Journal of Logistics and Supply Chain Operations (JLSCO)

Development and Improvement of Optimal Solution Finding Efficiency Using the Grey Wolf Optimization: In Case Study of Waste Collection Route Planning

2025-09-12T16:38:14+07:00

From the problem of inefficient waste collection routing operations due to starting from the farthest points, leading to increased total travel distance and fuel consumption. This research aims to develop and enhance the solution-finding efficiency of the Grey Wolf Optimizer (GWO) for waste collection vehicle routing, formulated as a Capacitated Vehicle Routing Problem (CVRP). The study employs waste collection data from three case study villages: Ban Pa Ha, Ban San Ton Kham, and Ban Daowadueng, comprising 52 nodes. The proposed improved GWO the two-group GWO (GWO2) and the three-group GWO (GWO3), are compared against the conventional routing method, the original GWO, Differential Evolution Optimization (DEO), and Particle Swarm Optimization (PSO). The experiments were conducted using the same number of waste collection rounds, set at four. The results showed that the traditional method produced a total distance of 72,200 m., while DEO and PSO yielded average distances of 62,970 and 62,550 m., respectively. The original GWO reduced the distance to 61,150 m., GWO2 further reduced it to 60,250 m., and GWO3 achieved the shortest distance of 58,350 m. or 19.183% compared to the traditional method. Moreover, GWO3 yielded the lowest standard deviation, indicating high consistency and stability in its results. Based on the findings, GWO3 demonstrates the fastest and most efficient convergence among all compared algorithms. Therefore, it is highly suitable for application in waste collection route planning.

Warehouse Layout Design Using FSN Analysis and Linear Programming to Enhance Warehouse Operation Efficiency: A Case Study of Aluminum Frying Pan Manufacturing

2026-01-14T21:37:53+07:00

This research aimed to design a warehouse layout to improve the warehouse management of an aluminum cookware manufacturing factory. The study applied the Fast-, Slow-, and Non-moving (FSN) Analysis technique to classify raw materials according to their usage frequency and developed three alternative warehouse layouts: a crosswise layout, a lengthwise layout, and a fishbone layout. Mathematical models were then developed and solved using Microsoft Excel Solver to determine the optimal locations of raw materials within each warehouse layout. The most suitable layout was selected based on the average warehouse performance improvement, evaluated using the percentage improvement in total travel distance, storage area, space utilization, and pallet capacity. The results showed that the fishbone layout provided the shortest total travel distance but reduced storage efficiency. Therefore, the crosswise warehouse layout was the most suitable layout for the case study warehouse because it achieved the highest average warehouse performance improvement across all performance indicators, with an overall improvement of 24.12%. In addition, it reduced the total material handling distance by 10.32%, increased the storage area by 25.38%, increased pallet capacity by 32.70%, and improved space utilization by 28.10%.

The influence of reverse logistics and product value recovery on sustainable industries through circular economy practices

2025-11-21T11:25:14+07:00

This research aimed to: 1) investigate the operational levels of reverse logistics, used-product value recovery, circular economy practices, and sustainable industry in Thailand's food manufacturing industry; and 2) examine the validity and reliability of the study constructs. This study employed a quantitative research approach. Data were collected from 400 personnel working in the food and beverage manufacturing industry in Thailand. The data were analyzed using descriptive statistics and Confirmatory Factor Analysis (CFA). The results revealed that: 1) the operational levels of reverse logistics, used-product value recovery, circular economy practices, and sustainable industry in Thailand's food manufacturing industry were at a high level in all aspects. This indicates that food manufacturers place importance on resource management, waste reduction, and the continuous implementation of sustainable operational practices. 2) The first-order Confirmatory Factor Analysis (CFA) model of the study constructs was consistent with the empirical data after model modification. By correlating 10 pairs of error terms, all goodness-of-fit indices satisfied the recommended evaluation criteria and were within acceptable levels. Therefore, food manufacturers should integrate reverse logistics, used-product value recovery, and circular economy practices into their operational processes to improve resource utilization, reduce waste, and support sustainable industrial development.

Digital Marketing Strategies Affecting Product Purchase Decisions: A Case Study of EdTech for Kids Co., Ltd.

2026-01-16T17:06:27+07:00

This research aimed to: (1) examine customers' opinions on digital marketing strategies and purchasing decisions in the case of Ed Tech for Kids Co., Ltd.; and (2) investigate the digital marketing strategies affecting purchasing decisions. This study employed a quantitative research approach. The population consisted of 2,110 customers, and the sample comprised 337 respondents selected using simple random sampling. The research instrument was a questionnaire. Data were analyzed using descriptive statistics and inferential statistics through multiple regression analysis. The results revealed that: (1) customers' opinions on digital marketing strategies were at a high level overall. The highest-rated dimension was social media, followed by content marketing, while email marketing received the lowest rating. Overall purchasing decisions toward the products of Ed Tech for Kids Co., Ltd. were also at a high level. The highest-rated item was comparing product quality, price, and product features before making a purchase decision. (2) The digital marketing strategies affecting purchasing decisions were Search Engine Optimization (SEO), followed by social media, websites, and influencer marketing. In contrast, content marketing and email marketing did not affect purchasing decisions. Therefore, the findings can be applied to strategic planning for Ed Tech for Kids Co., Ltd. and other companies operating in similar business contexts.

The Application of Value Stream Mapping to Improve Machine Spare Part’s Purchasing Process: A Case Study of Preventive Maintenance Booster Gas Compressor Company

2026-03-09T13:25:36+07:00

The objectives of this research were to (1) study the current purchasing process of machine maintenance spare parts in a case study company (2) analyze the causes and problems that lead to delays and inefficiencies in the purchasing process and (3) propose guidelines for improving the efficiency of the purchasing process for maintenance spare parts. The researcher applied Value Stream Mapping (VSM) to analyze the current workflow and identify non–value-added activities with a Fishbone Diagram to investigate the root causes of the problems. Subsequently, the ECRS principles (Eliminate, Combine, Rearrange, Simplify) were applied to improve the process by eliminating unnecessary steps, consolidating redundant tasks, rearranging workflow sequences, and simplifying work procedures. The results of this research revealed that the current purchasing process took more than 40 days to complete. However, with the proposed improvements such as implementing an electronic document system (E-MSR), establishing Price Agreements with regular suppliers and developing an Inventory Database with Power BI. The purchasing lead time could be reduced to only 7-10 days and for items under Price Agreements, the lead time could be shortened to merely 3-4 days. The application of Value Stream Mapping, Fishbone Diagram and ECRS principles significantly enhances efficiency by approximately 76–83%, eliminates waste and streamlines the spare parts purchasing process. Moreover, it supports preventive maintenance activities to be carried out on schedule, thereby improving the company’s competitiveness and long-term reliability.

Analyzing Tourist Activity Patterns Using Association Rule Mining: A Case Study of Chiang Rai Night Market

2026-04-01T12:25:58+07:00

This research aims to explore patterns of tourist activity participation and analyze the relationships between tourist activities at Chiang Rai night market in order to support effective urban tourism management. A descriptive quantitative research design was employed. Data were collected from 405 tourists visiting the Chiang Rai Night Market using a structured questionnaire covering demographic characteristics, travel behavior, and activity participation. Descriptive statistical analysis, including frequency distribution and percentages, was used to examine tourist characteristics and overall participation. In addition, activity relationship patterns were analyzed using association rule mining with the FP-Growth algorithm, utilizing minimum thresholds of 0.60 for support value and 0.95 for confidence value, implemented through RapidMiner. The results showed that tourist activities were highly interconnected, creating integrated experiences. Tourists who participate in sightseeing and cultural learning activities show a strong tendency to engage in food consumption activities. Moreover, food consumption appears to be the central activity in most activity patterns at the market. The most frequent pattern involves dining combined with watching local music performances, which subsequently leads to sightseeing experiences within the night market environment. The practical implications suggest that the spatial planning of night markets should position food-related activities as the core attraction, while integrating cultural performances and experiential activities along major circulation areas, and allocating infrastructure in alignment with identified activity clusters to enhance tourist experiences and support sustainable urban tourism management, contributing new evidence on activity-level behavioral patterns in informal tourism settings where such data-driven analysis remains limited.

Factors Influencing the Performance of the Organic Agricultural Supply Chain in Southern Thailand

2026-04-02T14:30:28+07:00

This study aims to (1) examine the factors influencing the performance of organic agricultural supply chains in Southern Thailand, and (2) test the causal relationship model among these factors using Structural Equation Modeling (SEM). The research adopts a quantitative approach, collecting data from 400 organic farmers. Data were analyzed using descriptive statistics and inferential statistics, including Confirmatory Factor Analysis (CFA) and Structural Equation Modeling. The results indicate that the model demonstrates a good fit with the empirical data (χ²/df = 1.067, GFI = 0.929, CFI = 0.996, RMSEA = 0.013, SRMR = 0.074). All latent constructs exhibit acceptable levels of validity and reliability. The findings reveal that production management, collaboration, organic certification, differentiation strategy, and customer responsiveness have significant positive effects on supply chain performance. In terms of effect size, production management has the strongest influence (β = 0.483), followed by collaboration (β = 0.399), organic certification (β = 0.307), differentiation strategy (β = 0.270), and customer responsiveness (β = 0.219), respectively. Collectively, these variables explain 73.60% of the variance in supply chain performance (R² = 0.736). The findings suggest that the performance of organic agricultural supply chains is driven by a systemic mechanism arising from the integration of multiple critical factors, which together enhance efficiency in terms of cost, quality, speed, and flexibility. This, in turn, strengthens competitiveness and long-term sustainability. Notably, the study identifies production management as the most influential factor, which differs from prior research, and highlights the expanded roles of collaboration and organic certification as strategic mechanisms within the supply chain.

Customer Segmentation Using a Hybrid Approach of K-Means Clustering and RFM: An Online Retail Case Study

2026-03-24T21:40:08+07:00

Traditional customer segmentation methods are inadequate for understanding complex consumer behaviors, as online shopping requires extensive data analysis. This research aims to formulate a hybrid customer segmentation methodology by amalgamating RFM scoring with K-Means clustering, employing a dataset from a genuine online retail transaction consisting of 406,573 records. The research methodology encompasses data cleansing and preprocessing, the computation of the Recency, Frequency, and Monetary (RFM) variables, and data normalization. We use RFM scoring to put customers into five groups and K-Means clustering with the Elbow method to find the best number of clusters to put customers into four groups. The findings demonstrate that RFM produces distinct and intelligible groups relevant to practical situations. K-Means, on the other hand, wants to find deeper structural differences between customers. This is especially helpful for finding customer groups that are worth a lot. Using both methods together makes customer segmentation more accurate and complete. Online stores can use this information to plan their marketing, keep customers, and increase the value of their business.

Sustainable Logistics Assessment of an Air Conditioner Manufacturing Company: ESG Approach

2026-06-08T11:13:48+07:00

This study aims to (1) assess the level of importance and performance of sustainability indicators within a logistics system based on the ESG (Environmental, Social, and Governance) framework, and (2) propose development guidelines for a sustainable logistics system in an air-conditioner manufacturing company as a case study. The Importance–Performance Analysis (IPA) technique was employed as a strategic gap analysis tool. This research adopts a quantitative approach, collecting data from employees involved in production and logistics functions. Mean scores of importance and performance were plotted on the IPA matrix to identify priority areas for improvement. The findings reveal that nine sustainability indicators are positioned in Quadrant II (High Importance – Low Performance). These indicators cover the environmental dimension, including energy efficiency, refrigerant management, waste management, and environmentally friendly packaging; the social dimension, including employee retention, fair compensation, and equal employment opportunity; and the governance dimension, particularly emergency and crisis management systems. The results indicate systemic gaps related to monitoring infrastructure, human capital management, and risk culture at the operational level. Based on these findings, the study proposes the “ESG-Driven Sustainable Logistics Development Framework,” integrating gap assessment into strategic formulation, implementation, and control processes. The framework synthesizes concepts from Sustainable Supply Chain Management, Resource-Based View, Enterprise Risk Management, and the Triple Bottom Line to enhance long-term sustainability performance in logistics systems.

Optimization of Customer Clustering for Goods Transportation: A Case Study of a Spice and Condiment Manufacturer

2026-05-26T09:04:57+07:00

This research aims to optimize customer clustering and route planning in the Customer Service and Logistics Department of a case-study spice and condiment manufacturer. The company currently outsources transportation services using a one-vehicle-per-customer delivery model, resulting in unnecessarily high transportation distances and costs. The study utilized domestic delivery data from 13 customers during January–March 2024. A Fishbone Diagram was used to analyze root causes, followed by K-Means Clustering to group geographically proximate customers, and the Clarke–Wright Saving Algorithm to determine optimal routes within each cluster. The results showed that customers could be grouped into 3 clusters, reducing delivery routes from 13 to 4. Total transportation distance decreased from 1,696.9 km to 662.60 km (a 60.96% reduction), and transportation costs decreased from THB 55,730.71 to THB 19,550.89 (a 64.92% reduction). These findings demonstrate that the combination of K-Means Clustering and the Saving Algorithm is an effective approach for reducing logistics costs and can be widely applied in food and logistics industries.