A Data Mining Practical Approach to Inventory Management and Logistics
Optimization

A Data Mining Practical Approach to Inventory Management and Logistics
Optimization

Data Mining; Logistics Optimization; Inventory Management

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.

Bambang Pujiarto 1,*, Mukhtar Hanafi 2, Arief Setyawan 3, Asti Nur Imani 4, Eky Rizky Prasetya

2021

PERI IRAWAN

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