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Engineering Changes (ECs) are facts of life for any company developing and introducing new products, despite a commonly held notion that they are distractions from normal operation. Companies can become more innovative by utilising ideas from the ECs or by learning how to handle the ECs. This paper presents a framework to manage the ECs effectively, particularly the issue of EC propagation. An EC seldom confines itself to a single change, but triggers other changes in different components. The framework is designed to identify the affected components automatically, capture the required knowledge during the design phase of the product life cycle, and use it during the Engineering Change Management (ECM) process.

The changes within a new product development (NPD) process are handled differently depending on the stage of the project. The changes during the initial stages of the project are addressed by design iterations, while the changes after the product design is complete are addressed using a formal engineering change management (ECM) process. The ECM process is a complex process, especially under a collaborative environment, where various independent entities work together for a common cause of product development. The interactions between the NPD and ECM processes have rarely been investigated in the research community. In this paper, we attempt to study the interactions between the various NPD and ECM process parameters by modelling the processes and simulated the model to understand the parameter interactions. The organisations in a supply chain have been characterised based on their interactions with the original equipment manufacturer (OEM) during the NPD process. The organisation process templates representing the NPD and ECM processes of each type of organisation in the supply chain have been modelled. The templates have been used to develop a simulation model representing the NPD and ECM processes for a supply chain. The process variables, such as processing rates, resources, resource composition, resource allocation priority, processing quality and phase overlap, have been included in the model. The results indicate that most of the variables and interactions among the variables have a significant influence on the NPD lead time. By identifying the status of the NPD process, the decision-makers can use these results to develop appropriate management policies to govern their product development projects.

PurposeThe purpose of this paper is to investigate the interactions between new product development (NPD) and engineering change management (ECM) processes in terms of their impact on organizational performance.Design/methodology/approachA system dynamics model of the NPD and ECM processes within an organization has been built and simulated for a range of parameter values to investigate the interactions between the two processes.FindingsThe effect of various parameters on the lead‐time of the NPD process varies with different process environments. No single process management policy is advantageous for most if not all process operating conditions, thus it is important to change the critical parameters of the process every time.Research limitations/implicationsThe accuracy of the estimated effect of parameters on the lead‐time depends on the accuracy of estimated parameter values.Practical implicationsThe insights developed from the results would be useful for managers in planning their process management policies under various circumstances.Originality/valueThe study of interactions between the ECM and NPD processes has been scarce. This research would be very helpful to managers who plan the process managing strategies given various circumstances such as limited resources.

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