Supply Chain Management (SCM): Its Future Implications

This keynote paper illustrates the theory and evolution of Supply Chain Management (SCM) as well as highlights chronological prospective of SCM in terms of time frame in different areas of manufacturing and service industries. The objective of SCM is to incorporate activities across and within organizations for providing the customer/stakeholders value. Supply Chain Management (SCM) has been widely researched in numerous application domains during the last few decades. 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. This paper demonstrates Educational Supply Chain Management, Integrated Tertiary Educational Supply Chain Management (ITESCM) model, as the application of SCM in the service industry, which would add the values towards academia. ITESCM model were identified and confirmed by 493 respondents, representing experts and administrators, faculty, staffs of the university, employers, graduates, etc. The resulting model was subsequently evaluated for accuracy and validity by multiple linear regressions (MLR) analysis and the structural equation modeling (SEM) technique. The research model provides a novel approach for decision makers of each supply chain components to review and appraise their performance toward fulfillment of ultimate goals i.e. producing high-caliber graduates and high-impact research outcomes, which represent two main contributions, for the betterment of the end customer, i.e., the society.

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.

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.

Contributors

PurposeThe purpose of this paper is to describe the development and teaching of graduate courses providing in‐depth experiential learning employing commercial supply chain management software. The benefits of teaching such courses are described, the challenges in offering such courses are identified, and some solutions to overcome the challenges are offered.Design/methodology/approachThe experiences of the authors in developing and teaching supply chain management courses utilizing commercial software provided a basis for discussing the benefits and challenges associated with teaching students the management of modern supply chains using commercial decision‐support software.FindingsIncorporating commercial software in university programs presents a myriad of challenges and therefore is rarely done. However, providing students with in‐depth knowledge of commercial logistics and supply chain management software improves their understanding of supply chain issues and provides a key differentiator in the marketplace. Modeling real‐world supply chains using commercial software enhances student's education by providing relevant experiential exposure to real‐world problems and decision‐support tools.Originality/valueThe observations of the authors in developing and teaching courses in supply chain management utilizing commercial software afford a unique viewpoint and roadmap for others regarding teaching supply chain management in academic environments.

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

This article argues that current Supply Chain Management (SCM) education and training is inadequate to meet the human capital needs of industry. To bridge this gap, we propose the development and deployment of SCM educational programs that provide learning opportunities earlier in students’ educational journeys. We argue that such programs should be based on self‐directed learning of SCM knowledge, business concepts/skills, and their real‐life application with help from industry partners. We present a case study of a four‐way partnership SCM educational program for high schoolers, which utilizes a project‐based learning method. Survey data and focus group discussion revealed positive developmental learning outcomes, the program's uniqueness, and challenges such as lack of awareness, expertise, and industry partners. We evaluate the efficacy of this four‐way partnership among industry, government agencies, universities, and high schools and discuss the importance of “champions” for each partner. Our findings offer a scalable educational model that leverages multi‐stakeholder engagement to address the SCM talent and educational conceptual gap.

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.

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

The importance of supply chain management to business operations and social growth cannot be overstated. Today's supply chains are very different from those of a few years ago and continually change in a highly competitive climate. Investing in technology that can handle the sheer complexity of dynamic supply chain operations is necessary. Current supply chain solutions cannot completely mitigate the risks of inefficiencies at various supply chain stages. Several functional supply chain applications based on Machine Learning (ML) have appeared in recent years; however, few studies have analyzed data-driven logistics and supply chain management applications. Robotics, machine learning, and natural language processing are potential supply chain transformation enablers. This paper offers a thorough and up-to-date literature review that examines what researchers have done regarding data-driven techniques in the supply chain context and identifies what needs further exploration. We reviewed 135 research articles published between 2008 and 2022 on the Scopus database and created a classification of the research material on data-driven logistics and supply chain management. This comprehensive literature evaluation will enable researchers and business administrators to undertake innovation initiatives better and redirect money and human resource efforts.

PurposeIn India, artificial intelligence (AI) application in supply chain management (SCM) is still in a stage of infancy. Therefore, this article aims to study the factors affecting artificial intelligence adoption and validate AI’s influence on supply chain risk mitigation (SCRM).Design/methodology/approachThis study explores the effect of factors based on the technology, organization and environment (TOE) framework and three other factors, including supply chain integration (SCI), information sharing (IS) and process factors (PF) on AI adoption. Data for the survey were collected from 297 respondents from Indian agro-industries, and structural equation modeling (SEM) was used for testing the proposed hypotheses.FindingsThis study’s findings show that process factors, information sharing, and supply chain integration (SCI) play an essential role in influencing AI adoption, and AI positively influences SCRM. The technological, organizational and environmental factors have a nonsignificant negative relation with artificial intelligence.Originality/valueThis study provides an insight to researchers, academicians, policymakers, innovative project handlers, technology service providers, and managers to better understand the role of AI adoption and the importance of AI in mitigating supply chain risks caused by disruptions like the COVID-19 pandemic.

Low priced and attainable information technology has made it possible to connect critical and proprietary information with supply chain partners, because of ever growing size, complexity, 7/24 system ability, evolved production processes etc. need flow of knowledge continuously. The knowledge integrated in the supply chain management (SCM) systems affects both supply chain and organizational performance. But since there is no single entity on performance an effective systematically approach to performance measurement could not be established. Generally in previous studies, organizational performance hasn`t been measured within the terms of SCM but if integral unit endeavor performance can be judged by so the overall performance can be judged by its departments` efforts. So SCOR model is chosen because important supply chain characteristics and their associated interactions aren't ignored. And organizational performance is reciprocal that performance measurement is taken a system for integrating the management of supply chain and knowledge. Therefore the objectives of the study were appeared to be correlation levels and relationships between knowledge sharing, SCM and organizational performance. This study is unique in terms of the dimensions that are being investigated in Turkey. While there are studies on SCM and knowledge sharing, each examined alone, this study tried to undercover the relationships between these two terms.

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.

The term supply chain management first appeared in the logistics literature in the mid-1980s. The goal of so-called supply chain management is to plan, implement and control in order to reduce production costs and improve production efficiency. This paper aims to analyze enterprise supply chain management. The supply chain involves most of the products in our lives. The paper starts by presenting an overview of supply chain and the importance of it. This paper chooses Apple as an example to analyze supply chain management. Apple is the world's leading high-tech company, which can offer me some experience. The supply chain was not pioneered by Apple, but they do a good job. In the supply chain, part of the enterprise's business is external, which reduces costs and improves production efficiency. Apple is consumer-oriented. Some businesses are external and so on, which makes it a win-win situation for him and his partners. But there are also some problems in Apple's supply chain management, such as excessive exploitation of downstream suppliers and neglect of employee rights and interests. Then this paper discusses how they make changes in order to improve supply management. Finally, we summarize how to effectively manage supply chains in businesses. Apple needs to pay attention to the low-end market, strengthen the supervision of suppliers, reduce the exploitation of suppliers, and truly protect the rights and interests of those suppliers' employees. In addition, it should shorten and optimize the supply chain to make the company's operations more stable.

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.