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Design project management is witnessing an increasing need for practitioners to rely on tools that reflect the integrated nature of the social and technical characteristics of design processes, as opposed to considering the two as separate concepts. For practitioners, this integration has the potential value of predicting the future behavior of design processes by allowing them to understand what task to do next, whom to assign a task given the availability of resource, and the levels of knowledge and expertise required. In response to these challenges, this paper contributes to the development of a new process modeling method, called actor-based signposting (ABS), that looks at the early stages of the product development processes from the perspective of integrated sociotechnical systems. The objective is to support managers and decision-makers on both typical planning issues, such as scheduling and resource allocation, and less conventional issues relating to the organizational planning of a design project, such as identification of criticalities, matching required skills and expertise, and factors of influence. Ultimately, the aim is to support organizations to be more adaptive in responding to change and uncertainty. Two case studies in the automotive and aerospace industries with different properties and modeling objectives were selected to demonstrate the utility of the proposed method. Experimental analysis of these cases led to a range of insights regarding the future of modeling for academia as well as the decision-making capabilities for managers and practitioners.

In real-world, making the right decision at the right time is very challenging. Additional complexity is related to the fact that, a change in one part may propagate across multiple subsystems and therefore, it is very difficult to predict how decisions behave. To respond this need, this paper contributes a conceptual framework to present a novel perspective on modelling decisions, so-called Decision Propagation System -DPS. It is an intelligent approach to reflect how organisational dynamics and information dependencies can affect decision propagations, thus helping make Smart decisions.

This paper proposes a model that integrates information from product, process and organisation domains with a view to help manage these complex interrelationships with multiple layers of interaction. This model incorporates an integrated mechanism that simulates change effects during the design of complex manufacturing system by populating a Multi-layered Domain Matrix (MDM) and applying a Change Prediction Model (CPM) propagation mechanism to interconnected elements.