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Presents an approach for assessing the overall performance index (i.e. the relative value) of the suppliers asset base of an organization. Four sets of end‐result variables, for example the total service level (i.e. service level and its reliability), the total quality level (i.e. quality and reliability), the after‐sales service level and the effective price level have been considered to reflect a supplier′s performance. Appropriate surrogate measures for assessing these variables have been suggested. The performance level of a supplier has been judged in relation to the nature of the item, its importance, criticality, situational context in which the supply has been made and the proportional volume of the total requirements supplied by that supplier. This is expressed as an overall performance index for the supplier. A study has been carried out in a small‐scale engineering unit to assess the relative value of its suppliers asset base for a five‐year period relative to the base period. The suppliers asset base has been found to be appreciating over the years reflecting the effectiveness of managing it.

The study explored how supplier evaluation, selection, development and segmentation affect supply chain performance in pharmaceutical organizations. It also determined the moderating influence of top management support on the link between supply chain performance and organizational performance.

The research philosophy of this study was positivism, leading to the adoption of a quantitative research method. Empirical data were gathered from a significant sample of supply chain experts at leading pharmaceutical companies in South Africa. Data collection scales were derived from existing studies. The collected data were analysed using structural equation modelling.

The results confirmed the validity of the proposed model, which is based on selected criteria (latent variables). This study emphasizes the crucial influence of supplier evaluation, selection, development and segmentation on supply chain performance in pharmaceutical organizations. The research shows a positive correlation between supply chain performance and organizational performance, with top management support playing a moderating role.

The study’s originality and value stem from its thorough examination of how supplier relationship management practices affect supply chain performance and organizational performance in the pharmaceutical industry of South Africa. Furthermore, the research adds to the current body of knowledge by considering the moderating influence of top management support on the link between supply chain performance and organizational performance. These findings offer valuable insights for academics and industry professionals in the realm of supply chain management.

This paper explores the application of supervised machine learning (ML) classification models to address supplier performance analysis and risk profiling as a multi-class classification problem. The research highlights that current applications of machine learning in supplier selection primarily focus on binary classification problems, underscoring a significant gap in the literature.

This research paper opts for a structured approach to solve supplier selection and risk profiling using supervised machine learning multi-class classification models and prediction probabilities. The study involved a synthetic data set of 1,600 historical data points, creating a supplier selection framework that simulates current supply chain (SC) performance. The “Supplier Analysis and Selection ML Module” guided supplier selection recommendations based on ML analysis. Real-world variability is introduced through random seeds, impacting actual delivery dates, quantity delivered and quality performance. Supervised ML models, with hyperparameter tuning, enable multi-class classification of suppliers, considering past delivery performance and risk calculations.

The study demonstrates the effectiveness of the supervised ML-based approach in ensuring consistent supplier selection across multi-class classification problems. Beyond evaluating past delivery performance, it introduces a new dimension by predicting and assessing supplier risks through ML-generated prediction probabilities. This can enhance overall SC visibility and help organizations optimize strategies associated with risk mitigation, inventory management and customer service.

The findings highlight the adaptability of ML-based methodologies in dynamic SC environments, providing a proactive means to identify and manage risks. These insights are vital for organizations aiming to bolster SC resilience, particularly amid uncertainties.

The practical implications of this study are significant for both commercial and humanitarian supply chain management (SCM). For commercial applications, the ML-based methodology allows businesses to make more informed supplier selection decisions, reducing risks and improving operational efficiency. In disaster and humanitarian SC contexts, the use of ML can improve preparedness and resource allocation, ensuring that critical supplies reach affected areas promptly.

The study’s implications extend to disaster and humanitarian SCM, where timely and efficient delivery is critical for saving lives and alleviating suffering. ML tools can improve preparedness, resource allocation and coordination in these contexts, enhancing the resilience and responsiveness of humanitarian supply chains.

Unlike conventional methods focused on quality, cost and delivery performance aspects, the current study introduces supervised ML to identify and assess supplier risks through prediction probabilities for multi-class classification problems (delivery performance as late, on-time and ahead), offering a refined understanding of supplier selection in dynamic SC environments.

This study aims to develop a comprehensive green supplier governance (GSG) concept and explore whether specific GSG approaches (green supplier assessment, green supplier assistance and green strategic partnership with suppliers (GSPS)) bring environmental and economic performance. Moreover, this study aims to reveal a synergistic effect of three GSG approaches on performance improvement.

Using data collected from 200 Chinese manufacturing firms, regression analysis was employed to reveal the relationship between specific GSG approaches and firm performance. Further, cluster analysis was used to identify groupings of firms regarding implementation levels of three GSG approaches and compare the performance of the firm groups.

Green supplier assessment (GSA) can bring environmental performance, but GSA is not associated with economic performance. Green supplier assistance is positively associated with economic performance, while green supplier assistance cannot improve environmental performance. Only GSPS leads to improvement for both environmental and economic performance. Furthermore, firms with high implementation levels of GSA and GSPS (whether with high or low implementation levels of GSAS) can achieve the best environmental and financial performance.

This study provides implications for firms to more strategically and comprehensively implement GSG approaches, which can be more effective in bringing environmental and economic performance.

The authors' study extends the GSG concept with two approaches by subdividing the collaborative approach into green supplier assistance and GSPS based on the collaboration levels. This study also sheds light on how to improve firm performance by different GSG approaches and reveals a synergistic effect of three GSG approaches on performance.

Green supplier selection (GSS) is acknowledged as important governance in green supply chain management (GSCM). However, this paper argues that GSS is not a stand-alone GSCM governance mode that determines manufacturers' environmental performance but rather one that needs to be aligned with contractual governance, particularly contractual control and adaptation, to promote environmental performance effects. This paper adopts GSS as ex ante governance and introduces behavior and outcome controls as ex post contractual control and adaptation, respectively. Thus, this paper addresses how GSS affects environmental performance directly and indirectly through behavior and outcome controls within transaction cost economics (TCE) theory.

This research model was tested on 300 Chinese manufacturing firms, and multiple regression analysis was used to validate our hypotheses.

A direct relationship was observed between GSS and environmental performance. This direct relationship is positively mediated by behavior and outcome controls.

This paper develops and elucidates an integrative green supply chain process proceeding from the implementation of ex ante GSS and ex post contractual governance to the realization of environmental performance. Furthermore, this paper considers two different forms of contractual governance, specifically contractual control and adaptation, and explains how they can be implemented using behavior and outcome controls from the perspective of TCE theory.

The growing focus on socially responsible supply chain management (SRSCM) has made it crucial to extend corporate social responsibility (CSR) to upstream suppliers. Drawing on resource dependence theory, this study aims to examine how supplier dependence upon socially responsible buyers impacts suppliers' CSR performance and how this relationship is moderated by network prominence and demand uncertainty.

The proposed hypotheses are tested using regression analysis with Heckman's two-stage model and a dyadic supply chain dataset constructed based on publicly traded Chinese firms between 2008 and 2016. This time window is selected due to a one-year lag of the dependent variable and the change in evaluation methods of the database providing CSR performance in 2018.

The empirical results indicate that supplier dependence upon socially responsible buyers is positively associated with suppliers' CSR performance. However, this positive relationship is attenuated when suppliers occupy a prominent position in the network or when they face high demand uncertainty.

This study extends knowledge about the role of relationship dependence in implementing SRSCM by highlighting its positive impact on suppliers' CSR. Thus, this study contributes to the buyer–supplier relationship literature and the power and relationship dependence literature. This study further advances the understanding of the factors that influence suppliers' behavior by exploring the moderating roles of network prominence and demand uncertainty. The results have several practical implications for managers and policymakers.

The purpose of this paper is to clarify the prerequisites for performance measurement supporting purchaser-supplier relationships and value co-creation. It also explains the causes for the limited use of collaborative measurement.

Four case companies representing different contextual settings are studied. The primary source of empirical material is an interview study addressed to 24 interviewees. The empirical data are analyzed according to the constructs created as a result of the literature review.

The results reveal that prevailing performance measurement practices represent a more transactional than relationship-oriented approach to purchaser-supplier collaboration. The technical prerequisites for collaborative performance measurement are mostly not fulfilled, inhibiting the use of performance measurement in a collaborative manner. It is proposed that the differentiation between project and process production types has implications on the importance of collaborative performance measurement.

The paper illustrates the desirable characteristics of performance measurement supporting collaboration. It also presents an application of collaborative performance measurement in a single case context. The research reveals the need to develop non-financial performance measures further in order to facilitate the more proactive use of performance measurement supporting true value co-creation between purchaser and supplier companies.

The empirical research on the topic of performance measurement in purchasing and supply management (PSM) is often limited to intra-organizational measurement and highlights transactional approach to collaboration between parties, although PSM research has otherwise acknowledged the importance of value creation and relationships between organizations.

The purpose of this paper is to provide an analytical model for low carbon supplier development. This study is focused on the level of investment and collaboration decisions pertaining to emission reduction.

The authors’ model includes a fuzzy c-means (FCM) clustering algorithm and a fuzzy formal concept analysis. First, a set of suppliers were classified according to their carbon performances through the FCM clustering algorithm. Then, the fuzzy formal concepts were derived from a set of fuzzy formal contexts through an intersection-based method. These fuzzy formal concepts provide the relative level of investments and collaboration decisions for each identified supplier cluster. A case from the Indian renewable energy sector was used for illustration of the proposed analytical model.

The proposed model and case illustration may help manufacturing firms to collaborate with their suppliers for improving their carbon performances.

The study contributes to the low carbon supply chain management literature by identifying the decision criteria of investments toward low carbon supplier development. It also provides an analytical model of collaboration for low carbon supplier development. Though the purpose of the study is to illustrate the proposed analytical model, it would have been better if the model was empirically validated.

Though the earlier studies on green supplier development program evaluation have considered a set of criteria to decide whether or not to invest on suppliers, these are silent on the relative level of investment required for a given set of suppliers. This study aims to fulfill this gap by providing an analytical model that will help a manufacturing firm to invest and collaborate with its suppliers for improving their carbon performance.

This study aims to examine the effect of supplier monitoring on procurement performance in the Tanzanian public sector, as well as how contract management difficulty moderates the effect of supplier monitoring on procurement performance.

In this paper cross-sectional data were collected from 179 Tanzanian public procuring organizations using a structured survey questionnaire. Confirmatory factor analysis (CFA) and the PROCESS macro were used to analyse the collected data.

Supplier monitoring has a positive and significant relationship with procurement performance in terms of cost reduction, lead times and buyer satisfaction. Furthermore, contract management difficulty has a negative moderating effect on the relationships between supplier monitoring and procurement performance dimensions.

Because public procurement is governed by laws and procedures, generalization of results should be done with caution. This is because the study is currently limited to Tanzanian public procurement. Apart from contract management difficulty, future research can look at other factors that may be needed to moderate the link between supplier monitoring and procurement performance.

Procurement practitioners must monitor major suppliers’ timeliness, product quality and order accuracy in order to improve procurement performance. Furthermore, proper contract management is required, which necessitates effectively reinforcing procurement contract managers’ responsibilities and providing contract management training for practitioners in order to control anomalies when suppliers and contracts are involved.

By adding a moderating variable, the study adds to the literature on supplier monitoring in public procurement and the on-going debate on supplier monitoring and performance.

Drawing on social capital theory, we extend the concept of supply chain capital to examine whether structural and relational capital can strengthen the complementary capabilities of suppliers and enhance their performance.

The empirical study was conducted on 161 precision mold equipment suppliers. To evaluate the mediated moderation model of supply chain capital, we applied multiple linear regression to test our hypotheses.

We found that both structural and relational capital positively affect the complementary capabilities of suppliers and that these capabilities mediate the relationship between supply chain capital and supplier performance. Furthermore, structural capital positively and significantly moderates the mediating effect on the relationship between complementary capabilities and supplier performance.

This study provides suggestions for suppliers that are equipped with sufficient structural and relational capital to effectively enhance their complementary capabilities. By considering the interaction between structural capital and complementary capabilities, suppliers can effectively improve their performance.

This novel research develops a theoretical model to examine the antecedents and consequences of supplier complementary capabilities. We contribute to a new line of research on supply chain capital, which aims to explore how it affects the complementary capabilities of suppliers by examining a practical supply chain activity setting.