Optimization Of Supply Chain Management Research Articles

Influence of Theory of Constraints on Optimizing Supply Chain Management in the USA

This study investigates the impact of applying the Thinking Process within the Theory of Constraints framework on supply chain management practices in the USA. Through a mixed-methods research approach, qualitative interviews and quantitative surveys were conducted to gather insights from supply chain professionals across various industries. Qualitative analysis revealed themes related to the identification of constraints within the supply chain, the application of Thinking Process methodologies, and the perceived impact on supply chain performance. Participants highlighted the effectiveness of the Thinking Process in problem-solving and decision-making, leading to improvements in efficiency and effectiveness within their supply chains. Quantitative analysis of survey data further supported these findings, indicating a high level of perceived effectiveness of Thinking Process techniques and a significant proportion of respondents identifying supply chain bottlenecks. Based on the study findings, recommendations are provided for organizations to enhance supply chain management practices, including the integration of Thinking Process training, continuous improvement initiatives, collaborative partnerships, and investment in technology. By implementing these recommendations, organizations can capitalize on the benefits of the Thinking Process to optimize supply chain performance and maintain competitiveness in the dynamic business landscape of the USA.

A Systematic Literature Review of ERP and RFID Implementation in Supply Chain Management

Abstract The integration of Enterprise Resource Planning (ERP) and Radio Frequency Identification (RFID) in Supply Chain Management (SCM) significantly enhances supply chain efficiency and effectiveness. ERP systems enable companies to optimize inventory and production management, while RFID technology facilitates precise tracking of products and raw materials, thereby improving production process efficiency. This research aims to systematically review the existing literature on implementing ERP and RFID technologies in SCM and their interconnected roles. Utilizing the Systematic Literature Review (SLR) methodology, the study highlights the crucial impact of ERP and RFID on supply chain performance efficiency. The findings indicate that ERP implementation in SCM offers numerous benefits, which is vital in boosting company efficiency, effectiveness, and overall performance. Additionally, integrating ERP and RFID with technologies like blockchain and cloud computing can further optimize supply chain management processes, providing even greater advantages.

Enterprise digital transformation and supply chain management

This paper empirically tests that enterprise digital transformation is an important means to strengthen supply chain management, providing a new research perspective and empirical evidence for improving the level of supply chain management. The empirical research results show that enterprise digital transformation can significantly improve the company's level of supply chain management. The results of heterogeneity analysis indicate that this effect is more pronounced in samples with a high level of marketization and high industry competition intensity. The conclusions of this study offer new insights for enterprises to use digital transformation to promote the optimization of supply chain management.

Digital Economy Enables Community Group-buying Fresh Agricultural Products Supply Chain Management Optimization

In the context of China's digital economy promoting social development, this paper based on the characteristics of fresh agricultural products perishable, difficult to preserve and easy to wear, with the help of digital economy enabling model, explores the practical problems of community group purchase of fresh agricultural products, and analyzes its supply chain management. The analysis results show that the optimization of supply chain management depends on the digital economy, which can effectively do a good job in factor allocation, model innovation and information transmission, and achieve win-win benefits of multiple entities by improving product quality control, forming core competitiveness, balancing supply and demand and controlling costs, and meeting the diversity requirements of consumers. This study can explain the potential problems of the current agricultural product supply chain to a certain extent, and also provide a reference for the digital economy enabling community group-buying fresh agricultural product supply chain management optimization.

Optimizing Supply Chain Management To Increase Efficiency And Profitability In The Manufacturing Industry

The purpose of this study is to explore how optimizing supply chain management can improve efficiency and profitability in the manufacturing industry. Using case studies of specific manufacturing companies, this study identifies the various factors that influence supply chain performance and how optimizing these processes can improve a company's overall performance. The research method used is qualitative and quantitative analysis through in-depth interviews, observations, and secondary data analysis. The results of the study show that vertical integration, the use of information technology, and increased communication between parts of the supply chain are key factors in optimizing supply chain management. This research provides valuable information for manufacturing companies seeking to improve their efficiency and profitability through better supply chain management.”
 

Optimizing Supply Chain in a Foundry Through Computer Simulation Using Flexsim – A Case Study

Abstract The article presents the optimization of supply chain management in a foundry using computer simulation with the FlexSim program. The authors analyze collaboration with external entities in the production process, focusing on the settlement of raw materials, transportation services, and storage costs. Special attention is given to the production plans of subcontractors integrated into the operational production schedule. Utilizing the 3D FlexSim environment, they showcase a simulation model optimized for minimizing the costs of production, transportation, and storage of alloying elements essential for iron casting production. The case study illustrates the effective use of computer simulation in refining supply chain management within the context of the foundry production process.

Repurposing existing skeletal spatial structure (SkS) system designs using the Field Information Modeling (FIM) framework for generative decision-support in future construction projects

Skeletal spatial structure (SkS) systems are modular systems which have shown promise to support mass customization, and sustainability in construction. SkS have been used extensively in the reconstruction efforts since World War II, particularly to build geometrically flexible and free-form structures. By employing advanced digital engineering and construction practices, the existing SkS designs may be repurposed to generate new optimal designs that satisfy current construction demands of contemporary societies. To this end, this study investigated the application of point cloud processing using the Field Information Modeling (FIM) framework for the digital documentation and generative redesign of existing SkS systems. Three new algorithms were proposed to (i) expand FIM to include generative decision-support; (ii) generate as-built building information modeling (BIM) for SkS; and (iii) modularize SkS designs with repeating patterns for optimal production and supply chain management. These algorithms incorporated a host of new AI-inspired methods, including support vector machine (SVM) for decision support; Bayesian optimization for neighborhood definition; Bayesian Gaussian mixture clustering for modularization; and Monte Carlo stochastic multi-criteria decision making (MCDM) for selection of the top Pareto front solutions obtained by the non-dominant sorting Genetic Algorithm (NSGA II). The algorithms were tested and validated on four real-world point cloud datasets to solve two generative modeling problems, namely, engineering design optimization and facility location optimization. It was observed that the proposed Bayesian neighborhood definition outperformed particle swarm and uniform sampling by 34% and 27%, respectively. The proposed SVM-based linear feature detection outperformed k-means and spectral clustering by 56% and 9%, respectively. Finally, the NSGA II algorithm combined with the stochastic MCDM produced diverse “top four” solutions based on project-specific criteria. The results indicate promise for future utilization of the framework to produce training datasets for generative adversarial networks that generate new designs based only on stakeholder requirements.

Analisis Manajemen Rantai Pasok pada UMKM Tahu Menggunakan Metode Economic Order Quantity (EOQ)

Tofu Micro, Small and Medium Enterprises (MSMEs) in Pandau Jaya Village are individual businesses operating in the food industry, especially tofu production. This research aims to analyze the supply chain performance of tofu MSMEs in Pandau Jaya Village and the impact of optimizing supply chain management on supply. This research method is descriptive qualitative using primary and secondary data. The research results show that the supply chain management of tofu products in Tofu MSMEs in Pandau Jaya Village is running well. All elements of the supply chain, including soybean agents, Tahu Pandau Jaya Village MSMEs, and consumers are running well. Supply chain management is considered effective, especially in transactions, policies and capital. Chain resources, such as people, technology and capital, operate optimally, supporting the smooth production of tofu. The chain business process is considered optimal, reflecting solid business relationships between supply chain members, based on good trust

A Review on the Healthcare Supply Chain Management in Transforming Hospital Services

The aim of this paper is to present a novel idea to revolutionize hospital services by integrating healthcare supply chain management principles. By using a web application which enables hospitals to establish a real-time connection and streamline patient transfers. Many sectors focus on supply chain management optimization and qualifications to construct a better network of suppliers for their end consumers. Especially in healthcare sector, not only for pharmaceutical products but also for hospital materials, supply chain management gets importance for service quality and patient satisfaction. Thus, there are many studies about supply chain management in healthcare to emphasize its importance. In this study it is aim to put forward more review of existing solution on healthcare management system and patient transfer. Keywords- Supply Chain Management, Healthcare Service Delivery, MERN stack web application, Real-time data processing, Patient Transfers, Predictive Analytics, Patient-Centric Care, Efficiency, Seamless Coordination, Fast Connectivity

Supply Chain Management Optimization Based on Bigdata Analysis

Contemporarily, in the context of big data, enterprises need to continuously optimize and innovate the supply chain, change the traditional supply chain management process pattern as well as optimization the whole supply processes. These actions are able to strengthen the actual effect level of supply chain process operation and achieve more benefits for the enterprise. Under the background of big data, more information management technologies are widely used, and supply chain management needs to clarify the deficiencies in its own operation, and reengineer and adjust the deficiencies. This paper will systematically discusses the implementation of the state-of-art bigdata analysis techniques in SCM. According to the analysis and evaluations of the measures, the usage of advanced big data technology will help increase the cultivation and establishment of management talent resources, so as to optimize the supply chain management process. Overall, these results shed light on guiding further exploration of supply chain management optimization.

Research on the Supply Chain Strategy of Freshippo in the Context of Epidemic

Since the end of 2019, the outbreak of COVID-19 has had a severe impact on the economy and peoples lives. In this paper, Freshippo was selected as the research object to discuss the problems and optimization strategies of its supply chain in the context of the epidemic, to provide reference for supply chain management of fresh food e-commerce enterprises. The research methods used in this paper are literature review and field research. The epidemic has brought historic opportunities to the whole fresh food e-commerce industry represented by Freshippo, but also put forward higher requirements for the efficiency and flexibility of the supply chain. The fresh food e-commerce industry should optimize supply chain management for improving response efficiency and coping with epidemic risk, improve emergency support capacity, ensure freshness of goods and reduce wastage, and share supply chain costs.

Machine-Learning-Based Spectroscopic Technique for Non-Destructive Estimation of Shelf Life and Quality of Fresh Fruits Packaged under Modified Atmospheres

The safety and quality of fresh fruits deserve the greatest attention, and are a priority for producers and consumers alike. Modern technologies are crucial to accurately estimating and predicting fresh fruits’ quality and shelf life, to optimize supply chain management. Modified atmosphere packaging (MAP) is an essential method that maintains quality parameters and increases the shelf life of fresh fruits by reducing their ripening rates. This study aimed to develop a cost-effective, non-destructive technique using tiny machine learning (TinyML) and a multispectral sensor to predict/estimate the quality parameters and shelf life of packaged fresh dates under the natural atmosphere (Control), vacuum-sealed bags (VSBs), and MAP with different gas combinations: 20% CO2 + N balance (MAP1), and 20% CO2 + 10% O2 + N balance (MAP2). The shelf life and quality parameters of the packaged fresh dates (pH, total soluble solids (TSSs), sugar content (SC), moisture content (MC), and tannin content (TC)) were evaluated under different storage temperatures and times. A multispectral sensor (AS7265x) was utilized to correlate the fruit quality parameters with spectrum analysis under the same storage conditions, to prepare the dataset to train the prediction models. The prediction models were trained in the Edge Impulse Platform, and deployed to an Arduino Nano 33 BLE sense microcontroller unit (MCU) for inference. The findings indicated that the vacuum and MAP1 efficiently increased the shelf life and maintained the quality parameters of the packaged fresh fruit to 43 ± 2.39 and 39 ± 3.34 days, respectively, at 5 °C. The optimal neural network consisted of the input layer with 20 nodes (the packaging type, storage temperature, and 18 channels of the spectral sensor data at 410 to 940 nm wavelengths), two hidden layers with 20 and 12 nodes, and an output layer with one node for the target quality parameter or shelf life. These optimal prediction models efficiently predicted the shelf life with R2 = 0.951, pH with R2 = 0.854, TSSs with R2 = 0.893, SC with R2 = 0.881, MC with R2 = 0.941, and TC with R2 = 0.909. The evaluation of the developed prediction models under each packaging condition indicated that these models serve as powerful tools for accurately predicting fruit quality parameters, and estimating the shelf life of fresh dates.

The impact of supply chain management on a company's operation and decision based on the multidimensional data analysis of upstream and downstream industry market states.

This study provides data analysis support for the entire enterprise procurement management process, thereby improving the management effectiveness of supply chain operations. It analyzes upstream and downstream industry market status data in the supply chain and various primary data in enterprise management activities. By utilizing the Delphi method to screen and verify multimode market status data indicators, which significantly impact upstream and downstream industries in multiple rounds, 28 types of market status data were selected for analysis. This analysis aimed to investigate the effect of supply chain management on operational decisions within the company. The data reduction method based on adaptive statistics was the most effective in revealing the market status and promoting efficient operation decision-making based on supply chain management. This study also suggests a brand-new technique for measuring supply chain performance based on the Levenberg-Marquardt Back Propagation (LMBP) algorithm, offering a more impartial manner of doing so. The performance evaluation results showed a maximum error level of less than 0.4% when paired with empirical analysis. The proposed optimization model provides strategic guidance for optimizing supply chain management and improving overall performance.

The Optimization of Global Organizational Communication for Enterprise Supply Organization Management by Using Big Data Text Mining

The purpose is to optimize supply chain management (SCM) programs in global organizations and promote international economic development. This paper first discusses the development of cloud technology and the international organization supply chain under big data, artificial intelligence, the internet of things, mobile internet and cloud computing (BIIMC). Then, the application design concept of cloud technology in the global organization supply chain is discussed. Finally, based on the current international organization supply chain foundation, the management optimization program is designed using cloud technology and evaluated. The results show that the program performs prominently in the SCM of Taobao and Amazon.

Improving Demand Management Performance using Knowledge Management Approach in an Oil and Gas Company

The oil and gas industry has become one of the most important sectors of the global economy, providing energy for transportation, industry and power generation. Indonesia is one of the world’s largest producers of oil and gas and the oil and gas industries have been operating in Indonesia for several decades, contributing to the country’s economic growth and development. PT. COPI, one of the entity in the oil and gas industry which focuses on downstream segment, plays an important role in this industry. However, to anticipate industrial competition, it requires the company to adapt and meet the minimum standards in order to remain competitive, through Continuous Improvement method which is implemented in departments, including Supply Chain Management (SCM) where, through the “Supply Chain Optimization (SCO)” approach, several obstacles are found, including Demand Management Process. This research uses the Knowledge Management (KM) approach to address the obstacles by conducting KM maturity analysis in PT. COPI’s Supply Chain division and perform analysis and evaluation of the current KM practices in order to provide recommendations for improvement. This research uses qualitative methodology to perform data collection, obtained through an interview with the head of Supply Chain Management division of PT. COPI, using Asian Productivity Organization (APO) KM assessment tool which comprises seven APO audit elements: Leadership, Process, People Technology, Knowledge Processes, Learning and Innovation, and KM Outcomes as the baseline for the interview questions. The research shows that KM maturity level of PT. COPI’s Supply Chain division sits at the fourth quadrant of the APO KM Maturity Model, which is refinement. Obstacles are identified from several audit elements of the APO KM assessment tool and marked as an area of improvement. This research provides recommendations for the identified area of improvements using the relevant KM method, which can be implemented by the company to increase their KM Maturity level, increase demand management performance and optimize Supply Chain Management process in the company.

Operation Strategies of Green Supply Chain Members with Short-Sighted and Far-Sighted Behavior: A Differential Game Theory Approach

The concept of “green supply chain” has gained increasing attention in the pursuit of sustainable development by enterprises globally, leading to the optimization of supply chain management. However, the behavior patterns of some members in green supply chains exhibit differences. Hence, analyzing the choices of different behavior patterns among green supply chain members is of practical significance in improving supply chain governance and achieving sustainable development. In this study, a differential game model is constructed using differential game theory to analyze the behavior patterns among members of a green supply chain. Analytical solutions under different models are obtained with the aid of Bellman continuous dynamic programming theory, and comparative analysis and numerical examples are carried out to explore the operation strategies of green supply chain members and the choice of short-sighted and far-sighted behavior patterns. Our findings indicate that suppliers playing a leading role in the green supply chain should exhibit far-sighted behavior as decision makers, while retailers should also display far-sighted behavior. This study adopts a dynamic perspective and is innovative and cutting-edge, providing targeted recommendations for enterprises and serving as a reference for the future development of green supply chain management.

Electricity supply chain hybrid long‐term demand forecasting approach based on deep learning—a case study of basic metals industry

AbstractDemand forecasting is a key parameter to achieve optimal supply chain management at different levels. The basic metals industry is one of the most energy‐intensive industries in the electricity supply chain. The impossibility of large‐scale energy storage, reservation constraints, and its high costs, limitations on electricity transmission lines capacity, real‐time response to high priority strategic demands, and variety of energy rates at different hours and seasons are issues that challenge the electricity supply chain. A hybrid approach is presented in this paper to improve the accuracy of long‐term demand forecasting in the electricity supply chain. The proposed approach uses wavelet decomposition and long short‐term memory (LSTM) neural networks to produce new predictors for demand forecasting in case of defective data. The data used in this study consists of the recorded hourly demand of Espidan Iron Stone (EIS) company and data of Mobarakeh Steel (MS) company in Isfahan Province. The understudy time series includes a lot of interruptions due to non‐production of the factory or power outages and spikes which cause uncertainty that makes the forecasting more difficult in comparison with continuous time series. Multiple machine learning models including decision trees (DTs), boosted and bagged trees (BGTs), support vector regression (SVR) models, extreme learning machines (ELMs), and a LSTM neural network model are employed to evaluate the effectiveness of the proposed approach in this paper. A comparison of the results obtained using the hybrid proposed approach and training machine learning (ML) methods shows a notable reduction in forecasting error.

Determining Distribution Center Locations to Optimize Food Supply Chain Integration (A Case Study of an Agribusiness Company in Indonesia)

PT Asagri Selaras Asia is an agribusiness company in Jakarta, Indonesia that provides food supply for entrepreneurs in the tourism sector, such as hotels and restaurants. In running their business, the company has a supply chain network that effectively integrates suppliers, manufacturers, warehouses, and stores. Supply chain management optimization is deemed necessary to maximize efficiency in every process carried out at PT Asagri Selaras Asia. One of the processes that can be optimized is the determination of facility location. The right facility location can minimize distance, delivery time, and operational costs and optimize the company's profit. Currently, the contract period for the distribution center (DC) used by PT Asagri Selaras Asia is about to expire therefore a decision is needed regarding the determination of the DC location for the next period, either extending the current DC contract or moving to a new location. This study aims to maximize the company's profit by comparing the profit obtained under existing and alternative DC, and the proposed DC from the experimental results using the Green Field Analysis (GFA) method. This study also aims to determine the feasibility of the three DC locations using the Network Optimization (NO) method with Anylogistix software. Based on the data analysis, the new DC is recommended to be located in the West Pejaten area, South Jakarta with latitude -6.2729150574 and longitude 106.8230798418 which will give an increase in profit of 80.53% compared to the existing DC.

ІНФОРМАЦІЙНА СИСТЕМА УПРАВЛІННЯ ДИСТРИБУЦІЙНИМИ ПРОЦЕСАМИ

Distribution processes play a crucial role in ensuring the efficient and optimal movement of goods from manufacturers to consumers. With intense competition and constant changes in demand, companies must develop and refine their distribution strategies, ensuring speed, accuracy, and efficiency in managing these processes. An appropriate information system and the software tools that form its foundation are essential instruments for optimizing and automating distribution processes. They not only assist in tracking the movement of goods but also provide analytical tools for making strategic managerial decisions. Furthermore, the systematic implementation and utilization of information technologies in managing distribution processes, along with the creation of a flexible and efficient mechanism facilitating the interaction of key distribution elements – supply, storage, transportation, and sales – represent one of the key directions in the post-war economic reform in Ukraine. The aim of the article is to investigate the information system for distribution management, focusing on its software components, in terms of implementing key functions and elements of distribution to enhance the efficiency of implementing a distribution strategy. Methodology. The theoretical and methodological basis of the research includes works by domestic and foreign scholars on defining the specifics and practical implementation of distribution management software. The information base comprises scientific, economic, and reference literature, works of leading domestic and foreign researchers, methodological materials, information portals, and periodicals of Ukraine. A combination of methods and approaches is employed: system analysis for studying theoretical foundations, forming an information system for distribution management, theoretical generalization for defining the essence of such a system, and analysis and synthesis methods for analyzing and systematizing distribution management software related to order management, sales, customer relations, warehousing, and transportation. Results. The author formulates the definition of an information system for managing distribution processes as a set of software tools and technologies specifically designed for the automation and optimization of supply chain management, ensuring the coordination and implementation of economic interests of participants in this process. The essence of distribution management software closely linked to the implementation of distribution strategies is revealed. The research investigates distribution management software components, grouped according to the implementation of key distribution functions: order management, sales and customer relations management; warehouse management; transportation management. Scientific novelty lies in a deeper understanding of the essence of the information system for managing distribution processes, which, unlike existing ones, clearly outlines the need for aligning the interests of participants involved in the supply of goods and services for the effective realization of the functional content of the distribution strategy. Practical significance is determined by increasing the rationale for the comprehensive application of distribution management software across all components: order management, sales and customer relations management; warehouse management; transportation management.

Efficiency and optimization of supply chain management in the indian food processing industry: A case study of Jammu district

Efficiency and optimization of supply chain management in the indian food processing industry: A case study of Jammu district