Quantum Computing for Real-Time Decision Making in Supply Chain Operations

Contributors

Implementation and adoption of new technologies are gaining the result of smooth supply chain and logistics operations. Internet of things (IoT) artificial intelligence, including data mining, intensified in all fields of life, particularly in supply chain management and operations. Blockchain technology has the capability to reform the supply chain and logistics operations management. Blockchain provides digital database solutions for all transactions across supply chain and operations management. Radio frequency identification device (RFID) is also helping technology transmit electromagnetic waves to radio-compatible integrated circuits to look after and manage the entire supply chain and logistics operations management. The Fourth Industry Revolution 4.0 refers to the automation, interconnectivity, machine learning, and real-time data that help supply chain and logistics operations management in the 21st century.

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.

PurposeDespite the economic growth in Ghana, the manufacturing industry faces numerous challenges in their supply chains. The study aims to investigate the mediated-moderated role of supply chain technological innovation (SCTI) in the relationship between supply chain resilience (SCR) and supply chain performance (SCP) of manufacturing firms. By exploring this relationship, the study seeks to provide insights that can help manufacturing firms overcome the challenges they face and improve their overall supply chain performance.Design/methodology/approachThe quantitative research approach and explanatory research design were utilised. A sample of 345 manufacturing firms was drawn from a population of 2495 manufacturing firms in the Accra metropolis. Analysis of this study was performed using the Partial Least Squares Structural Equation Modelling (PLS-SEM).FindingsIt was revealed that SCTI positively mediates the nexus between SCR and SCP. However, we document that SCTI negatively moderates the nexus. It is instructive to advocate that a mere presence of a more enhanced SCTI is not enough to improve upon SCP of manufacturing firms, but should be a channel through which SCR can improve SCP.Practical implicationsThis study highlights the need for managers of firms to prioritise investment in technological innovation as a means of enhancing SCR and ultimately improving supply chain performance. By understanding the SCTI mediated-moderated relationship between SCR and SCP, supply chain managers, logistics managers, operation managers, as well as procurement managers can develop more effective strategies to optimise their operations. This study provides valuable insights for managers and policymakers in developing and implementing supply chain resilience strategies that take into account the important role of SCTI.Originality/valueThe originality of the study lies in exploring the mediated-moderated effect of technological innovation on the nexus between resilience and performance of supply chains in developing economies, where firms often face unique challenges such as infrastructure limitations, political instability and economic uncertainty. By investigating the interplay of SCTI between SCR and SCP, researchers can develop new insights and strategies to help navigate these challenges and achieve success.

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 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.

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.

PurposeThis study aims to examine the impact of supply chain digitalization (SCD) on supply chain performance (SCP) with the integration of supply chain agility (SCA) and supply chain resilience (SCR) in manufacturing firms. Additionally, it investigates the moderating role of environmental dynamism (ED) in these relationships in the context of manufacturing firms.Design/methodology/approachA quantitative research design was employed, utilizing a structured questionnaire to collect data. The sample consisted of 374 valid responses from various managerial levels within manufacturing firms in Jordan. A purposive sampling technique was used to ensure that participants had relevant expertise in supply chain management. The data were analyzed using structural equation modeling (SEM) through Smart PLS software to test the proposed hypotheses.FindingsThe study found that SCD positively impacts SCP by enhancing both SCA and SCR. Digital technologies were shown to improve supply chain visibility, speed of decision-making and resource efficiency, which are essential for maintaining performance under uncertain conditions. Furthermore, ED significantly moderates the relationship between digitalization and SCP, indicating that the benefits of digitalization on SCA and SCR are more pronounced in highly dynamic environments.Originality/valueThis study contributes to the literature by offering an investigation on the role of SCD in influencing SCA, SCR and ultimately SCP in manufacturing firms. It also sheds light on the moderating effect of ED, providing a deeper understanding of the conditions under which digital transformation in supply chains is most beneficial. The study was based on three theoretical foundations: the resource-based view (RBV), the dynamic capabilities theory (DCT) and the contingency theory (CT).

The integration of Artificial Intelligence (AI) into Supply-Chain Management (SCM) has revolutionized operations, offering avenues for enhanced efficiency and decision-making. AI has become pivotal in tackling various Supply-Chain Management challenges, notably enhancing demand forecasting precision and automating warehouse operations for improved efficiency and error reduction. However, a critical debate arises concerning the choice between less accurate explainable models and more accurate yet unexplainable models in Supply-Chain Management applications. This paper explores this debate within the context of various Supply-Chain Management challenges and proposes a methodology for developing models tailored to different Supply-Chain Management problems. Drawing from academic research and modelling, the paper discusses the applications of AI in demand forecasting, inventory optimization, warehouse automation, transportation management, supply chain planning, supplier management, quality control, risk management, and customer service. Additionally, it examines the trade-offs between model interpretability and accuracy, highlighting the need for a nuanced approach. The proposed methodology advocates for the development of explainable models for tasks where interpretability is crucial, such as risk management and supplier selection, while leveraging unexplainable models for tasks prioritizing accuracy, like demand forecasting and predictive maintenance. Through this approach, stakeholders gain insights into Supply-Chain Management processes, fostering better decision-making and accountability.

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.

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.

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.

PurposeThe purpose of this paper is to examine how e-procurement (EP) and supply chain technology internalization (SCTI) influence supply chain performance (SCP) through supply chain integration (SCI).Design/methodology/approachThis research analyzed 214 survey responses from project managers who have prior experience in the field of supply chain management. Structural equation modeling was used to analyze the data.FindingsThe results show that EP and SCTI positively influence SCI and SCP. The effects of EP and SCTI on SCP are found to be mediated by SCI in the context for the construction industry.Research limitations/implicationsFuture studies should focus on quantitative measures of SCP like budget overrun, supply chain efficiency and project success. Further research can be done through the exploration of moderating interactions of the proposed model.Practical implicationsFirst, the study highlights the importance of SCTI. Supply chain managers should first focus on effective utilization of different technologies used to support supply chain. Second, the research gives the guidelines to the supply chain managers and project managers about the benefits of EP. They should focus on proper implementation of EP in their organizations.Originality/valueThis paper contributes to the literature by proposing and testing the influences of EP and SCTI on SCI. This allows a strategic viewpoint when implementing SCTI, EP systems and SCI, intended to improve SCP.

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