Selecting new product development team members with knowledge sharing approach: A fuzzy bi-objective optimization model. Issue 2 (4th April 2016)

• Record Type: Journal Article
• Authors: Akhavan, Peyman; Hosseini, S. Mahdi; Abbasi, Morteza
• Is Part Of: Program. Volume 50:Issue 2(2016)
• Page Start: 195
• Page End: 214
• Publication Date: 2016-04-04
• Subjects: New product development -- Project team -- Knowledge sharing -- Fuzzy sets -- Bi-objective optimization -- MOA framework; Libraries, University and college -- Great Britain -- Automation -- Periodicals; 025.30285
• DOI: 10.1108/PROG-04-2015-0033
• Languages: English
• ISSNs: 0033-0337

Abstract

Abstract : Purpose: – The purpose of this paper is to provide a method for selection of the new product development (NPD) project team members, in such a way to maximize the expertise level of team members and at the same time, optimize knowledge sharing in the organization. Design/methodology/approach: – According to the motivation-opportunity-ability framework, knowledge sharing antecedents were determined. Then, the problem of selecting appropriate members of the project team was formulated as a bi-objective integer non-linear programming model. Due to the uncertainty conditions in the evaluation of candidates, the fuzzy sets approach was used for modeling. To solve the problem, first, the non-linear programming model was converted to a linear model. Subsequently, the fuzzy bi-objective linear programming problem was solved by using an approximate algorithm. Findings: – Results of applying the proposed method to an Iranian ship-building company showed its effectiveness in selecting appropriate members of the project team. Practical implications: – With the aid of the proposed approach, project managers will be able to form effective project teams that while increasing the success probability of the project, facilitate the maintenance of knowledge acquired during the project lifecycle. Originality/value: – This paper, for the first time, has tried to provide a method for selecting the NPD project team members, in a way that while selecting candidates with highest expertise, maximizes the sharing of knowledge among them.