A Simulation-Optimization Technique for Service Level Analysis in Conjunction with Reorder Point Estimation and Lead-Time consideration: A Case Study in Sea Port

TL;DR

This paper presents a practical simulation-optimization approach for analyzing service levels and estimating reorder points in complex spare parts inventory systems at a seaport, integrating data analysis, ABC classification, and simulation techniques.

Contribution

It introduces a structured, step-by-step procedure combining data cleaning, ABC analysis, AHP, and simulation to optimize inventory control in a complex port environment.

Findings

Identified 1416 critical inventory items using ABC and AHP.

Simulated reorder points and quantities with Arena software for near-optimal results.

Demonstrated the feasibility of the approach with a real-world case study.

Abstract

This study offers a step-by-step practical procedure from the analysis of the current status of the spare parts inventory system to advanced service-level analysis by virtue of simulation-optimization technique for a real-world case study associated with a seaport. The remarkable variety and immense diversity on one hand, and extreme complexities not only in consumption patterns but in the supply of spare parts in an international port with technically advance port operator machinery, on the other hand, have convinced the managers to deal with this issue in a structural framework. The huge available data require cleaning and classification to properly process them and derive reorder point (ROP) estimation, reorder quantity (ROQ) estimation, and associated service level analysis. Finally, from 247000 items used in 9 years long, 1416 inventory items are elected as a result of ABC analysis integrating with the Analytic Hierarchy Process (AHP), which led to the main items that need to be kept under strict inventory control. The ROPs and the pertinent quantities are simulated by Arena software for all the main items, each of which took approximately 30 minutes run-time on a personal computer to determine near-optimal estimations.