Narrow Results

Project selection can be a real problem of the multi-criteria group decision making if a group of decision makers express their preferences depending on the nature of the alternatives and different criteria with respect to their knowledge about them. The purpose of the project selection process is to analyze project viability and to approve or reject project proposals based on established criteria. Such decisions are often complex, because they require the identification, consideration and analysis of many tangible and intangible factors. This paper presents a multi-criteria group decision-making approach for project selection problem in the type-2 fuzzy environment. The proposed method is an extended version of Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method with interval type-2 fuzzy numbers; it is called type-2 fuzzy VIKOR (T2F-VIKOR). A stepwise procedure is used for ranking and evaluating the alternatives in the developed method, and the best solution is selected considering both the beneficial and nonbeneficial criteria. An illustrative example is presented to show the applicability of the proposed approach in the project selection problems, and the results are analyzed. The results are compared with some existing methods to show the validity of the extended method. We also utilize six sets of criteria weights for analyzing the stability of the proposed method. These analyses show that the obtained results of the proposed method are relatively consistent with other methods and have good stability in different criteria weights.

Nowadays most of the organizations tend to outsource their projects. In these cases, two major problems arise: project selection and contractor selection. There are many papers which have argued separately about project and contractor selections. In this paper, we propose a decision support system for selecting projects and related contractors. The proposed approach contains three phases including contractors' prequalification (Phase 1), computing success coefficient of each contractor in projects using fuzzy TOPSIS method (Phase 2), and selecting projects and assigning each project to the most appropriate contractor using a linear programming model (Phase 3). Finally, a numerical example is illustrated to show the application of the proposed method.

This paper explores the front end innovation activities in a multinational Global Healthcare Company (GHC). A questionnaire was designed and distributed to front end innovators from 20 operating companies to understand team composition, essential skill sets, and the methodology used to assess customer needs, generate ideas, and define the selection criteria used during the go/no go decision. For each category the current state and best practice (based on the views of the individual respondents) for front end innovation was analyzed and reported in this paper. The results of this study reflect the opinions and suggestions of 37 respondents (23 that were at directorship level or above). These results were amalgamated and used to develop existing models of innovation to produce a framework (termed the Total Front End Framework (TFEF)) that should be useful for both senior managers and innovation teams to optimize their activities and increase the efficiency of the innovation process.

Innovation Management Control (IMC) supports innovation management with valuable information, thus improving innovation effectiveness and efficiency. Literature has shown that different elements of a management control system must be interdependent and that its design must form a coherent system of controls that spans the entire process under control. Accordingly, IMC covers the entire innovation process. However, IMC literature is spread over numerous journals from different disciplines, which may prevent the development of holistic IMC systems. We present a bibliometric analysis that provides insights into the development of IMC as a research field and examines the severity of knowledge dispersal. We build on a database covering 549 papers. The aim of the study is to reveal IMCs emergence and evolution as a research field, and future research directions. Our study shows that IMC combines several subfields and that dispersion is present in IMC, thus slowing knowledge transfer and hindering rapid progress.