Using Business Intelligence in Supply Chain Management

Contributors

Supply chains have received significant attention in recent years. Neural networks (NN) are a technique available in artificial intelligence (AI) which has many supporters due to their diverse applications because they can be used to move towards complete harmony. NN, an emerging AI technique, have a strong appeal for a wide range of applications to overcome many issues associated with supply chains. This study aims to provide a comprehensive view of NN applications in supply chain management (SCM), working as a reference for future research directions for SCM researchers and application insight for SCM practitioners. This study generally introduces NNs and has explained the use of this method in five features identified by supply chain area, including optimization, forecasting, modeling and simulation, clustering, decision support, and the possibility of using NNs in supply chain management. The results showed that NN applications in SCM were still in a developmental stage since there were not enough high-yielding authors to form a strong group force in the research of NN applications in SCM.

A bibliometric analysis of IoT applications in logistics and supply chain management

This keynote paper represents theory of Supply Chain Management (SCM) and its future implications as well as demonstrates chronological prospective of SCM in terms of time frame in different areas of manufacturing and service industries. SCM has been widely researched in numerous application domains during the last decade. Despite the popularity of SCM research and applications, considerable confusion remains as to its meaning. There are several attempts made by researchers and practitioners to appropriately define SCM. Amidst fierce competition in all industries, SCM has gradually been embraced as a proven managerial approach to achieving sustainable profits and growth. Finally, this study demonstrates Educational Supply Chain Management, as the application of SCM in the service industry, which would unlock other applications of SCM in different arenas. Integrated Tertiary Educational Supply Chain Management (ITESCM) model would be verified through Structural Equation Modeling (SEM) Techniques that would describe in this paper. The ITESCM model furnishes stakeholders of the supply chain with appropriate strategies to review and appraise their performance toward fulfillment of ultimate goals, i.e. producing high-ca- liber graduates and high-impact research outcomes, which represent two main contributions, for the betterment of the society.

Artificial intelligence applications in supply chain management

Abstract—To date, the knowledge regarding the potential uses of Geographic Information Systems (GIS) in Supply Chain Management (SCM) and how GIS applications can deliver business benefits are limited. To explore the current uses of GIS in SCM, a systematic literature review (SLR) was conducted. Three databases (Web of Science, ProQuest, and Business Source Premier) were explored using the PreferredReporting Items for Systematic Reviews and Meta-Analyses(PRISMA) methodology. A total of 79 papers were included in the literature review. The combined findings showed that the GIS applications in SCM were a) network/transportation analysis/ routing algorithms (53 papers), b) Location-Allocation Site Search/ Selection (using multiple-criteria decision analysis [MCDA]) (45 papers), c) spatial analysis(people or land) (34 papers), d) connection with optimisation tools (32 papers) and e) visualisation/ monitoring or BuildingInformation Modelling (BIM) (8 papers). The industries and related Supply Chain (SC) domains that used GIS were a)Biomass Biofuel/Wood (33), b) Logistics (22), c)Humanitarian/ Emergency/ Healthcare (10), d) Food/ Agro-Industry (5), e) Petroleum/ Coal/ Shale Gas (3), f) FaecalSludge (2), g) Recycle & product footprint (2) and h)Construction (2). The research case studies took place in 26 countries, and the most prominent GIS software provider wasEsri’s ArcGIS. The findings of the first literature review ofthe GIS applications in SCM showed that many businessindustries have not utilised the GIS capabilities, such asvisualisation, spatial analysis, optimisation, and site research. However, GIS can significantly contribute to SCM decisionmaking by providing answers to cost minimisation, supplierselection, facility location, SC network configuration andasset management. Keywords— Supply Chain Management, Logistics, SystematicLiterature Review, GIS

The advent of the Internet of Things (IoT) has ushered in a transformative era in supply chain management, revolutionizing the way organizations monitor, analyze, and optimize their operations. This comprehensive survey paper explores the multifaceted landscape of IoT applications in supply chain management, shedding light on the challenges, opportunities, and best practices that define this technological paradigm shift. The paper delves into the fundamental principles of IoT, elucidating how sensor-laden devices, real-time data streams, and advanced analytics empower organizations with unprecedented visibility and control across their supply chains. It systematically examines IoT applications in key supply chain domains, including inventory management, asset tracking, cold chain monitoring, predictive maintenance, route optimization, and waste reduction. Each application is scrutinized for its role in enhancing efficiency, reducing costs, ensuring product quality, and advancing sustainability. Furthermore, this paper addresses the challenges inherent in implementing IoT within supply chains, such as data security, interoperability, scalability, and regulatory compliance. It underscores the importance of change management and workforce development in harnessing the full potential of IoT and presents a roadmap for best practices to overcome these obstacles. The paper culminates in a forward-looking exploration of future trends and innovations in the IoT-driven supply chain landscape. By offering a comprehensive overview of IoT's role in supply chain management, this paper equips practitioners, researchers, and decision-makers with a holistic understanding of the transformative power of IoT, empowering them to navigate the complexities, seize opportunities, and implement best practices that will define the future of supply chain management.

This paper presents a systematic review of studies related to Artificial Intelligence (AI) and Machine Learning (ML) applications in Supply Chain Management (SCM). Our systematic search studied 109 journal articles published between 2007 and 2025. It was presented definitions for AI, ML, and SCM and a bibliometric analysis was carried out to examine trends and patterns in the existing literature. The 109 articles were grouped into five clusters based on keywords and abstracts, identifying promising research areas and proposes directions for future investigations. The current literature on AI and ML in SCM is expanding, with an increasing number of publications and growing diversity in applications, although significant gaps remain warranting attention. Various AI and ML techniques, including supervised learning algorithms, neural networks, and optimization techniques, have been studied in scenarios such as demand forecasting, inventory management, and risk analysis. Additionally, AI and ML methods are expected to gain broader acceptance in SCM scenarios, such as, application of Generative AI (GenAI) and Large Multimodal Models (LMMs); Quantum ML techniques to handle with large SCM data; to implement SHAP (SHapley Additive exPlanations) for demand forecasting and fraud detection; and to use Generative AI models (like, GPT-4) to generate synthetic supply chain scenarios.

Supply chain management has an integrated system that can manage the entire process in preparing a product or service for all consumers. For this reason, supply chain management is one of the most important strategies in knowing the needs of customers. One example of the application in supply chain management that is currently done in Malang is a small and medium enterprise (MSME). These small and medium businesses use worm media as fertilizer. The business has two manufacturing processes namely solid fertilizer and liquid fertilizer. In the supply chain management model using 3 different models and can be known the comparison between the first SCM model to the third SCM model. The difference starts from the first SCM model that is 9:11 wherein a supply chain process from the beginning to the end it can still have an error of 2 times the error in structured management. And in the second SCM model that is 10:11 were in a supply chain process from beginning to end still has an error of 1 time in structured management. In the third SCM model 11:11 which states that in a supply chain process from beginning to end there are no errors in structured management. And in this 3rd SCM model, it can be said to be a very efficient model in the supply chain process.

Nowadays, the e-commerce sector has emerged as a significant player in the current financial landscape. It offers a wide range of advantages for global customers, and given the increasing demand for various services and products, there is a pressing need for the development of efficient Web applications. This e-commerce application serves as the bridge between small businesses and their clientele. The application harnesses the capabilities of various technologies, including MongoDB for data storage, Node.js and Express.js for a fast and efficient backend, and HTML CSS for constructing a user-friendly interface for clients. This thesis underscores the proficiency of the (MongoDB, Express.js, HTML, CSS, Node.js) technology stack in the creation of a comprehensive e-commerce application with robust scalability options. The study delves into a meticulous development process, spanning from the backend to the frontend. The results unequivocally underscore the indispensable role in crafting a customizable Web application. We have also investigated the realm of big data analytics and its applications in logistics and supply chain management. It delves into innovative methods, practices, and prospects within this field. The articles within this collection scrutinize multiple avenues for enhancing big data analytics and its applications in logistics and supply chain management. These include the exploration of technology-centric tracking approaches, the examination of the correlation between financial performance and data-driven supply chains, as well as the assessment of implementation challenges and the maturity of supply chain capabilities in the context of big data.

The transition from linear to circular economic systems represents a critical step towards achieving global sustainability goals. At the core of this transformation lies the development of supply chain management that needs to deviate from the traditional “take-make-dispose” approach to one focused on resource efficiency, waste reduction, and material recycling. The use of Business Intelligence (BI) technologies has great potential to support this shift through enhanced decision-making and operational efficiency. This study adopts an assessment framework drawing upon an extensive review of literature, empirical analysis, and case studies from various industries. A theoretical concept map was created to evaluate the application of circular economy principles to supply chain management, highlighting the role of business intelligence technology. The findings highlight how BI tools (such as advanced analytics, predictive modeling, and real-time monitoring) enable organizations to identify inefficiencies, streamline operations, and optimize resource usage across the supply chain. BI contributes to circular economy practices in areas such as product life cycle management, manufacturing, and reverse logistics by facilitating material tracking, recycling, and reintegration into the value chain. This study analyzes the relevance of strong technology infrastructure, facilitatory policy and regulatory arrangements, and collaboration between industries in the development of circular supply chain models. In addition, this study recommends capacity building initiatives that would enable organizations to acquire skills and capabilities to employ business intelligence-based strategies. Finally, the study provides recommendations to businesses, policy makers, and stakeholders on how business intelligence can support circularization. The proposed framework offers practical application and contributes to the body of literature relating to the incorporation of circular economy concepts into supply chain management.

This chapter examines the relationship between supply chain management and quantum computing, with a particular emphasis on real-time decision-making in supply chain operations. Quantum computing offers a solution to traditional supply chain decision-making by enabling faster computations at unprecedented speeds. This technology can improve inventory management, transportation routing, and demand forecasting. However, it faces challenges in real-time decision-making due to compatibility with current technologies and hardware limitations. The chapter explores the fundamentals of quantum computing and its potential applications in supply chain management, highlighting real-time decision-making-friendly quantum algorithms and quantum-inspired optimization strategies. Despite these challenges, quantum computing offers revolutionary possibilities for enhancing supply chain flexibility and effectiveness.

Decision-making in supply chains is challenged by high complexity, a combination of continuous and discrete processes, integrated and interdependent operations, dynamics, and adaptability. The rapidly increasing data availability, computing power and intelligent algorithms unveil new potentials in adaptive data-driven decision-making. Reinforcement Learning, a class of machine learning algorithms, is one of the data-driven methods. This semi-systematic literature review explores the current state of the art of reinforcement learning in supply chain management (SCM) and proposes a classification framework. The framework classifies academic papers based on supply chain drivers, algorithms, data sources, and industrial sectors. The conducted review revealed a few critical insights. First, the classic Q-learning algorithm is still the most popular one. Second, inventory management is the most common application of reinforcement learning in supply chains, as it is a pivotal element of supply chain synchronisation. Last, most reviewed papers address toy-like SCM problems driven by artificial data. Therefore, shifting to industry-scale problems will be a crucial challenge in the next years. If this shift is successful, the vision of data-driven decision-making in real-time could become a reality.

For modern industry, supply chain optimization is becoming very important.To stay ahead of the competition, companies must be able to optimize their supply chain.Customers expect fast order fulfilment and delivery, as well as product options, styles and features.Companies that meet these expectations are expected to succeed.Big Data plays an important role in various areas of supply chain management, such as demand forecasting, product development, delivery decisions, sales and customer feedback.The increasing amount of data shared by supply chains in manufacturing and service sectors justifies the use of Big Data in supply chain management.This paper reviews research activities in the area of Big Data in supply chain management.It also examines the applications of Big Data in supply chain management, opportunities, challenges and future trends.

The Internet of Things (IoT) envisions an ecosystem where smart and interconnected objects can sense surrounding changes, communicate with each other, process information and take active roles in decision making. Optimizing supply chain performance is a primary concern of manufacturing and logistics organizations. Radio Frequency Identification (RFID) is helping organizations to build automated and interconnected smart environment by object identification and tracking, motivating the first step towards an IoT-enabled world. This chapter attempts to understand extant literature studying applications of RFID in implementing the IoT in supply chain management. We categorize extant literature, firstly, based on research methodology and secondly, based on supply chain processes. We find that presently academic activity is around conceptualizing the usability of RFID in the IoT with limited analytical and empirical evidence. Supply chain processes such as demand planning, procurement, retail shelf space management and product returns are prospective areas for interesting future research.