Integrating machine learning to optimize stock flow and goods replenishment in intralogistics

Abstract

The intralogistics area has emerged and developed as a result of increasing the importance of Industry 4.0. Autonomously running intralogistics systems are gaining more and more attention as a result of digitalization. Intralogistics is a process of organization, control, execution, and optimization of internal material and information movement. It is a complex interplay of several logistical operations. Hie literature study highlighted how logistics inside the warehouse facility transformed with the major trends in the context of optimization, automation, system integration, and mathematical modeling techniques. Further, this study proves that numerous technologies can be altered and combined to improve intralogistics in terms of accuracy, quality, efficacy as well as sustainable aspects even in more complex settings in the discussion. In the paradigm shift of moving traditional internal logistics systems to autonomous advanced intralogistics systems, integrating various operations research applications with industry 4.0 concepts is vital. It will support to develop optimized, organized, and automated logistic systems within the warehouse. In this study, we have considered developing a storage and goods retrieval system amalgamating the intralogistics concept. In order to develop a demand-driven optimized storage plan for the warehouse, storage allocation was focused. The storage allocation was done by classification technique mainly considering the total consumption value of the items. The used classification mechanism was improved by integrating Machine learning (ML) approach. ML-based classification addresses the question that how a computerized system can automatically identify the correct storage segmentation for the items in the inventory including newly added items. Apart from the consumption value, cost of the item issued quantity of the item, and frequency of issuing were considered to develop the model. It is easy to retrieve the items from the warehouse as it is in the correct demarcated location to fulfill the orders received for the warehouse. The developed storage allocation will be used to perform a simulation model in order to identify how inventory segmentation utilizes and eases the internal logistics.