Supply Chain Management Practitioners Research Articles

Artificial Intelligence Enabled Supply Chain Management: Unlocking New Opportunities and Challenges

This paper delves deeper into the potential of Artificial Intelligence (AI)-enabled Supply Chain Management (SCM) as a groundbreaking technology capable of revolutionizing supply chain operations and ushering in a new era of possibilities. In today's dynamic business landscape, where agility and efficiency are paramount, AI plays a pivotal role in redefining how supply chains operate. The journey commences with an in-depth exploration of AI's fundamental concepts and its manifold applications within SCM, shedding light on its adaptability across various aspects of the supply chain, from demand forecasting to inventory optimization. Moreover, this paper illuminates the myriad benefits that AI brings to SCM practitioners. These advantages encompass heightened operational efficiency through real-time data analysis, cost reduction through predictive maintenance and optimized routing and a superior customer experience resulting from improved demand prediction and personalized service offerings. However, acknowledging the transformative power of AI in SCM, we must also acknowledge the hurdles in its implementation. This paper underscores the significant challenges that organizations may face while integrating AI into their SCM processes, ranging from data quality issues and concerns regarding privacy and security to the need for domain-specific human expertise. To address these hurdles effectively, the paper proposes a comprehensive framework. This framework encompasses a holistic strategy that aligns AI initiatives with organizational goals, governance and ethics considerations to ensure responsible AI deployment, and a clear roadmap that guides the implementation journey from inception to full integration. In conclusion, this paper offers valuable insights into the opportunities and challenges that AI-powered SCM presents in the ever-evolving business landscape. By providing practical recommendations, it equips organizations with the knowledge and tools needed to successfully harness the potential of AI in their supply chain operations, ultimately paving the way for enhanced competitiveness and sustainability in the future.

Analysis of the Impact of Big Data and Artificial Intelligence Technology on Supply Chain Management

Differentiated production and supply chain management (SCM) areas benefit from the IoT, Big Data, and the data-management capabilities of the AI paradigm. Many businesses have wondered how the arrival of AI will affect planning, organization, optimization, and logistics in the context of SCM. Information symmetry is very important here, as maintaining consistency between output and the supply chain is aided by processing and drawing insights from big data. We consider continuous (production) and discontinuous (supply chain) data to satisfy delivery needs to solve the shortage problem. Despite a surplus of output, this article addresses the voluptuous deficiency problem in supply chain administration. This research serves as an overview of AI for SCM practitioners. The report then moves into an in-depth analysis of the most recent studies on and applications of AI in the supply chain industry. This work introduces a novel approach, Incessant Data Processing (IDP), for handling harmonized data on both ends, which should reduce the risk of incorrect results. This processing technique detects shifts in the data stream and uses them to predict future suppressions of demand. Federated learning gathers and analyzes information at several points in the supply chain and is used to spot the shifts. The learning model is educated to forecast further supply chain actions in response to spikes and dips in demand. The entire procedure is simulated using IoT calculations and collected data. An improved prediction accuracy of 9.93%, a reduced analysis time of 9.19%, a reduced data error of 9.77%, and increased alterations of 10.62% are the results of the suggested method.

Neutrosophic-based Machine Learning Techniques in the Context of Supply Chain Management: A Survey

Supply Chain Management (SCM) plays a critical role in the success of any business organization. Individuals involved in business activities often have to make decisions regarding different aspects of the supply chain, including planning, procurement, production, inventory management, transportation, distribution, and customer relationship management. The combination of neutrosophic logic and machine learning has gained significant attention in the field of SCM as a means to tackle uncertainties and improve decision-making. This paper highlights the potential benefits and applications of integrating neutrosophic reasoning and machine learning in SCM. Neutrosophic reasoning provides a framework for handling imprecise and uncertain information, while machine learning techniques offer powerful tools for data analysis, pattern recognition, and predictive modeling. By leveraging machine learning algorithms within the context of neutrosophic logic, SCM practitioners can enhance demand forecasting accuracy, optimize inventory management, improve transportation and routing decisions, and strengthen supply chain collaboration. The integration of neutrosophic logic and machine learning enables the handling of complex supply chain data, accommodates dynamic and uncertain environments, and supports proactive decision-making. Furthermore, the combination of these approaches can contribute to improved supply chain resilience, sustainability, and customer satisfaction. This paper applied the neutrosophic AHP method as a feature section to select the highest importance criteria as an input to machine learning. Then we applied two machine learning models named random forest and decision. The results show the random forest has the highest accuracy followed by a decision tree.

Reviewing the Applications of Neural Networks in Supply Chain: Exploring Research Propositions for Future Directions

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.

Exploring the impact of logistics and supply chain management scholarship: Why pursue practical relevance and are we successful?

AbstractThis study explores the level of relevance of logistics and supply chain management research and probes underlying motives prompting scholars to value and pursue managerial (vs. purely academic) contributions. At its core, the concerns over rigor and relevance of higher education comprise a crisis of trust and credibility that appeared decades ago and continues to provoke concern from the field's thought leaders. While exploratory in nature, the results suggest both practitioners and academics agree at differing levels that logistics and supply chain management research is relevant, but pressure to chase indices and rankings has influenced behavior of academics. Consequently, the academic peer‐reviewed process no longer seems to focus on managerial implications. The article begins with theories pertinent to human motivation, namely, self‐determination theory (SDT), and employs qualitative interviews of academics to help develop surveys of both academics and logistics and supply chain management practitioners. It then presents the methodologies of quantitative data collection and analysis—combined with interviews of deans and editors—to explore the research questions to offer discussion, implications, future research directions, and conclusions.

How Corporate Social Responsibility and External Stakeholder Concerns Affect Green Supply Chain Cooperation among Manufacturers: An Interpretive Structural Modeling Analysis

The past decade saw an increasing development and improvement of green supply chain, but the environmental performances cannot be achieved by single enterprises. Therefore, it is necessary to find out the influencing factors that hinder the cooperation of green supply chain, so as to make full use of these influencing factors to improve environmental performance. In consultation with supply chain management practitioners and academics, this paper establishes a set of 19 influencing factors for green supply chain cooperation among Chinese manufacturers The corporate social responsibility as internal effect and external stakeholder concern as external effect is considered in this study and the interpreted structural model (ISM) method is used to analyze the interaction among these influencing factors and its impact on green supply chain cooperation. This study aims to identify the most significant impacts on environmental issues with supply chain partners and to understand how these impacts can help improve the environmental performance throughout the supply chain.

Mapping the Knowledge Base of Sustainable Supply Chain Management: A Bibliometric Literature Review

Supply chain management (SCM) concerns itself with the integration of a firm’s internal management processes with the external environment. This could explain why sustainability has been embraced by scholars who study SCM. This bibliometric review was untaken with the explicit goals of updating and extending prior reviews of research on sustainable SCM (S-SCM). The goals of this research were to document the scope and development of S-SCM research; identify influential journals, authors, and documents; analyze the intellectual structure of this field of sustainability inquiry, and highlight emerging topics on the frontier of S-SCM inquiry. By using bibliometric tools, a relatively large and rapidly growing corpus of peer-reviewed research documents concerned with S-SCM were found. Citation analyses of journals, authors, and documents yielded a surprisingly high level of scholarly content for a literature body of such recent vintage. The author co-citation analysis revealed three coherent but closely connected groups of thought that comprise the intellectual structure of this knowledge base. Finally, the analysis identified a constellation of topics concerned with the integration of internal processes (e.g., decision-making, manufacturing, sensitivity analysis, risk assessment, life-cycle assessment) and the organizational environment (e.g., climate change, gas emissions, carbon emissions, greenhouse gases, energy utilization, climate change). The results of this research concluded that SCM practitioners and scholars may have embraced sustainability more than any other field of management.

A systematic review of the research trends of machine learning in supply chain management

Research interests in machine learning (ML) and supply chain management (SCM) have yielded an enormous amount of publications during the last two decades. However, in the literature, there was no systematic examination on the research development in the discipline of ML application, in particular in SCM. Therefore, this study was carried out to present the latest research trends in the discipline by analyzing the publications between 1998/01/01 and 2018/12/31 in five major databases. The quantitative analysis of 123 shortlisted articles showed that ML 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 ML applications in SCM and their publications were still at a low level; even though 10 ML algorithms were found to be frequently used in SCM, the use of these algorithms were unevenly distributed across the SCM activities most frequently reported in the articles of the literature. The aim of this study is to provide a comprehensive view of ML applications in SCM, working as a reference for future research directions for SCM researchers and application insight for SCM practitioners.

Smart industry and the pathways to HRM 4.0: implications for SCM

PurposeThe purpose of this paper is to address the potential impacts of Industry 4.0 on human resource management (HRM) – with a particular focus on employment, job profile and qualification and skill requirements in the workforce – which can have implications for supply chain management (SCM). Consequently, exploratory relationships among Industry 4.0, HRM and SCM are presented based on a systematic review.Design/methodology/approachTo explore Industry 4.0 literature and its impact on employment, the authors used a systematic literature review to identify, classify and analyze current knowledge, flagging trends and proposing recommendations for future research in this area. Using the Web of Science database, the authors utilized co-citation software to visualize the networks which emerged from recurrent terms and which were then used to develop the categories of analysis.FindingsThe authors can affirm that the literature in this field is in a transition process, from the early studies of German academics to the current development of new impacts worldwide. Industry 4.0 is the central theme of the literature analyzed and is accomplished through the development of employment, qualifications, skills and learning frameworks. The results reveal that most papers are conceptual, with quantitative studies still lacking. Developed countries have a leading role in terms of research production, while Latin America and Asia are far behind. Clustering reveals four dominant themes (educational changes, employment scenario, work infrastructure resources and work meaning and proposal). The first refers to labor changes around working conditions, the work environment and new skills which are required. The second main theme concerns the potentially unstable shift in the labor market has toward a high-level context. The third is about the technical interface of humans and machines, and finally, the fourth understands the German industry as a starting point for global industrial improvements and work proposal changes. Furthermore, socio-technical systems cover the implications of HRM for SCM in three different dimensions: qualification and education (human competences), collaboration and integration of SCM (organizational competences) and data and information management (technical competences).Research limitations/implicationsAn original research agenda for further development of the topic. Additionally, the implications of the findings for SCM practitioners are presented.Practical implicationsSCM managers can benefit from the results of this paper by developing adjusted polices for organizational and human aspects. Specially about training programs to improve technology skills and education programs for cyber-human new plataforms.Originality/valueSo far, Industry 4.0, HRM-related topics and implications for SCM have generally been considered separately. This paper elucidates the few important studies on the impacts of Industry 4.0 on human-related topics, such as the labor market, building a research framework using the main contributions highlighted in the literature. An original research agenda is presented, as well as potential implications for SCM.

A case-based roadmap for lateral transshipment in supply chain inventory management

Manufacturers and wholesalers are increasingly cost conscious in response to today’s hyper-competitive environment. Lateral transshipment (LT) has been proposed as a viable solution to drive total inventory costs down whilst increasing customer service level. Our study proposes five LT decision rules with a case-based roadmap to guide professional inventory management. Results of this large fast moving consumer goods case study company demonstrate superior inventory management performance with implementing a combined reactive and proactive LT strategy to determine whether to transship emergency stock from other warehouse or to backorder from suppliers, size of transshipment, favorite wholesaler, preferred supplier, and extra quantity for preventive LT, which are the key LT decision points among the professional supply chain management practitioners.

Supply chain management practices in state-owned enterprises environment

The South African government’s supply chain management (SCM) system is not adequately implemented in state-owned enterprises (SOEs). There are weaknesses in the SOEs SCM enablers, strategies, policy implementation and poor enforcement of government SCM rules and regulations. This is resulting in governance and compliance failures, waste and fraudulent activities. Also, top management of SOEs still do not see SCM as strategic enough to deserve their attention, and therefore, there is little support from senior management, thus making it even more difficult for SCM practitioners to execute their day-to-day functions, let alone deliver the letter and spirit of the relevant legislation. In most SOEs Senior SCM practitioners have not been involved in the corporate strategy development. This has led to supply chain and organisational strategies being misaligned. The misalignment has resulted in service delivery backlog, high levels of corruption and continuous strikes across the country for better services. Given the massive expenditure and the financial challenges facing government, an effective public-sector SCM becomes a primary requirement. Government leaders should be more strategic around using SCM as a tool to improve service delivery. This article explores supply chain practices in SOEs. It is based on a conceptual review of SCM practices in the South African SOE environment and suggests that the supply chain and organisational strategies of SOEs must be aligned to ensure efficient delivery of public services.

A Descriptive Analysis of the Possible Causes of the Inverse Audit Outcomes: Case of Supply Chain Management of the KwaZulu-Natal Province, South Africa

Determining the most quantitative possible root causes of the inverse audit reports, will discover whether inadequate training and compliance enforcement are among the top possible determinants of the inverse audit reports received by the KwaZulu-Natal (KZN) Provincial Administration for the financial year 2007/2008. It is recommended that management consider building professional networking opportunities, identify opportunities for country-level networking and mentoring, as well as communities of practice related to procurement through supply chain management (SCM); engage existing professional associations (private and public sector) for example Chartered Institute of Purchasing and Supply. It is also recommended that segregation of duties among SCM practitioners will bring about efficiency in procurement practices. This implies that employees understand and see the need for the segregation of duties, as one of the elements with which to curb fraud and bring about efficiency in SCM operations, this was strongly agreed by 63 percent of the respondents. It is further recommended that there must be a review of current legislation that regulates Public Sector procurement. This infers that Public Sector procurement legislation does not address the needs of public procurement ultimately service delivery, a collective 59 percent of the respondents strongly agreed. DOI: 10.5901/mjss.2014.v5n16p82