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The purpose of this study is to address supplier evaluation and selection in a constrained environment of advance purchasing. The study presents the potential solutions to supplier evaluation and selection issues in the Indian automobile sector where advance purchases are carried out to fulfill the supply chain demand.

Based on the literature review and expert elicitation, nine major factors which are responsible for the successful implementation of supplier selection in a constrained environment of advance purchasing are identified. This paper explores supplier selection in constrained environment issues based on an integrated method based on Shannon entropy, analytic hierarchy process (AHP) techniques and failure mode effects analysis (FMEA).

Analysis of the results of the study suggests that traditional suppliers are not suitable in advance purchasing scenarios; hence, criteria developed in this paper to accommodate the requirement of advance purchasing with possible risk considerations are of high importance. This research paper is an original attempt to develop supplier selection criteria for advance purchasing with special identification of deliverability, flexibility, innovation and productivity factors through a case demonstration.

This study uses data from secondary sources, literature reviews and expert opinions. It formalizes the important factors of successful supplier selection in the constrained environment of advance purchasing in the automotive industry context.

The paper shows how the engagement of suppliers through advance purchasing helps automotive companies in developing a competitive advantage. The integrated approach of Shannon entropy, AHP techniques and FMEA is an effective and useful method that can be applied to the supplier selection process.

The proposed FMEA-AHP method integrated with Shannon entropy used for evaluation represents a useful tool to embrace the suitable functioning tactics for efficient supplier selection. The study is unique as supplier evaluation and selection in a constrained environment of advance purchasing is not investigated much and has good industry applicability.

Today the role of industry 4.0 plays a very important role in enhancing any supply chain network, as the industry 4.0 supply chain uses Big Data and advanced analytics to inform the complete visibility. Latest data are available to bring clarity and support real-time decision-making in the entire supply chain that’s why adopting optimization techniques such as lean manufacturing and lean supply chain concept for enhancing the supply chain network of the organizations is a good idea and would benefit them in increasing their cost efficiency and productivity. The purpose of this work is to develop a technique, which may be useful for future researchers and managers to identify and classification of the significant lean supply chain enablers.

In this paper, the authors considered hybrid analytical hierarchy process to find the ranking of the identified lean supply chain enablers by calculating their weightage. Interpretive structural modeling (ISM) is applied to develop the structural interrelationship among various lean supply chain management enablers. Considering the results obtained from ISM the Matrices d'Impacts Croises Multiplication Appliqué a un Classement (MICMAC) analysis is done to identify the driving and dependence power of Lean Supply Chain Management Enablers (LSCMEs).

Further, the best results applying these methodologies could be used to analyze their inter-relationships for successful Lean supply chain management implementation in an organization. The authors developed an integrated model after the identification of 20 key LSCMEs, which is very helpful to identify and classify the important enablers by ISM methodology and explore the direct and indirect effects of each enabler by MICMAC analysis on the LSCM implementation. This will help organizations optimize their supply chain by selective control of lean enablers.

For lean manufacturing practitioners, the result of the study can be beneficial where the manufacturer is required to increase efficiency and reduce cost and wastage of resources in the lean manufacturing process, as well as in enhancing the supply chain.

This paper is the first research paper that considered firstly deep literature review of identified lean supply chain enablers and second developed structured modeling of various lean enablers of supply chain with the help of various methodologies.

In recent years, scholarly focus has shifted towards exploring the applications of disruptive technologies in professional services. These studies emphasise the need for further research in this domain. This research aims to comprehensively review the existing literature on the uses of blockchain, artificial intelligence (AI) and machine learning (ML) algorithms in professional services such as higher education, healthcare, financial securities firms and smart energy consulting. The rapid innovation and advancement in technology have led to substantial improvements in work efficiency and productivity. As industries transition towards sustainability and digitalisation, the role of energy-efficient systems becomes important in shaping smart factory designs and in further implementations. The uses of AI and other disruptive technologies for business operations not only boost production efficiency but also enhance customer satisfaction. Central to this transformation are strategies like deep learning and data/text mining, which facilitate the shift from conventional manufacturing practices to smart manufacturing. Apart from smart energy systems, the fields of higher education, healthcare and financial securities are witnessing a surge in the applications of AI, ML algorithms and blockchain technology and their contributions in emerging service economy. This study undertakes a comprehensive investigation into various factors associated with the application of disruptive technologies, evaluating their impact on the operational efficiency of professional service firms (PSFs). Through this research, we aim to identify gaps in the current literature and will suggest the directions for future research.

Supply chain integration (SCI) dominates supply chain strategy and is receiving increasing academic attention. The purpose of this paper is to provide a systematic review of the knowledge trajectory and structure of the SCI field.

Based on 3,533 papers extracted from the Web of Science (WoS), this paper adopts the main path analysis (MPA) method to detect three distinct knowledge development trajectories. Coupling-based clustering is combined with MPA to reveal three critical subfields.

The findings show that the definition, content and dimensions of SCI lack unified conclusions. The influencing factors and performance consequences of SCI are long-standing research elements. Building theoretical models and integrated systems and applying blockchain technology to improve SCI are the key research contents. The intertwining of collaboration and SCI cannot be ignored, and the green SCI may be a hot topic in the future.

This study explores knowledge in the SCI field based on the limited literature collected by WoS rather than all published papers. The omissions of some relevant papers and books may exist.

The study methodology provides a framework for similar studies in the future, and the results help researchers to get a comprehensive picture of the knowledge trajectory and structure of the SCI field.

Compared to existing reviews, MPA combines cluster analysis to develop a synthetic framework of the knowledge trajectory and structure in the SCI domain. It contributes to a systematic review of the development of SCI.

My way or the highway: A stronger partner in humanitarian relief collaboration often seeks control over its weaker partners by leveraging diverse power tactics that may be non-coercive or coercive in nature. In this backdrop, this research accentuates the perspective of weaker partners to understand how a weaker partner drives collaborative synergy under power tactics. Weaker partner in the collaborative dyad resembles the humanitarian organization (HO) who is less capable in terms of access to resources. This partner is further dependent on the stronger HO's directives to participate in the relief work.

Based on social exchange theory (SET), a collaborative dyad in humanitarian relief work is visualized from the perspectives of power and power disparity. In terms of power, mediated power tactics such as legal, coercive and reward powers are considered. Disparity recognizes the presence of a stronger and a weaker partner. Set also magnifies the insight on conflict and trust that can respectively hamper and improve the synergy between partners. In total, 295 executives representing self-reported weaker HOs are surveyed.

Legal power, as experienced by the weaker partner, bears a positive impact over collaborative performance to improve collaborative synergy. Coercive power shows a negative impact. Reward power again bears a positive impact. Coercion can complicate collaborative synergy by establishing conflicts. Yet, the trust of weaker partner on stronger HO positively moderates the negative impact of conflict over collaborative performance.

This research invokes the view of power disparity to explain synergy in inter-organizational collaboration from the perspective of weaker partners.

This study aims to (1) validate the efficacy of contractual and relational governance in enhancing operational performance and (2) explore the influence of product complexity on the effectiveness of these governance mechanisms, thereby determining the optimal approach for varying levels of product complexity.

By utilizing a comprehensive theoretical framework encompassing transaction cost economics, social exchange theory and contingency theory, this research explores the intricate interplay between governance mechanisms, product complexity and operational performance, drawing insights from a dataset comprising 246 responses within Mainland China’s manufacturing sector. To rigorously test the proposed hypotheses, this study employed a hierarchical regression analysis.

The findings of this study are summarized as follows: (1) while both contractual governance and relational governance have a significant impact on operational performance, relational governance is found to be more effective than contractual governance in enhancing operational performance; and (2) the moderation effect of product complexity is evident, as it weakens the impact of contractual governance while simultaneously enhancing the positive influence of relational governance on operational performance.

The study uncovers a moderation effect of product complexity on the relationship between governance mechanisms and operational performance. This finding adds an original contribution to the literature by highlighting how product complexity can interact with governance strategies, providing practical insights for industries dealing with varying levels of product complexity.

This study aims to investigate the factors influencing the adoption of data analytics in performance management. By examining the role of organizational and environmental contexts, this study contributes to the existing literature by proposing a novel and detailed technology-organization-environment (TOE) model for the complex interplay between firm characteristics and the adoption of data analytics. The results offer valuable insights and practical implications for organizations seeking to leverage data analytics for effective performance management.

The research draws upon a data set encompassing over 21,869 companies operating across all European Union member states. A multilevel logistic regression model was developed to evaluate the influence of organizational and environmental factors on the likelihood of adopting performance analytics in organizations.

The findings indicate that the lack of awareness of the benefits of data analytics and its practical application to address specific business challenges is a significant barrier to its adoption. Organizational contexts, such as variable-pay systems, employee training, hierarchical structures and frequency of monetary rewards, also influence the adoption of data analytics.

The study informs managers about the strategic role of data analytics capabilities in performance management for improved business intelligence and driving data culture.

The study helps managers understand the strategic role of data analytics capabilities in performance management, leading to improved business intelligence and fostering a data-driven culture in five key areas: structural alignment, strategic decision-making, resource allocation, performance improvement and change management.

The study advances the TOE theory, making it a more detailed and complete framework, particularly applicable to the adoption of performance analytics. It identifies the main factors of adoption that play a crucial role in this process.

This study aims to examine the full mediating role of supply chain flexibility (SCF) between lean production (LP) and business performance (BP) found in the previous literature. This effect negates the direct LP-BP effect (the so-called “total eclipse effect”). The authors analyze the individual contributions that the different SCF dimensions (sourcing flexibility; operating system flexibility, distribution flexibility and information system [IS] flexibility) make to the “total eclipse effect” between LP and BP produced by SCF. The relational resources-based view and resource orchestration theory are used to support the theoretical framework.

Covariance-based structural equations modeling (CB-SEM) is used to test the SCF LP-BP total eclipse hypothesis and four additional mediation hypotheses, one for each of the SCF dimensions. Data obtained via a questionnaire given to 260 companies are analyzed with CB-SEM, and SPSS Process is used to evaluate the mediation effect.

Research results indicate that only one of the dimensions (operating system flexibility) has a full mediation effect between LP and BP and is, therefore, the main contributor to the eclipse effect. Two other dimensions (sourcing flexibility and distribution flexibility) have partial mediation effects, so they also contribute to developing the eclipse effect, although to a lesser extent. Finally, IS flexibility is neither a full nor a partial mediation factor and does not contribute to the eclipse effect.

These findings have some important implications. For academia, they generate new knowledge of the role that each of the SCF dimensions or components plays in the LP-BP relationship. For company management, the findings offer supply chain managers specific information on the individual effects that the different types of SCF flexibility have between LP and BP. This will allow companies to target their efforts to develop certain types of flexibility in LP contexts depending on the outcomes that senior managers want to achieve with their SCs.

The purpose of this paper is to investigate the potential synergy between companies’ sustainable supply chain management (SSCM) activities and their supply chain resilience (SCRES). The authors propose hypotheses about the impact of buying companies SSCM activities on the inflicted damage by unexpected supply chain disruptions and the recovery time afterwards and test these empirically using data from companies during the global COVID-19 pandemic.

The authors investigate a sample of 231 of the largest publicly traded companies in the European Union with 4.158 firm-year observations. For the analysis, the authors generate variables capturing the companies’ intensity and years of experience of their SSCM activities targeted at the supply chain and run regression analyses on the inflicted damage due to the COVID-19 pandemic and the recovery time after the disruption.

Buying companies’ SSCM activities have a positive effect on their SCRES. The damage inflicted by unexpected supply chain disruptions is lower when companies have higher levels of SSCM and longer experience with it. The recovery time afterwards is significantly reduced by longer experience with SSCM efforts.

The authors suggest SCRES is reinforced by transparency, situational awareness, social capital and collaboration resulting from companies SSCM activities translate into increased SCRES.

The authors show that companies with superior SSCM are more resilient in a crisis and conclude that, therefore, companies should invest in SSCM to prevent future supply disruptions.

To the best of the authors’ knowledge, this is the first empirical study analyzing a data set of multi-industry companies, linking their SSCM activities to SCRES during the pandemic.