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An Adaptive Process Model to Support Product Development Project Management

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2 Author(s)
Viktor LÉvÁrdy ; 3D Syst. Eng. GmbH, Munich, Germany ; Tyson R. Browning

Projects are temporary allocations of resources commissioned to achieve a desired result. Since each project is unique, the landscape between the current state (the start of the project) and the desired state (the successful end of the project) is often dynamic, uncertain, and ambiguous. Conventional project plans define a set of related activities (a work breakdown structure and activity network) with the assumptions that this set is necessary and sufficient to reach the project's desired result. Popular models for project planning (scheduling, budgeting, etc.) and control are also based on a set of project activities that are specified and scheduled a priori. However, these assumptions often do not hold, because, as an attempt to do something novel, the actual path to a project's desired result is often revealed only by the additional light provided once the work is underway. In this paper, we model a product development process as a complex adaptive system. Rather than prespecifying which activities will be done and when, we set up: 1) a superset of general classes of activities, each with modes that vary in terms of inputs, duration, cost, and expected benefits; and 2) simple rules for activity mode combination. Thus, instead of rigidly dictating a specific project schedule a priori, we provide a ldquoprimordial souprdquo of activities and simple rules through which the activities can self-organize. Instead of attempting to prescribe an optimal process, we simulate thousands of adaptive cases and let the highest-value process emerge. Analyzing these cases leads to insights regarding the most likely paths (processes) across the project landscape, the patterns of iteration along the paths, and the paths' costs, durations, risks, and values. The model also provides a decision support capability for managers. For researchers, this way of viewing projects and the modeling framework provide a new basis for future studies of agile and adaptive processe- s.

Published in:

IEEE Transactions on Engineering Management  (Volume:56 ,  Issue: 4 )