Optimization of knowledge transferring costs in designing product portfolio: a fuzzy binary linear programming model

Purpose The success of any organization in a knowledge-based economy depends on effective knowledge transferring and then proper use of the transferred knowledge. As is known, optimizing the knowledge transferring costs in a product portfolio plays an important role in improving productivity, compet...

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Bibliographic Details
Published in:VINE journal of information and knowledge management systems Vol. 52; no. 1; pp. 18 - 32
Main Authors: Dorostkar-Ahmadi, Nahid, Shafiei Nikabadi, Mohsen, babaie-kafaki, Saman
Format: Journal Article
Language:English
Published: Bingley Emerald Publishing Limited 11.01.2022
Emerald Group Publishing Limited
Subjects:
Algorithms
Business competition
Comparative advantage
Competition
Competitive advantage
Consumers
Costs
Decision making
Documentation
Empowerment
Experiments
Heuristic methods
Information systems
Knowledge
Knowledge economy
Knowledge management
Knowledge sharing
Linear programming
Manufacturing
Optimization
Portfolio management
Portfolios
Product development
Product life cycle
Productivity
Profitability
Profits
Rule ordering
Software
Knowledge transferring costs
Knowledge sharing
KM
Fuzzy binary linear programming
Manufacturing
Product portfolio
ISSN:2059-5891, 2059-5905
Online Access:Get full text
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Summary:Purpose The success of any organization in a knowledge-based economy depends on effective knowledge transferring and then proper use of the transferred knowledge. As is known, optimizing the knowledge transferring costs in a product portfolio plays an important role in improving productivity, competitive advantage and profitability of any organization. Therefore, this paper aims to determine an optimal product portfolio by minimizing the konlwedge transferring costs. Design/methodology/approach Here, a fuzzy binary linear programming model is used to select an optimal product portfolio. The model is capable of considering the knowledge transferring costs while taking into account the human-hours constraints for each product by a fuzzy approach. Using fuzzy ranking functions, a reasonable solution of the model can be achieved by classical or metaheuristic algorithms. Findings Numerical experiments indicate that the proposed fuzzy model is practically effective. Originality/value The contributions of this work essentially consist of considering knowledge transferring costs in selecting an optimal product portfolio and using the fuzzy data which make the model more realistic.
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ISSN:2059-5891
2059-5905
DOI:10.1108/VJIKMS-02-2020-0019