EMPIRICAL STUDY OF AN AUTOMATED INVENTORY MANAGEMENT SYSTEM WITH BAYESIAN INFERENCE ALGORITHM

Abstract

This research addresses the inefficiency and inconsistency associated with asset tracking and inventory management. This paper focuses on the algorithm and framework of an automated system for large quantities of inventory control, with the features of geographically-widely located facility and heterogeneous assets. Further, the authors investigate two major asset-tracking practices: manual or barcode inventory management system versus automated inventory management system. The authors empirically validate both systems with modeling, simulation, and observation of the system performances. The evidence provides statistically strong support for the improvement of efficiency and accuracy of automated inventory management system, although the impacts of environmental circumstances and human factors suggest that precautions should be taken to minimize the influences of such factors. The proposed automated system is ideal for large quantities of inventory control. Small scale of inventory control depends more on manual inspection or barcode technology. This research provides a possible solution to the growing concerns over asset tracking and protection. In addition, it has the potential to change the facilities management process and policy of the organizations with widely located properties. This empirical study verified the practicability of implementing an automated inventory management system and validated the design by comparing the performances of the manual system and the