The objective of this paper is to explore the potential future changes on the value and feasibility of Outcome-Based Contracts (OBC) and Outcome Business Models (OBM) in the setting of industrial production equipment. As companies and industries are implementing these business models characterized by long value co-creation contracts, the impact of the world around changing is becoming both a risk as well as in other cases an opportunity for increased value creation. We conducted a futures research project with the emphasis in understanding the external changes impacting the value of the OBCs and OBMs for the contract parties. The paper contributes by highlighting the impact of Drivers of Change on OBCs for both parties as well as identifying a set of Drivers of Change for industrial equipment manufacturers and users.

Change management is a key process in the Product Development Process. In a context of availability of large data on the process through Enterprise Information Systems like PDM (Product Data Management) or PLM (Product Lifecycle Management) systems, the structured data network can be analysed to identify key feature to improve the process performance.

In this work, we propose a multi-layer network to engineering change management that models product, organisation and change facets together. Once build, this network is analysed with some classical graph tools to identify some key features allowing to elicit some organisational behaviours. This approach is applied on two pedagogic examples.

Researchers are continuously trying to deliver products on time. However, traditional tools such as product lifecycle management, critical path method, and program evaluation and review technique cannot predict product development span time with reasonable accuracy. Product development involves large, multidisciplinary teams designing complex, interdependent systems; so, predicting span time is challenging. We have modelled product development micro-activities to predict project span time and effort. The model uses a knowledge perspective where the difference between product knowledge requirements and designer knowledge capability drives product development speed. We simulate both process tasks and team behaviour, which are influenced by product complexity, development process complexity, and the difficulty of both technical and interface design. Our method identifies specific bottlenecks and poor levels of designer performance. It also predicts the span time and effort at the start of a project or at any point during the product development process as conditions change.