Forecasting collaboration capacity by driving output in product teams

Abstract

It is no doubt that globalization and free trade have brought competitive advantages of Innovation Driven Enterprises (I.D.E.s) to new levels. More specifically, managers are focused on improving and maximizing team collaboration to both increase capacity and utilization of their in-house talent and optimize company throughput and output. It has long been argued that performance and output are attributed to leadership, management and/or recruiting. However, new management and cognitive theories argue that optimizing output and team performance is now more of a science rather than just a conventional wisdom; conceiving a winning team now involves both predictive and nurturing responsibilities. This applies to all fields be it military, product development, medical, business, engineering and others to name a few. This thesis focuses on the new field of Integrated Design Management whereby multidisciplinary, innovative engines or product development teams are becoming essential entities for entrepreneurial survival and versatility during economic uncertainty. How can a product designer, an engineer and a businessman work together efficiently'? What makes the team perform better? Are there any rules for engagement or does skill lead output? How are people selected as part of a team? This thesis argues that creating an optimal product team should not be a stroke of luck but rather the result of applying new management sciences and team dynamics to better recruit and build for collaboration in today's fast-changing and competitive world. I believe that studying the correlation between three variables: compassion, collective intelligence and output in Integrated Design Management (I.D.M.) teams can lead to positive inferences relating to team formation and competitive work cultures. Through methods of measuring the latter variables in addition to observing product team habits and recording interviews with individuals enrolled in the I.D.M. program at MIT, the following thesis maps predictive variables across a system for nurturing successful team collaboration and output. The model constructed to forecast product team output, produced an r2 of 0.57, setting a benchmark for future models. This research also provides a template for future applications across multiple industries aimed at conceiving more collaborative teams with exceptional skills whose members may have been more comfortable working in isolation at the expense of creativity and efficiency.