Supplier evaluation and selection problem has been studied extensively. Various decision making approaches have been proposed to tackle the problem. In contemporary supply chain management, the ...performance of potential suppliers is evaluated against multiple criteria rather than considering a single factor-cost. This paper reviews the literature of the multi-criteria decision making approaches for supplier evaluation and selection. Related articles appearing in the international journals from 2000 to 2008 are gathered and analyzed so that the following three questions can be answered: (i) Which approaches were prevalently applied? (ii) Which evaluating criteria were paid more attention to? (iii) Is there any inadequacy of the approaches? Based on the inadequacy, if any, some improvements and possible future work are recommended. This research not only provides evidence that the multi-criteria decision making approaches are better than the traditional cost-based approach, but also aids the researchers and decision makers in applying the approaches effectively.
•Developed decision support system for logistics 4.0 industries to select resilient suppliers.•Developed methodologies to process heterogeneous decision relevant information.•Supplier's Cost versus ...Resilience Index is proposed and evaluated.•Goal programming approach is used for determining optimal order allocation plans.
Supplier selection problem has gained extensive attention in the prior studies. However, research based on Fuzzy Multi-Attribute Decision Making (F-MADM) approach in ranking resilient suppliers in logistic 4.0 is still in its infancy. Traditional MADM approach fails to address the resilient supplier selection problem in logistic 4.0 primarily because of the large amount of data concerning some attributes that are quantitative, yet difficult to process while making decisions. Besides, some qualitative attributes prevalent in logistic 4.0 entail imprecise perceptual or judgmental decision relevant information, and are substantially different than those considered in traditional suppler selection problems. This study develops a Decision Support System (DSS) that will help the decision maker to incorporate and process such imprecise heterogeneous data in a unified framework to rank a set of resilient suppliers in the logistic 4.0 environment. The proposed framework induces a triangular fuzzy number from large-scale temporal data using probability-possibility consistency principle. Large number of non-temporal data presented graphically are computed by extracting granular information that are imprecise in nature. Fuzzy linguistic variables are used to map the qualitative attributes. Finally, fuzzy based TOPSIS method is adopted to generate the ranking score of alternative suppliers. These ranking scores are used as input in a Multi-Choice Goal Programming (MCGP) model to determine optimal order allocation for respective suppliers. Finally, a sensitivity analysis assesses how the Supplier's Cost versus Resilience Index (SCRI) changes when differential priorities are set for respective cost and resilience attributes.
The concept of structural embeddedness refers to the importance of framing suppliers as being embedded in larger supply networks rather than in isolation. Such framing helps buying companies create ...more realistic policies and strategies when managing their suppliers. Simply put, the performance of a supplier is dependent on its own supply networks. By adopting the concept of structural embeddedness, we learn that a buying company needs to look at a supplier's extended supply network to arrive at a more complete evaluation of that supplier's performance. By doing so, a buying company may do a better job of selecting suppliers for long‐term relationships and may also find value in maintaining relationships with poorly performing suppliers who may potentially act as a conduit to other companies with technological and innovative resources.
Due to growing concerns regarding sustainability, purchasing decisions are challenging and difficult tasks for decision-makers. This difficulty has compelled purchasing companies to know and ...understand the whole purchasing process and to give importance to purchasing associated decisions. A long-term relationship and investment are required for purchasing decisions because they impact significantly on a company’s performance and supply chain. Hence, supplier selection and sourcing strategy selection decisions are among a firm’s most important problems. In this study, sourcing strategy decisions include supplier development, which helps suppliers to improve their performance, and supplier switching, which searches for more proficient alternatives for supply. To solve these problems, this study provides an integrated multi-criteria decision-making (MCDM) model. The model contains four stages. First, the right set of sustainable key performance indicators (SKPIs) for evaluating the performance of suppliers is identified through a literature survey and discussions with the decision-making team. Second, the best worst method-measurement of alternatives and ranking according to the compromise solution method (BWM-MARCOS) approach is applied to determine the priority weights of SKPIs and the priority weights of incumbent and new suppliers based on identified SKPIs. Third, a bi-objective mathematical model is developed to determine which optimum sourcing strategy and potential supplier should be chosen based on the priority of incumbent and new suppliers while optimizing cost and sustainable performance. Fourth, the mathematical model is solved using Epsilon constraint method and min–max fuzzy approach. The applicability and efficiency of the proposed integrated MCDM model is demonstrated with a real case study from a home appliance manufacturing company. The key findings reveal that the proposed model can be utilized for strategic and effective sourcing planning. One of the important contributions of this work is to provide suggestions for deciding the appropriate sourcing strategy for suppliers using the outputs of the mathematical model.
A rich research stream investigates the drivers and enablers of supplier sustainability practices, usually classified into suppliers' monitoring and collaboration with suppliers. Differently from ...previous works analysing relationships between supplier sustainability practices and drivers or enablers, this research investigates how well-defined configurations of monitoring and collaboration can be characterised in terms of drivers and enablers. In this way, it intends to advance knowledge by identifying what drivers and enablers are important and distinctive for the different configurations of supplier sustainability practices. A first result is that moving from configurations of plants which less adopt supplier sustainability practices (i.e. non-adopters) to those which invest on monitoring and/or collaboration to a limited extent (i.e. partial adopters) up to the most advanced ones (i.e. full-adopters), the pressure due to cost reduction lessens its relative importance as a driver, while the pressure due to regulations remains essential. Other relevant results are that plant size acts as a barrier for non-adopters, and the alignment between the sustainability project and plant goals results determinant especially for full-adopters. This research also enriches the debate on the opportunity of differentiating between supplier monitoring and collaboration when investigating drivers/enablers, providing evidence of the risk of oversimplifications for some enablers/drivers.
Manufacturers undergoing servitization resort to an increased number of suppliers to deliver services. Although managing upstream relationships is particularly critical in servitized contexts, theory ...development on this topic is still at an early stage. This study analyses the linkages between the types of services that servitized manufacturers outsource and the relationships they establish with their suppliers.
First, we present a framework that is based on a multidimensional description of buyer–supplier relationships and on a categorisation of services. Second, we present the empirical findings coming from multiple case studies and discuss the characteristics of buyer-supplier relationships for Product support, Customer support and Process related services (the three categories investigated) in the light of the presented framework.
The paper contributes to the servitization literature by showing that there is no one best way to shape buyer–supplier relationships in servitized environments. Instead, the type of service being outsourced is one of the key factors that influence the way upstream relationship should be crafted.
•The paper addresses buyer–supplier relationships in servitized contexts.•A classification of services offered in product-centric servitization is developed.•The linkages between service types and relationship characteristics are analysed.•Findings can help companies to align supplier relationships with service offerings.
If one customer accounts for a large portion of a supplier's sales, then the loss of that one customer can cripple the supplier's financial health. As a precaution against the additional operating ...risk induced by being in an important relationship with a customer, I find that suppliers in such relationships hold more cash on average than suppliers that are not in important relationships. Additionally, supplier's cash holdings increase proportionately with the importance of their customer relationships. Being in an important relationship affects cash holdings and leverage differently, indicating that firms manage cash and debt for different purposes. I find that suppliers in relationships primarily accrue cash through issuance of stock as opposed to debt or retained earnings. The results highlight the importance of understanding buyer–supplier relationships when evaluating a firm's financing policy.
► Suppliers with an important buyer hold more cash than other suppliers. ► As relationship importance increases, so does suppliers' cash holdings. ► Buyer–supplier relationships affect cash holdings and leverage differently. ► Relationship risk motivates suppliers to hold additional cash.
•We conceptualize a new approach to analyzing the risk profiles of supplier performance under uncertainty by utilizing the data analytics capabilities in digital manufacturing.•We develop a hybrid ...technique, combining simulation and machine learning and examine its applications to data-driven decision-making support in resilient supplier selection.•We consider on-time delivery as an indicator for supplier reliability, and explore the conditions surrounding the formation of resilient supply performance profiles.•We theorize the notions of risk profile of supplier performance and resilient supply chain performance.•We show that the associations of the deviations from the resilient supply chain performance profile with the risk profiles of supplier performance can be efficiently deciphered by our approach.
There has been an increased interest in resilient supplier selection in recent years, much of it focusing on forecasting the disruption probabilities. We conceptualize an entirely different approach to analyzing the risk profiles of supplier performance under uncertainty by utilizing the data analytics capabilities in digital manufacturing. Digital manufacturing peculiarly challenge the supplier selection by the dynamic order allocations, and opens new opportunities to exploit the digital data to improve sourcing decisions. We develop a hybrid technique, combining simulation and machine learning and examine its applications to data-driven decision-making support in resilient supplier selection. We consider on-time delivery as an indicator for supplier reliability, and explore the conditions surrounding the formation of resilient supply performance profiles. We theorize the notions of risk profile of supplier performance and resilient supply chain performance. We show that the associations of the deviations from the resilient supply chain performance profile with the risk profiles of supplier performance can be efficiently deciphered by our approach. The results suggest that a combination of supervised machine learning and simulation, if utilized properly, improves the delivery reliability. Our approach can also be of value when analyzing the supplier base and uncovering the critical suppliers, or combinations of suppliers the disruption of which result in the adverse performance decreases. The results of this study advance our understanding about how and when machine learning and simulation can be combined to create digital supply chain twins, and through these twins improve resilience. The proposed data-driven decision-making model for resilient supplier selection can be further exploited for design of risk mitigation strategies in supply chain disruption management models, re-designing the supplier base or investing in most important and risky suppliers.
•Main focus of the study is to select suppliers being involved in green supplier development programs.•Two-step supplier evaluation was applied via fuzzy c-means clustering.•Supplier were evaluated ...via performance criteria and green criteria.•After performance and green evaluation, VIKOR method is used to sequence suppliers.•An illustrative example was provided in an automobile company.
Green supplier development has became necessity as organizations increasingly compete on environmental supply chain capabilities. This paper aims to determine green/environmental performance of supplier, and define which suppliers need to improve their conditions about environmental issues, and identify which suppliers should be included to green supplier development programs to enhance their environmental performance. Therefore, primarily, performance criteria and green supplier evaluation criteria were determined via a survey, then factor analysis was conducted to evaluate validity of factors. Then two step clustering was performed by using c-means clustering method. In the first step of clustering, all suppliers of a firm performing in automobile industry were clustered according to criteria-delivery, quality, cost and service. Thus best performing suppliers were determined. In the second step of clustering, best performing suppliers determined in the first step of clustering were evaluated with environmental/green criteria. As a result of clustering, best performing suppliers were splitted to three groups according to green criteria-good, medium, poor. Lastly, suppliers within the poor group were sequenced by using VIKOR method in order to include green supplier development programs.
Purchasing occupies a strategic role in supply chain management for a firm and is the driver of competitive advantage. Owing to the high purchase cost to revenue ratio, decisions such as evaluation, ...selection, and performance management of suppliers are of the matter of immense interest to firms. Multi-criteria decision making tools allow the purchasing managers to evaluate the suppliers holistically. One such tool, data envelopment analysis (DEA) has been used extensively for supplier evaluation and selection. This paper presents a comprehensive review of 161 articles published since 2000, on the application of DEA in supplier selection. These articles are located from the Scopus database. With little existing literature on a full-fledged review, this work envisages to be first of its kind, by aiding DEA practitioners in purchasing function. The analysis of the study indicates the emergence of the theme of green supply chain and sustainability in recent years as well as the adoption of hybrid approaches to solving the problem of supplier selection using DEA. The paper presents various classifications of DEA methods based on input criteria, sectors of application, and industry-wide case studies, which can be used as a quick reckoner by an academician or a purchasing manager.
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