AI-Augmented SAP EWM For Predictive Warehouse Optimization In Life Sciences Supply Chains
DOI:
https://doi.org/10.29284/xae4ay23Keywords:
Artificial Intelligence; SAP Extended Warehouse Management (EWM); Predictive Warehousing; Life Sciences Supply Chain; Regulatory Compliance (21 CFR Part 11)Abstract
The supply chains of life sciences are very controlled and entail sensitive products and complicated procedures, which greatly determine the functions at the warehouse. This has facilitated the SAP Extended Warehouse Management (EWM) to become popular in such settings in that it allows standard, auditable and compliant operations to be deployed in the warehouse. Nevertheless, optimization systems developed by SAP EWM are predominantly rule-based, which prevents the opportunity to predict changes in the processes occurring in warehouses, and respond proactively to disruption. Artificial intelligence offers predictive and reactive decision support on the basis of past and current warehouse data. This review describes the ways in which artificial intelligence can be implemented to enhance SAP EWM to provide predictive warehouse optimization within the supply chains in the life sciences. The conceptual framework is based on the synthesis of peer reviewed research on the sphere of warehouse management systems, logistics analytics and controlled supply chain activities. The implications on the operational performance are also discussed in the review, and compliance assurance and adoption by the organizations are also discussed in the review. Lastly, the research directions are also discussed for future research wherein the explainability and validation are proposed as the conditions of scalable and reliable warehouse management using AI-enhanced warehouse management. The results can be summarized as offering an academic unified view of the implementation of artificial intelligence within the warehouse system in life sciences indicating both the opportunity and the barriers of predictive warehousing in controlled supply chains.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
