نوع مقاله : پژوهشی
مجتمع مدیریت و فنّاوریهای نرم، دانشگاه صنعتی مالک اشتر
عنوان مقاله [English]
As the rate of business change continues to accelerate, organizations face challenging situations like rapid technological developments, corporate restructuring, emerging technologies, and globalization. Hence, the use of project teams in the performance of daily activities is increasingly gaining popularity among many new product development (NPD) projects. Project teams are highly advantageous, because the team members share common project goals and handle technical complexity and change with the assistance of their collective cross-functional knowledge. Additionally, these cross-functional teams are
often temporary organizations that are able to respond quickly to changing environmental conditions by adjusting the composition of the team members. In addition, by the use of cross-functional project teams, organizations attempt
to improve coordination and integration, span organizational boundaries, improve timing of technology developments, and reduce uncertainty levels. However, a significant challenge remains for project managers or other decision-makers to assemble project teams that are able to effectively preserve acquired knowledge during project lifecycle by project team members. Usually, three types of knowledge sharing take place in such projects: 1) knowledge sharing among team members in their domain of expertise; 2) knowledge sharing between team members and their co-workers in related functional departments in the domain of expertise; 3) knowledge sharing between team members and their co-workers in related functional department in the domains of non-expertise. Therefore, the problem of selecting proper project team's members is formulated in this paper as a mixed-integer nonlinear programming (MINLP) model to optimize these three types of knowledge sharing. We used the model for selecting a NPD project team in automotive industry. The experimental results indicated that the proposed approach is effective in selecting proper members based on their expected performance in knowledge sharing in and outside the project team.