A Data Mining Practical Approach to Inventory Management and Logistics Optimization

Abstract

The latent demand to optimize costs and customer service has been fostered in the current economic situations, characterized by high competitiveness and disruption in supply chains, placing inventories as a vital sector with significant potential to implement improvements in firms. Inventory management that is done correctly has a favorable impact on logistics performance indexes. Warehousing operations account for around 15% of logistics expenditures in terms of dollars. This article employs a method based on the Partitioning Around Medoids algorithm that incorporates, in a novel way, the application of a strategy for locating the optimal picking point based on cluster classification, taking into account the qualitative and quantitative factors that have the greatest impact or priority on inventory management in the company. The results obtained with this model improve the routes of distributed materials based on the identification of their characteristics such as the frequency of collection and handling of materials, allowing for the reorganization and expansion of storage capacity of the various SKUs, moving from a classification by families to a cluster classification. This article shows a suggestion for a warehouse distribution design using data mining techniques, which uses indicators and key qualities for operational success for a case study in a corporation, as well as an approach to improve inventory management decision-making.

A Methodology for Clustering Items with Seasonal and Non-seasonal Demand Patterns for Inventory Management

“…(Raja et al, 2016) used hierarchical clustering in which they have converted non-metric variables to metric for improving inventory performance in inventory management of spare parts. (Pujiarto et al, 2021) developed a method based on Partitioning Around Medoids algorithm for locating optimal picking points based on cluster classifications. (Razavi Hajiagha et al, 2021) proposes a hybrid fuzzy-stochastic multi-criteria method using possibilistic chance-constrained programming.…”