The model explores the combined effects of progressive iteration, rework and change propagation during design of interconnected parts in a product architecture. Design progression is modelled as an increase in the maturity of parts; rework and change propagation cause maturity levels in certain parts to reduce. Twelve product architecture models ranging in size from 7 to 32 elements are simulated to draw qualitative and general insights. Sensitivity of the findings to assumptions and model inputs is tested. Generally effective priority policies are identified, and their impact is shown to depend on the interconnectedness and organisation of product architecture, as well as the degree of concurrency in the design process.@en