Resilient supplier selection to mitigate uncertainty: soft-computing approach

Abstract

Purpose In recent years, determining the effective and suitable supplier in the supply chain management (SCM) has become a key strategic consideration to the success of any manufacturing organization in terms of business intelligence (BI), as many quantitative and qualitative critical factors are measured from big data. In today’s competitive business scenario, the main purpose of this study is to determine suitable and sustainable suppliers during supplier selection process is to reduce the risk of investment along with maximize overall value to the customer and develop closeness and long-term relationships between customers and suppliers to build a resilient SCM to mitigate uncertainty for automotive organizations. Design/methodology/approach As these types of decisions generally involve more than a few criteria and often necessary to compromise among possibly conflicting factors, the multiple-criteria decision-making becomes a useful approach to solve this kind of problem. Considering both tangible and intangible criteria, the aim of this paper is the presentation of a new integrated fuzzy analytic hierarchy process and fuzzy additive ratio assessment method with fuzzy entropy using linguistic values to solve the supplier selection problem to build the resilient SCM under uncertain data. Fuzzy entropy is used to obtain the entropy weights of the criteria. Findings Organizations gather massive amounts of information known as BD on the basis of historical records of uncertainties from several internal and external sources to manage uncertainty to improve the overall performance of organizations using BI strategy for analyzing and making effective decision to support the managements of automotive manufacturing organizations in an information system. Research limitations/implications Although this study tries to represent a full analysis on suitable and resilient global supplier selection under various types of uncertainty, still there are some improvements that can be made in the future by developing a more refined and more sophisticated approach to further enhance the performance of the proposed scheme to calculate overall rating scores of the alternatives. Originality/value The novelty of this paper is to propose a framework of BI in SCM to determine a suitable and resilient global supplier where all the meaningful information, relevant knowledge and visualization retrieved by analyzing the huge and complex set of data or data streams, i.e. BD based on decision-making, to develop any manufacturing organizational performance worldwide.

An overview of ARAS method: Theory development, application extension, and future challenge

“…Similarly, Martinez‐Gomez and Narvaez 73 and Martinez‐Gomez 74 combined the AHP method and the entropy method together to determine the attributes weights, and they also integrated the weights in the compromise way. In addition, Pramanik et al 76 used the AHP method and the entropy method to determine the weights of main attributes and subattributes, respectively.…”

“…Considering the convenience of the linguistic information, many original evaluations of experts are linguistic information. To handle the calculation of the linguistic information, some researchers converted the linguistic variables to triangular fuzzy number 23,27,29,31,34,41,43,46,48,50,51,60,70,72,76,80,82,84 some converted linguistic variables to gray numbers, 42,58,104 some converted the linguistic variables to rough numbers 106 …”

“…Triangular fuzzy set The operation rules in Reference [108] Trapezoidal fuzzy set The operation rule in Reference [100] Gray set The operation rules in Reference [109] Intuitionistic fuzzy set The operation rules in Reference [90] Interval-valued triangular fuzzy set The operation rules in Reference [110] Interval-valued Intuitionistic fuzzy set The operation rules in Reference [111] Interval-valued Pythagorean fuzzy set The operation rules in Reference [112] Hesitant fuzzy linguistic term set The operation rules in Reference [113] Interval type-2 hesitant fuzzy set The operation rules in Reference [114] Rough set The operation rules in Reference [115] Probability multivalued neutrosophic set The operation rules in Reference [116] Z-number The operation rules in Reference [117] Picture fuzzy set The operation rules in Reference [118] information, some researchers converted the linguistic variables to triangular fuzzy number 23,27,29,31,34,41,43,46,48,50,51,60,70,72,76,80,82,84 some converted linguistic variables to gray numbers, 42,58,104 some converted the linguistic variables to rough numbers. 106 In addition, combining with the situation that experts are usually hesitant when giving their evaluations, Liao et al 8 extended the ARAS method to hesitant fuzzy linguistic environment and proposed a BWM-ARAS framework for finance supplier selection in digital supply chain, in which they used the operation rules for hesitan...…”

Savunma Sanayindeki Bir İşletmede Tedarik Zinciri Risk Azaltma Stratejilerinin Bulanık DEMATEL Yöntemiyle Analizi

“…Dördüncü sırada yer alan tedarikçi seçimi ve geliştirme stratejisi odak işletmenin belirsizliklere ve değişimlere zamanında ve doğru tepkiler verebilmesi bakımından kritik öneme sahip olup literatürde bu bulguyu destekleyen diğer bazı çalışmalar da bulunmaktadır [88]. Şekil 1'de pozitif net değere sahip def def ii DR − değerlerine bakıldığında sırasıyla standartlaştırma/ortak parça kullanımı (S6), talep tahmininin doğru yapılması (S1), bilgi paylaşımı (S2), yerel tedarikçilerle çalışılması (S8), işbirliğinin artırılması (S5) ve tedarikçi seçimi ve tedarikçi geliştirme (S10) stratejilerinin diğer stratejileri daha çok etkileyen konumunda olduğu görülmektedir.…”

Spectroelectrochemical Investigation of Cholesterol Oxidase from Streptomyces lividans at Different pH Values

“…[15][16][17][18][19][20][21][22][23][24][25][26][27] In some papers the authors even postulate direct electron transfer between ChOx and electrodes. [28][29][30][31] However, when direct electron transfer between enzymes and electrodes is postulated great care has to be taken that the electrochemical signal originates indeed from the intact enzyme rather than from a degradation product or the free cofactor being liberated upon degradation. [32][33] The difference between direct and mediated electron transfer is illustrated in Scheme 2.…”