Data In Supply Chain Management Research Articles

Improved Association Rule Mining-based Data Sanitisation with Blockchain for Secured Supply Chain Management

A supply chain management (SCM) method must include information sharing as a vital component in order to improve supply chain performance and boost an organisation’s strategic advantage. Since, due to a lack of trust concern over information leakage, and security breaches by nefarious individuals or groups, several organisations are hesitant to share information with their supply chain partners. This work presents a new supply chain management-based secure data transmission method. By using blockchain-based data storage, it is assumed that the manufacturers, suppliers, and customers would transfer data that must be kept private during transmission. As a consequence, this paper aims to provide an improved association rule mining with a data sanitisation scheme with an improved Apriori algorithm used in the proposed data sanitisation process. In particular, the Long Short-Term Memory (LSTM) will generate keys by considering the objective relying on the value of the preservation ratio, false rule generation, hiding failure, and degree of modification. The weights are adjusted via a novel Minkowski distance-based Namib beetle optimisation (MDNBO) technique, which also improves the performance of the LSTM model. The reverse process of encryption occurs when encrypted data are restored at the receiving end. By contrasting it with the old methods with regard to security as well, the proposed protected data in SCM with blockchain technology will be proved to be efficient.

Secure pharmaceutical supply chain using blockchain in IoT cloud systems

Supply Chain Management (SCM) systems require time sequencing, coordination and tracking the movement of goods and processes. Internet of Things (IoT) and Blockchain technologies are useful to develop a secure automated SCM. IoT devices with in-built sensors and actuators help to keep track of the state, location, temperature or other parameters of an entity, and control the automation of routine as well as hazardous tasks. Blockchain technology supports time-stamping, authentication, process coordination, non-repudiation, commercial transactions, and also provides security for transactions and storage. The pharmaceutical SCM demands accurate, immediate and secure control system. Additionally, the supply chain process data from IoTs is stored and processed in Cloud by Analytics applications, for business planning and improvement. An efficient and secure IoT-Cloud-Blockchain based system for both SCM automation and analytics has been proposed in this work. It leverages a hierarchical IoT, Mist, Edge, Fog, Cloud computing (IMEFC) architecture to enhance Communication-Response-Compute-Security-Storage (CRCSS) in the system. Blockchain technology provides security for the SCM transactions and data. The efficiency of the Blockchain is measured in terms of upload time, download time and transaction fees for Bitcoin, Ethereum and Filecoin platforms. The Filecoin blockchain platform is quicker and cost-effective for larger file sizes, compared to Ethereum and Bitcoin, making it suitable for Pharmaceutical SCM systems.

Organic Food Traceability System using Blockchain Technology

Traditional traceability system has problems of centralized management, opaque information, untrustworthy data, and easy generation of information islands. To solve the above problems, this paper designs a traceability system based on blockchain technology for storage and query of product information in supply chain of agricultural products. Leveraging the characteristics of decentralization, tamper-proof and traceability of blockchain technology, the transparency and credibility of traceability information increased. A dual storage structure of ‘‘database + blockchain’’ on-chain and off-chain traceability information is constructed to reduce load pressure of the chain and realize efficient information query. Blockchain technology combined with cryptography is proposed to realize the safe sharing of private information in the blockchain network. In addition, we design a reputation-based smart contract to incentivize network nodes to upload traceability data. Furthermore, we provide performance analysis and practical application, the results show that our system improves the query efficiency and the security of private information, guarantees the authenticity and reliability of data in supply chain management, and meets actual application requirements.

Reviewing the use of big data in supply chain optimization in the USA

This paper provides a succinct overview of the review conducted on the utilization of Big Data in supply chain optimization within the United States. The advent of Big Data analytics has revolutionized traditional supply chain management, offering unprecedented opportunities for enhancing efficiency, reducing costs, and improving overall operational performance. This comprehensive review examines the current state of Big Data applications in supply chain optimization, focusing specifically on the context of the United States. The review begins by outlining the fundamental role of Big Data in transforming supply chain processes. It delves into various aspects, including data collection, processing, and analysis, emphasizing their impact on decision-making and strategic planning. Notably, the study highlights real-world examples of successful Big Data implementations within the U.S. supply chain landscape, showcasing how companies have leveraged data-driven insights to streamline operations and gain a competitive edge. Furthermore, the paper explores the challenges associated with the adoption of Big Data in supply chain management. It addresses concerns related to data security, privacy, and the need for advanced technological infrastructure. Additionally, the review considers the organizational and cultural shifts required for successful integration, emphasizing the importance of a holistic approach to implementation. The paper concludes by pointing towards future trends and potential developments in the use of Big Data for supply chain optimization in the U.S. It underscores the evolving nature of technology and its continued impact on reshaping supply chain strategies. The insights derived from this review contribute to a deeper understanding of the current landscape, offering valuable implications for practitioners, researchers, and policymakers engaged in optimizing supply chain processes through the harnessing of Big Data analytics.

An Overview of the Application of Big Data in Supply Chain Management and Adaptation in Nigeria

An Overview of the Application of Big Data in Supply Chain Management and Adaptation in Nigeria

An Improved Integrity-Based Hybrid Multi-User Data Access Control for Cloud Heterogeneous Supply Chain Databases

Cloud-based supply chain applications play a vital role in the multi-user data security framework for heterogeneous data types. The majority of the existing security models work effectively on small to medium-sized datasets with a homogenous data structure. In contrast, Supply Chain Management (SCM) systems in the real world utilize heterogeneous databases. The heterogeneous databases include a massive quantity of raw SCM data and a scanned image of a purchase quotation. In addition, as the size of the database grows, it becomes more challenging to provide data security on multi-user SCM databases. Multi-user datatypes are heterogeneous in structure, and it is complex to apply integrity and confidentiality models due to high computational time and resources. Traditional multi-user integrity algorithms are difficult to process heterogeneous datatypes due to computational time and variation in hash bit size. Conventional attribute-based encryption models such as "Key-policy attribute-based encryption" (KP-ABE), "Ciphertext-Policy Attribute-Based Encryption" (CP-ABE) etc., are used to provide strong data confidentiality on large textual data. Providing security for heterogeneous databases in a multi-user SCM system requires a significant computational runtime for these conventional models. An enhanced integrity-based multi-user access control security model is created for heterogeneous databases in the cloud infrastructure to address the problems with heterogeneous SCM databases. A non-linear integrity model is developed to provide strong integrity verification in the multi-user communication process. A multi-user based access control model is implemented by integrating the multi-user hash values in the encoding and decoding process. Practical results proved that the multi-user non-linear integrity-based multi-access control framework has better runtime and hash bit variation compared to the conventional models on large cloud-based SCM databases.

XGBoost with Q-learning for complex data processing in business logistics management

The modern business landscape is characterized by complex technical information, economic globalization, and high customer expectations. These factors have led to significant changes in various industries. To ensure customer satisfaction, companies rely on supply chain management (SCM) for the timely delivery of products and gathering feedback for analysis. The collected customer data is often complex and requires advanced methods for processing and management. To effectively manage demand and supply in real-time, businesses must have the ability to handle complex data. Due to the inefficiency and ineffectiveness of traditional methods for the increased data volume and speed, much-emerging research is being conducted on how to harness complex data in SCM. This paper examines the limitations of conventional methods and introduces an Artificial Intelligence (AI) approach based on Q-Learning algorithm with Extreme Gradient Boosting (QL-XGB) model. The QL-XGB method is applied to select suppliers and predict their future demand for the production of products. It is built on the foundation of accurate data and analysis of supply chain characteristics using metrics such as MAE and RMSE. The results show that the QL-XGB model with an accuracy rate of 96.02% outperforms QL and XGB models with respective accuracy rates of 93.44% and 94.68%.

Big data optimisation and management in supply chain management: a systematic literature review

The increasing interest from technology enthusiasts and organisational practitioners in big data applications in the supply chain has encouraged us to review recent research development. This paper proposes a systematic literature review to explore the available peer-reviewed literature on how big data is widely optimised and managed within the supply chain management context. Although big data applications in supply chain management appear to be often studied and reported in the literature, different angles of big data optimisation and management technologies in the supply chain are not clearly identified. This paper adopts the explanatory literature review involving bibliometric analysis as the primary research method to answer two research questions, namely: (1) How to optimise big data in supply chain management? and (2) What tools are most used to manage big data in supply chain management? A total of thirty-seven related papers are reviewed to answer the two research questions using the content analysis method. The paper also reveals some research gaps that lead to prospective future research directions.

Digital transformation of supply chain management in retail and e-commerce

PurposeThe purpose of studying digitization transformation of the supply chain is to understand how digital technologies and processes are changing the way supply chains operate and to identify the opportunities and challenges associated with this transformation. Studying digitization transformation of the supply chain is important because it can help global businesses in identifying the best practices in supply chain management (SCM) systems and enhance supply chain performance. Hence, this research study is contributing in revealing the outcomes of digital inclusiveness in overall SCM for the growth of retail and e-commerce based platforms.Design/methodology/approachThis research is using both descriptive and explanatory research designs to provide a comprehensive understanding of the problems in SCM. Descriptive research provides a detailed description of the characteristics of the population under study, while explanatory research identifies the causal relationships between the variables. Descriptive research has helped us to develop hypotheses about the relationships between variables that can be tested using explanatory research. Explanatory research has been used to validate the findings of descriptive research. By using both descriptive and explanatory research designs, our research design has increased the generalizability of our findings.FindingsAccording to this study, businesses intend to change their supply chain strategies after the wake of competitive era to make them more robust, sustainable and collaborative with suppliers, customers and stakeholders by investing more in SCM technology like Blockchain, AI, analytics, robotic process automation and data control centers. This study evaluates the impact of digitization on supply chain systems. This includes assessing the benefits of digitization and identifying the factors that contribute to successful implementation. This research is studying the role of data analytics in SCM and how it can be leveraged to improve efficiency, reduce costs and increase transparency.Research limitations/implicationsThe study highlights the importance of adopting digitization in supply chain systems to improve supply chain robustness, sustainability and collaboration with stakeholders. This study's emphasis on data analytics in SCM presents an opportunity for businesses to gain insights into their supply chain systems and make data-driven decisions. This can enhance efficiency, reduce costs and improve overall supply chain performance. The study's focus on SCM technology and data analytics may overlook other factors that contribute to successful SCM, such as organizational culture, human resources and supply chain governance.Originality/valueThis study will complement to the existing body of information, management theory and practice and will benefit all. The research work is original and can be implemented worldwide to promote digitization in SCM for smooth transactions in the entire chain of wholesalers, retail distributors and customers.

Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management

The supply chain management (SCM), data science, predictive analytics, and big data (together referred to as DPB) confluence offers a wealth of study options. We show how the increased use of these terminology might help supply chain education and research. Data science necessitates both domain expertise and a broad range of quantitative skills, despite the paucity of research on the topic and the abundance of open issues. We suggest further study into the competencies required of SCM data scientists and examine the relationship between domain expertise and the efficacy of SCM data scientists. This expertise is crucial for the advancement of future supply chain executives. We suggest data science and predictive analytics definitions that are particular to SCM. We examine real-world instances of DPB uses and propose both DPB-based research issues derived from these applications and from management theories. Last but not least, we provide a detailed explanation of the steps researchers might take to respond to our request for research on the confluence of SCM and DPB.

SCMBQA: Design of a Customised SCM-Aware Sidechaining Model for QoS Enhancement under Attack Scenarios

Storing& processing data for supply chain management (SCM) systems requires design of high-security and quality of service (QoS) aware models. These modelsassist in improving traceability performance of SCM deployments via integration of transparent & distributed mechanisms. A wide variety of security models are proposed by researchers to perform these tasks, and it is observed that blockchain-based SCM implementations outperform other models in terms of security & QoS metrics.But most of these implementationsare general-purpose and do not incorporate SCM-specific consensus & mining rules. It is also observed that, mining speed& throughput performance of these blockchain-based implementations reduces exponentially w.r.t. number of SCM transactions. To resolve these issues, this paper discusses design of a novel Proof-of-Supply Chain (PoSC) based consensus model, which is specifically designed for sidechain based SCM deployments. The PoSC consensus model is used for high-efficiency SCM-based data storage and communication scenarios. The proposed PoSC consensus model is capable of resisting selfish mining, time jacking, and sybil attacks, which are targeted towards SCM deployments. The model uses temporal performance metrics of miner nodes, and combines them with relationship graphs to form an SCM miner rank. Based on this rank, miner nodes are selected, and their consensus responses are recorded. These responses are processed using an augmented deep learning model, that is trained over 8 different SCM implementations via machine learning. After successful mining, responses obtained from these miners are used to incrementally train the machine learning model which assists in continuous performance improvement. The SCMBQA model was tested on milk supply chain, agriculture supply chain, and electronic supply chain applications, in terms of computational speed, throughput, energy requirement, retrieval & verification delay, and storage requirements. It was observed that the proposed PoSC consensus was capable of improving the computational speed by 8.5%, reduce energy consumption by 4.9%, improve throughput by 9.6%, and reduce storage costs by 15.4% when compared with standard blockchain-based SCM consensus models. This is because the proposed model deploys an intelligent sidechaining approach, that is capable of optimizing number of generated sidechains via temporal QoS & security performance metrics. Due to use of smaller chain lengths, the proposed model is capable of integrating privacy-aware & secure approaches depending upon different SCM stages. Thus, distributor-level security models are different than retailer-level security models, which assists in context-sensitive block deployments. Due to use of PoSC, the proposed model was observed to be 99.5% resilient against internal and external attacks, which makes it useful for real-time SCM deployments.

Big Data in Supply Chain Management: A Systematic Literature Review

Big data analytics (BDA) have the potential to improve upon and change conventional supply chain management (SCM) techniques. Using BDA, organisations need to build the necessary skills to use big data effectively. Since BDA is relatively new and has few practical applications in SCM and logistics, a systematic review is needed to emphasise the most significant advancements in current research. The objectives are to evaluate and categorise the literature that addresses the big data potential in SCM and the current practises of big data in SCM. The Systematic Literature Review (SLR) was conducted to analyse several published papers between 2017 and 2022. It follows four steps: the literature collection, descriptive analysis, category selection, and material evaluation in a systematic review. The finding reveals that BDA has been applied in many supply chain functions. Furthermore, integrating BDA in SCM has several advantages, including improved data analytics capabilities, logistical operation efficiency, supply chain and logistics sustainability, and agility. Finally, the study emphasises the importance of using BDA to support the success of SCM in businesses.

Impact of Predictive Analytics of Big Data in Supply Chain Management on Decision-Making

The beginning of information technology has led to a burst of data in every sector of operation. Handling huge volume of data to mine useful information to support decision making is one of the current sources of competitive advantage for organizations. However, preceding research literature on predictive analytics has attributed a lack of direct causal influence on predictive analytics in a manner that support Supply Chain Management in utility companies’ performance. This is as a result of huge data posing great challenges to practitioners when incorporating it into their complex decision making which adds business value. The purpose of this study was to introduce predictive analytics in supply chain management framework that enhances decision making in Kenya Power and lighting Company in Kenya. The study was guided by the following research objectives; to assess the existing predictive analytics in Supply Chain Management, to analyse existing supply chain management systems in utility companies in Kenya and to develop an integrated predictive analytics framework for big data in supply chain management for decision making in Kenya Power and lighting Company in Kenya. This research employed the Design Science research design because one of the key outcomes of the research was framework development. The study was carried out in Kenya Power & Lighting Company in Western Region in the republic of Kenya. The target population was 10 regional finance officers, 10 regional procurement officers, 47 county stores in-charges, 47 county project supervisors and 47 county business managers totalling to 161 as the sample size. The main tools for data collection were questionnaires, interview schedules and documentary review.

Big data analytics in smart cities’ transportation infrastructure modernization

Using big data in supply chain management (SCM) has the potential to have a significant impact on the industry in general and international transportation in particular. Big data have a direct influence on transportation capacity in future cities. There has been a significant increase in urbanization over the last decade in which one in three people will live in an urban area by 2050. An updated transportation infrastructure is essential to keep up with the present flow of goods, while also limiting its impact on the environment and human health and this is likely to be achieved using big data analytics technique.To overcome this problem, smart cities are becoming more popular. With the use of information and communication technology (ICT), a smart city aims to address public concerns in an inclusive, municipally-based partnership. A big data transportations system may be built using the superstructure of a smart city. A good way to define it is the modeling and analysis of urban transportation and distribution networks using enormous data sets created by GPS, mobile phones, and transactional data from company activities.Big Data analytics may be used in public transportation to better understand how people go about the city. A better understanding of passengers’ travel patterns might help transportation providers make better judgments regarding service quality. People who travel by automobile on a regular basis may now be predicted based on the triangulation of mobile phone data from millions of anonymous users. Local and national polls may demonstrate the paradigm’s applicability. To compute the time it takes for passengers to board and exit trains, Metro and iBus vehicle position data may be combined with information from smart cards. Big Data analytics for traffic management may benefit from these findings.

Use of Big Data in Supply Chain Management

Abstract: The rapidly expanding interest in the application of big data analytics (BDA) in supply chain management (SCM) among academics and practitioners has prompted an assessment of current research progress in order to define a new agenda. the use of sophisticated analytics tools to improve supply chain management The apps are divided into three categories: descriptive, predictive, and prescriptive analytics, as well as the supply chain operations reference (SCOR) model domains of plan, source, make, deliver, and return. This review answers to the demand by offering a new classification scheme that provides a comprehensive picture of current literature on where and how BDA has been used in SCM. The classification system is based on Mayring's (2008) content analysis method and addresses four research questions: (1) In which aspects of SCM is BDA used? (2) To what extent is BDA employed in these SCM domains in terms of analytics? (3) What are the different types of BDA models utilised in SCM? (4) How are these models developed using BDA techniques? The consideration of these four topics reveals several research gaps, pointing to future study directions. Purpose - Rapid innovation and globalisation have created a plethora of opportunities and choices for businesses and consumers in the marketplace. Due to competitive pressures, sourcing and manufacturing are now done on a global basis, resulting in a huge increase in product availability. The purpose of this article is to determine whether real-time business intelligence (BI) is required in supply chain analytics. Design/methodology/approach – The paper argues for and analyses the benefits and drawbacks of BI. Findings – The article focuses on the need to review the classic BI notion of integrating and consolidating information in an organisation in order to help service-oriented businesses that want to keep their customers. Using a BI methodology to improve the effectiveness and efficiency of supply chain analytics is a vital component of a company's ability to establish a competitive edge. Originality/value – This study contributes to a better understanding of the difficulties surrounding the usage of business intelligence tools in supply chains. Keywords: Supply chain management, Business analytics, Information systems, Big Data, Big data analysis

Artificial Intelligence, Social Media and Supply Chain Management: The Way Forward

Supply chain management (SCM) is a complex network of multiple entities ranging from business partners to end consumers. These stakeholders frequently use social media platforms, such as Twitter and Facebook, to voice their opinions and concerns. AI-based applications, such as sentiment analysis, allow us to extract relevant information from these deliberations. We argue that the context-specific application of AI, compared to generic approaches, is more efficient in retrieving meaningful insights from social media data for SCM. We present a conceptual overview of prevalent techniques and available resources for information extraction. Subsequently, we have identified specific areas of SCM where context-aware sentiment analysis can enhance the overall efficiency.

Application of Machine Learning in Supply Chain Management: A Comprehensive Overview of the Main Areas

In today’s complex and ever-changing world, concerns about the lack of enough data have been replaced by concerns about too much data for supply chain management (SCM). The volume of data generated from all parts of the supply chain has changed the nature of SCM analysis. By increasing the volume of data, the efficiency and effectiveness of the traditional methods have decreased. Limitations of these methods in analyzing and interpreting a large amount of data have led scholars to generate some methods that have high capability to analyze and interpret big data. Therefore, the main purpose of this paper is to identify the applications of machine learning (ML) in SCM as one of the most well-known artificial intelligence (AI) techniques. By developing a conceptual framework, this paper identifies the contributions of ML techniques in selecting and segmenting suppliers, predicting supply chain risks, and estimating demand and sales, production, inventory management, transportation and distribution, sustainable development (SD), and circular economy (CE). Finally, the implications of the study on the main limitations and challenges are discussed, and then managerial insights and future research directions are given.

A Trusted Blockchain-Based Traceability System for Fruit and Vegetable Agricultural Products

Traditional traceability system has problems of centralized management, opaque information, untrustworthy data, and easy generation of information islands. To solve the above problems, this paper designs a traceability system based on blockchain technology for storage and query of product information in supply chain of agricultural products. Leveraging the characteristics of decentralization, tamper-proof and traceability of blockchain technology, the transparency and credibility of traceability information increased. A dual storage structure of “database + blockchain” on-chain and off-chain traceability information is constructed to reduce load pressure of the chain and realize efficient information query. Blockchain technology combined with cryptography is proposed to realize the safe sharing of private information in the blockchain network. In addition, we design a reputation-based smart contract to incentivize network nodes to upload traceability data. Furthermore, we provide performance analysis and practical application, the results show that our system improves the query efficiency and the security of private information, guarantees the authenticity and reliability of data in supply chain management, and meets actual application requirements.

Big data supply chain analytics: ethical, privacy and security challenges posed to business, industries and society

This study conducted a comprehensive review of big data supply chain analytics (BDSCA). The paper explored the application of big data in supply chain management and its benefits for organisations and society. The paper also examined the ethical, security, privacy and operational challenges of big data techniques, as well as the potential reputational damages to businesses. The review outlined four principal facets, namely: Big data analytics, applications, ethics and privacy issues, and how organizations employed this emerging tool to anticipate and even predict the future and direct their operations. These principle facets are built across the multiple levels and unique conceptual standpoints indicated by 7 themes and 14 sub-themes. These themes were generated based on 120 articles (2005−2020) drawn mainly from leading academic journals. Overall, there is a considerable consensus across current literature that big data analytics extend far beyond just reinventing the supply chain. It has the potential to support more responsive next-generation of global companies who are operating in an increasingly challenging and uncertain environment.

Big Data Application in Supply Chain Management: Scopus Based Literature Review

For modern industry, supply chain optimization is becoming very critical. To win the competitive edge, companies should be able to optimize its supply chain. Customers are expecting quick order fulfillment and fast delivery in addition to options, styles and features of products. It is expected that companies who meets these expectations will be recording success stories. Big data plays a key role in several areas of supply chain management such as demand predictions, product development, supplying decisions, distribution, and customer feedback. The increasing amount of data exchange by supply chains in manufacturing and service sectors justify the use of big data in supply chain management. This paper outlines research activities review in the field of big data in supply chain management. In addition, it investigates applications of big data in supply chain management, its opportunities, challenges and future trends.