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The purpose of the study is to shed light on how to implement Industry 4.0 effectively across companies in buyer–supplier relationships.

The study follows an exploratory research design and analyzes qualitative empirical data of eight case companies from the German automotive industry. The data are inductively categorized to uncover patterns and structures in a qualitative content analysis, whereupon a deeper data structure is developed.

The research reveals that a comprehensive implementation approach is required to pave the way for digitalized and interconnected supply chains. Several challenges occur during the implementation, such as system heterogeneity and resource scarcity. Prerequisites and fundamentals for a successful implementation include a vision and strategy, management involvement, and sufficient resources. Lastly, indications on how to conduct the implementations were found.

The study is based on an exploratory methodology, analyzing data from the German automotive industry. The methodology entails some limitations, and caution must be given when transferring the results to different industries and national contexts. Future studies could complement the findings by studying different contexts and including further supply chain levels.

Managers and practitioners can study the recurring themes in the implementation approaches and the best practices and subsequently learn from the experiences. This knowledge could aid to shape the strategy of companies accordingly.

The study empirically sheds light on the Industry 4.0 implementation approach across companies in buyer–supplier relationships and helps to understand the success factors and underlying mechanisms.

Supply chains (SCs) have been growingly virtualized in response to the market challenges and opportunities that are presented by new and cost-effective internet-based technologies today. This paper designed a virtual closed-loop supply chain (VCLSC) network based on multiperiod, multiproduct and by using the Internet of Things (IoT). The purpose of the paper is the optimization of the VCLSC network.

The proposed model considers the maximization of profit. For this purpose, costs related to virtualization such as security, energy consumption, recall and IoT facilities along with the usual costs of the SC are considered in the model. Due to real-world demand fluctuations, in this model, demand is considered fuzzy. Finally, the problem is solved using the Grey Wolf algorithm and Firefly algorithm. A numerical example and sensitivity analysis on the main parameters of the model are used to describe the importance and applicability of the developed model.

The findings showed that the Firefly algorithm performed better and identified more profit for the SC in each period. Also, the results of the sensitivity analysis using the IoT in a VCLSC showed that the profit of the virtual supply chain (VSC) is higher compared to not using IoT due to tracking defective parts and identifying reversible products. In proposed model, chain members can help improve chain operations by tracking raw materials and products, delivering products faster and with higher quality to customers, bringing a new level of SC efficiency to industries. As a result, VSCs can be controlled, programmed and optimized remotely over the Internet based on virtual objects rather than direct observation.

There are limited researches on designing and optimizing the VCLSC network. This study is one of the first studies that optimize the VSC networks considering minimization of virtual costs and maximization of profits. In most researches, the theory of VSC and its advantages have been described, while in this research, mathematical optimization and modeling of the VSC have been done, and it has been tried to apply SC virtualization using the IoT. Considering virtual costs in VSC optimization is another originality of this research. Also, considering the uncertainty in the SC brings the issue closer to the real world. In this study, virtualization costs including security, recall and energy consumption in SC optimization are considered.

1. Investigates the role of IoT for virtual supply chain profit optimization and mathematical optimization of virtual closed-loop supply chain (VCLSC) based on multiperiod, multiproduct with emphasis on using the IoT under uncertainty.

2. Considering the most important costs of virtualization of supply chain include: cost of IoT information security, cost of IoT energy consumption, cost of recall the production department, cost of IoT facilities.

3. Selection of the optimal suppliers in each period and determination of the price of each returned product in virtual supply chain.

4. Solving and validating the proposed model with two meta-heuristic algorithms (the Grey Wolf algorithm and Firefly algorithm).

Investigates the role of IoT for virtual supply chain profit optimization and mathematical optimization of virtual closed-loop supply chain (VCLSC) based on multiperiod, multiproduct with emphasis on using the IoT under uncertainty.

Considering the most important costs of virtualization of supply chain include: cost of IoT information security, cost of IoT energy consumption, cost of recall the production department, cost of IoT facilities.

Selection of the optimal suppliers in each period and determination of the price of each returned product in virtual supply chain.

Solving and validating the proposed model with two meta-heuristic algorithms (the Grey Wolf algorithm and Firefly algorithm).

This paper aims to examine how interfirm transactional and relational assets drive firm performance (FP) in digitally integrated supply chains.

The authors combine the Transaction Cost Economics (TCE) and Relational Exchange Theory (RET) frameworks to hypothesize that FP will be a function of Asset Specificity (AS), Digital Technology Usage (DTU) and Collaborative Information Sharing (CIS). In addition, the authors hypothesize that Supply Chain Integration (SCI) will partially mediate the effect of DTU and fully mediate the impact of AS and CIS on FP. A cross-sectional survey of supply chain managers is used to test the hypotheses.

Findings indicate that specific investments in digitally integrated supply chains would increase FP. In addition, SCI fully mediates the relationships between AS and FP and CIS and FP, while SCI partially mediates the influence of DTU on FP.

Managers could strategically engage in the technologies that effectively fit within the firm’s supply chain strategies and seek to develop a pragmatic expertise that enables the effective use of technology in a comprehensive setting.

The study enriches the extant literature by incorporating TCE and RET as contradictory viewpoints on AS and investigating how transactional and relational assets affect FP in digitally integrated supply chains.

This study aims to develop a hierarchical model that uncovers the relationships between challenges confronting Africa's organ transplant supply chain systems.

Eleven challenges (variables) were identified after a comprehensive review of the existing literature. The contextual interactions among these variables were analysed from the perspectives of health-care stakeholders in two sub-Saharan Africa (SSA) countries (Nigeria and Uganda), using Delphi-interpretive structural modelling-cross-impact matrix multiplication applied to classification (MICMAC) techniques.

The findings reveal that weak regulatory frameworks, insufficient information systems and a lack of necessary skills make it challenging for critical actors to perform the tasks effectively. The interaction effects of these challenges weaken organ supply chains and make it less efficient, giving rise to negative externalities such as black markets for donated organs and organ tourism/trafficking.

This paper establishes a solid foundation for a critical topic that could significantly impact human health and life once the government or non-profit ecosystem matures. The MICMAC analysis in this paper provides a methodological approach for future studies wishing to further develop the organ supply chain structural models.

The study provides valuable insights for experts and policymakers on where to prioritise efforts in designing interventions to strengthen organ transplantation supply chains in developing countries.

This study is one of the first to empirically examine the challenges of organ transplant supply chains from an SSA perspective, including theoretically grounded explanations from data collected in two developing countries.

The purpose of this paper is to investigate, from a thorough review of the literature, the role of metaverse-based quality 4.0 (MV-based Q4.0) in achieving manufacturing resilience (MFGRES). Based on a categorization of MV-based Q4.0 enabler technologies and MFGRES antecedents, the paper provides a conceptual framework depicting the relationship between both areas while exploring existing knowledge in current literature.

The paper is structured as a comprehensive systematic literature review (SLR) at the intersection of MV-based Q4.0 and MFGRES fields. From the Scopus database up to 2023, a final sample of 182 papers is selected based on the inclusion/exclusion criteria that shape the knowledge base of the research.

In light of the classification of reviewed papers, the findings show that artificial intelligence is especially well-suited to enhancing MFGRES. Transparency and flexibility are the resilience enablers that gain most from the implementation of MV-based Q4.0. Through analysis and synthesis of the literature, the study reveals the lack of an integrated approach combining both MV-based Q4.0 and MFGRES. This is particularly clear during disruptions.

This study has a significant impact on managers and businesses. It also advances knowledge of the importance of MV-based Q4.0 in achieving MFGRES and gaining its full rewards.

This paper makes significant recommendations for academics, particularly those who are interested in the metaverse concept within MFGRES. The study also helps managers by illuminating a key area to concentrate on for the improvement of MFGRES within their organizations. In light of this, future research directions are suggested.

This research aims to review relevant documentation on performance measurement in aircraft maintenance. The approaches and models used to measure and manage aircraft maintenance performance in civilian and military contexts are classified and examined during this process. Based on the results of this study, future directions and research topics are identified.

The review included an initial database of 200 publications published over the past 20 years. Published work included contributions from institutions, companies, and scholars.

The present literature review showed the distribution of themes related to measuring and improving of aviation maintenance performance. These areas and themes cover the perspectives of the customer, technicians and managers and range from the effective management of material resources, the contribution of mathematical models and the help of information systems, and finally, the involvement of the human factor. Thus, given this documentary base, a conceptual framework, which retraces the different operational and organizational facets of the evolution of maintenance performance management, is presented.

Based on the results obtained, the measurement of performance in aeronautical maintenance requires more research and the development of theoretical models.

Findings from this investigation have relevant implications for aircraft maintenance. In this context, it is essential to understand the different approaches to “performance measurement” and management used in airlines and maintenance centers.

Given the competitiveness of the aviation sector, it is important to understand the types and scope of different approaches and models used to manage and measure aeronautical maintenance performance in civilian and military operational contexts.

This study considers transitive service triads, which consist of three dyads formed by three actors: supplier, logistics service provider and customer, who remain directly linked by one or more of the upstream and downstream flows of products, information and finances. This paper aims to explore the link between information governance, decentralized information technologies and supply chain self-organization, and their resulting impact on network performance in the transitive service triads.

Drawing upon the tenets of the theory of complex adaptive systems and supply chain practice view, this paper involves an empirical investigation that uses survey data gathered from transitive service triads in the European countries. The study uses partial least squares structural equation modeling to estimate the formative-reflective hierarchical component model and test the research hypotheses.

Information governance defines how supply chain information flows are controlled, accessed and used by a focal organization and its business partners. As empirically evidenced in this study, it can be depicted as a latent construct consisting of three distinct dimensions of information custody, information ownership and right to data access. Likewise, the study also indicates that supply chain self-organization, as a second-order construct, consists of three interactive self-organization actions undertaken by specific firms participating in the triadic arrangement. Supply chain self-organization is thus produced by firms that are reciprocally interrelated and interacting, having effects on one another. Furthermore, the study also highlights that information governance creates an environment for applying decentralized information technologies, which then positively affects supply chain self-organization. Finally, the research also empirically operationalizes the construct of network performance within the transitive service triads.

Although the results provide several major contributions to theory and implications for practitioners, the study still demonstrates some methodological constraints. Specifically, although the study uses a relatively large research sample of 350 transitive service triads, it still focuses only on a selected group of industries and is limited to investigating solely a particular type of service triads.

Given the increasing interest in investigating triads, this study examines how information governance and decentralized information technologies support supply chain self-organization to yield network performance in transitive service triads.

Identifying and prioritizing supply chain risk is significant from any product’s quality and reliability perspective. Under an input-process-output workflow, conventional risk prioritization uses a risk priority number (RPN) aligned to the risk analysis. Imprecise information coupled with a lack of dealing with hesitancy margins enlarges the scope, leading to improper assessment of risks. This significantly affects monitoring quality and performance. Against the backdrop, a methodology that identifies and prioritizes the operational supply chain risk factors signifies better risk assessment.

The study proposes a multi-criteria model for risk prioritization involving multiple decision-makers (DMs). The methodology offers a robust, hybrid system based on the Intuitionistic Fuzzy (IF) Set merged with the “Technique for Order Performance by Similarity to Ideal Solution.” The nature of the model is robust. The same is shown by applying fuzzy concepts under multi-criteria decision-making (MCDM) to prioritize the identified business risks for better assessment.

The proposed IF Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) for risk prioritization model can improve the decisions within organizations that make up the chains, thus guaranteeing a “better quality in risk management.” Establishing an efficient representation of uncertain information related to traditional failure mode and effects analysis (FMEA) treatment involving multiple DMs means identifying potential risks in advance and providing better supply chain control.

In a company’s supply chain, blockchain allows data storage and transparent transmission of flows with traceability, privacy, security and transparency (Roy et al., 2022). They asserted that blockchain technology has great potential for traceability. Since risk assessment in supply chain operations can be treated as a traceability problem, further research is needed to use blockchain technologies. Lastly, issues like risk will be better assessed if predicted well; further research demands the suitability of applying predictive analysis on risk.

The study proposes a hybrid framework based on the generic risk assessment and MCDM methodologies under a fuzzy environment system. By this, the authors try to address the supply chain risk assessment and mitigation framework better than the conventional one. To the best of their knowledge, no study is found in existing literature attempting to explore the efficacy of the proposed hybrid approach over the traditional RPN system in prime sectors like steel (with production planning data). The validation experiment indicates the effectiveness of the results obtained from the proposed IF TOPSIS Approach to Risk Prioritization methodology is more practical and resembles the actual scenario compared to those obtained using the traditional RPN system (Kim et al., 2018; Kumar et al., 2018).

This study provides mathematical models to simulate the supply chain risk assessment, thus helping the manufacturer rank the risk level. In the end, the authors apply this model in a big-sized organization to validate its accuracy. The authors validate the proposed approach to an integrated steel plant impacting the production planning process. The model’s outcome substantially adds value to the current risk assessment and prioritization, significantly affecting better risk management quality.

In the digital age, emerging technologies have affected every industry. Information and communications technology and digital technologies have transformed traditional supply chains into smart and more resilient ones, enabling effective management of challenges. Given the importance of the two topics, namely sustainable supply chain management and Industry 4.0 in supply chain management, on the one hand, and the dearth of theoretical research performed in this area on the other, this study aims to propose a conceptual model on a sustainable digital supply chain management in manufacturing companies.

This study utilized a qualitative approach. First, an in-depth review of the relevant literature was done. Then, following a multi-grounded theory methodology, relevant data were gathered by reviewing 92 papers and conducting nine semi-structured interviews with industry experts. These data were analyzed using the MAXQDA software.

A total of 41 concepts, ten sub-components and three main components (dimensions) were extracted, and the proposed conceptual model was presented. Finally, based on this conceptual model, three propositions were suggested.

Considering that the present study was performed in the context of Iranian manufacturing companies, caution should be exercised in relation to the generalizability of the obtained results. Also, due to the problems in the digital technology infrastructure and the limited use of these technologies by manufacturing companies (emphasized by the interviewees), this study focused on the theoretical dimension of using digital technologies by these companies.

The proposed comprehensive model can help academicians as well as practitioners to focus better and explore the variables and constructs of the model, paving the way toward successful implementation of digital technologies in the manufacturing supply chain.

To the best knowledge of the authors, this study is among the first of its kind which presents a holistic and comprehensive digital supply chain model aimed at guiding companies to consider sustainability from all the main dimensions and their relevant indicators.