A modified cross-abc analysis for direct inventory management

Data analytics represents an important step for manufacturing companies aiming at optimizing warehouse management. Moreover, in a Decision Support System perspective, companies require automated methods and processes for warehouse activities planning able to identify the most appropriate inventory management policy. Moving towards a data-driven environment, a deeper understanding of data provided by warehouse operations and supplier's orders is needed. In this context, a procedure aiming at reducing the problem of out of stock and overstock by developing a Decision Support System is deployed through a data-driven approach. The standard crossABC analysis is revised as a top-down methodology: in this way, cross-ABC analysis is applied iteratively by ranking items according to their physical features, e.g., color or size, in order to identify the weight of the items for each attribute classification, then combining the results for the final rating. The first step of the proposed procedure requires the identification of a weight for each feature, based on the influence of its importance. Then, a Decision Support System is developed in order to customize and automate the following daily activities: (a) application of the revised cross-ABC analysis, (b) warehouse management, and (c) orders planning. The main contribution of this work is the development of a versatile and dynamic procedure applying the well-known cross-ABC analysis in a different way, also developing and integrating a direct warehouse management system. A case study of a manufacturing company is also presented to explain the proposed procedure, as well as to analyze its performance and the different results compared to the standard analysis.