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Organizations are continually improving their practices to improve operational performance. They already employ Lean Manufacturing techniques (LM) to reduce unnecessary waste. Industry 4.0 techniques enhance operational performance in association with LM. Despite the proven benefits of LM principles and the advancements offered by Industry 4.0 technologies, many organizations struggle to integrate these approaches effectively. This research paper explores how LM principles can be combined with Industry 4.0 technologies to provide valuable guidance for businesses looking to adopt lean automation strategies.

A systematic literature review on LM and Industry 4.0 was done to investigate the possible technical integration of both methods. Ninety-two articles are extracted systematically from the Scopus and Web of Science databases. This study states a systematic literature review, including quantitative analysis of bibliographic networks and cluster analysis, to identify emergent ideas and their further implementation.

The research findings highlight the positive impact of integrating lean production with Industry 4.0 techniques, benefiting organizations in achieving their goals. A lean automation integration framework is proposed based on the literature review and the findings.

This study provides industry administrators and practitioners valuable guidance for enhancing organizational productivity. These implications can provide businesses with competitive advantages, enhance customer satisfaction, and enable them to adapt to the dynamic demands of the contemporary business environment.

This literature review study has substantially contributed to the technological integration of lean and Industry 4.0. The work has also identified potential emerging areas that warrant further research.

Demand planning (DP) is a key element of supply chain management (SCM) and is widely regarded as an important catalyst for improving supply chain performance. Regarding the availability of technology to process large amounts of data, artificial intelligence (AI) has received increasing attention in the DP literature in recent years, but there are no reviews of studies on the application of AI in supply chain DP. Given the importance and value of this research area, we aimed to review the current body of knowledge on the application of AI in DP to improve SCM performance.

Using a systematic literature review approach, we identified 141 peer-reviewed articles and conducted content analysis to examine the body of knowledge on AI in DP in the academic literature published from 2012 to 2023.

We found that AI in DP is still in its early stages of development. The literature is dominated by modelling studies. We identified three knowledge clusters for AI in DP: AI tools and techniques, AI applications for supply chain functions and the impact of AI on digital SCM. The three knowledge domains are conceptualised in a framework to demonstrate how AI can be deployed in DP to improve SCM performance. However, challenges remain. We identify gaps in the literature that make suggestions for further research in this area.

This study makes a theoretical contribution by identifying the key elements in applying AI in DP for SCM. The proposed conceptual framework can be used to help guide further empirical research and can help companies to implement AI in DP.