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Aiming at the problem of supplier evaluation and selection in B2B e-commerce, a supplier evaluation and recommendation method based on improved k-means algorithm is proposed. Firstly, this paper analyzes the supplier evaluation and recommendation ideas based on the purchase and supply platform, and proposes the data mining algorithm ideas of clustering analysis and AHP evaluation; secondly, K-means algorithm is proposed based on the data mining model, and the algorithm is optimized according to the data characteristics of the purchase and supply platform; finally, taking the business data of the industrial product purchase platform of the volume purchase network as an example, not only the effectiveness of the algorithm is verified, but also the clustering effect of the algorithm is good and the calculation speed is fast, which provides a practical and effective supplier evaluation and recommendation method for B2B trading website.

The paper is dedicated to the problem of the subjective factors influence on the choice of supplier. To make an objective decision on choosing a product supplier, machine learning models applying is suggested. Due to the use of machine learning models, the evaluation of suppliers is formed based on the analysis of the results of their activities, which minimizes the influence of subjective factors on the choice of supplier. The paper presents the results of the research based on a set of data, including the information obtained during the analysis of the annual report of the purchasing department of a meat processing enterprise, as well as open information published on the Rosselkhoznadzor (Federal Service for Veterinary and Phytosanitary Surveillance of Russian Federation) official site. A sample was formed for training the model for classifying suppliers into reliable and unreliable ones. Methods such as logistic regression and decision tree are used to solve the problem of supplier classification. Ordinal scales are proposed for evaluating suppliers, based on such criteria as the availability and correctness of the design of the product accompanying documentation, compliance with labeling, the presence of reactions to reviews, etc. This made it possible to design the structure of a database containing information about suppliers. In accordance with the specified structure, a sample was formed for training the model for classifying suppliers into reliable and unreliable ones. Methods such as logistic regression and decision tree are used to solve the problem of supplier classification. A comparative analysis of the selected methods was performed using the AUC metric. Modifications of the composition criteria will allow to use the proposed method not only for evaluating the suppliers of food products. Due to machine learning models using, the evaluation of suppliers is formed based on the analysis of their performance, which reduces the influence of subjective factors. The obtained results can simplify the process of selecting suppliers, promote competition in the commodity markets of the Russian Federation, allow enterprises to reduce management costs and save time on searching, evaluating and selecting bona fide suppliers.

Purpose: The research explores and examines factors for supplier evaluation and its impact on process improvement particularly aiming on a steel pipe manufacturing firm in Gujarat, India. Design/Methodology/approach: The conceptual research framework was developed and hypotheses were stated considering the analysis of literature and discussions with the managers and engineers of a steel pipe manufacturing company in Gujarat, India. Data was collected using in-depth interview. The questionnaire primarily involves the perception of evaluation of supplier. Factors influencing supplier evaluation and its influence on process improvement is also examined in this study. The model testing and validation was done using partial least square method. Outcomes signified that the factors that influence evaluation of the supplier are quality, cost, delivery and supplier relationship management. Findings and Originality/value: The study depicted that quality and cost factors for supplier evaluation are insignificant. The delivery and supplier relationship management have significant influence on evaluation of the supplier. The research also depicted that supplier evaluation has significant influence on process improvement. Research limitations/implications: The study has been made specifically for ABC steel pipe manufacturing industry in Gujarat, India and may not be appropriate to the other industries or any parts of the world. There is a possibility of response bias as the conclusions of this research was interpreted on survey responses taken from the employees of case study company, so it is suggested that future research can overcome this problem by employing various methodologies in addition to surveys like carrying out focus group and in-depth interviews, brainstorming sessions with the experts etc. Originality/value: Many researchers have considered quality, cost and delivery as the factors for evaluating the suppliers. But for a company it is quintessential to have good relationship with the supplier. Hence, the factor, supplier relationship management is considered for the study. Also, the case study company focused more on quality and cost factors for the supplier evaluation of the firm. However delivery and supplier relationship management are also equally important for a firm in evaluating the supplier.

Organisations need to develop long-term strategies to ensure they incorporate innovation for environmental sustainability (IES) to remain competitive in the market. This can be challenging given the high level of uncertainty regarding the future (e.g., following the COVID pandemic). Supplier selection is an important decision that organisations make and can be designed to support IES. While the literature provides various criteria in the field of IES strategies, it does not identify the criteria which can be utilised to assist organisations in their supplier selection decisions. Moreover, the literature in this field does not consider uncertainty related to the occurrence of possible future events which may influence the importance of these criteria. To address this gap, this paper develops a novel criteria decision framework to assist supplier evaluation in organisations, taking into consideration different events that may occur in the future. The framework that combines three decision-making methods: the stratified multi-criteria decision-making method, best worst method, and technique for order of preference by similarity to ideal solution. The framework, proposed in this paper, can also be adopted to enable effective and sustainable decision making under uncertainty in various fields.

Purpose – In the rapidly changing business environment, companies must align with suppliers to streamline operations, as well as working together to achieve a level of agility beyond individual companies (Lin et al., 2006). Today’s more dynamic business environment increases the need for greater agility in supply chains, which increases both the importance and frequency of supplier/partner evaluation and benchmarking decision making. The purpose of this paper is to develop a multiple criterion appraisement index (model/module) for supplier/partner alternative firm benchmarking perspective under similar agile supply chain architecture. Design/methodology/approach – In this reporting, evaluation information against subjectivity (uncertain environment) indices has been transformed mathematical dimensionless numbers by fuzzy-based computation module. A new interval-valued fuzzy number set conjunction with modified “technique for order preference by similarity to ideal solution” methodology has been explored from benchmarking (ranking order of firm under similar criterion) point of view of supplier firms. Findings – In this context, a novel “fuzzy mathematical equation” has been developed in perceptive to compute the priority weights and appropriateness ratings of first-level measures which reduced the acquisition of supplementary priority weights and appropriateness ratings assessment in linguistic terms from group decision makers (DMs) for first-level indices. An empirical case study has been carried to ranking order the candidate partner/supplier alternative via collective index (CI) value. Lower value of “CI” reflected higher degree of performance extent. The authors found out the effectiveness and validity of proposed methodology for constructed appraisement module. Originality/value – This research work shall be valuable for that organization which volunteer to obtain the ranking order of partner/supplier alternative (benchmark) under similar agile supply chain architecture in accordance to group DMs’ comprehensive information for select best one supplier for own firm. In this reporting, a novel fuzzy mathematical equation has been developed in order to compute the important weights as well as priority rating of first-level indices/measure which reduced the supplementary important weights and priority rating assessment from group DMs in linguistic terms in order to obtain the measures rating and weights.

In spite of the increasing awareness apparent in the previous studies regarding the evaluation suppliers considering sustainability aspects, there are limitations on the incorporation of sustainable performance in terms of traditional, green and social aspects in supplier selection and order allocation. This paper presents the development of an integrated fuzzy TOPSIS-possibilistic multi objectives model to (1) solve a two-stage sustainable supplier selection problem; and (2) allocate the optimal flow of products quantity that should be ordered from suppliers towards the minimization of expected costs, environmental impact and travel time and maximization of social impact. Suppliers’ sustainable performance was based on traditional, green and social criteria, and quantified by using fuzzy TOPSIS and then integrated into the possibilistic multi objective model. The latter helps decision makers in having an order allocation plan that considers sustainability aspect. Furthermore, the multi-objective optimization model was re-developed as a possibilistic multi-objective optimization model (PMOOM) to handle the dynamic nature in some of the input data. Next, the LP-metrics method was employed to derive Pareto solutions out of the PMOOM. The quality of the obtained Pareto solutions was evaluated using the global criterion approach aiming to help decision makers in selecting the final Pareto solution. The applicability of the developed integrated fuzzy TOPSIS-possibilistic multi-objective approach was proven with sensitivity analysis on a case study of a meat supply chain.

PurposeE-commerce refers to the facilitation and delivery of goods and services to the customers employing an electronic arrangement. For an e-commerce firm, the customer service level provided by its suppliers can make or break the firm. The purpose of this research is to help e-commerce enterprises in addressing the vast challenge of complex supplier selection and evaluation process that must be performed vigilantly.Design/methodology/approachThe present study utilizes a three-pronged approach that integrates supplier management practices with the operational business practices of an e-commerce enterprise. In the first step, key performance factors for e-commerce capable suppliers are identified through an expert opinion and existing supplier management literature. Further, Data Envelopment Analysis (DEA) is employed to obtain the efficiency score for each supplier that enables their ranking on various performance parameters. Lastly, the suppliers are classified into different categories based on their performance and efficiency.FindingsUnder the proposed classification scheme, top five suppliers, i.e. supplier 1, 7, 9, 11 and 17 are categorized as HE (High Performance and Efficient). It is suggested that e-commerce enterprises must build long-term relationship with the identified top performing suppliers. The study also provides real insights into supplier's performance on a number of objective criteria. Further, the present study enhances the overall performance and productivity of an e-commerce firm by achieving input cost minimization and output quality maximization, simultaneously.Research limitations/implicationsThe results are valid for e-commerce enterprises in general. However, the present DEA model can be further evolved when applied in case of any particular e-commerce enterprise depending upon the internal capabilities of that firm. The nuances related to a firm's own supply capability development can be further explored by practitioners and researchers.Practical implicationsThe proposed approach is expected to motivate decision-makers to consider using more sophisticated approached like DEA in supplier evaluation processes. Also, as a benchmarking technique, the proposed supplier classification approach is expected to be highly useful for practitioners in real-life settings.Originality/valueThe novel contribution of this study includes the supplier evaluation, ranking and classification for e-commerce enterprises based on the real-life data. The insights would help the practitioners to formulate novel strategies for appropriately investing in supplier relationships.

This study aspires to empirically evaluate the effect of firm‐specific resources and/or capabilities on sustainable supply management (SSM) and sustainability performance. Specifically, enviropreneurship and strategic purchasing are, respectively, recognized as firm‐specific capabilities and resources that are fundamental to pursuing sustainable supply practices. SSM is forwarded as a key relational capability that can result in significant improvements in organizational sustainability. Using data collected from 145 U.S. firms and advanced structural equation modeling approaches, a number of direct, mediation and moderation effects are hypothesized and tested. Five of the six proposed hypotheses were found to be significant, providing strong support for the significant role that internal resources/capabilities can play in managing sustainable supply practices as well as organizational sustainability. Surprisingly, the hypothesis suggesting that strategic purchasing could moderate the relationship between enviropreneurship and SSM was found to be insignificant. This result suggests that managers need to realize that a strategic purchasing function alone cannot help in achieving the lofty goals of sustainability. On the contrary, the prime objective of firms must be to nurture an enviropreneurial orientation within their organization. Further implications for future research and practice within SSM are offered.

For any industry to improve and expand, the proper evaluation and selection of suppliers is essential. In order to establish whether a supplier is appropriate for working with a company, a system for selecting green suppliers is required. A variety of Decision-Making (DM) models have been created by researchers to address the problems associated with evaluating and choosing green suppliers. In order to address the Green Supplier Evaluation and Selection (GSES) challenge, we did a thorough investigation of ten works of literature, in order to find out which approach is the most widely used and which is more efficient. This study primarily focuses on the findings of ten reviews that examined 1098 research publications from academic journals between 1990 and 2020. 271 DM models examined that were broken down into 170 individual models and 101 combination models, our analysis only looked at the single models. The method of Analytic Hierarchy Process (AHP) is the dominant model used by 160 articles, 122 studies used Data Envelopment Analysis (DEA), and finally 101 research works that utilized the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) model. In addition, we found that the biggest percentage “62%” of studied articles used multi-criteria decision-making (MCDM) models. As a result, the most widely utilized Decision Making models to address the evaluation and selection of green supplier were found to be AHP, DEA, and TOPSIS.

The construction industry is an important industry because of its effects on different aspects of human life experiences and circumstances. Environmental concerns have been considered in designing and planning processes of construction supply chains in the recent past. One of the most crucial problems in managing supply chains is the process of evaluation and selection of green suppliers. This process can be categorized as a multi-criteria decision-making (MCDM) problem. The aim of this study is to propose a novel and efficient methodology for evaluation of green construction suppliers with uncertain information. The framework of the proposed methodology is based on weighted aggregated sum product assessment (WASPAS) and the simple multi-attribute rating technique (SMART), and Fermatean fuzzy sets (FFSs) are used to deal with uncertainty of information. The methodology was applied to a green supplier evaluation and selection in the construction industry. Fifteen suppliers were chosen to be evaluated with respect to seven criteria including “estimated cost”, “delivery efficiency”, “product flexibility”, “reputation and management level”, “eco-design”, and “green image pollution”. Sensitivity and comparative analyses were also conducted to assess the efficiency and validity of the proposed methodology. The analyses showed that the results of the proposed methodology were stable and also congruent with those of some existing methods.