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Supplier selection in the supply chain network (SCN) has strategic importance and involves multiple factors. The multi-criteria nature of the problem coupled with environmental uncertainty requires several procedures and considerations. The issue of decision-making in selecting the best among various qualified suppliers remains the major challenge in the pharmaceutical industry. This study investigated the multi-criteria multi-supplier decision-making process and proposed a model for supplier selection problems based on mixed-integer linear programming.

The concept of principal component analysis (PCA) was used to reduce data dimensionality, and the four best criteria have been considered and selected. The result is subjected to decision-makers’ (DMs’) reliability test using the concept of a triangular fuzzy number (TFN). The importance of each supplier to each measure is established using fuzzy technique for order preference by similarity to an ideal solution approach, and the suppliers have ranked accordingly.

This study proposes a mixed integer linear programming model for supplier selection in a pharmaceutical company. The effectiveness of the proposed model has been demonstrated using a numerical example. The solution shows the model's applicability in making a sound decision in pharmaceutical companies in the space of reality. The model proposed is simple. Readily commercial packages such as LINDO/LINGO and GAMS can solve the model.

This research contributed to the systematic manner of supplier selection considering DMs’ value judgement under a fuzzy environment and is limited to the case study area. However, interested researchers can apply the study in other related manufacturing industries. However, the criteria have to be revisited to suit that system and might require varying ratings based on the experts' opinions in that field.

This work suggests more insights practically by considering a realistic and precise investigation based on a real-life case study of pharmaceutical companies with six primary criteria and twenty-four sub-criteria. The study outcome will assist organizations and managers in conducting the best decision objectively by selecting the best suppliers with their various standards and terms among many available contenders in the manufacturing industry.

In this paper, the authors attempted to identify the most critical attributes to be preserved by the top managers (DMs) while selecting suppliers in pharmaceutical companies. The study proposed an MILP model for supplier selection in the pharmaceutical company using fuzzy TOPSIS.

This paper aims to propose a new approach called “analytical hierarchy process weighted fuzzy linear programming model (AHP‐FLP)” for supplier selection.

A hybrid method of supplier selection, AHP‐FLP is applied to a real industry case. The weights of the various criteria, taken as local weights from a given judgment matrix, are calculated using analytical hierarchy process (AHP) that are also considered as the weights of the fuzzy linear programming model. This new model is compared with the classical AHP method.

This study concluded that the AHP‐FLP method outperforms the AHP method for supplier selection with respect to restricted supplier selection criteria. Drawing on a real case, Supplier 1 was identified to be the best supplier through the AHP model under no restrictions, which contradicts the finding that Supplier 2 was selected as the best supplier by the AHP‐FLP model subject to constraints.

More research is definitely called for within the context of studying a more complex supply chain with multiple supply network and nodes. There is also a crucial need for investigating other hybrid methods to find the optimum supplier.

The findings of this study indicate that the weights of supplier selection criteria calculated by the AHP‐FLP model are in line with the actual supplier selection decision of purchasing managers. Since the AHP‐FLP model is relatively more difficult to implement compared with the crisp AHP, its application will be more appropriate for high‐value components where stringent purchasing criteria are required. In contrast, AHP remains an appropriate approach for relatively lower value components (C class).

The novelty of this study lies in the application of a hybrid approach to a real industry case. This study has dealt with one of the most important subjects in supply chain management, providing a better decision for supplier selection using appropriate quantitative techniques.

A key driver of procurement effectiveness is the alignment of the procurement function with interlinked functions, such as R & D, engineering, production, and marketing. In the strategic management literature, the degree of alignment of individual team members on strategic objectives is termed “consensus.” The purpose of this paper is to investigate antecedents of consensus on objectives in cross-functional sourcing teams, the relationship between the degree of consensus and supplier performance, and moderators of the consensus-performance relationship. To do so, it ties strategic management literature to SCM and supplier selection research. As a result of these investigations, this research holistically introduces the concept of consensus to the discipline.

The study analyzes a sample of 88 sourcing teams (233 team members) from three manufacturing companies using regression analysis and moderated regressions.

Consensus on objectives for supplier selection among sourcing team members is positively related to the selection of higher performing suppliers. Sourcing team member experience is positively related to the level of consensus, and formalization of the selection process positively moderates the consensus-performance relationship. Team demographic diversity does not affect consensus among team members or supplier selection effectiveness.

This study investigates consensus on objectives as a state within the sourcing team; it does not analyze how decision-making processes unfold in situations of low- or high-initial consensus among sourcing team members.

This paper provides insights into the drivers and effects of consensus on objectives and formalization of supplier selection in cross-functional setups.

This research addresses a gap in the SCM literature by investigating the role of consensus on objectives and thereby contributes to a better understanding of cross-functional sourcing team setups and effectiveness. The study introduces a key construct from the strategic management literature to supply management research, and empirical evidence shows how consensus can improve supplier selection performance.

Selection of resilient suppliers has attracted the attention of researchers in the past one decade. The devastating effect of COVID-19 in emerging economies has provided great impetus to the selection of resilient suppliers. Under volatile and uncertain business scenarios, supplier selection is often done under imprecise and incomplete information, making the traditional decision-making methods ineffective. The purpose of this paper is to demonstrate the application of a fuzzy decision-making method for resilient supplier selection.

A group of three decision makers was considered for evaluating various alternatives (suppliers) based on their performance under different primary, sustainability and resilience criteria. Experts' opinion about each criterion and alternative was captured in linguistic terms and was modelled using fuzzy numbers. Then, an algorithm for solving resilient supplier selection problem based on the trapezoidal intuitionistic fuzzy technique for order preference by similarity to ideal solution (TrIFTOPSIS) was introduced and demonstrated through a case study.

A closeness coefficient was used to rank the suppliers based on their distances from intuitionistic fuzzy positive-ideal solution and intuitionistic fuzzy negative-ideal solution. Finally, the proposed fuzzy decision making model was applied to a real problem of supplier selection in the clothing industry.

The presented TrIFTOPSIS model provides an effective route to prioritise and select resilient suppliers under imprecise and incomplete information. This is the first application of intuitionistic fuzzy multi-criteria decision-making for resilient supplier selection.

This study aims to focus on sustainable supplier selection in a construction company considering a new multi-criteria decision-making (MCDM) method based on dominance-based rough set analysis. The inclusion of sustainability concept in industrial supply chains has started gaining momentum due to increased environmental protection awareness and social obligations. The selection of sustainable suppliers marks the first step toward accomplishing this objective. The problem of selecting the right suppliers fulfilling the sustainable requirements is a major MCDM problem since various conflicting factors are underplay in the selection process. The decision-makers are often confronted with inconsistent situations forcing them to make imprecise and vague decisions.

This paper presents a new method based on dominance-based rough sets for the selection of right suppliers based on sustainable performance criteria relying on the triple bottom line approach. The method applied has its distinct advantages by providing more transparency in dealing with the preference information provided by the decision-makers and is thus found to be more intuitive and appealing as a performance measurement tool.

The technique is easy to apply using “jrank” software package and devises results in the form of decision rules and ranking that further assist the decision-makers in making an informed decision that increases credibility in the decision-making process.

The novelty of this study of its kind is that uses the dominance-based rough set approach for a sustainable supplier selection process.

The supplier selection problem has gained great attention in business management literature. The first objective of this study is to determine the required variables in selecting the best suppliers and to develop a supplier selection model based on these selected variables. The second objective is to explain how an integrated AHP‐PGP model can be used in supplier selection decisions while minimizing suppliers' defects rate, rate of late order delivery, purchasing costs, and maximizing suppliers' scores and after‐sales service levels.

An integration of an analytic hierarchy process (AHP) and multi‐objective pre‐emptive goal programming (PGP) is used to consider both quantitative and qualitative factors in selecting the best suppliers and allocating the optimum order quantities among them.

The integrated model is presented with a real‐world application using source data provided by the manufacturing firm operating in an automotive industry in Turkey. Findings demonstrate that the integrated AHP‐PGP model can be useful to all firms in their supplier selection decisions.

The integrated model presented in this study could be used to incorporate criteria such as quantity discount, demand satisfaction and budget constraints, etc. Further, the AHP approach could be extended with the genetic algorithm or data envelopment analysis techniques in solving such a problem.

Although there is considerable research in the literature, this study differs from the literature by introducing stages of the AHP model extensively, and adding a service objective function related to the suppliers' after‐sales service levels. In addition, the paper is especially of interest to both practitioners and researchers.

The paper addresses supplier selection in the public procurement sector. The motivation is providing a decision-making tool that mimics the intuitive behavior of the public tender committee in evaluating the bid according to predefined (quantitative and qualitative) criteria by a strict and transparent procedure, in accordance with governmental procurement regulations and requirements. To this aim, we address the supplier selection decision problem in the public procurement sector by a well-known fuzzy multiple criteria decision- making approach. Using the presented technique enables evaluation of both imprecise information and quantitative data in vendor selection, while keeping the transparency features requested in public procurement. We show the effectiveness and efficiency of the approach with a case study involving an Italian public administration.

The purpose of this paper is to propose a comprehensive methodology and a problem-specific model for the configuration of the optimal strategic supplier portfolio in terms of traditional, performance-related objectives and sustainability targets.

To bridge the research gap, i.e., to align strategic supplier portfolio selection with corporate sustainability targets, a hybrid model of the analytic network process (ANP) and goal programming (GP) is developed. To validate the model, a case example is presented and managerial feedback is collected.

By enabling the integration of sustainability targets into strategic supplier portfolio configuration, the hybrid ANP-GP model contributes to research in the area of sustainable supply chain management. Results indicate that simplifying the model by omitting one or more details may lead to unfortunate actions.

The model has been applied using a case example in the automotive industry. To strengthen the findings, it should be examined under other terms as well.

Integrating economic, environmental, and social targets into strategic supplier portfolio configuration reduces supply risks and promotes the achievement of the sustainability goals of the purchasing company.

Strategic supplier selection counts among the decisions that have an impact on the environment and society for several years. Configuring economically rational, environmentally friendly, and socially responsible supplier bases supports worldwide efforts towards sustainable development.

Although sustainable supplier selection has gained importance in recent years, this is the first time that a comprehensive model for the determination of the optimal strategic supplier portfolio in terms of performance-related objectives and sustainability targets has been proposed.

Suggests that the evaluation and selection of potential suppliers is becoming a more important and critical decision in today’s competitive operating environment. Reveals that many firms are reducing their number of suppliers and the supplier base may provide a company with a strategic competitive advantage. Explains that the supplier selection decision now involves many judgemental factors in addition to the historical selection criteria. Offers a prototype expert system which enables users to evaluate and select a potential supplier based on the strategic importance of the product involved, quantitative data and qualitative factors. The system’s early validation results show its potential as an effective decision‐making aid.

The application of theories on group decision support is yet to be explored extensively in supplier selection literature, although the literature in both domains is extremely rich, in isolation. The purpose of this paper is to explore the application of group decision support theories for supplier selection.

The row geometric mean method (RGMM) of the analytic hierarchy process (AHP) has been used in this study for the prioritization of group preferences under consensus. A case study was conducted to test the theories of consensual group decision making and compare it with other approaches based on AHP.

The study establishes that the application of decision support theories for group decision making can improve the supplier selection process. Findings further imply that RGMM is more effective than eigen value method, for group decision making under consensus.

Methodologically, the study highlights the greater regularity in outcome of group decision making, vis-à-vis individual decision making, for the same decision-making context. Also, it highlights how RGMM is more effective since it preserves reciprocal properties and diversity in preferences better.

The study establishes that firms can improve supplier selection processes by leveraging on the collective expertise of a group rather than depending on individual decision-making expertise.

This study explores the application of different theories based on AHP for consensual group decision making. It compares different approaches based on AHP and establishes that RGMM is a superior approach for supplier selection.