In response to the requirement to address the global climate crisis in urban areas caused by the logistics sector, an increasing number of governments around the world have begun aggressive strategic actions to encourage manufacturers and consumers to adopt electric vehicle (EV) technology. One of the most beneficial aspects of driving an EV is that it reduces pollution while also reducing the use of fossil fuels, as well as improving public health by improving local air quality. Nevertheless, the level of EV adoption differs significantly across markets and geographies. EV adoption barriers slow the overall rate of electric mobility. This study ranks a list of obstacles and sub-hindrances to the adoption of electric vehicles in Thailand using the Fuzzy Analytical Hierarchy Process (FAHP), a Multi-Criteria Decision Making (MCDM) technique. The results showed that infrastructure policy barrier (A), which had the highest weight of 0.6058, was the biggest barrier to EV adoption, followed by technological barrier (B) with a weight of 0.2657, and then by market barrier with a weight of 0.1285. Insufficient charging infrastructure network (A3), lack of proper government support/incentives and collaboration (A1), insufficient electric power supply (A2), high capital cost (C3), and EV charging time (B3) were key sub-barriers to EV adoption in Thailand. Decision Making Systems (DMS) have additionally been created to assist executives in making decisions about the aforementioned barriers. The DMS is based on the concept of computer-aided decision making in that it allows for direct user interaction, analysis, and the ability to change circumstances and the decision-making process based on the executives’ own experience and abilities. Thus, the findings of this study aid in the formulation of market strategies for relevant stakeholders and shed light on potential policy responses.

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.

This study proposed a decision support system (DSS) for optimizing transportation routing and disposal hub location for Thailand’s local administrative organizations’ hazardous waste management. The first step is to choose the origin and destination, as well as to collect data for each route. Each route’s decision criteria were based on traffic quality and risk measures from the perspective of users. An analytic hierarchy process (AHP) was used to compute effective weights for decision criteria. To achieve optimal routing, AHP weights were combined with a zero-one goal programming technique. The DSS operates systematically and successfully, taking into account both transportation risk and socioeconomic factors, and provides appropriate routes. Overall, this waste management system may provide useful information to decision-makers for prioritizing disposal site alternatives and implementing feasible waste management actions. The zero-one location design was then used to determine the best hub location. Based on two scenarios of six and fifteen CoG hubs, the results revealed that the best model for managing a case company’s hazardous waste was to establish 15 hubs scattered throughout the country for collecting points and then transporting them to the waste disposal plant in Phitsanulok province. Locating the hub near sink points would reduce transportation costs and backhaul issues, reducing the environmental impact of GHG emissions and increasing system efficiency.

Supply chain consists of all integrated activities that bring products to markets in the righttime, at the right place while creating value added in order to satisfy customers. Theperformance of all activities in the supply chain requires the cooperation and coordination ofcomplex processes across all members in organizations. Successful supply chainmanagement relies on great leaders, who can communicate with their supply chain membersand encourage feedback systems to improve the workplace environment. The leaders havethe principal goal of making use of modern, scientific, technical and innovative methods inthe implementation of tasks and activities. Therefore, the aim of this study was to investigatekey factors affecting visionary leadership in the context of Thai manufacturing industry.Data were collected using survey questionnaires administered to supply chain managers ineach manufacturing company located in industrial estates across the country. A total of 500valid questionnaires were received and analyzed using structural equation modeling. Theresults revealed that communication skill had the highest impact on visionary leadership,followed by organization climate and creativity. The study outcome could be to the benefitof managers, who strive to generate visionary leadership and realize how important it is toincorporate communication skills as one of the most powerful instruments to improveleadership

Locations of storage area and hub are important factor of transport cost. To determine locations, increased transport cost against variable numbers of storage areas and hubs must be considered at the same time. This research shows how to manage locations, via the zero-one integer programming model, which considers total transport costs as incurred by every activity in the transport process. An Enhanced Elevator Kinematics Optimization method was developed from the Aungkulanon, Luangpaiboon, and Montemanni’s Elevator Kinematics Optimization algorithm to determine the optimal locations of storage areas and hubs. It was concluded that the company should have one storage area located in Samut Prakan Province and 12 hubs around the country, compared to the existing 11 hubs, which would result in 3.1% cost reduction. Also, from a further study on storage area fixed cost reduction, it would be possible to increase storage area size leading to a 4.1% cost reduction.