Multi-warehouse Relief Aid Pre-position Planning for Thai Red Cross

Abstract

Relief supply pre-positioning is essential for effective disaster management, particularly when demand is uncertain and varies across regions. This study proposes a data-driven approach to analyze and forecast the demand for relief aid packages of the Thai Red Cross to support resource planning at the cluster level of Red Cross stations and improve the efficiency of storage and distribution. Monthly demand data from Red Cross stations and the Disaster Relief Division during 2011–2023 were analyzed. Due to missing records in certain periods and changes in station service areas, the dataset was restructured by reallocating historical demand to the original coverage areas to maintain data continuity prior to the analysis. Statistical analysis using the Kolmogorov–Smirnov test indicated that the demand exhibited a right-skewed pattern and was best represented by a lognormal distribution. Based on this distribution, demand levels corresponding to an 80% confidence level were estimated and compared between decentralized station-level storage and cluster-based management, showing that demand aggregation at the cluster level reduced the total safety stock required by the system. For forecasting, several Machine Learning techniques were applied with time, location, and rainfall variables serving as model inputs. where rainfall was incorporated as weighting factor during model training to reflect disaster risk conditions. Experimental results indicate that cluster-based inventory planning significantly reduces overall stock requirements compared with independent station storage, while the selected forecasting model provides reliable demand estimates. Evaluation using the cumulative distribution function (CDF) shows that the proposed approach can support demand coverage of approximately 80–90% at the cluster level and about 60–80% at the station level.