Improve Supply Chain Performance Research Articles

SCM Meets Big Data: Transforming Supply Chain Management

This paper examines the transformative impact of integrating Big Data technologies into Supply Chain Management (SCM). It highlights how advanced analytics, machine learning, and cloud computing are reshaping traditional supply chain processes by enabling real-time insights, predictive capabilities, and enhanced decision-making. The study focuses on three critical areas: Demand forecasting, inventory optimization, and logistics planning. In demand forecasting, Big Data leverages historical and real-time data to improve accuracy, helping organizations better align supply and demand. Inventory optimization benefits from predictive analytics that reduce overstocking and understocking risks, ensuring cost efficiency and improved service levels. Logistics planning is enhanced through real-time data processing, enabling dynamic route optimization and reduced delivery times. The paper includes detailed case studies from various industries to demonstrate how Big Data has driven measurable improvements in supply chain performance, including reduced costs, increased agility, and enhanced customer satisfaction. Challenges in implementing Big Data solutions, such as data integration and security concerns, are also discussed, along with strategies to overcome them. This research underscores the pivotal role of Big Data in modernizing SCM and creating a competitive edge for businesses.

The fusion of Lean Manufacturing with Industry 4.0 technologies towards a new pillar for improving supply chain performance, the case of the automotive industry in Morocco

In response to the COVID-19 pandemic, a Moroccan automotive glass company embarked on a strategic overhaul to improve its supply chain performance and resilience. Faced with excessive working capital tied up in inventory (Working Capital = $200M), the company chose to integrate advanced artificial intelligence (AI) technologies with Lean Manufacturing principles, including Just-In-Time (JIT) production and workload balancing. The goal was to lower inventory levels while maintaining a high service rate to meet customer demands. AI tools have played a crucial role in predicting quality defects and forecasting equipment availability, facilitating streamlined operations and cost reduction. This initiative also aims to enhance the supply chain's ability to withstand and adapt to future disruptions

The mediating effect of innovative behavior on supply chain performance of water supply companies

This research investigates the mediating effect of Innovative Behavior on the relationship between Emotional Intelligence, Intellectual Intelligence, and supply chain performance. Data were collected from 198 employees of water supply companies across five major cities in Central Java, using purposive sampling and analyzed through structural equation modeling. The findings indicate that both Emotional Intelligence and Intellectual Intelligence positively and significantly impact supply chain performance. Additionally, Innovative Behavior not only positively influences supply chain performance but also mediates the relationship between Emotional and Intellectual Intelligence with supply chain performance. The study suggests that enhancing Emotional and Intellectual Intelligence through Innovative Behavior can significantly improve supply chain performance in water supply companies. Organizations are recommended to incorporate Emotional Intelligence into selection and training programs, develop initiatives to boost Emotional and Intellectual Intelligence through training in areas such as emotional management, interpersonal communication, problem-solving, critical thinking, and creativity, and create a supportive work environment that encourages innovative behavior. Implementing these strategies can lead to more efficient operations, better resource management, and increased customer satisfaction, thereby enhancing overall supply chain performance.

Effects of material master data management on supply chain performance at FLSmidth: the moderating role of PiLog external service provider

In the dynamic landscape of industrial operations, the effective management of material master data within the supply chain is paramount for organizational success. This study investigates the challenges and solutions associated with Material Master Data Management (MMDM) at FLSmidth (FLS), a leading supplier in the cement and mining sectors. FLS operates within a complex and diverse supply chain environment, serving global clientele across various sectors. The study aims to understand the impact of MMDM implementation on supply chain performance at FLS, specifically focusing on performance metrics such as inventory turnover, order fulfillment accuracy, and lead time reduction. The moderating role of PiLog, an External Service Provider (ESP), is explored in this context. Employing a mono-qualitative approach, the study delves into MMDM implementation through in-depth interviews with 18 FLS employees and 4 PiLog customers. Thematic analysis reveals that well-implemented MMDM systems enhance visibility, planning, and inventory management, leading to improvements in supply chain performance. Challenges in stakeholder persuasion and infrastructure access are identified as common implementation hurdles. PiLog emerges as a crucial moderator, providing structured frameworks, emphasizing standardization, and addressing governance challenges. The findings underscore the importance of strategic partnerships between organizations and ESPs in successfully implementing and sustaining MMDM initiatives. Recommendations include strategies for overcoming infrastructure challenges, leveraging ESP expertise, and aligning with best practices in standardization and governance. This study contributes to the academic discourse on MMDM implementation and offers practical insights for organizations seeking to enhance supply chain performance through meticulous MMDM practices.

Enhancing business efficiency through effective inventory management: a systematic literature review

Efficient inventory management is crucial for various industries' operational performance and financial success. As market demands evolve and technological advancements progress, the complexity and importance of inventory management have increased, making it essential for maintaining a competitive advantage. This study aims to systematically review existing literature to understand the impact of inventory management practices on operational performance across different industries and contexts. Employing the PRISMA framework for systematic literature review, the Scopus database was the primary source for selecting relevant articles. The initial search yielded 791 results, refined through rigorous inclusion and exclusion criteria, ultimately resulting in the thorough analysis of 12 critical articles. This review's findings highlight that advanced inventory management systems, such as deep reinforcement learning and real-time analytics, significantly enhance operational efficiency. These systems facilitate more effective information sharing and enable tailored inventory strategies, which improve supply chain performance. This improvement is particularly evident in sectors such as healthcare and manufacturing, where precise inventory control is critical. Furthermore, financial metrics like days inventory outstanding correlate positively with firm performance, underscoring the strategic importance of inventory management in achieving financial success. The study highlights the importance of advanced inventory management practices in enhancing operational efficiency and financial performance despite high setup costs, technological complexity, and data quality issues. This study synthesizes research on inventory management practices, offering insights for businesses to enhance operational performance and emphasizes the need for continuous innovation for competitiveness.

Boosting Supply Chain Effectiveness with Lean Six Sigma

This article explores the integration of supply chain management (SCM) and Lean Six Sigma (LSS), which can create a powerful systematic methodology for achieving manufacturing excellence and improving customer satisfaction. SCM in industry refers to the systematic coordination and integration of all activities involved in the procurement, production, and delivery of goods and services, from the sources of raw materials to the end customer. It ensures the right products are delivered in the right quantities, at the right time, and at the lowest possible cost. In this context, enhancing SCM has become a fundamental necessity for organizations aiming to improve operational efficiency, effectiveness, market share, and customer satisfaction. In this effort, LSS is a systematic methodology for improving process efficiency and effectiveness through continuous improvement by eliminating waste, minimizing defects, reducing lead time, and improving customer satisfaction. This research presented a comprehensive literature review focusing on applying the LSS methodology in SCM, as no comprehensive review is available. This study highlights the integration of LSS practices into SCM and provides a roadmap for future studies that focus on exploring the applications of LSS and its impacts on SCM. In conclusion, by aligning LSS principles with SCM strategies, organizations can achieve operational excellence, reduce costs, and improve overall supply chain efficiency while effectively meeting customer demands. Furthermore, this study aims to propose a framework that can be used to explain how the LSS methodology can be applied in different SCM fields. This comprehensive analysis aims to guide academics and professionals in improving supply chain performance using LSS tools.

Harnessing Technology for Enhanced Efficiency in Supply Chain Operations

The international supply chain environment is quickly changing due to innovations that are reinventing not just the method companies operate. Artificial Intelligence (AI), Internet of Things (IoT), Blockchain, And Automation are some technologies that change the supply chains with more efficiency, better decisions, and live views. The technologies simplify processes like inventory management, demand forecasting, order fulfillment, and logistics optimization, which can keep businesses ahead of the competition in a fast-paced global marketplace. In this paper, we look at how organizations use these technologies to drive operational excellence, entitlement, and responsiveness to customer demand. The report presents some case studies of the successful applications of these technologies and talks about what companies are facing when incorporating these new systems into their existing supply chain process. It also explores strategic best practices in addressing these challenges, beginning with defining clear goals, aligning technology, and ensuring their workforces are ready for change. Finally, the paper looks at the future for supply chain technology. With burgeoning trends such as hyper-automation (driven by AI and IoT), we can expect things to keep improving and improving supply chain performance in years to come. The results show that despite challenges, successfully implementing these technologies increases efficiency and reduces costs, improving customer satisfaction and providing firms with a sustainable comparative advantage. Keywords Supply Chain Management, Technology, Artificial Intelligence, Internet of Things, Blockchain, Automation, Operational Efficiency, Demand Forecasting, Logistics, Inventory Management, Hyper-Automation.

IoT-Enabled Cross-Platform Applications for Real-Time Logistics Monitoring

Internet of Things (IoT)The integration of Internet of Things (IoT) technology and cross-platform mobile applications has revolutionized real-time logistics monitoring, enabling unprecedented visibility, efficiency, and proactive management of supply chain operations. This article explores the key components of IoT-enabled logistics monitoring, including IoT sensors, data integration, real-time tracking, automated notifications, predictive maintenance, and proactive supply chain management. The benefits of implementing these solutions are highlighted, such as reduced risks, optimized routes, improved operational efficiency, and enhanced customer satisfaction. The article also discusses the challenges associated with IoT implementation, including data integration, connectivity, security, and privacy, and provides strategies for overcoming these obstacles. Through real-world case studies, the tangible impact of IoT-enabled cross-platform applications in logistics is demonstrated, showcasing the potential for significant improvements in cost savings, asset utilization, and overall supply chain performance. As the IoT landscape continues to evolve, the adoption of these technologies will become increasingly critical for logistics providers looking to maintain a competitive edge in the market.

The Role of Business Intelligence in Enhancing the Performance of Supply Chains in Jordan

The study problem is represented by the question: What is the role of business intelligence in enhancing the performance of supply chains in industrial companies in Jordan?. A quantitative descriptive design was used in this study to explore the role of business intelligence in improving supply chain performance in Jordan. The study sample was selected using a stratified random sample. The expected number of participants in the study is 300 participants to ensure adequate representation of industrial companies in Jordan. The results showed that there is a positive role for business intelligence in enhancing the performance of supply chains (agility, integration, efficiency, customer responsiveness) in industrial companies in Jordan. The Jordanian companies are recommended to increase their investments in enhancing the performances of supply chains by adaption of business intelligence tools and technologies. In addition, it should focus on the development of human skills through training programs, which will definitely enhance employees' capabilities to make better use of business intelligence technologies and data.

Digital Twin Technology, Predictive Analytics, and Sustainable Project Management in Global Supply Chains for Risk Mitigation, Optimization, and Carbon Footprint Reduction through Green Initiatives

This review explores the integration of digital twin technology, predictive analytics, and sustainable project management to enhance global supply chain efficiency, resilience, and environmental sustainability. Digital twins provide real-time virtual representations of physical supply chain systems, enabling predictive analytics to identify potential disruptions and optimize decision-making processes. By combining these advanced technologies with sustainable project management practices, such as circular supply chains and green logistics, organizations can proactively address risks while reducing their carbon footprint. The focus on data- driven insights and scenario analysis facilitates informed risk mitigation and resource optimization. The integration of frameworks like the Triple Bottom Line emphasizes the importance of balancing economic, social, and environmental objectives in project management. This approach aims to improve supply chain performance, drive sustainability efforts, and create a resilient logistics network that adapts effectively to market uncertainties and environmental challenges.

Examining the effect of AI-BDA on manufacturing firm performance: An Indian approach

Manufacturing firms face an uncertain and continuosly changing environment because of innovations, technological changes, and globalization. To cope with this quick and uncertain environment, firms need to be flexible. Artificial Intelligence (AI) and Big Data Analytics (BDA) are must for manufacturing firms to achieve the flexibility in procurement to manufacturing to marketing. This study explores role of AI-BDA played between Supply Chain Flexibility (SCF) and Supply chain firms performance(SCFP) through six hypothesis. A sample data of 297 responses from forty Indian manufacturing firms were collected. Exploratory and confirmatory factorial analysis were used to analyse the collected data. Out of six hypothesis, four hypothesis are supported. The results show positive impact of AI, BDA and SCF on supply chain firm performance. Also AI positively impacts on BDA. However two hypothesis not supported are positive effect of AI and BDA on SCF needs further investigated. The study can guide decision makers to understand role of AI and BDA to improve supply chain performance.

Measurement and Improvement of Fresh Milk Supply Chain Performance (Case Study of XYZ Farmers Group, Bogor Regency)

The challenges in the small-scale dairy industry often stem from outdated business management practices and a lack of comprehensive performance evaluation. This study adopts a structural approach by analyzing the performance of the fresh milk supply chain within Farmers Group XYZ, located inBogor Regency. We employed the SCOR and AHP methods for our performance analysis. Data were garnered through questionnaires, interviews, and observations. The findings reveal that the overall performance of the fresh milk supply chain was moderate, with performance scores of 84.25% forfeed suppliers, 77.47% for Farmers Group XYZ, and 82.05% for consumers. Notably, areas requiring enhancement include reliability, responsiveness, and cost-efficiency. Consequently, we have developed strategic recommendations aimed at augmenting supply chain effectiveness. These include enhancing the transportation system to ensure timely deliveries (Reliability), shortening order fulfillment times and improving the flexibility to adapt to demand fluctuations (Responsiveness), and optimizing logistics and storage to reduce costs (Cost). Implementing these strategies will bolster collaboration and fortify relationships among suppliers, producers, and consumers, contributing to a more robust dairy industry.

Dynamics analysis of green supply chain under the conditions of demand uncertainty and blockchain technology

This research investigates the implications of incorporating blockchain technology into the process of making decisions for green supply chains, particularly under conditions of demand uncertainty. A model was formulated to encompass both environmentally friendly products enabled by blockchain technology and those without such enabling technology. The study further explores the optimal method of introducing green input in a duopoly market using game theory. It also examines how consumer uncertainty about green products and acceptance of the technical parameters of blockchain influence this strategy. The findings suggest that increased consumer uncertainty can, in some instances, motivate manufacturers to enhance the eco-friendliness of their products and improve supply chain performance. However, the universal adoption of blockchain does not necessarily ensure better results; on the contrary, it may compromise product sustainability while enhancing supply chain profitability. Moreover, research has indicated that products enabled by blockchain typically have lower prices, thereby offering potential benefits to consumers when acceptance of sustainable energy solutions is high or uncertain. Additionally, this paper analyzes the impact of changes in green supply chain decision-making on system reliability. This involves exploring the relationship between decision parameters and consumer reluctance towards sustainable products and the adoption of blockchain technology. To ensure stable market competition in dynamic complex systems, research shows that feedback control technology can effectively regulate unpredictable behavior.

Analysing the feasibility of agro-based value chain configurations in the rural context with B2B marketing

Purpose This paper aims to advocate for small-scale entrepreneurship with commercial cultivation to earn better profits at the farmer level. This paper explores potential value chain configurations of vertical coordination. Design/methodology/approach The aloe vera supply chain was examined for the study where aloe vera juice is the product. The various configurations are complete vertical coordination, semi-vertical coordination and non-vertical coordination. These were analysed to find feasibility and market prospects based on parameters. The cyclic view of supply chains, comparative analysis and fuzzy-analytical hierarchy process algorithm are presented. Findings The semi-vertically coordinated supply chain is indicated as the optimum setup to initiate value chains. The entrepreneurs in this configuration will be able to sell semi-finished produce to a brand that will process and distribute the final product. This B2B setup will help the farmer to earn better revenues in the post-COVID-19 scenario. Effective marketing with improved execution and innovation will boost the food processing industry. Research limitations/implications This study uses an established methodology to distinguish among alternatives based on underlying factors. The study is based on data collected from stakeholders. The study reflects the most accurate picture of real-world scenarios at the farm and market level in the Indian context. Practical implications This study’s learnings will help understand the organisational approach to serve demand by designing unique business configurations. Adapting the fundamental model with technological intervention will improve supply chain performance and sustainability. The most modern approaches in agri-business, such as subscription models, vertical farming, corporate entrepreneurship and technological intervention, will be covered in future work. Social implications This study will help stakeholders, including the government, decide whether to introduce training skills, subsidy policies, business expansion ideas and feasibility studies. Originality/value The study highlights the strategic role of developing the B2B platform to support customer values and customer ecosystem with better product availability.

Study on Supply Chain Rearrangement to Improve Supply Chain Performance of Sambal Sauce in Company X Case Study using the Enterprise Architecture Design method that utilizes Industrial Revolution 4.0 Technology based on Hybrid Technology

Company X is a large enough producer of Sambal Sauce to supply Meatball traders in Indonesia. Many problems in this supply chain include the occurrence of unhealthy competition between internal networks, both between agents and other networks that intersect between supply regions, market prices are not uniform, the availability of field products is not maintained, the uncertainty of delivery from the previous supply from the Supply Chain, high Debts and Receivables from Distributors, the certainty of product delivery to the Supply Chain from Producers, The certainty and stability of the delivery of Sambal Sauce Raw Materials to the Factory, unstable quality of raw materials, and many other problems. Of course, this needs to be fixed immediately, considering that it will be a turning point for product returns if this problem continues. The following is a deeper study of what and why this happens, how to solve the problem, and concrete efforts to solve the problem analytically using the Enterprise Architecture Design method by utilizing Hybrid Technology-based Industrial Revolution 4.0 Technology.

The Effect of Information Sharing, Supply Chain Collaboration and Supply Chain Agility on Supply Chain Performance in Food and Beverage SME in the Bogor Region

This study analyzes the influence of information sharing, supply chain collaboration, and supply chain agility on supply chain performance in the food and beverage industry in the Greater Jakarta area. The main focus of this study is to evaluate the extent to which information sharing practices and collaboration between supply chain partners, as well as the ability of the supply chain to respond to change, affect overall supply chain performance. The method used was a survey with SME respondents in the food and beverage sector. The results showed that information sharing has a significant positive influence on supply chain collaboration and supply chain agility, which in turn improves supply chain performance. This research makes an important contribution to the development of supply chain management theory and practice, particularly in the context of SME in the food and beverage sector.

Enhancing supply chain performance by integrating knowledge management and lean, agile, resilient, and green paradigms

Due to global market competition, there is an increasing emphasis on utilizing lean, agile, resilient, and green (LARG) paradigms to improve Supply Chain (SC) performance. Furthermore, as technology advances, Knowledge Management (KM) is essential in improving SC performance through information sharing, improving processes, and increasing coordination and collaboration. Thus, this research proposes a novel approach to integrating KM with LARG paradigms to enhance SC performance. Also, the decision framework is presented using Multi-Criteria Decision-Making (MCDM) approaches, including the Grey Step-wise Weight Assessment Ratio Analysis (SWARA-G) and Grey Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS-G) method. The presented approach is implemented in an automobile enterprise to improve SC performance. The findings indicated that building coordination and collaboration to improve efficiency, facilitate operations, and reduce communication and exchange costs was introduced as the most critical strategy in the automotive industry.

Enhancing the supply chain performance of Kayumas specialty coffee based on the SCOR-ANP approach

Introduction: Performance evaluation is essential in defining supply chain strategies because the firm can find out how the business processes in the supply chain are running and formulate strategic policies. Kayumas coffee is one of Indonesia’s specialty coffee with a unique aroma and flavor originating from Kayumas Village, Situbondo District, East Java. Kayumas coffee supply chain needs to be evaluated because of the lack of competitiveness with other specialty coffees. Strategies are needed to improve supply chain performance, improve product quality, and reduce costs. This study aims to configure the supply chain of Kayumas specialty coffee structure, evaluate its performance, and determine performance improvement strategies. Methods: This study uses descriptive analysis to configure the supply chain structure and flow. Analysis of supply chain performance based on Supply Chain Operation Reference (SCOR) model then weighted with Analytic Network Process (ANP). Performance improvement strategies were formulated by a discussion with experts and then weighted with ANP. Results: The research results show that the Kayumas specialty coffee supply chain comprises farmers, farmer groups, distributors or resellers, and consumers. There are 6 performance indicators (metrics) to measure performance: perfect order fulfillment, order fulfillment cycle time, flexibility, total supply chain management cost, cost of goods sold, and cash-to-cash cycle time. Performance measurement shows that Kayumas specialty coffee supply chain performance is in the inferior category. Conclusion: The strategy needed to improve supply chain performance by reducing cost is a partnership between supply chain members to reach a price agreement.

Advanced Queueing and Location-Allocation Strategies for Sustainable Food Supply Chain

Background: This study presents an integrated multi-product, multi-period queuing location-allocation model for a sustainable, three-level food supply chain involving farmlands, facilities, and markets. The model employs M/M/C/K queuing systems to optimize the transportation of goods, enhancing efficiency and sustainability. A mixed-integer nonlinear programming (MINLP) approach is used to identify optimal facility locations while maximizing profitability, minimizing driver waiting times, and reducing environmental impact. Methods: The grasshopper optimization algorithm (GOA), a meta-heuristic algorithm inspired by the behavior of grasshopper swarms, is utilized to solve the model on a large scale. Numerical experiments demonstrate the effectiveness of the proposed model, particularly in solving large-scale problems where traditional methods like GAMS fall short. Results: The results indicate that the proposed model, utilizing the grasshopper optimization algorithm (GOA), effectively addresses complex and large-scale food supply chain problems. Compared to GAMS, GOA achieved similar outcomes with minimal differences in key metrics such as profitability (with a gap ranging from 0.097% to 1.11%), environmental impact (0.172% to 1.83%), and waiting time (less than 0.027%). In large-scale scenarios, GOA significantly reduced processing times, ranging from 20.45 to 64.78 s. The optimization of processing facility locations within the supply chain, based on this model, led to improved balance between cost (up to $74.2 million), environmental impact (122,112 hazardous units), and waiting time (down to 11.75 h). Sensitivity analysis further demonstrated that increases in truck arrival rates and product value had a significant impact on improving supply chain performance.

Virtual warehousing through digitalized inventory and on-demand manufacturing: A case study

Novel digital on-demand manufacturing technologies provide a significant opportunity to support development of virtual warehousing and in turn improve supply chain performance. However, the implementation of virtual warehouse comes with a set of challenges, especially where the objective is to virtually warehouse standard or legacy parts that have been developed and verified initially for conventional (non-digital) manufacturing. In this paper, we explore the key elements required for successful implementation of a virtual warehouse for legacy parts based on a combination of part digitalization, on-demand manufacturing, and part validation. Our proposed framework for adoption of virtual warehouse includes development of a digital inventory which includes supply chain and manufacturability data, identification, and selection of suitable parts for on-demand manufacturing, selection of on-demand manufacturing technology, fit-for-purpose validation of the parts. Our framework is exemplified through a case study, and we conclude that the building of an effective virtual warehouse requires several enablers, including availability of digital data about technical and supply chain characteristics of parts, but also a suitable part identification tool. This part identification tool needs to be flexible to include comparison with reference parts already produced by different on-demand manufacturing technologies.