The integration of the global market makes the supply chain more complex and has great impacts on efficient supplier management strategies. The aim of this study is to present a systematic supplier management framework to integrate supplier selection and monitoring phases, which are not independent of each other. However, only a few previous studies have pointed out the differences between the two phases. The proposed methodology integrates a quantitative and qualitative approach, formulating multi-criteria decision making (MCDM) using the analytic hierarchy process (AHP) to evaluate the priorities of these criteria. This research explores the difference in the set of criteria for supplier selection and supplier monitoring. The results provide comprehensive insights into the criteria to help decision-makers, managers, and practitioners select appropriate suppliers and monitor suppliers’ performances in the automotive industry. Based on the result, it can be said that a company should integrate the supplier selection and monitoring process. Furthermore, the purchasing and manufacturing manager should continuously collaborate and synchronize the relative weights for the critical factors.

A cold chain for perishable fresh products aims to preserve the quality of the products under the control of a predefined temperature range. To satisfy the delivery conditions within appropriate time windows, the most critical operations in cold chain management is the transportation and distribution of fresh products. Due to rapid population growth and increasing demand for high-quality fresh foods, it becomes critical to develop advanced transportation and distribution networks for fresh products, particularly in urban areas. This research aims to design different scenarios based on mathematical models for fresh products transportation and distribution network in the Bangkok metropolitan area using Geographic Information system (GIS). The proposed methodology integrates location–allocation and vehicle routing problem analysis. The performance of all possible scenarios is evaluated and compared by considering the number of required distribution centers and trucks, total travel time, total travel distance, as well as fairness among drivers. The results of the scenario analysis highlight that the alternative scenarios show a better performance as compared to the current network. In addition, the administrator can make a different decision among several alternatives by considering different aspects, such as investment cost, operating cost, and balance of using available resources. Therefore, it may help a public officer to design the fresh products logistics network considering actual demand and traffic conditions in Bangkok.

Energy Storage Systems (ESSs) have recently been highlighted because of their many benefits such as load-shifting, frequency regulation, price arbitrage, renewables, and so on. Among those benefits, we aim at evaluating their economic value in frequency regulation application. However, unlike previous literature focusing on profits obtained from participating in the ancillary service market, our approach concentrates on the cost reduction from the perspective of a utility firm that has an obligation to pay energy fees to a power exchange. More specifically, we focus on the payments between the power exchange market and the utility firm as a major source of economic benefits. The evaluation is done by cost- benefit analysis (CBA) with a dataset of the Korean market while considering operational constraint costs as well as scheduled energy payments, and a simulation algorithm for the evaluation is provided. Our results show the potential for huge profits to be made by cost reduction. We believe that this research can provide a guideline for a utility firm considering investing in ESSs for frequency regulation application as a source of cost reduction.

Previous studies have proposed various frameworks and algorithms to optimize routes to reduce total transportation cost, which accounts for over 29.4% of overall logistics costs. However, it is very hard to find cases in which mathematical models or algorithms are applied to practical business environment cases which require reusable packaging, especially daily operating logistics services like convenience store support systems. Most previous studies have considered developing an optimal algorithm which can solve the mathematical problem within a practical amount of time while satisfying all constraints, such as the capacity of delivery and pick-up, and hard or soft time windows. For daily delivery and pick-up services, like those supporting several convenience stores, it is required to consider the unit transporting the container, as well as the demand, capacity of trucks, travel distance, and traffic congestion. In particular, reusable transport containers and trays should be regarded as important assets of logistics centers. However, if the mathematical model focuses on only satisfying constraints related to delivery and not considering the cost of trays, it is often to leave the empty trays on the pick-up points when there is not enough space in the track. In this study, we propose a mathematical model for optimizing delivery and pick-up plans by extending the general vehicle routing problem of simultaneous delivery and pick-up with time windows, while considering left-over cost. With numerical experiments it has been proved that the proposed model may reduce the total delivery cost. Also, it seems possible to apply the proposed approach to the various logistics businesses which require reusable transport containers like shipping containers, refrigerating containers, trays, and pallets.

The global supply chain is facing unprecedented challenges due to pandemics, geopolitical tensions, volatile commodity prices, and rapid changes in consumer demand. The Sales and Operations Planning (S&OP) process can be an important solution by integrating supply and demand with business strategy and operational planning. However, previous studies have developed single frameworks or maturity models without considering industry-specific factors, overlooking the structural and operational characteristics of different industries that can have a significant impact on the approach and success factors of S&OP. Therefore, this study identifies and compares the key success factors of S&OP in the manufacturing and retail industries using the Analytic Hierarchy Process (AHP) technique. The results show clear differences in the prioritization of success factors between two industries. In the manufacturing industry, strategic support and leadership were evaluated as the most critical factor, while in the retail industry, operational processes were emphasized. In addition, the relative importance was concentrated on the top-ranked factor in the manufacturing industry, but the relative importance was distributed among several factors in the retail industry. This study provides valuable insights into the critical success factors for S&OP implementation in different industries and offers an understanding of how industry-specific characteristics influence the effectiveness of S&OP strategies.