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 major aspects and impacts of the interrelationships between Industry 4.0 (I4.0) technology and Lean Supply Chain Management are discussed in this article (LSCM). Many practical LSCM systems based on I4.0 have lately appeared. Despite this, there has been little research into the use of I4.0 technologies within LSCM. Machine learning, smart factories, blockchain, and the internet of services (IoS) are all possible LSCM revolution enablers. The goal of this research is to find out more about present and potential I4.0 technologies that can improve LSCM research and application in order to fill a gap in the current literature. A Systematic Literature Review (SLR) technique was used for the collection, selection, and evaluation of published literature. We looked at 79 studies published between 2010 and 2021 that were found in the Scopus database.

No abstract