EWM vs. WM Migration Patterns in Legacy Manufacturing Landscapes: Throughput, Labor, and Accuracy Impacts

Abstract

Enterprise Warehouse Management (EWM) systems are the forthcoming progressive development of conventional Warehouse Management (WM) modules to allow captive logistics operations, superior analytics, and real-time flexibility within an overview of manufacturing realms. This paper will explore the migration trends between SAP WM and SAP EWM in the old manufacturing resides with emphasis on throughput efficiency, labor productivity, and inventory accuracy. Based on the data gathered on five separate manufacturing companies (automotive, electronics, consumer goods, heavy machinery, and process manufacturing), this paper offers a comparative study of the pre- and post-migration operational measures. The analysis includes both mixed-method procedures that combine statistical process control (SPC), statistical overall equipment effectiveness (OEE) computation, and labor standard cost model to determine tangible and intangible performance increase. Results have shown that EWM movement produces an average throughput of 18-24 or labor optimization of 12-16 or an inventory accuracy of up to 9. Nonetheless, the issues of migration, including harmonization of legacy data, lack of workforce training, and the necessity to fully synchronize with a MES/ERP system are also very high-risk areas of transitional risks. At the end of the paper, a list of suggestions is provided, which includes designing a migration optimization framework based on applying hybrid data pipelines, gradual rollouts, and slotting algorithms that are aided by machine learning. The findings can be used as a theoretical basis and practical guidelines that can be afforded to manufacturing enterprises that are to undergo the transition to digital supply chain infrastructures.