LAUSR

This paper proposes a discrete-time Markov chain-based ranking algorithm to rank critical activities in development processes for complex systems. Critical activities are those that lead to greater rework, involve close coordination or interdependence between development units, or produce task volatility. For example, the product development processes of the Ford Motor Company have several activities that are sensitive to information variability which can lead to higher task volatility or higher probability of rework. It is important to identify such activities early to ensure that the requirements for these activities are met and risk mitigation efforts can be planned. Analysis of the results from a variety of process architectures across a range of systems engineering domains shows that the proposed algorithm is more effective in identifying these critical activities than existing network ranking algorithms.