Interest in applying Industry 4.0 technologies in supply chain operations has increased significantly due to the urgent need to combat climate change and achieve net-zero emissions. This study aims to thoroughly comprehend how Industry 4.0 technologies affect the efficiency of net-zero supply chains. To do so, the study systematically reviews the existing research using 68 academic papers that are thematically analysed and classified by potentials associated with Industry 4.0 in the context of net zero supply chain performance. The findings of this systematic literature review highlight the multifaceted role of Industry 4.0 technologies in achieving net-zero supply chain performance. However, the study also identifies challenges related to policy, technology, economy, and markets to harness these technologies effectively. A conceptual framework is proposed to help organizations strategically leverage Industry 4.0 technologies for sustainable supply chain performance. By identifying knowledge gaps, the review provides a roadmap for future research to explore the complex dynamics at the intersection of Industry 4.0 and sustainability. Practically, the study offers valuable insights for supply chain managers and policymakers on the opportunities and challenges associated with adopting Industry 4.0 technologies for sustainable practices. With the goal of achieving net-zero supply chain performance, this paper emphasizes the importance of a holistic, integrated approach to technology adoption and sustainability strategies.

The green supplier selection presents numerous challenges, from initial assessment to final selection, which specialists in this field of supplier management often encounter. Among the techniques that aim to meet these challenges that are continually progressing is the creation and development of high-quality decision-making tools. In this study, the assessment of suppliers hinges on both traditional and environmental factors. A framework of multi-criteria decision-making (MCDM) is announced in order to appraise green supplier selection. This framework integrates Fuzzy Analytical Hierarchy Process (AHP) combined with two additional methods: WASPAS (“Weighted Aggregated Sum-Product Assessment”) and TOPSIS (“Technique for Order Preference by Similarity to Ideal Solution”). At the outset, there were five environmental criteria for green supplier selection: “Health and Safety”, “Sustainable Product Design”, “ISO 14001 Certification”, “Investment Recovery”, and “Green Packaging”, along with three conventional criteria including quality, price, and delivery, were pinpointed through a review of literature and expert input to facilitate the MCDM approach. As part of demonstrating the pertinency of the recommended framework, a practical case study of the automotive industry in Morocco is discussed. The results demonstrate that the utilized Fuzzy hybrid methods yield consistent rankings for green suppliers. Additionally, among the four green suppliers, number three obtained the best ranking, which indicates the robustness and performance of the chosen models. Furthermore, this study offers a unified platform for selecting green suppliers under a Fuzzy environment. Therefore, the chosen strategy and its analysis provide relevant data and information to decision-makers for the choice and selection of suppliers. It can also provide and help procurement departments and decision-makers to choose and select the efficient ecological supplier in the global market supply chain.

Purpose: This study aims to address Industry 4.0 (I4.0) technologies that can improve the research and implementation of lean supply chain management (LSCM) and the enhanced LSCM subfields in I4.0 technologies. Design/methodology/approach: The authors conducted a systematic literature review to detect, categorize and assess recent data, highlighting patterns and providing suggestions for potential research in this field, to investigate I4.0 literature and its effect on LSCM. The authors examined 79 published types of research from the Scopus database that were published between 2010 and 2021 and classified them into four LSCM fields: logistics, production, supply chain and marketing. Findings: The authors can emphasize the fact that the literature on this topic is in progress, from early German academic research to the current creation of new effects around the world. The majority of the potential effects investigated were discovered to improve specific areas that ultimately enhance the practices of the four LSCM domains as well as performance outcomes. The authors were also able to assess the extent to which present and upcoming I4.0 technologies can improve LSCM research and implementation. Originality/value: To the best of the authors’ knowledge, this is the first study of its kind. Although some research looked into various areas of I4.0 and LSCM topics, there has been no research specifically looking into the impact of I4.0 on LSCM. The originality of this study lies in the treatment of the main fields and sub-fields of LSCM, which can benefit from the technologies of I4.0. Academic scholars interested in the research topics may benefit from the findings of this study. Organizations in various industrial sectors, particularly manufacturing, where lean thinking is used, business professionals specialized in lean operations and supply chain management, along with anyone else who wants to learn more about the interrelationships between I4.0 and LSCM.

The accuracy of demand forecasting has a significant impact on the supply chain system's performance, which in turn has a major effect on company performance. Accurate forecasting will allow the organization to make the best use of its resources. The synchronization of customer orders to support production is critical for on-time order fulfillment. However, In fact many organizations report that their forecasting method is not working as effectively as they had hoped because orders regularly alter due to client demands. The purpose of this paper is to present an Internet of Things (IoT)-based inventory management system (IMS) that combines a causal method of multiple linear regressions (MLR) with genetic algorithms (GA) to improve the accuracy of demand forecasting in the future period by the customer as closely as feasible and enable smart inventory for Industry 4.0. Based on the data gathered from a semiconductor company that specializes in low-volume, high-mix contract manufacturing equipment and services integration, the suggested IoT-based IMS indicates that inventory productivity and efficiency could be enhanced, and it is resilient to order fluctuation.

The present paper reports on studying synchronous flow implementation, as a lean supply chain tools, through a collaborative relationship with suppliers. This involves consolidating with a new contribution to the development and application of a supply chain collaboration framework between automotive constructor and first-tier equipment suppliers to achieve the synchronous flow of components. The objective is to provide the automotive companies with a decision-making tool for selecting strategic suppliers to collaborate with, examining the collaboration context in terms of motivators, drivers, and barriers and evaluating the collaboration performance. Therefore, our contribution is structured as follows. As a first step, an overview of papers reporting on collaboration, lean supply chain, and synchronous flow is provided to identify the key elements of successful collaboration relationships. As a result, a preliminary framework is elaborated. The second step described the case study of a leading automotive firm “RENAULT” and its suppliers in Morocco. Based on semi-structured interviews conducted with participants from these companies, the preliminary framework was improved. The next section discusses the obtained results as well as the improved framework. Finally, conclusions and suggestions for further works are included.