This study provides a comprehensive analysis of supply chain management practices based on survey responses from a sample of enterprises. Through descriptive statistics, hypothesis testing, predictive modeling, advanced analytics techniques such as classification, clustering, and association rule mining, the research offers valuable insights into key areas of collaboration, quality management, technology adoption, agility, risk management, and customer responsiveness within supply chains. The findings highlight the importance of strategic integration, proactive problem-solving, customer-centric practices, and agility in meeting changing demands. The study also identifies distinct profiles of practice adoption and reveals intricate relationships between different supply chain practices. Overall, the research contributes to a deeper understanding of supply chain dynamics and offers actionable insights for improving operational performance and strategic decision-making.

Each company will make every effort to improve productivity, efficiency, fast, easy service, and continue to create various new innovations to stay ahead and survive in the market. In addition to productivity and efficiency that need to be improved, companies must also understand and know what consumers need. This study aims to determine and explain the effect of information sharing and innovation in Supply Chain Management partially on company performance. Data was collected by distributing questionnaires. The unit of research analysis is the tourism sector SMEs in Bogor, West Java, Indonesia. Respondents who were used as samples were 210 people. The analytical method used in testing the hypothesis is Structural Equation Modeling (SEM) using Partial Least Square (PLS). This research has succeeded in proving the existence of mediation of information sharing and significant innovation in the relationship between supply chain management and performance.

The COVID-19 outbreak has illustrated the wide range of issues that supply networks confront when they are subjected to major interruptions. The supply networks for vaccines are no exception. To get out of this pandemic, it's critical to identify and address problems with the COVID-19 vaccine supply chain (VSC). This work identifies 13 challenges and prioritizes those. The findings provide stakeholders and government policymakers with realistic advice for developing a better VSC. This paper proposes a methodology based on fuzzy analytical hierarchy process (Fuzzy-AHP) with the use of triangular fuzzy numbers for prioritizing VSC barriers. It has been found that the impact of poor health worker training facilities becomes maximum with the highest weightage. Moreover, the managerial implication of the results is also provided, which will be useful for VSC sectors to take suitable decisions to overcome these obstacles.

To acquire the competitive advantages in order to survive in the global business scenario, modern companies are now facing the problems of selecting key supply chain strategies. Strategy selection becomes difficult as the number of alternatives and conflicting criteria increases. Multi criteria decision making (MCDM) methodologies help the supply chain managers take a lead in a complex industrial set-up. The present investigation applies fuzzy MCDM technique entailing multi-objective optimization on the basis of ratio analysis (MOORA) in selection of alternatives in a supply chain. The MOORA method is utilized to three suitable numerical examples for the selection of supply chain strategies (warehouse location selection and vendor/supplier selection). The results obtained by using current approach almost match with those of previous research works published in various open journals. The empirical study has demonstrated the simplicity and applicability of this method as a strategic decision making tool in a supply chain.

The increase competition and decline economy has increased the relevant importance of having reliable supply chain. The primary objective of many supply chain problems is to reduce the cost of services and, at the same time, to increase the quality of services. In this paper, we present a multi-level supply chain network by considering multi products, single resource and deterministic cost and demand. The proposed model of this paper is formulated as a mixed integer programming and we present two metaheuristics namely MOPSO and NSGA-II to solve the resulted problems. The performance of the proposed models of this paper has been examined using some randomly generated numbers and the results are discussed. The preliminary results indicate that while MOPSO is able to generate more Pareto solutions in relatively less amount of time, NSGA-II is capable of providing better quality results.

The aim of this paper is to present mathematical models optimizing all materials flows in supply chain. In this research a fuzzy multi-objective nonlinear mixed- integer programming model with piecewise linear membership function is applied to design a multi echelon supply chain network (SCN) by considering total transportation costs and capacities of all echelons with fuzzy objectives. The model that is proposed in this study has 4 fuzzy functions. The first function is minimizing the total transportation costs between all echelons (suppliers, factories, distribution centers (DCs) and customers). The second one is minimizing holding and ordering cost on DCs. The third objective is minimizing the unnecessary and unused capacity of factories and DCs via decreasing variance of transported amounts between echelons. The forth is minimizing the number of total vehicles that ship the materials and products along with SCN. For solving such a problem, as nodes increases in SCN, the traditional method does not have ability to solve large scale problem. So, we applied a Meta heuristic method called Genetic Algorithm. The numerical example is real world applied and compared the results with each other demonstrate the feasibility of applying the proposed model to given problem, and also its advantages are discussed.

Employment of supply chain management could impact the entire functional and practical aspects of industry and services positively and change the manager’s attitude toward the implementation of supply chain management systems from a cost-oriented attitude to an enabling attitude towards better activities with other chain members. Yet still many organizations do not have the required vision within the identification of strategic appropriate measure in implementation, evaluation and usage of proper benchmarks to attain an integrated chain. Constantly supply chain management activity can be introduced as a tied up with strategic level of organization. Hence, it is a necessary with an overall strategic approach to identify the criteria, which enable us to include the whole process level of chain, financial matter of chain activities, visible and invisible properties, human activity and turn all these activities into an integrated approach to resolve supply chain difficulties. In this paper, we present an integrated balanced score card combined with DEMATEL technique to prioritize different alternatives for supply chain implementation.

Radio-frequency identification (RFID) is the wireless non-contact implementation of radio-frequency electromagnetic fields to transfer necessary data to identify and to track tags attached to objects, automatically. RFID has been successfully used for supply chain management (SCM) problems in order to reduce errors associated with inventory. This paper uses RFID technique in five different scenarios of labeling, errors in stolen parts, waste, wrong replacement and delivery. The study uses simulation technique based on Enterprise Dynamics to analyze three parts in an auto industry. The study considers different criteria including inventory error estimation, profitability, profitability, customer satisfaction and SCM performance. The preliminary results indicate that the proposed method is capable of optimizing most objectives, significantly.