The impact of design debugging on new product development speed : the significance of improvisational and trial-and-error learning

Investigating the antecedents of cycle time reduction is a continuing concern within new product development (NPD) research (Chen et al., 2010; Cankurtaran et al., 2013). A number of researchers have reported the effects of team learning on NPD speed (Dayan and Di Benedetto, 2008; Cankurtaran et al., 2013), while others relate learning to overall team performance (Magni et al., 2013). However, few studies have systematically researched the effects of improvisation and trial-and-error learning on NPD cycle time. The aim of this study is to shine new light on NPD learning and cycle time reduction through an examination of the effects of improvisation and trial-and-error. To that end, this study conceptualizes and tests the settings wherein improvisation and trial-and-error might contribute or hinder NPD cycle time reduction. The authors develop hypothesis to investigate the effects of improvisation - and trial-and-error learning on NPD cycle time. Based on a review of the literature and in-depth interviews measures are defined to approximate improvisation and trial-and-error using secondary data from over 200 projects with absolute objective measures of cycle time. In addition, 1000s archival records of debugging incidents and engineering changes are used to approximate the impact of improvisation and trial-and-error. To estimate their impact on cycle time a learning curve model is developed (Argote, 2012) which offers an effective way of identifying the conditions that drive cycle time learning and performance (Wiersma, 2007). Based on this model the hypotheses are tested. The findings suggest that improvisation and trial-and-error contribute to cycle time learning in the prototyping and pilot phases only, and that they hinder learning during later stages in the NPD process. These findings contribute to the extant literature by providing an important new organizational learning perspective on NPD speed. The study contributes to practice by relating firms’ improvisation and trial-and-error practices to learning and speed performance

Quantifying the impact of product changes on manufacturing performance

Every adjustment to a physical product disrupts the manufacturing organization, requiring adaptation in tools and processes. The resulting disruption to manufacturing performance is poorly understood. We use design structure matrices and a complexity metric to quantify the complexity and change of product architecture in an explorative, small scale experiment. Based on the results we develop two propositions to guide further research into the factors that affect the shape of consecutive learning curves upon product changes. The first proposition is that after product change, the complexity of the novel part of product architecture is responsible for the initial decrease in manufacturing performance. Second, we propose that the asymptote of a learning curve and the complexity of a product’s architecture are inversely related.Every adjustment to a physical product disrupts the manufacturing organization, requiring adaptation in tools and processes. The resulting disruption to manufacturing performance is poorly understood. We use design structure matrices and a complexity metric to quantify the complexity and change of product architecture in an explorative, small-scale experiment. Based on the results we develop two propositions to guide further research into the factors that affect the shape of consecutive learning curves upon product changes. The first proposition is that after product change, the complexity of the novel part of product architecture is responsible for the initial decrease in manufacturing performance. Second, we propose that the asymptote of a learning curve and the complexity of a product’s architecture are inversely related.</p

Product platform life cycles : a multiple case study

Product platforms are used to specify a (virtual) company's offering to the market in terms of functionality and performance. The specification covers a range of actual products and services and the platform includes choice features, options and external interfaces. Usually, the platform also specifies a number of internal interfaces, in such a way that the common architecture of the products is specified by the platform. The components of a product platform can by itself act as a recursively-defined smaller platform. Like many other artefacts, platforms have a life cycle. In this paper, we discuss the life cycle of the platform in various industries, such as industrial machinery, aerospace, automotive and product software. This paper describes requirements for platform life cycle management and concludes that there are strong arguments to distinguish platform life cycle management from the more common Product Life Cycle (PLC) management, and that best practices in various industries deserve to be generalised