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Showing 2 results for Inventory Optimization
A Novel Integrated decision-making support via probabilistic piecewise optimization model for integrated supply chain management involving discounts under probabilistic uncertainty
Widowati Widowati, Sutrisno Sutrisno, Robertus Heri Soelistyo Utomo,
Volume 0, Issue 0 (10-2025)
Abstract
In the manufacturing and retail sectors, the challenges of supplier selection revolve around determining the most efficient allocation of raw materials to various suppliers to minimize procurement costs. Concurrently, production planning issues focus on optimizing the quantity of products to be manufactured.  Simultaneously, warehouses used to store raw materials and products must also be optimally managed to reduce holding costs. To achieve maximum revenue, decision-makers must make optimal arrangements regarding these three problems. This study introduces a novel mathematical model within the realm of dynamic expected-based probabilistic piecewise programming as a decision support tool for those three problems, which are solved in an integrated manner. It aids in identifying optimal solutions for the combined issues of supplier selection, inventory management, and production planning, which encompass discount and uncertainty factors. The primary objective is to enhance supply chain performance, specifically by maximizing profits derived from production activities. The model accommodates supply chain scenarios involving multiple raw materials, suppliers, products, and buyers. Furthermore, the problem is modeled with numerous observation time instants. Numerical experiments were conducted to assess the proposed model and to demonstrate how optimal decisions can be made. Compared to the deterministic model, the proposed model increased the profit by 6%. The results indicate the model's effectiveness in resolving these challenges and providing optimal solutions. As a result, decision-makers and managers in various industries can consider implementing this proposed model.

Dynamic Inventory Risk Profiling Using PCA and Clustering: A Data-Driven Approach to Supply Chain Optimization
Ida Lumintu, Achmad Maududie,
Volume 36, Issue 3 (9-2025)
Abstract
Effective inventory management is critical for mitigating inefficiencies such as overproduction, excessive holding costs, and stockouts. This study leverages DBSCAN and GMM clustering methods, combined with Principal Component Analysis (PCA) for dimensionality reduction, to categorize inventory data into distinct risk-based clusters. The analysis highlights that DBSCAN outperformed GMM, achieving a silhouette score of 0.62 compared to 0.49, while identifying three meaningful inventory clusters. Each cluster reflects unique combinations of risk factors, providing actionable insights for optimizing inventory levels. The study demonstrates how these clusters enable targeted strategies to address inefficiencies and improve overall inventory management. Limitations include the reliance on historical data, which may not fully capture dynamic market conditions, and the assumption of fixed clustering parameters. The findings underscore the importance of choosing clustering algorithms suited to the data's characteristics and highlight the potential of PCA in enhancing computational efficiency. Future research should explore dynamic clustering techniques and integrate real-time data streams to refine inventory management strategies further.

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