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.

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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 paper focuses on Information and communication technologies (ICT) deployed by Logistics service providers (LSP), particularly for the monitoring and companies. Indeed, nowadays ICT are essential for any implementation of efficient traceability, notably for the outsourcing of logistics activities. This paper is structured around two parts: the first part presenting the state of the art in terms of outsourcing logistics activities and traceability. The second part concerns a comparative study combining a set of LSP operating in Morocco and those operating abroad through several criteria. These criteria include the services offered and the technology deployed for rigorous traceability. The comparative study will determine the profile of the LSP operating in Morocco and adopting an effective and efficient traceability strategy based on innovative technology.

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