Hospitals play a crucial role in delivering high-quality patient care, and the healthcare industry is experiencing significant changes driven by the integration of advanced technology. This integration aims to enhance patient care, streamline operations, and minimize errors. Therefore, this study seeks to propose a comprehensive decision-making framework for selecting appropriate technologies, focusing on the medication dispensing process within hospital settings. Through an extensive literature review and expert input, seven critical criteria for technology adoption in hospitals have been identified. Additionally, the study considers the Pick-to-Light system, Carousel Storage System, and Robotic Dispensing System as alternative technologies for integrating medication dispensing processes, with a focus on enhancing labeling, picking, and packing activities. The evaluation involves participants from five tertiary care hospitals in Thailand. The Analytic Hierarchy Process (AHP) is used to assess the relative importance of criteria, followed by the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) for technology selection. The analysis results, including criteria weights and technology selection, are presented separately for each hospital case and overall. The top-three criteria identified as having high priority weights across hospitals are budget and funding support (BF) (0.329), staff technological proficiency levels (TP) (0.147), and leadership support (LS) (0.128). These findings can guide healthcare organizations in making informed decisions during technology implementation, particularly in improving medication dispensing processes. The study identifies the “Pick-to-Light” technology as a suitable solution across all hospital cases, as it can streamline dispensing processes, improve accuracy, and enhance overall operational efficiency. This research, along with the proposed decision-making framework, contributes valuable insights for healthcare institutions seeking to effectively select and integrate technologies, ultimately leading to improved outcomes in hospital medication services.

Globalization and multinational commerce have increased the dynamism and complexity of supply networks, thereby increasing their susceptibility to disruptions along interconnected supply chains. This study aims to tackle the significant concern of supplier selection disruptions in the Thai agri-food industry as a response to the aforementioned challenges. A novel supplier evaluation system, PROMETHEE II, is suggested; it combines the Fuzzy Analytical Hierarchy Process (FAHP) with inferential statistical techniques. This investigation commences with the identification of critical indicators of risk in the sustainable supply chain via three phases of analysis and 315 surveys of management teams. Exploratory factor analysis (EFA) is utilized to ascertain six supply risk criteria and twenty-three sub-criteria. Following this, the parameters are prioritized by FAHP, whereas four prospective suppliers for an agricultural firm are assessed by PROMETHEE II. By integrating optimization techniques into sensitivity analysis, this hybrid approach improves supplier selection criteria by identifying dependable solutions that are customized to risk scenarios and business objectives. The iterative strategy enhances the resilience of the agri-food supply chain by enabling well-informed decision-making amidst evolving market dynamics and chain risks. In addition, this research helps agricultural and other sectors by providing a systematic approach to selecting low-risk suppliers and delineating critical supply chain risk factors. By bridging complexity and facilitating informed decision-making in supplier selection processes, the results of this study fill a significant void in the academic literature concerning sustainable supply chain risk management.

Background: The freight forwarding industry is undergoing digital transformation through the implementation of online platforms designed to enhance operational efficiency and transparency. Despite these benefits, the adoption of these platforms has been slower than anticipated due to customer concerns and industry-specific challenges. Methods: This study investigates the factors influencing the intention to adopt and the actual use of online platforms for freight forwarding services among business customers in Thailand. A modified Unified Theory for Acceptance and Use of Technology (UTAUT) model, incorporating perceived risk, serves as the theoretical framework. Survey data were collected from 400 respondents in managerial or higher-level positions involved in freight shipping within Thai firms and analyzed using a structural equation model (SEM). Results: The analysis reveals that performance expectancy, effort expectancy, social influence, and facilitating conditions positively influence adoption intention, while perceived risk negatively impacts it. Firm size moderates the effect of social influence, with a stronger impact observed in larger enterprises. Conclusions: The findings offer practical insights for Thai freight forwarders, suggesting strategies to improve customer acceptance and encourage the adoption of online platforms. Addressing the identified factors could lead to improved efficiency and greater integration of digital technologies in the logistics industry.

Nowadays, several hospitals are still suffering from inefficient processes. Redesigning business processes is an effective way for improvement. However, business process mapping and analysis in healthcare environment has become a complex task. Besides, most healthcare professionals often resist change. To deal with these obstacles, a design thinking framework and design patterns useful for modelling and simulation are proposed in this article. Initially, current problems focusing on hospital pharmacy management are identified through empirical investigation. According to the problems, the suitable solutions are determined based on the related literature. Afterwards, the holistic view of problems and their solutions with different aspects can be structured as the design thinking framework. For each solution, the design patterns are represented through various attributes including pattern name, purpose, description, modelling structure using a business process modelling notation. Likewise, the attributes facilitated to simulation modelling are also combined such as control variables and performance measures. Here, based on the crucial problem that mostly concerned in any hospital case studies, the design pattern related to the problem of inefficient inventory management policy is illustrated and verified. Through a design thinking framework and design patterns, business users or process modellers can rapidly understand the context of problems and conduct a successful simulation study. It can be a valuable tool to redesign healthcare systems.

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.