In Product Development (PD), there is an inherent complexity in deciding what resources should perform which tasks taking into account their effectiveness towards completing the task, while adjusting to their availabilities. The right resources must be applied to the right tasks in the correct order. In this context, process modeling and simulation could aid in resource management decision making. However, most approaches define resources as elements needed to perform the activities without defining their characteristics, or use a single classification such as "designers". Despite their crucial importance to the delivery of the product, resources such as computational hardware, software, testing resources, amongst others have been overlooked during process planning stages. This paper presents a new method to model different resource types (designers, computational, testing) and studies the impact of using different options of those resources by simulating the model and analyzing the results. Thus, the new approach, which extends a task network model with Bayesian Networks (BN), allows testing the influence of using different resources on process performance. The method uses BN within each task to model different instances of resources that carries out the design activities (computational, designers and testing) along with its configurable attributes (time, risk, learning curve etc.), and tasks requirements. Thus, activity behavior is shaped depending on the chosen resource option to perform it. The approach enhances the capability to explore resource combination design space. It was applied to an aerospace case study to identify insights such as the best performing resource combinations, critical resources, resource sensitive activities, and the probability of a resource reaching performance targets.@en

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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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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