Background: This study introduces a novel supply chain management (SCM) model tailored for the hospital industry in Thailand. The model emphasises the integration of third-party logistics (3PL) providers to streamline supply-processing-distribution (SPD) functions. By outsourcing non-core activities like SPD to 3PL providers, hospitals can enhance their operational efficiency, allowing healthcare professionals to focus on core tasks and ultimately improving service delivery. Methods: This research employed a dual methodology, combining an analytic hierarchy process (AHP) with a Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS). These approaches evaluated various SCM models based on multiple hospital logistics performance attributes. Results: The AHP results highlighted on-time delivery, patient safety, utilisation rate, and emergency procurement as critical criteria for selecting the optimal model. Fuzzy TOPSIS analysis identified the SCIII: W-G-H model as the most suitable for implementation in Thai hospitals. This model incorporates a centralised warehouse for negotiation leverage, a Group Purchasing Organisation (GPO) for cost efficiency, and regional SPD hubs for effective inventory management and rapid responses to demand fluctuations or emergencies. Conclusions: Adopting this SCM model is expected to significantly enhance supply chain performance, reduce operational costs, and improve the quality and safety of patient care in Thai hospitals.

Thailand's port logistics sector faces significant challenges in achieving secure and interoperable electronic data exchange across stakeholders. Although a standardized Port Community System (PCS) has been promoted, persistent issues-including fragmented digital platforms, security vulnerabilities, and institutional mistrust-impede Electronic Data Interchange (EDI) effectiveness. This study investigates the key barriers (BAR) to EDI implementation and examines how blockchain technology can be a foundational solution to overcome (OVB) them. A structured survey of 350 PCS service users and providers was administered. The data were examined using Confirmatory Factor Analysis (CFA) to check the different parts of the study and Canonical Correlation Analysis (CCA) to look at the relationships between seven identified barrier factors (like infrastructure, legal issues, and budget) and seven strategies to overcome them (such as technology acceptance, organizational readiness, and management support). The first canonical function showed a strong relationship, with a correlation coefficient of 0.652 (p < 0.01), meaning that certain organizational actions are closely linked to reducing important barriers. This research is the first empirical study in Thailand to apply multivariate statistical modeling to blockchain-enabled PCS adoption. It contributes methodological rigor through validated measurement models, structural analysis, and practical insight for policymakers, port administrators, and logistics managers. The findings suggest blockchain can enhance transparency, trust, and secure data integration in national logistics infrastructures.

Currently, the criteria for determining the optimal order quantity and the holding quantity to achieve maximum cost-effectiveness have not yet been applied in the inventory management of the factory in the case study. Therefore, to reduce the factory's costs and improve the efficiency of raw material management to meet demand, this research aims to study the improvement of inventory management in the factory by applying the theory of different order quantity methods and comparing them with the actual inventory costs from April 2022 to March 2023 using three heuristic methods: 1. Economic Order Quantity (EOQ) 2. The Periodic Order Quantity (POQ) method, as per the Silver-Meal approach, found that all three heuristic order size determination methods had lower inventory costs than the actual inventory costs. The method that provided the lowest cost for this case study was the POQ method, which reduced the factory's inventory costs by 79.59%. Additionally, the calculation mechanism of the POQ method is not complex and is easy to implement in practice.

Flow shop scheduling problem is a type of scheduling dealing with sequencing jobs on a set of machines in compliance with predetermined processing orders. Each production stage to be scheduled in typical flow shop scheduling contains only one machine. However, in automotive part industry, many parts are produced in sequential flow shop containing more than one machines in each production stage. This circumstance cannot apply the existing method of flow shop scheduling. The objective of this research is to schedule the production process of automotive parts. The feature production is hybrid flow shop which consists of two-stages. In each stage, there are several manufacturing machines and each machine can produce more than one product. Thus, production scheduling is a complex problem. This paper, therefore, develops mathematical model to solve the hybrid flow shop production scheduling under different constraints of each machine. The setup time and production time of each machine can be different for each part. The solution for the experimental data sets from an automotive part manufacturer reveals that the process time can be reduced by 34.29%.