Improve Supply Chain Efficiency Research Articles

AI-Driven Transport and Distribution Optimization Model (TDOM) for the downstream petroleum sector: enhancing sme supply chains and sustainability

The downstream petroleum sector faces significant challenges in transport and distribution logistics, particularly for small and medium enterprises (SMEs) that rely on efficient supply chains to remain competitive. Inefficiencies such as fluctuating demand, fuel shortages, and route optimization hurdles contribute to increased operational costs and environmental impact. Addressing these persistent issues requires innovative solutions that balance efficiency, cost reduction, and sustainability. This study introduces the AI-Driven Transport and Distribution Optimization Model (TDOM), a transformative approach designed to revolutionize logistics in the downstream petroleum sector. TDOM leverages advanced machine learning algorithms and predictive analytics to optimize delivery routes, reduce fuel consumption, and ensure real-time adaptability to market demands. By integrating data from diverse sources, including traffic patterns, fuel station inventories, and weather conditions, TDOM provides SMEs with actionable insights for enhanced decision-making. The primary objectives of this research are threefold: to improve supply chain efficiency for SMEs in the downstream petroleum sector, to minimize environmental impacts through reduced carbon emissions, and to ensure cost-effective transport and distribution processes. The methodology includes developing a robust AI-driven model utilizing supervised and unsupervised learning techniques, followed by simulation testing on real-world logistics data. Key performance metrics such as delivery time, cost savings, and carbon footprint reduction will be analyzed to validate the model's efficacy. Anticipated outcomes of the TDOM implementation include a 20-30% reduction in transport costs, a significant decrease in fuel consumption, and improved supply chain resilience for SMEs. Furthermore, the model is expected to enhance sustainability by promoting eco-friendly logistics practices, thereby aligning with global environmental standards. By addressing inefficiencies in downstream petroleum logistics and supporting SMEs, the AI-driven TDOM represents a paradigm shift in how the industry approaches transport and distribution challenges. This innovative solution underscores the potential of artificial intelligence to transform traditional industries and foster a more sustainable future.

The impact of contract types on carbon-capped food supply chains: options versus wholesale

Given the rising awareness of environmental issues and the growing demand for eco-friendly food products, stakeholders within the food supply chain are investing more in green technologies to reduce carbon emissions. However, the efficacy of carbon caps in achieving emission reductions is challenged by unpredictable consumer demand, complicating both emission control and supply chain efficiency. This study investigates a scenario involving a food supply chain with an environmentally responsible supplier and a retailer facing demand fluctuations. We explore the incentives for suppliers to adopt option contracts and evaluates their impact on carbon emissions through a Stackelberg game model with strict carbon emission caps. Our analysis reveals that while higher green investment generally enhances supplier profitability, it may not always benefit retailers. Although option contracts can improve supply chain efficiency, they might also lead to increased carbon emissions and food waste. Notably, when green investments are decision-based, option contracts tend to encourage greater investments than wholesale contracts, helping to mitigate the financial burdens of high costs. This trend persists even in competitive markets. Our research suggests that option contracts can effectively balance the dual objectives of maintaining supply chain performance and complying with carbon emission limits.

Sustainable Digital Exports from China to Africa Based on Innovation and Predictive Analytic to Improve Supply Chain Efficiency

Sustainable Digital Exports from China to Africa Based on Innovation and Predictive Analytic to Improve Supply Chain Efficiency

Artificial Intelligence-Driven Inventory Management: Optimizing Stock Levels and Reducing Costs Through Advanced Machine Learning Techniques

This study investigates the implementation of artificial intelligence (AI) algorithms to enhance inventory management processes in small and medium-sized enterprises (SMEs) within the retail sector. Accurate inventory level determination is a critical factor in improving organizational performance. Inventory levels are subject to a wide range of influences, including seasonal fluctuations, promotional campaigns, and macroeconomic conditions, which introduce significant complexity and variability. Such complexities often render manual management approaches inefficient. This research focuses on addressing these challenges through AI-based methodologies, particularly by employing machine learning and data analytics techniques to optimize inventory control. The findings of the study contribute to the literature by highlighting the potential of AI-driven approaches in reducing inventory costs and improving supply chain efficiency.

A Study of the Multidimensional Impact of International Trade on Firm Productivity

In the context of economic globalization, international trade, as a key factor in promoting economic growth, greatly affects enterprise productivity. This paper synthesizes the research results of scholars and modern economic theories to explore the multidimensional impact of international trade on enterprise productivity. This paper firstly reviews the development of neoclassical international trade theory and new trade theory, deeply analyses the impact of market access policy, export market diversification, and technology spillovers on enterprise productivity, and explores the positive effect of international trade on enhancing enterprise productivity. It is found that the opening of market access can reduce enterprise costs, improve supply chain efficiency, and promote the diffusion of technology and knowledge. Economies of scale and technological spillovers can further reduce the cost structure of enterprises, stimulate technological innovation, and enhance their competitiveness. These findings provide important guidelines for policymaking and business strategies, emphasizing the importance of international trade in promoting sustainable economic development and enterprise competitiveness.

Consumer Behavior on Organic Food in Kathmandu Valley

The increasing consumer interest in organic food reflects a global shift toward healthier lifestyles and sustainable consumption. This study explores the factors influencing consumer behavior and the challenges faced by the organic food market. The objective is to identify the drivers of consumer preference for organic food and evaluate the barriers that limit its market expansion. A mixed-method approach was employed, combining an extensive review of relevant literature with the analysis of empirical data from surveys and market reports. Findings indicate that health consciousness, environmental awareness, and the perceived quality of organic food are the primary motivators for consumer preference, with a willingness to pay a premium price being common among consumers. However, challenges such as limited accessibility, high costs, lack of standard certification systems, and insufficient consumer knowledge about organic products undermine the market's growth potential. The study concludes that addressing these barriers through targeted interventions, such as improving supply chain efficiency, standardizing certifications, and increasing consumer awareness campaigns, could significantly enhance the adoption of organic food. The research contributes to the existing literature by offering a holistic view of consumer behavior in the organic food market, emphasizing the need for collaborative efforts among stakeholders to promote sustainable consumption.

The Role Of Cost Accounting In Enhancing Supply Chain Efficiency In International Business Operations

To improve supply chain efficiency in global corporate operations, this study investigates the function of cost accounting. The growing interconnectedness of global marketplaces has made efficient cost management a crucial component for businesses looking to stay ahead of the competition. The main goal of this study is to investigate how supply chain performance can be maximized through the use of cost accounting techniques to pinpoint major cost drivers and enhance decision-making. A thorough analysis of different cost accounting approaches and how they are used in supply chains, with an emphasis on Activity-Based Costing and technological integration, is part of the objective of the study. The primary issue discussed is the dearth of empirical knowledge about how cost accounting is implemented in various business contexts, especially in multinational settings and service-oriented industries. A qualitative research methodology is used, and the analysis is supported by secondary data from company reports, academic publications, and related literature. Supply chain efficiency is greatly increased by precise cost identification, strong cost management systems, and cooperative supplier relationships, according to key results. Limitations, however, include issues with cultural and legal disparities in global settings as well as a dearth of empirical data in some businesses. The consequences of the study are both theoretical and practical, providing firms looking to enhance their cost management plans and operational effectiveness with insightful information. By filling in current research gaps, this study advances our knowledge of the relationships between supply chain performance and cost accounting.

An examination of food insecurity among Canadian Aboriginal people

Background Food insecurity is a significant problem affecting many Indigenous people in Canada. This paper examines the prevalence, causes, and effects of food insecurity among Aboriginal populations. Methods Using a review of peer-reviewed articles, government reports, policy evaluations, and data from Statistics Canada, it highlights how factors such as remoteness, mental illness, traditional food consumption patterns, and socioeconomic conditions contribute to food insecurity. The paper also discusses existing policies, such as the Canada Child Benefit (CCB) and Nutrition North Canada (NNC), which are designed to address this issue. Additionally, it offers policy recommendations, including improving supply chain efficiency, monitoring subsidy programs, reevaluating eligibility issues associated with the NNC, providing support to improve access to existing government policies, and dismantling racist structures through initiatives such as Canada’s Anti-Racism Strategy 2024-2028. Results One important link made in the study is how educational attainment and income levels among Indigenous people are a reflection of structural injustices that lead to greater vulnerability to food insecurity, as well as the effectiveness of government policies designed to mitigate food insecurity. Conclusions The study emphasizes the need for a multi-pronged approach that combines modern strategies with traditional Indigenous values to build resilience against food insecurity. Addressing these factors can enhance the impact of policies targeted at effectively reducing vulnerability and improving food security among Indigenous communities in Canada.

Green design and information sharing in a horizontally competitive supply chain

This study explores the impact of information sharing on green supply chains with horizontal competition, addressing an important yet underexplored issue. Using a Stackelberg game framework, we examine green product design and information-sharing dynamics in a supply chain consisting of a common retailer and two competing manufacturers. The retailer decides whether to share green demand information with manufacturers, who then determine product greenness, while the retailer sets the retail price. The findings reveal that information sharing can have mixed effects on manufacturers’ profitability, depending on competitive dynamics and demand sensitivity to product greenness. Additionally, the interplay between information sharing and green design strategies emphasizes the importance of aligning product design decisions with optimal information-sharing practices. While information sharing consistently improves environmental performance in non-competitive settings, it can lead to negative environmental outcomes in competitive scenarios. Extending the analysis to cases where manufacturers control wholesale pricing and information is shared sequentially, the findings remain robust. This study highlights the dual role of information sharing as a driver of supply chain efficiency and a potential source of environmental inefficiencies in competitive markets. The results offer actionable insights for crafting green supply chain strategies that balance economic and environmental objectives.

MCDM Approaches for Supplier Selection in Sustainable Supply Chain Management

This article examines the significance of Sustainable Supply Chain Management (SSCM) within the Iranian automotive sector, emphasizing the assessment of suppliers’ sustainability performance in light of increasing global environmental concerns and regulatory demands. This study employs the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method to evaluate five principal suppliers—SAPCO, MEGA Motor, Crouse Company, Sazehgostar, and ITMCO—based on twelve criteria: green initiatives, quality, waste reduction, eco-design, reverse logistics, sustainable investments, financial costs, management commitment, strategic capabilities, customer satisfaction, innovation, and trust. Findings indicate that SAPCO and Sazehgostar demonstrate superior sustainability owing to their strong environmental policies, whereas MEGA Motor, Crouse Company, and ITMCO reveal aspects requiring enhancement. The results underscore the imperative of incorporating SSCM principles to improve supply chain efficiency and sustainability. Despite the apparent high costs of SSCM implementation, research indicates that the advantages include enhanced productivity and regulatory compliance. The research additionally advocates for more investigation into SSCM-related variables and the influence of upcoming technologies. Adopting SSCM enables automotive firms to comply with regulatory mandates and satisfy consumer demands, promoting a more sustainable and competitive sector.

Blockchain-Enabled Smart Contracts Revolutionizing Supply Chain Management: A Case Study

Inefficiencies, a lack of transparency, and trust issues are common challenges in today's globalized and complex supply chains. Smart contracts combined with the decentralized, immutable ledger system of blockchain technology have made it appear like a viable answer to these problems. The application of blockchain-enabled smart contracts to supply chain management is examined in this research paper's case study. This study explores the potential of blockchain technology to improve supply chain efficiency, traceability, and transparency by closely examining a real-world case. This case study explores the planning, execution, and results of incorporating smart contracts and blockchain technology into different phases of the supply chain, such as manufacturing, distribution, logistics, and procurement. The study also addresses the advantages, difficulties, and lessons discovered during the implementation of blockchain-enabled smart contracts, offering insightful information to enterprises thinking about implementing such systems. The results demonstrate how blockchain technology has the ability to completely transform supply chain management by increasing stakeholder collaboration, lowering costs, minimizing risks, and fostering trust. This case study adds to the expanding body of research on supply chain management applications of blockchain technology and provides useful advice for companies looking to use blockchain technology to improve their operations and gain a competitive edge in the fast-paced market of today

Optimizing Agility in the Pharmaceutical Supply Chain Using Digital Twins to Cope with the Ripple Effect

The pharmaceutical supply chain is a multilayered and complex structure designed to deliver medicines to customers in a timely manner while ensuring optimal quality and quantity of medicines. The COVID-19 outbreak exposed the vulnerability and uncertainty of the pharmaceutical supply chain, so managing the risks in the pharmaceutical supply chain has become particularly important. This study demonstrates that digital twin (DT) technology can improve pharmaceutical supply chain agility and reduce the ripple effect caused by disruptions. The ripple effect refers to the effect that a sudden disruption at one node in the supply chain has on causing a chain reaction in the rest of the supply chain. The most used risk management method today is the Enterprise Resource Planning (ERP) system, which allows for real-time data sharing but has limitations in predicting and modeling the entire supply chain and lacks the ability to make quick decisions and simulate the entire supply chain. DT technology creates a virtual model of the supply chain, which enables continuous communication and information exchange between real assets and the virtual model, offering a promising alternative to risk management for pharmaceutical supply chains. risk management by providing a promising alternative. This study utilizes the AnyLogistix platform to construct a DT model and demonstrates the effectiveness of DT technology in reducing ripple effects and improving supply chain agility using quantitative analysis. This paper focuses on analyzing the pharmaceutical supply chain for 75mg aspirin tablets in London. A supply chain that operates normally, a supply chain that introduces a disruptive event (closure of the logistics center due to an earthquake), and a supply chain that applies a proactive strategy (activation of an alternate logistics center) to mitigate risk are simulated separately. The positive impact of DT technology on the supply chain is evaluated by analyzing the methods of key performance indicators (KPIs) such as inventory level, order fulfillment rate, and delivery time. The experimental results show that DT technology enhances the responsiveness, flexibility, speed, and proactivity of the supply chain, which significantly improves the agility of the supply chain. Proactive and effective strategies also reduce the financial impact of disruptions and service levels are quickly recovered. In addition, the proactive strategy stabilized inventory levels and reduced delayed orders. The above key metrics confirm the hypothesis that DT technology can improve supply chain agility and effectively reduce the ripple effect. Despite the significant conclusions drawn in this paper, there are some limitations. Firstly, sensitivity analysis and t-test were not used in the study for secondary testing, which resulted in the lack of reliability of the validation results. In addition, the possible interaction between the ripple effect and the bullwhip effect was not discussed, leading to a confounding factor in the experimental results. The above issues can be considered in further research in the future. Finally, this paper explores the possibilities and uses of introducing machine learning techniques and considers the supply chain risk factors of globalization. In summary, this study validates the potential of digital twin technology in improving agility and coping with ripple effects in pharmaceutical supply chains. As research continues, DT technology is expected to improve supply chain efficiency and reduce supply chain risk while ensuring patient safety.

축제 산업생태계 네트워크 구조 분석 : 명예 문화관광축제를 중심으로

The demand for the industrialization of festivals has become increasingly prominent both academically and in policy discussions. Approaching festivals from an industrial perspective is regarded as a key strategy for improving supply chain efficiency and expanding the festival business ecosystem. The demand for the industrialization of festivals is increasingly strong, both academically and in policy discussions. This study aims to analyze the structural characteristics (connectivity and cohesion) and positional characteristics (influence) of South Korea's festival industry through social network analysis, grounded in industry ecosystem theory. The research utilizes 595 expenditure data points from four major cultural tourism festivals in Korea. The data were preprocessed in Excel, using pivot tables to identify relationships between attributes. After data cleaning, the dataset was divided into node and edge files, which were analyzed using Gephi 0.10, an open-source Java-based software optimized for large-scale network analysis. This multidimensional approach was critical in visualizing and optimizing the network data. The findings reveal that Korea’s festival business ecosystem displays characteristics of early-stage industrialization, characterized by low network density and limited two-way interactions between actors. While cohesive clusters within the network show diversity, the influence of major networks remains low, highlighting the need for focused efforts to strengthen them. In terms of positional characteristics, festival organizations, venue construction industries, and content development companies were identified as influential actors. Academically, this study contributes to the empirical identification of the network structure within the festival business ecosystem, providing foundational data for understanding its dynamic changes. Practically, it offers policy recommendations, including fostering key actors to enhance cohesion and expanding opportunities for inter-company collaboration to further develop the ecosystem.

Can cross-border e-commerce development improve supply chain efficiency? Empirical evidence from China

ABSTRACT Cross-border e-commerce (CBEC) is becoming critical for a strong supply chain. Using data from Chinese listed firms and a difference-in-difference (DID) model, this paper examines the impact of CBEC development on supply chain efficiency from the perspective of enterprise inventory. CBEC significantly reduces enterprises’ non-finished goods inventory and improves supply chain efficiency. The mechanism tests reveal three ways in which CBEC optimizes enterprises’ inventory and enhances supply chain efficiency: improving the performance of upstream-facing sourcing, increasing the efficiency of enterprises’ product transformation, and optimizing downstream-facing demand coordination. The heterogeneous results suggest that CBEC has a greater impact on the supply chain efficiency of firms with high operational volatility and capital intensity and low digitization levels. Further, the effect of CBEC on the reduction of non-finished goods inventory is more significant in regions with weak infrastructure development and location endowment. This study provides empirical evidence for the emerging development topic of new international trade patterns empowering industrial supply chains.

Improving efficiency and sustainability via supply chain optimization through CNNs and BiLSTM

Supply chain management is changing rapidly due to increasing complexity, uncertain demand, and the requirement for sustainable methods. Advanced technologies like Bidirectional Long Short-Term Memory networks (BiLSTM) and Convolutional Neural Networks (CNNs) can enhance supply chain processes. This paper proposes integrating CNNs and BiLSTM models to improve supply chain efficiency and sustainability. The proposed model employs CNNs to optimize resource allocation, uncover trends, and evaluate supply chain spatial linkages. Using BiLSTM models to capture temporal correlations allows accurate demand forecasting and proactive decision-making. Combining these models explains supply chain dynamics. CNNs and BiLSTM models' adaptive learning and real-time monitoring boost efficiency by responding quickly to changing situations. Predictive analytics optimizes inventory, lowers stock outs, and cuts lead times. Sustainability factors include transportation route optimization, carbon footprint minimization, and intelligent green-sourcing decision assistance. The proposed Hybrid Model achieved 94.65 % Specificity, 96.57 % Accuracy, 95.67 % Sensitivity and 0.85 % MCC. The result analysis demonstrates that the proposed model significantly improved the accuracy level. This research sheds light on supply chain difficulties from all sides. CNNs and BiLSTM models can boost operational efficiency and link supply chain practices with sustainability goals to produce a more sustainable global supply network.

The Role of Artificial Intelligence in Improving the Efficiency of the Company's Supply Chain

The development of technology, especially in the era of Industry 4.0, has significantly impacted various sectors, including supply chain management. Artificial Intelligence (AI), one of the leading technologies in Industry 4.0, has excellent potential to improve the efficiency and effectiveness of the supply chain. This study aims to identify the relationship between AI and the concept of supply chain management and analyze the practice of using AI to improve supply chain efficiency at PT. Pelita Media Nusantara. Using a descriptive-analytical method, this study examines various relevant literature and data. The results of the study indicate that the application of AI at PT. Pelita Media Nusantara has increased the supply chain's visibility, accuracy, and responsiveness. Implementing big data analytics, IoT sensors, and AI-based predictive systems allows companies to optimize procurement, production, and distribution processes. This case study also reveals that complex technology integration and the need for skilled human resources are the main challenges in implementing AI. This study provides important insights for academics and practitioners in developing more effective and efficient AI-based supply chain management strategies.

Optimizing the Agricultural Supply Chain Through the Internet + Model: Enhancing Efficiency with a Case Study of TikTok

This study uses the TikTok platform as a case study to explore the application of the Internet+ model in the agricultural supply chain and analyses the impact of Internet+ model on improving supply chain efficiency and reducing costs. With the development of science and technology, Internet+ has become an important trend in modern industry, which deeply integrates traditional agriculture with information technology and promotes the innovation and efficiency of supply chain management. This study not only discusses the practical application of the Internet + model in agriculture but also proposes to solve the bottlenecks in the traditional agricultural supply chain using logistics informatization, intelligent scheduling system, cold chain monitoring and intelligent warehouse management. Through case studies, the effectiveness of Internet technology in optimizing the supply chain, improving the circulation efficiency of agricultural products and reducing costs are verified, providing theoretical and case study support for promoting the development of agricultural modernization.

Integrating the Technology Acceptance Model (TAM) into the Supply Chain of India's Passenger Car Industry: Mitigating Technical Uncertainties

This study explores the application of the Technology Acceptance Model (TAM) in the context of the Indian passenger car industry's supply chain, with a specific focus on addressing technical uncertainties. The research aims to investigate how TAM can be leveraged to enhance the adoption and utilization of advanced technologies, thereby improving supply chain efficiency and resilience. The Indian passenger car industry is experiencing rapid growth, driven by increasing demand and technological advancements. However, technical uncertainties pose significant challenges to supply chain management. The Technology Acceptance Model (TAM) offers a valuable framework for understanding the factors influencing technology adoption. This study integrates TAM into the supply chain of the Indian passenger car industry to identify key drivers and barriers to technology acceptance.

Leveraging Blockchain for Transparent and Efficient Supply Chain Management: Business Implications and Case Studies

Blockchain technology has surfaced as a revolutionary response to important challenges in supply chain management, such as a scarcity of transparency, inefficiencies, and potential for fraud. This study looks into the implementation of blockchain in supply chain systems, focusing especially on improving transparency, operational efficiency, and the trust among stakeholders. The analysis of genuine case studies in retail, logistics, and manufacturing illustrates the ways blockchain facilitates better traceability, cuts transaction times, and increases security via immutable and valid records. This study uses a mixed-methodology by blending quantitative data analytics from secondary sources with qualitative observations from reports in the industry. Findings point to the fact that adopting blockchain technology has improved supply chain efficiency by 15-30% and reduced fraud by as much as 40%, together with a striking decrease in processing times throughout global logistics networks. The originality of this research is found in its complete examination of the economic and operational effects of blockchain, offering business leaders tactical insights on maximizing their supply chains through this technology. The paper culminates with suggestions for resolving implementation issues and tactics for extending blockchain solutions in complex international networks.

Optimizing the food supply chain through the integration of financial models and big data in procurement: A strategy for reducing food prices

Optimizing the food supply chain through the integration of financial models and big data analytics presents a transformative strategy for reducing food prices. In an increasingly complex global market, managing procurement effectively is crucial for cost control and price stabilization. Financial models, such as cost-benefit analysis, financial forecasting, and risk assessment, provide valuable insights into procurement decisions, helping organizations anticipate price fluctuations and manage expenses. When combined with big data analytics, these models enable more precise predictions and strategic planning. Big data analytics harnesses vast amounts of information from various sources, including market trends, supplier performance, and consumer behavior. By analyzing this data, organizations can identify patterns, optimize inventory levels, and forecast demand with greater accuracy. This data-driven approach enhances decision-making in procurement, allowing for better negotiation with suppliers, efficient inventory management, and the reduction of wastage. The integration of financial models with big data provides a holistic view of the supply chain, enabling companies to implement dynamic pricing strategies and adjust procurement practices based on real-time insights. This integration helps in minimizing operational costs, improving supply chain efficiency, and ultimately reducing food prices for consumers. For example, predictive analytics can identify potential supply disruptions and adjust procurement strategies proactively, while financial models can evaluate the cost-effectiveness of various sourcing options. Challenges such as data integration, quality, and privacy must be addressed to fully leverage these technologies. Moreover, investment in advanced analytics tools and skilled personnel is essential for successful implementation. In conclusion, the synergy between financial models and big data analytics offers a strategic approach to optimizing the food supply chain. By enhancing procurement practices and leveraging real-time insights, this strategy contributes to reducing food prices and improving overall supply chain efficiency, benefiting both businesses and consumers. Keywords: Food Supply Chain, Financial Models, Big Data Analytics, Procurement Optimization, Cost Control, Price Reduction, Inventory Management, Predictive Analytics, Supply Chain Efficiency.