Faster or fewer iterations? A strategic perspective of a sequential product development project. (2nd November 2021)

• Record Type: Journal Article
• Authors: Liu, Maoqi; Zheng, Li; Liu, Changchun
• Is Part Of: IISE transactions. Volume 53:Number 11(2021)
• Page Start: 1196
• Page End: 1214
• Publication Date: 2021-11-02
• Subjects: Product development -- iteration -- lead time -- Markov process; Industrial engineering -- Periodicals; Systems engineering -- Periodicals; Industrial engineering; Systems engineering; Electronic journals; Periodicals; 670.285
• DOI: Https://www.tandfonline.com/doi/10.1080/24725854.2020.1830207
• Languages: English
• ISSNs: 2472-5854

Abstract

Abstract: Shortening the lead time for Product Development (PD) provides enterprises with a competitive advantage. Given the iterative nature of PD projects, two aspects are regularly considered to shorten the PD lead time, that is, conducting faster or fewer iterations. However, executing faster iterations usually causes more iterations and vice versa . Therefore, suitable coordination between faster and fewer iterations is necessary to minimize the PD lead time. We investigate this coordination from a strategic perspective, whereby a PD project is considered as a sequence of stages and characterized by the design rates and rework probabilities of those stages. We model the coordination as a decision to choose the appropriate design rates for each stage, wherein the rework probabilities are negatively related to the design rates. An absorbing Markov process is applied to calculate the expected lead time of a PD project. Further, we formulate a geometric programming model to determine the optimal design rates of the stages with respect to the minimal expected lead time. Several insights are extracted from the model to provide general guidance on the coordination, including the effect of the acceptance check rate of the project, rework risk of the stages on the optimal design rates, and decomposability of the coordination. Inspired by these insights, an efficient heuristic algorithm is designed. The algorithm performs well in numerical experiments, which in turn validates the insights. Additionally, a field case proves the effectiveness of our model. Compared with the current policy, 12.25% of the PD lead time is saved through appropriate coordination between faster and fewer iterations.