Leveraging AI in Embedded and Extended Warehouse Management for Enhanced Efficiency

Leveraging AI in Embedded and Extended Warehouse Management , Industry 4.0, Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Smart Manufacturing (SM )

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Vol. 10 No. 06 (2022)
Engineering and Computer Science
June 9, 2022

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Speed and accuracy of decision-making at the operational and tactical levels are critical in warehouse management. This paper conceptually presents decision support systems (DSS) powered by artificial intelligence (AI) at two levels – embedded warehouse management at the operational level and extended warehouse management at the tactical level. For enhanced efficiency, suggestions are categorized at the tactical level into system-front-end/back-end-heavy lifting, other back-end system suggestions, and system extensions. Several AI technologies such as expert system rule engines, machine learning models, and natural language understanding models can be applied at both levels. Efforts required for data preparation and model training are highlighted.

Warehouse management takes place in a dynamic environment. New inventory arrives, and orders for shipping out inventory are constantly issued. There is a large number of decisions to be made regularly to coordinate the flow of materials in and out of a warehouse. Speed and accuracy of operational and tactical decision-making are important in warehouse management. This paper begins by discussing decision support systems (DSS) enabled by artificial intelligence (AI) for efficient decision-making at both the operational and tactical levels. Subsequently, several AI technologies that can be applied to offer intelligence at both levels are discussed. Throughout the paper, suggestions are made about how to apply these technologies to enhance efficiency. Furthermore, the effort required in terms of data preparation and model training is discussed. The pathways presented are only feasible with a supporting, intelligent IT infrastructure. Intelligence needs to be built not only within the warehouse system but also extended out to the surrounding ecosystem. The paper wraps up by either highlighting or reiterating the suggestions and insights to help stakeholders make the most of the possibilities AI offers for decision-making in warehouse management. With the rapid growth of e-commerce, flexible, adaptable AI-driven DSS could be the solution needed to help warehouse management keep up with the ever-increasing pace and dynamism of the industry.