The Application of a Decision - making Approach based on Fuzzy ANP and TOPSIS for Selecting a Strategic Supplier

Govindaraju, Rajesri; Akbar, Muhammad; Gondodiwiryo, Leksananto; Simatupang, Togar M.

doi:10.5614/j.eng.technol.sci.2015.47.4.5

Cited by 13 publications

(11 citation statements)

References 63 publications

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“…Financial health Ability to identify needs (Park et al, 2011); (Ellram 1990) (Govindaraju et al, 2015) Source: Adapted Ku et al, 2010 Currently, four suppliers were considered qualified for procuring milk and are still transactional. It is expected that relations within KUD Dau Batu and suppliers can reduce risk and maximize the total value of its purchases.…”

Section: Relationshipmentioning

confidence: 99%

Supplier Selection for Improving Supply Chain Performance

Rohmanna

Santoso

Majid

2022

AGROINTEK: Jurnal Teknologi Industri Pertanian

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Supplier selection played a role in developing industry performance. It was one of the critical issues in supply chain management. This study illustrated applying a decision-making procedure for supplier selection on KUD Dau Batu by ANP and fuzzy TOPSIS. Four suppliers (Princi, Gading Kulon, Petung Sewu, and Kucur) thought qualified for procuring milk. The first step determined the weight of criteria and sub-criteria that impacted strategic supplier selection by ANP. This approach in a KUD Dau Batu showed that cost was the top priority. The next step was to defuzzification of criteria and ranking the four suppliers by fuzzy TOPSIS. The supplier from Kucur was the best milk supplier compared to other supplier.

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Section: Relationshipmentioning

confidence: 99%

Supplier Selection for Improving Supply Chain Performance

Rohmanna

Santoso

Majid

2022

AGROINTEK: Jurnal Teknologi Industri Pertanian

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“…The advantage of using fuzzy-TOPSIS is its ability to apply fuzzy sets in order to express the relative importance of the applied criteria. The procedure for fuzzy-TOPSIS has been well established in several applications (e.g., Co 2017;Govindaraju et al 2015). This subsection briefly presents the fuzzy-TOPSIS model and the process to calculate relocation decision recommendations.…”

Section: Fuzzy-topsismentioning

confidence: 99%

A hybrid fuzzy-AHP-TOPSIS model for evaluation of manufacturing relocation decisions

2022

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Manufacturing relocation decisions are complex because they involve combinations of location modes like offshoring or reshoring, and governance modes like insourcing or outsourcing. Furthermore, the uncertainty involved in the decision-making process makes it challenging to reach a right-shoring decision. This study presents a hybrid fuzzy-AHP-TOPSIS model to support generic relocation decisions. Industry experts were involved in a pairwise comparison of the competitive priorities’ decision criteria. A meta-synthesis of empirical studies is used to generate theoretical relocation scenarios. The presented hybrid model is used to rank the relocation scenarios in order to identify the most pertinent alternative. The resiliency of the solution is presented through a sensitivity analysis. The results indicate that the proposed hybrid model can simultaneously handle all the main relocation options involving governance modes. Based on the input data in this study, the competitive priorities criteria quality, time and cost are shown to have a strong impact, whereas the sustainability criterion has a weak impact on the choice of relocation option. The research presented in this paper contributes to the research field of manufacturing relocation by demonstrating the suitability of the hybrid fuzzy-AHP-TOPSIS model for relocation decisions and the resilience of the results. Furthermore, the research contributes to practice by providing managers with a generic relocation decision-support model that is capable of simultaneously handling and evaluating various relocation alternatives.

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“…The use of fuzzy ratios in F-AHP is due to the inability of AHP to accommodate inaccuracy and subjectivity factors in the process of pair-wise comparisons or pair-wise comparisons for each criterion and alternative [9]. Meanwhile, F-ANP, as ANP development can overcome the shortcomings of the AHP method, especially in terms of the reciprocal relationship between decision levels and attributes through vague data processing [10] [11]. F-AHP and F-ANP implementations in assorted decision-making platforms including Mahdiyar et al that utilized F-ANP on green roof type selection [12]; Büyüközkan and Ifi employed F-ANP integrated with MADM hybrids to evaluate the green suppliers [13]; Bhattacharya et al enforced F-ANP based on balanced scorecard for green supply chain performance measurement [14].…”

Section: Introductionmentioning

confidence: 99%

Decision Support System for Smartphone Recommendation: The Comparison of Fuzzy Ahp and Fuzzy Anp in Multi-Attribute Decision Making

Okfalisa

Rusnedy

Iswavigra

et al. 2020

Sinergi

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Previous researches outlined the advantages of the Analytical Hierarchy Process (AHP) and Analytic Network Process (ANP) methods in solving Multi-Attribute Decision Making (MADM) problems. The advancement of the above methods was continually developed as an effort to cover up various weaknesses. Mainly related to the consistency and linguistic variables in translating the expert opinions. Thus, it initialized the emergence of Fuzzy AHP (F-AHP) and Fuzzy ANP (F-ANP). Due to the restricted operation of these algorithms in smartphone selection, this research attempted to investigate the effectiveness of both methods in providing the analysis of criteria weight, the final recommendation weight, the product recommendation weight, and the execution time in DSS-SmartPhoneRec application development. A survey of one hundred respondents of University students identified the dominant criteria in selecting the smartphone, namely price, Random Access Memory (RAM), processor, internal memory, and camera. Hence, five alternative products were then chosen as the appropriate smartphones’ recommendations based on the respondent’s preferences. As an automatic tool, a DSS-SmartPhoneRec application was built to analyze and compare between F-AHP and F-ANP methods in resolving the smartphone selection cases. It revealed that the level of consistency of criteria weight, the final weight of recommendation, and the weight that the product-based F-ANP was 40% greater than F-AHP. In terms of execution time, F-AHP had a shorter time than F-ANP. Meanwhile, the comparison of products recommendation from DSS-SmartPhoneRec and a manual test showed that F-ANP was 16% more in line with the respondents’ predilection. In a nutshell, the DSS-SmartPhoneRec administered the devote smartphone recommendations based on the user’s expectation. The comparison analysis furnished a learning outcome for the users in determining the appropriate MADM method tailored to the type of cases.

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The Application of a Decision - making Approach based on Fuzzy ANP and TOPSIS for Selecting a Strategic Supplier

Cited by 13 publications

References 63 publications

Supplier Selection for Improving Supply Chain Performance

Supplier Selection for Improving Supply Chain Performance

A hybrid fuzzy-AHP-TOPSIS model for evaluation of manufacturing relocation decisions

Decision Support System for Smartphone Recommendation: The Comparison of Fuzzy Ahp and Fuzzy Anp in Multi-Attribute Decision Making

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