Supply Chain Optimization Research Articles

Optimizing Company Performance Through Effective Logistics Management

In the contemporary business environment, effective supply chain and logistics management are pivotal for minimizing costs, enhancing competitiveness, and optimizing operational efficiency. This study investigates the effects of key logistics practices—transportation, warehousing, packaging, inventory, and information management—on organizational performance. Leveraging a ‘Supply Chain Optimization’ dataset from Kaggle, the research analyzes how cost reductions across these logistics activities influence overall expenses and performance metrics. By categorizing Supply Chain Management (SCM) performance metrics into strategic, tactical, and operational levels, the study reveals how decisions at each level, from location strategy to daily inventory management, interact to impact overall effectiveness. The findings emphasize the critical role of optimizing logistics functions to boost business efficiency, customer satisfaction, and competitive advantage. The study provides actionable insights for logistics managers aiming to enhance performance through improved management practices in various Industries and organizations.

Optimal supply chain performance: risk aversion to green innovation

PurposeAlthough many countries are focusing on the management of construction and demolition waste (CDW) resource utilization, the effect of risk aversion of the green innovation-led enterprise on the performance of the CDW resource utilization supply chain is unclear when considering different green innovation contexts (green innovation led by the building materials remanufacturer or by the construction waste recycler). This study aims to investigate how the level of risk aversion of the green innovation-led enterprise affects CDW resource utilization under different green innovation contexts based on contingency theory.Design/methodology/approachUsing Stackelberg game theory, this study establishes a decision model consisting of a building materials remanufacturer, construction waste recycler and CDW production unit and investigates how the level of risk aversion of the green innovation-led enterprise under different green innovation contexts influences the performance level of the supply chain.FindingsThe conclusions are as follows. (1) For the green innovation-led enterprise, the risk-averse behaviour is always detrimental to his own profits. (2) For the follower, the profits of the construction waste recycler are negatively correlated with the level of risk aversion of the green innovation-led enterprise in the case of a small green innovation investment coefficient. If the green innovation investment coefficient is high, the opposite result is obtained. (3) When the green innovation investment coefficient is low, the total supply chain profits decrease as the level of risk aversion of the green innovation-led enterprise increases. When the green innovation investment coefficient is high, total supply chain profit shows an inverted U-shaped trend with respect to the degree of risk aversion of the green innovation-led enterprise.Originality/value(1) This study is the first to construct a green innovation context led by different enterprises in the CDW resource utilization supply chain, which provides a new perspective on green management and operation. (2) This study is the first to explore the operation mechanism of the CDW resource utilization supply chain based on contingency theory, which provides new evidence from the CDW resource utilization supply chain to prove contingency theory. At the same time, this study examines the interactive effects of the green innovation cost coefficient and the degree of risk aversion of green innovation-led enterprises on the performance of supply chain members, expanding the contingency theory research on contingencies affecting enterprise performance. (3) This study will guide members of the CDW resource utilization supply chain to rationally face risks and achieve optimal supply chain performance.

Optimizing Vendor-Managed Inventory in Multi-Tier Distribution Systems

This paper addresses the concept of the vendor-managed inventory (VMI) problem, focusing on the replenishment policy and the vehicle routing problem (VRP) model. These components are integrated to tackle a three-echelon distribution issue comprising a single plant, multiple depots, and multiple retailers, with the primary objective of minimizing transportation and inventory costs within this complex distribution network. A three-phase methodology is proposed to optimize the entire supply chain, from the plant to the final retailer, and its performance is evaluated through computational experiments. This research is motivated by a real-life supply network, highlighting its practical relevance and applicability. To extend the capabilities of existing methods for solving the combined inventory and routing problem, an insertion heuristic is incorporated to enhance vehicle utilization, thereby reducing total costs. Computational results demonstrate the effectiveness of the improved algorithm, indicating that it is sufficiently robust for practical application. Significant cost savings can be achieved with the proposed approach, making it a valuable contribution to the field of supply chain optimization.

Data-Driven decision making in agriculture and business: The role of advanced analytics

Advanced analytics has revolutionized decision-making processes in agriculture and business by harnessing data-driven insights to optimize operations, manage risks, and drive innovation. This paper explores the transformative role of advanced analytics in these sectors, highlighting key benefits, challenges, and future directions. In agriculture, advanced analytics enables precision farming by integrating AI, IoT sensors, and satellite imagery. Predictive models forecast crop yields, optimize irrigation, and enhance soil management practices, improving productivity and sustainability. Similarly, advanced analytics supports strategic decision-making in business by analyzing consumer behavior, predicting market trends, and optimizing supply chain operations. However, adopting advanced analytics faces challenges such as data quality, technical expertise, cost constraints, and ethical considerations. Addressing these challenges requires investments in data infrastructure, talent development, and regulatory compliance to ensure secure and ethical data usage. Emerging trends include AI-driven automation, blockchain for supply chain transparency, and augmented analytics for democratizing data access. Recommendations for stakeholders include investing in data capabilities, fostering collaborative partnerships, and promoting a culture of data-driven decision making. In conclusion, advanced analytics offers profound opportunities to enhance efficiency, inform decision making, and drive sustainable growth in agriculture and business. Embracing these technologies is essential for organizations seeking to thrive in a data-driven economy. Keywords: Advanced Analytics, Precision Farming, Predictive Analytics, Data-driven Decision Making, Business Intelligence.

Predictive analytics on artificial intelligence in supply chain optimization

AI-powered predictive analytics is among the most important ways of optimizing supply chains. This paper on AI-powered predictive analytics will address improving the competitiveness and effectiveness of supply chain operations. Nevertheless, current methods are not always scalable or adaptable to complex supply networks and changing market environments. Therefore, this paper posits that Supply Chain Optimization using Artificial Intelligence (SCO-AI) systems can help with these concerns. SCO-AI employs real-time data analysis and advanced machine learning algorithms which results to reduced response time, enhanced logistics route optimization, improved demand planning as well as real-time inventory control. Thus, the idea herein suggested fits smoothly into existing supply chain frameworks for data-driven decisions that make companies remain agile in ever-changing market dynamics. SCO-AI implementation has seen significant improvements in inventory turnover rate, rates of on-time delivery as well as overall supply chain costs. In this period of high business turbulence, such kind of research builds up the robustness of a given supply chain while at the same time minimizing operational risks by means of simulations and case studies

Optimization of straw supply chain considering carbon emissions, supply uncertainty and facility disruption risk

Optimization of straw supply chain considering carbon emissions, supply uncertainty and facility disruption risk

The Role of Supply Chains as an Input to Improve Institutional Performance: Prospective Study

This article examines the pivotal position of delivery chains in enhancing overall institutional performance, highlighting their importance as essential for organizational success. It begins by defining delivery chains and institutional overall performance, establishing a framework for understanding their interconnections. Essential additives of robust supply chains, such as logistics, procurement, and facts flow, are explored to demonstrate how they contribute to institutional efficiency and effectiveness. The look at analyzes the effect of strong deliver chains on institutional overall performance, demonstrating that optimized supply chain management can result in value reduction, progressed provider delivery, and stronger aggressive gain. Strategies for reinforcing delivery chains within institutions are mentioned, imparting actionable insights for leaders searching to leverage delivery chain abilities to force overall performance upgrades. Finally, the thing identifies future developments in delivery chain control, such as digital transformation, sustainability, and collaboration, suggesting that establishments must adapt to these changes to stay aggressive in a rapidly evolving landscape. By integrating those views, this study aims to offer treasured steering for institutional leaders trying to harness the energy of supply chains to enhance overall performance and obtain strategic goals. Keywords : Supply chain, Input institutional Improvement, Quality improvement

Supply Chain Optimization: Machine Learning Applications in Inventory Management for E-Commerce

This study delves into the potential impact of machine learning (ML) on supply chain optimization and inventory management for e-commerce. Our primary focus is analyzing the accuracy of demand forecasting, optimizing inventory levels, and evaluating the impact of real-time decision-making on supply chain efficiency. Using a secondary data-based review methodology, this study explores the implementation of advanced predictive analytics, real-time data processing, autonomous operations, and personalized customer experiences in prominent e-commerce companies like Amazon, Walmart, and Alibaba. Our findings show impressive advancements in demand forecasting accuracy, dynamic inventory management, and operational responsiveness. These improvements have led to cost savings and increased customer satisfaction. Nevertheless, some drawbacks exist, such as the significant expenses associated with implementation, concerns about data privacy, and the potential for overfitting the model. Policy implications call for solid data protection regulations, financial assistance for smaller businesses, and ethical guidelines for AI to promote fair and responsible machine learning applications. By tackling these obstacles, companies can maximize the potential of ML technologies to enhance efficiency, promote sustainability, and gain a competitive edge in the ever-changing world of e-commerce.

Optimizing supply chain management: strategic business models and solutions using SAP S/4HANA

In today's highly competitive and rapidly evolving business environment, optimizing supply chain management (SCM) has become a critical imperative for organizations aiming to enhance operational efficiency, reduce costs, and improve customer satisfaction. SAP S/4HANA, a next-generation enterprise resource planning (ERP) suite, offers a comprehensive and integrated platform for streamlining and modernizing SCM processes. This review explores the strategic business models and solutions facilitated by SAP S/4HANA, focusing on its impact on various facets of supply chain operations, including procurement, manufacturing, logistics, and inventory management. The implementation of SAP S/4HANA enables organizations to leverage advanced analytics, real-time data processing, and intelligent automation to optimize supply chain functions. By providing a unified view of supply chain activities, SAP S/4HANA enhances visibility, enabling proactive decision-making and fostering a more responsive and agile supply chain. Key features such as predictive analytics and machine learning algorithms facilitate demand forecasting and inventory optimization, reducing the risk of stockouts and overstock situations. Furthermore, SAP S/4HANA supports the integration of Internet of Things (IoT) technologies, allowing for real-time monitoring of assets and operations. This connectivity enhances the ability to manage logistics and transportation more efficiently, reducing lead times and improving delivery performance. The platform's ability to harmonize data from various sources also aids in supplier collaboration, ensuring better alignment with suppliers' capabilities and enhancing the overall supply chain resilience. Strategic business models enabled by SAP S/4HANA include just-in-time (JIT) production, which minimizes inventory costs, and demand-driven replenishment, which aligns production schedules with actual customer demand. Additionally, the adoption of digital twin technology within SAP S/4HANA allows for virtual modeling and simulation of supply chain scenarios, facilitating strategic planning and risk management. In conclusion, SAP S/4HANA provides a robust foundation for optimizing supply chain management through its advanced technological capabilities and integrated approach. Organizations adopting SAP S/4HANA can achieve significant improvements in supply chain efficiency, agility, and resilience, positioning themselves for sustained competitive advantage in the dynamic global market. This exploration highlights the transformative potential of SAP S/4HANA in redefining supply chain strategies and operational excellence.

Multi-objective Optimization Model for Medical Waste Supply Chain Management Without Landfill Tax During COVID-19 In Indonesia

The COVID-19 pandemic has brought various problems into our daily life, and one of them is an increase in the volume of medical waste, which poses a risk to the environment and health.An evaluation of how optimal the supply chain management of medical waste as a whole can assist in the development of medical waste management and policy making. In this paper, the study on observing and reformulating the existing medical waste supply chain optimization model using Mixed Integer Linear Programming (MILP) is discussed. This study is done by modifying the objective function and the constraints with considering elimination of the landfill tax variable. The resulted model is also a multi-objective optimization problem. To this end, the Lexicographic Method is employed to obtained the optimal solution. In this model, the most prioritizes that is considered in the environmental condition to the medical waste supply chain. A numerical experiment is done in this paper which it results suggests two things, first, the cost of medical waste processing is much greater than its revenue, and second, the waste processing through incineration is not favorable. Thus, it can be concluded that this study provides a modified medical waste optimization model and decisions in determining the combination of the amount and method for each subtype of medical waste to be processed by maximizing profit and the amount of waste sent to the landfill.

Optimizing Distribution Center Network Design for a Cosmetic Manufacturer: A Case Study on Lightening Series Package Product

Ensuring customer satisfaction while maintaining optimal cost efficiency is paramount in designing a successful supply chain structure. This study explores the vital role of service quality and delivery efficiency in achieving customer satisfaction and a superior overall experience through a cost-effective supply chain structure. Furthermore, the strategic positioning of Distribution Centers (DC) and the geographical distribution of consumers serve as additional determinants in the quest to devise the most effective DC scheme that meets consumer expectations. Four simulation scenarios, each with measurable parameters including response time, lead time, and operational costs, were evaluated to identify the most cost-efficient and responsive solution. These parameters encompassed the performance metrics of the supply chain structure model, namely response time and lead time, in conjunction with the total operational cost of each scenario. Our findings reveal that scenario 1 emerges as the most cost-effective with the lowest distribution costs and shortest customer lead time. This scenario consistently maintains the lowest distribution costs from the first to the seventh year. Despite a surge in forecasted demand in the 7th year, resulting in an increase in total distribution costs to Rp. 3,392,793,440, scenario 1 still prevails with overall vehicle costs decreasing and the smallest percentage cost at 17.50%, thus yielding the most economical outcome compared to alternative scenarios. Scenario 4 offers a similar outcome to scenario 1 but incurs higher daily vehicle operating costs. The cost-effectiveness demonstrated by scenario 4 further highlights the significance of strategic DC placement in achieving optimal supply chain outcomes.

Considering economic-environmental dimension in the integrated strategic and tactical optimization of Ethiopia's bioethanol supply chain coupled with operational planning

The global supply chain has garnered significant attention in recent years due to its intricate nature and operational challenges. Companies face the crucial task of integrating various components to maintain competitiveness and enhance overall performance across the supply chain. Achieving this goal necessitates vertical integration to ensure coherence and coordination among decision-making processes at different temporal and spatial scales, bridging the gap between strategic, tactical, and operational decisions. This article proposes a mathematical model that integrates the three previous decisions within the supply chain context, aiming to maintain coherence while considering economic and environmental objectives (few papers cover this challenge). By addressing the complexities of multi-scale modeling and optimization, the model seeks to provide a holistic view of supply chain operations. The model focuses on the transport decisions at the operational level, resulting in the coupling between a strategic and tactical model and a vehicle routing problem. Nonetheless, achieving this integration effectively from a modeling and resolution point of view leads to computational and methodological challenges due to the multi-scale, multi-site, and multi-objective nature of the problem, requiring an innovative approach for effective model resolution. The contributions of this article pave the way towards more sustainable and efficient supply chain operations. The model capabilities are exemplified through the real case study of the bioethanol supply chain in Ethiopia. For the location of the different firms of the supply chain, the country is divided into 58 zones (second level administrative subdivision in Ethiopia) to have a precise mapping of the area. The result of the case study shows that in adding the operational level decisions to the strategic-tactical ones has not only changed the topology of the network configurations but has greatly affected the economic and environmental criteria due to more precise estimation of all activities. The results put also in highlight that there is a compromise to find between both previous objectives by identifying Pareto alternative scenarios.

Enhancing digital transformation: exploring the role of supply chain diversification and dynamic capabilities in Chinese companies

PurposeDrawing upon the extended resource-based view (ERBV), this research aims to examine the effects of supply chain diversification (SCD) on enterprise digital transformation (DT), focusing on the external perspective of the supply chain.Design/methodology/approachLeveraging panel data from Chinese A-share listed companies from 2015 to 2022, this research employs multiple regression analysis to empirically examine the relationship between SCD and enterprise DT.FindingsThe results of analysis indicate that: (1) SCD positively influences enterprise DT, a relationship that persists even after rigorous endogenous and serial robustness testing. (2) SCD fosters enterprise DT by bolstering its absorption, innovation, and adaptive capabilities. (3) Executive risk preferences and Pilot Policies positively mediate the effects of SCD on enterprise DT.Originality/valueThis research provides novel empirical insights into the underlying mechanism linking SCD and enterprise DT. The findings offer valuable guidance for enterprises seeking to optimize supply chain management and embrace DT, while also informing policymakers on strategies to facilitate SCD and DT enhancement among enterprises.

Production Planning Optimization in a Two-Echelon Multi-Product Supply Chain with Discrete Delivery and Storage at Manufacturer’s Warehouse

In today’s competitive world, customers expect their demands to be met at the shortest possible time, while manufacturers aspire to deliver the orders within a convenient time and at a minimum cost. Thus, manufacturers are compelled to seek ways of lowering the costs of their services in order to satisfy customers and survive the competition in their respective industries. This research paper investigates a multi-product problem in a two-echelon supply chain consisting of a single manufacturer and several retailers. The main objective of this research is to develop and present a multi-product optimization model in which retailers receive their orders through discrete delivery and surplus manufactured goods are stored in the manufacturer’s warehouse. The objective function of the mathematical model in the economic dimension includes the minimization of the total supply chain costs and the maximization of profit. The retailers in this model place new orders when their inventory level drops to zero, and the manufacturer responds to the retailers’ orders at the same time as it begins processing each product. After delivering the last set of orders, the manufacturer stores surplus items in its warehouse in case the retailers place new orders. This optimization problem is modeled using mixed integer nonlinear programming, while numerical scenarios are coded using the MATLAB software which helps estimate the total cost within a short time. Finally, a sensitivity analysis is performed to determine the effects of a number of factors on the total cost, including problem parameters, demand and production rates, the production quantity, and the number of times the manufacturing machines are operated at each production cycle.

A novel hydrogen supply chain optimization model – Case study of Texas and Louisiana

The increasing political momentum advocating for decarbonization efforts has led many governments around the world to unveil national hydrogen strategies. Hydrogen is viewed as a potential enabler of deep decarbonization, notably in hard-to-abate sectors such as the industry. A multi-modal, hourly resolved, linear programming model was developed to assess the infrastructure requirements of a low-carbon supply chain over a large region. It optimizes the deployment of infrastructure from 2025 up to 2050 by assessing four years: 2025, 2030, 2040, and 2050, and is location agnostic. The considered infrastructure encompasses several technologies for production, transmission, and storage.Model results illustrate supply chain requirements in Texas and Louisiana. Edge cases considering 100% electrolytic production were analyzed. Results show that by 2050, with an assumed industrial demand of 276 TWh/year, Texas and Louisiana would require 62 GW of electrolyzers, 102 GW of onshore wind, and 32 GW of solar panels. The resulting levelized cost of hydrogen totaled $5.6–6.3/kgH2 in 2025, decreasing to $3.2–3.5/kgH2 in 2050. Most of the electricity production occurs in Northwest Texas thanks to high capacity factors for both renewable technologies. Hydrogen is produced locally and transmitted through pipelines to demand centers around the Gulf Coast, instead of electricity being transmitted for electrolytic production co-located with demand. Large-scale hydrogen storage is highly beneficial in the system to provide buffer between varying electrolytic hydrogen production and constant industrial demand requirements. In a system without low-cost storage, liquid and compressed tanks are deployed, and there is a significant renewable capacity overbuild to ensure greater electrolyzer capacity factors, resulting in higher electricity curtailment.A system under carbon constraint sees the deployment of natural gas-derived hydrogen production. Lax carbon constraint target result in an important reliance on this production method due to its low cost, while stricter targets enforce a great share of electrolytic production.

Production of methanol from renewable sources in Mexico: Supply chain optimization

Methanol is one of the most important chemical compounds, as it is the basis for producing a wide variety of derivatives. Its production through fossil sources such as natural gas in countries like Mexico is not entirely viable due to the fluctuations in the availability of this resource. The use of renewable sources to produce methanol represents an interesting area of opportunity to reduce the dependence on a single raw material. This work proposes the design of the methanol supply chain in Mexico using residual materials, finding a solution with the best compromise between profit, social impact, and CO2 emissions. The solution with the best compromise corresponds to a profit of 7,334,100 USD/y, a marginalization index of 2592.536 and CO2 emissions of -0.021 Mt/y. This solution has 8 different types of raw materials, 18 process plants and the use of three processing technologies: gasification, anaerobic digestion, and catalysis from CO2.

Reshaping the Industrial Policy Framework of Turkiye: Integrating Exports at the Core of AI-Driven Development

This study explores the evolving landscape of industrial policy amid deglobalization, protectionism, and the technological revolution driven by artificial intelligence (AI). The shift in industrial policies across the US and EU, driven by geopolitical tensions, climate change, and economic rivalry, has resulted in increased protectionist measures. The US, once an advocate of free-market principles, now implements policies like the CHIPS Act to enhance domestic manufacturing and reduce reliance on China. For developing countries like Turkiye, integrating exports into industrial policy is crucial. Despite Turkiye's significant export growth, challenges such as low R&D spending and a small share of high-tech exports persist. The article emphasizes the need for a dedicated institution, the Industrial Policy Institutes of Turkey (TEPE), to focus on innovation, competitiveness, and sustainable development. TEPE would encompass various sub-institutions dedicated to high technology, energy, defense, and digitalization. AI can play a strategic role in analyzing sectors, optimizing supply chains, and enhancing export competitiveness. By leveraging AI, Turkiye can transform its industrial policies and position itself more robustly in the global market, ensuring economic resilience and growth.

Collaborative system model construction and simulation optimization of e-commerce enterprise supply chain

ABSTRACT With the rise and rapid development of the e-commerce industry, the pressure borne by the supply chain of e-commerce enterprises is increasing, which greatly increases the complexity and uncertainty of inventory management in the e-commerce supply chain. The study integrates system dynamics and collaborative supply chain inventory management to construct a new model. The study first uses the concept of system dynamics to optimize the supply chain inventory management system; then the collaborative supply chain is used to construct a system model by combining the optimized management system. The results show that the collaborative supply chain inventory management model constructed by the study can better accelerate the product turnover rate, improve the overall operational efficiency and market competitiveness of the supply chain, and can provide new support for the development of e-commerce enterprises.

Risk Identification and Optimization of Clean Energy Supply Chain under the "Dual Carbon" Goal

The rational development and utilization of renewable clean energy resources such as hydropower, wind energy, and biomass energy is in line with the trajectory of energy development, and plays a major role in establishing a sustainable energy system and promoting national economic development and environmental protection.The race to net zero will redefine global energy security, with secure, resilient and sustainable clean energy supply chains at the heart of the global energy transition.This paper firstly screens and analyzes the risk factors of clean energy supply chain in the context of China's "dual carbon" goals, and preliminarily constructs a risk assessment index system with reference to the SCOR model.The fuzzy comprehensive evaluation method and entropy value method were used to evaluate the impact of risk factors in China's clean energy supply chain, establish a risk assessment model, and then analyze the risk factors of China's clean energy supply chain.Finally, based on the previous research, some suggestions are put forward for the risk management and optimization of China's energy supply chain.

How to Improve Sustainability Performance Through Human Resource Management and green Supply Chain Management Strategies in the industrial Manufacturing Sector

This research investigates the impact of Green Human Resource and Green Supply Chain strategies on improving sustainable performance in the manufacturing industry. The main focus of this research is to understand how the implementation of these green strategies can improve a company's operational efficiency and productivity. This approach includes human resource management with a focus on environmental sustainability and supply chain optimization to minimize environmental impacts. The research methodology involved quantitative data analysis measuring the impact of green strategies on sustainable performance using random sampling. The research technique was carried out by means of observation, distributing questionnaires and processing data using SEM-AMOS. The sample used was 310 respondents in manufacturing companies located in the Bekasi area. The results of this study are expected to provide valuable insights for the manufacturing industry in facing sustainability challenges and increasing competitiveness through environmentally friendly business practices