An integrated fuzzy-goal-programming-based framework for selecting suppliers in strategic alliance formation

Famuyiwa, Oluwafemi; Monplaisir, Leslie; Nepal, Bimal

doi:10.1016/j.ijpe.2007.12.003

Cited by 37 publications

(8 citation statements)

References 19 publications

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“…Rodríguez-Ferradas and Alfaro-Tanco (2016) found that collaborating with suppliers may generate higher competitiveness due to "innovative workable parts codeveloped and provided by the suppliers" (Chung and Kim, 2003, p. 600). Collaboration with suppliers may involve outsourcing (Pun and Heese, 2014), strategic alliances (Famuyiwa et al, 2008), and joint ventures (Johnson and Houston, 2000). As well, other studies found competitors (Gnyawali and Park, 2011).…”

Section: Examining Open Innovation Practicesmentioning

confidence: 96%

Examining open innovation practices in low-tech SMEs: insights from an emerging market

Oduro

2019

JSTPM

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Purpose Much of the scholarly works on open innovation have significantly highlighted the application of the model in high-tech industries in the developed world. However, how the phenomenon applies in low-tech small and medium-sized enterprises (SMEs) in developing countries is still marginal and lacks substantive research. This study aims to draw on the network theory of innovation to examine the open innovation orientations of low-tech SMEs in an emerging market context, particularly Ghana. Design/methodology/approach The research design used was a qualitative–quantitative approach: the qualitative phase of the study, involving 31 low-tech SMEs, used a multiple case approach through semi-structured interviews and analyzed the interview responses using NVivo statistical tool; the quantitative phase, including 706 low-tech SMEs, also used a survey questionnaire approach and descriptively analyzed data collected using SPSS statistical tool. Findings Results disclose that the low-tech SMEs’ employment of the open innovation model are preponderantly driven by commercialization purposes, knowledge acquisition motives, financial motives and strategic motives, whereas their open innovation approaches include inbound strategies (collaboration with suppliers, co-creation/customer immersion), outbound strategies (IP licensing out) and coupled strategies (strategic alliances, contract manufacturing, and joint ventures). Moreover, the findings show that the SMEs’ preferred open innovation partners include suppliers, customers, private universities and non-industry, in that order. Finally, results show that the low-tech SMEs’ open innovation advantages include market gains, strategic gains, knowledge gains, operational gains, financial gains and network gains, whereas their open innovation challenges colossally were collaboration barriers and organizational barriers. Practical implications These findings purvey valuable perceptiveness for managers, academicians and policymakers alike; they highlight the importance of open innovation to low-tech SMEs, proven strategies, challenges involved and the mechanisms for effective and efficient adoption of the open innovation model. Originality/value The value of this study reclines in the extension of open innovation research from high-tech industries in the advanced world to low-tech SMEs in emerging economies. Results of the study enrich the knowledge and understanding of how the theoretical model of open innovation is adopted and implemented by the low-tech SME sector in emerging economies.

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Section: Examining Open Innovation Practicesmentioning

confidence: 96%

Examining open innovation practices in low-tech SMEs: insights from an emerging market

Oduro

2019

JSTPM

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“…By incorporating such factors, we can certainly improve the probability and stability of success of the entire ASC (Famuyiwa et al, 2008). Secondly, in practical situations of designing the fuzzy intelligent model, the decision makers are not required to give deterministic values to the system's parameters, such as threshold value, joint weight and activation value etc.…”

Section: Discussionmentioning

confidence: 99%

“…Then the resulting set is defuzzified to a single number. The smallest of max (Z SOM ) and largest of max (Z LOM ) defuzzification methods are not used as the other three dufuzzification methods because of their obvious bias (Famuyiwa et al, 2008). However, the centroid of area (Z COA ), which can be defined as…”

Section: Defuzzification Of the Fuzzy Outputsmentioning

confidence: 99%

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Partner selection in agile supply chains: a fuzzy intelligent approach

Barnes

2013

Production Planning & Control

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-2 - Partner selection in agile supply chains: a fuzzy intelligent approachPartner selection is a fundamental issue in supply chain management as it contributes significantly to overall supply chain performance. However, such decision making is problematic due to the need to consider both tangible and intangible factors, which cause vagueness, ambiguity and complexity. This paper proposes a new fuzzy intelligent approach for partner selection in agile supply chains (ASC) by using fuzzy set theory in combination with radial basis function artificial neural network. Using these two approaches in combination enables the model to classify potential partners in the qualification phase of partner selection efficiently and effectively using very large amounts of both qualitative and quantitative data. The paper includes a worked empirical application of the model with data from eighty-four representative companies within the Chinese electrical components and equipment industry, to demonstrate its suitability for helping organizational decision makers in partner selection.

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“…As pointed out by Dickson (1966), multiple criteria have to be considered in order to select an appropriate set of partners for creation of a new VO. However, most often, the key factors to be addressed are grouped into three categories: structural compatibility goals, managerial compatibility goals, and financial goals (Famuyiwa et al, 2008). Several attempts were made to create a general framework to map the relationships among the organizational units and the partnering companies (Camarinha-Matos et al, 2005;Stock et al, 2000;Fischer et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

An intelligent modeling method based on genetic algorithm for partner selection in virtual organizations

Dinu¹,

Raluca²

2011

BEH

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The goal of a Virtual Organization is to find the most appropriate partners in terms of expertise, cost wise, quick response, and environment. In this study we propose a model and a solution approach to a partner selection problem considering three main evaluation criteria: cost, time and risk. This multiobjective problem is solved by an improved GA that includes meiosis specific characteristics and step-size adaptation for the mutation operator. The algorithm performs strong exploration initially and exploitation in later generations. It has high global search ability and a fast convergence rate and also avoids premature convergence. On the basis of the numerical investigations, the incorporation of the proposed enhancements has been successfully proved.

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An integrated fuzzy-goal-programming-based framework for selecting suppliers in strategic alliance formation

Cited by 37 publications

References 19 publications

Examining open innovation practices in low-tech SMEs: insights from an emerging market

Examining open innovation practices in low-tech SMEs: insights from an emerging market

Partner selection in agile supply chains: a fuzzy intelligent approach

An intelligent modeling method based on genetic algorithm for partner selection in virtual organizations

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