Big Data Analytics applied in Supply Chain Management: A Systematic Mapping Study

The advent of information and communication technologies (ICT) ushers a cost-effective prospect to take care of large volumes of complex data, commonly known as “big data” in the supply chain operational environment. Big data is being generated today by web applications, social media, intelligent machines, sensors, mobile phones, and other smart handheld devices. Big data is characterized in terms of the velocity, volume, and variety with which it produces along the supply chain. This is due to recent advances in telecommunication networks along with centralized and decentralized data storage systems, which are processed thanks to modern digital computational capabilities. There is a growing interest in the use of this large volume of data and advanced analytics for diverse types of business problems in supply chain management (SCM). Such decision-support software applications employ pure mathematical techniques, artificial intelligence techniques, and sometimes uses both techniques to perform analytical operations that undercover relationships and patterns within supply chain generated big data. This chapter proposes a framework for the utilization of big data in SCM decision making. The framework is based on the SCOR (supply chain operations reference) model, which is endorsed by Supply Chain Council (SCC). The proposed framework is influenced by the enterprise potential of augmented reality and virtual reality in supply chain applications, and it identifies key categories of big data analytics applications for the key businesses of SCOR model. Finally, the chapter highlights research issues to extract insight from big data sources for enterprise decision making.

Domain-Specific Languages: A Systematic Mapping Study

Purpose Journals in business logistics, operations management, supply chain management, and business strategy have initiated ongoing calls for Big Data research and its impact on research and practice. Currently, no extant research has defined the concept fully. The purpose of this paper is to develop an industry grounded definition of Big Data by canvassing supply chain managers across six nations. The supply chain setting defines Big Data as inclusive of four dimensions: volume, velocity, variety, and veracity. The study further extracts multiple concepts that are important to the future of supply chain relationship strategy and performance. These outcomes provide a starting point and extend a call for theoretically grounded and paradigm-breaking research on managing business-to-business relationships in the age of Big Data. Design/methodology/approach A native categories qualitative method commonly employed in sociology allows each executive respondent to provide rich, specific data. This approach reduces interviewer bias while examining 27 companies across six industrialized and industrializing nations. This is the first study in supply chain management and logistics (SCMLs) to use the native category approach. Findings This study defines Big Data by developing four supporting dimensions that inform and ground future SCMLs research; details ten key success factors/issues; and discusses extensive opportunities for future research. Research limitations/implications This study provides a central grounding of the term, dimensions, and issues related to Big Data in supply chain research. Practical implications Supply chain managers are provided with a peer-specific definition and unified dimensions of Big Data. The authors detail key success factors for strategic consideration. Finally, this study notes differences in relational priorities concerning these success factors across different markets, and points to future complexity in managing supply chain and logistics relationships. Originality/value There is currently no central grounding of the term, dimensions, and issues related to Big Data in supply chain research. For the first time, the authors address subjects related to how supply chain partners employ Big Data across the supply chain, uncover Big Data’s potential to influence supply chain performance, and detail the obstacles to developing Big Data’s potential. In addition, the study introduces the native category qualitative interview approach to SCMLs researchers.

PurposeBased on previous literature on big data analytics (BDA) and supply chain (SC) management, the purpose of this paper is to address the factors determining firms’ intention to adopt BDA in their daily operations. Specifically, this study classifies potential factors into four categories: technological, organizational, environmental factors, and SC characteristics.Design/methodology/approachDrawing on the innovation diffusion theory, a model consisted of direct technological and organizational factors as well as moderators was proposed. Subsequently, survey data was collected from 210 organizations. Then we used SPSS and SmartPLS to analyze the collected data.FindingsThe empirical results revealed that perceived benefits and top management support can significantly influence the adoption intention. And environmental factors, such as competitors’ adoption, government policy, and SC connectivity, can significantly moderate the direct relationships between driving factors and the adoption intention.Research limitations/implicationsGiven the fact that big data (BD) usage in logistics and SC management is still in the start-up stage, the interpretations toward BDA might vary from different perspectives, thus causing some ambiguity in understanding the meaning and potential BD has. In addition, we collected data through questionnaires completed by IT managers, whose viewpoint may not fully represent that of an organization.Practical implicationsThis paper tests the organizational adoption intention of BDA and extends the literature streams of BD and SC management simultaneously.Social implicationsThis research helps top managers assess the benefits of BDA as well as how to adjust their business strategy along the changes of environment and SC maturity.Originality/valueThis paper contributes to the literature of organizational adoption intention of BDA and extends the literature streams of BD and SC management simultaneously.

SummaryContext: Software performance is crucial for ensuring the quality of software products. As one of the non‐functional requirements, the few efforts devoted to software performance have often been neglected until a later phase in the software development life cycle (SDLC). The lack of clarity of what software performance research literature is available prevents researchers from understanding what software performance research fields are available. It also creates difficulty for practitioners to adopt state‐of‐the‐art software performance techniques. Software performance research is not as organized as other established research topics such as software testing. Thus, it is essential to conduct a systematic mapping study as a first step to provide an overview of the latest research literature available in software performance. Objective: The objective of this systematic mapping study is to survey and map software performance research literature into suitable categories and to synthesize the literature data for future access and reference. Method: This systematic mapping study conducts a manual examination by querying research literature in noble journals and proceedings in software engineering in the past decade. We examine each paper manually and identify primary studies for further analysis and synthesis according to the pre‐defined inclusion criteria. Lastly, we map the primary studies based on their corresponding classification category. Results: This systematic mapping study provides a state‐of‐the‐art literature mapping in software performance research. We have carefully examined 222 primary studies out of 2000+ research literature. We have identified six software performance research categories and 15 subcategories. We generate the primary study mapping and report five research findings. Conclusions: Unlike established research fields, it is unclear what types of software performance research categories are available to the community. This work takes the systematic mapping study approach to survey and map the latest software performance research literature. The study results provide an overview of the paper distribution and a reference for researchers to navigate research literature on software performance.

The advancements in technology over the past quarter-century have yielded significant transformations in supply chain management, alongside several other sectors. The utilization of Artificial Intelligence (AI) and Big Data Analytics (BDA) has the capacity to greatly enhance the resilience of supply networks and enable more efficient management of resources within the supply chain. The advancements in AI and BDA have brought about significant transformations in the operational practices related to supply chain resilience, making them the forefront of these innovations. It is vital to elucidate the correlation between AI, BDA and supply chain resilience in order to comprehend the manner in which these transformations manifest themselves within this domain. The integration of AI and BDA technologies into supply chain management has emerged as a crucial avenue for enhancing efficiency, agility, and responsiveness in modern business operations. The objective of this study is to explore the benefits of AI and BDA in boosting supply chain resilience and visibility many. The researchers employed the Kitchenham guidelines to conduct a systematic literature review. The investigation is carried out utilizing online scholarly database systems, yielding a total of 207 initial publications, which are further narrowed down to 30 published studies spanning the period from 2013 to 2023. The research paper could be of interest to pertinent players in the business sector due to its provision of a comprehensive examination of the potential benefits associated with AI and BDA in the context of supply chain resilience and visibility.

PurposeThe purpose of this study is to provide insights into the way in which understanding and implementation of disruptive technology, specifically big data analytics and the Internet of Things (IoT), have changed over time. The study also examines the ways in which research in supply chain and related fields differ when responding to and managing disruptive change.Design/methodology/approachThis study follows a four-step systematic review process, consisting of literature collection, descriptive analysis, category selection and material evaluation. For the last stage of evaluating relevant issues and trends in the literature, the latent semantic analysis method was adopted using Leximancer, which allows more rapid, reliable and consistent content analysis.FindingsThe empirical analysis identified key research trends in big data analytics and IoT divided over two time-periods, in which research demonstrated steady growth by 2015 and the rapid growth was shown afterwards. The key finding of this review is that the main interest in recent big data is toward overlapping customer service, support and supply chain network, systems and performance. Major research themes in IoT moved from general supply chain and business information management to more specific context including supply chain design, model and performance.Originality/valueIn addition to providing more awareness of this research approach, the authors seek to identify important trends in disruptive technologies research over time.

Background: Change is inevitable and as supply chain managers prepare for the future they face many challenges. Two major trends over the last few years are the growing importance of ‘big data’ and analysing these data though ‘analytics’. The data contain much value and companies need to capitalise on the variety of data sources by in-depth and proper analysis through the use of ‘big data’ analytics.Objective: This article endeavours to highlight the evolving nature of the supply chain management (SCM) environment, to identify how the two major trends (‘big data’ and analytics) will impact SCM in future, to show the benefits that can be derived if these trends are embraced and to make recommendations to supply chain managers.Method: The importance of extracting value from the huge amounts of data available in the SCM area is stated. ‘Big data’ and analytics are defined and the impact of these in various SCM applications clearly illustrated.Results: It is shown, through examples, how the SCM area can be impacted by these new trends and developments. In these examples ‘big data’ analytics have already been embraced, used and implemented successfully. Big data is a reality and using analytics to extract value from the data has the potential to make a huge impact.Conclusion: It is strongly recommended that supply chain managers take note of these two trends, since better use of ‘big data’ analytics can ensure that they keep abreast with developments and changes which can assist in enhancing business competitiveness.

Technology has revolutionized business operations around the world. There is an ever-increasing trend to digitize business operations around the world. It is not uncommon to see the application of Big Data Analytics (BDA) throughout a vast range of industries and organizations in today's extremely competitive world. However, BDA adoption in supply chain (SC) is less accepted and research in this area remains at its infancy. BDA is extremely useful throughout supply chain functions like manufacturing, distribution, procurement, and marketing. However, a sizable portion of sectors continues to hold divergent opinions regarding big data's alleged benefits. Moreover, there is not a lot of empirical studies that have been published that addresses the adoption of such a tool or better yet, that investigates what factors affect the decision to adopt BDA in supply chain management (SCM). This study aims to develop and extend technology acceptance model (TAM) that considers the critical factors that may affect BDA adoption in SCM by critically analyzing the existing literature. Upon reviewing the literature, it became evident that the main categories of factors that have been thoroughly examined and recognized to be crucial in comprehending BDA adoption and acceptability in SCM are the organizational- and individual-related factors, such as top management and computer self-efficacy.

A Proposed Architecture for Big Data Driven Supply Chain Analytics

Despite the breadth of supply chain management (SCM) research, little attention has been paid to the application of Big Data Analytics to maximize the utilization of information in a supply chain. The objective of this article is to contribute to the development of SCM theory by examining the potential effects of Big Data Analytics on information utilization in a business and supply chain setting. Because it is critical for supply chain firms to have access to current, accurate, and useful data, the exploratory research will shed light on the opportunities and problems associated with the implementation of Big Data Analytics in SCM. While management is increasingly focusing on Big Data Analytics, actual research on the subject is still sparse. Due of the scarcity of relevant content at the nexus of Big Data Analytics and Supply Chain Management, the authors employ the Delphi research technique. The given Delphi survey findings complement to existing knowledge by identifying 43 opportunities and problems associated with the emergence of Big Data Analytics from a corporate and supply chain perspective. These structures provide the research community with a starting point for tailoring future research at the nexus of Big Data Analytics and SCM. The research contributes to the current body of knowledge by examining possibilities and challenges at the corporate and supply chain level with a particular emphasis on the consequences imposed by Big Data Analytics.

Supply chain management is essential to a company's success in today's fiercely competitive business environment, regardless of the industry. Businesses are increasingly turning to big data analytics as a useful tool for supply chain improvement to meet the expanding needs of customers, save operational costs, and boost overall efficiency. In order to improve critical areas including demand forecasting, inventory management, logistics optimization, and risk reduction, research has been conducted on the collecting and use of big data in supply chain management. Big data analytics and supply chain management integration can result in more cost-effective operations, better customer service, and increased resilience to disturbances. Organisations that effectively use big data will gain a competitive advantage in the constantly changing supply chain landscape as technology and data capabilities continue to grow.

Big Data refers to complex and unstructured data that is difficult to analyse and utilize with traditional applications and analyses. Big Data comes from a variety of sources, including tracking and sensor devices which are widely used in logistics and supply chain management, and relate to Radio Frequency Identification (RFID) technology. Thus, this chapter reviews the literature on RFID adoption in supply chain/logistics management from 1995-2015. We identify current trends in the literature, drawing on the three levels of decision making, that is, strategic, tactical, and operational. We suggest that more research needs to be conducted with regards to the intangible benefits of RFID, the use of RFID big data for achieving higher performance, and to shift the focus from the ‘what' and the impacts on performance to the ‘how' and the ways RFID is adopted and assimilated in organizations and supply chains. Finally, the managerial implications of our review as well as the limitations and future research directions are outlined.

Modern companies face numerous challenges in supply chain management, particularly due to globalization, increasing competition, and the need for quick adaptation to market changes. Inefficient supply chain management can lead to higher costs, supply delays, and reduced customer satisfaction, which directly impacts financial performance and competitiveness. The study aims to identify methods for improving supply chain management efficiency, taking into account current economic and technological conditions. Given the volatility of global markets, it is crucial to develop adaptive and resilient supply chain strategies that align with both short-term objectives and long-term sustainability goals. The study uses methods of analysis, synthesis, comparative analysis, as well as a systems approach to assess modern supply chain management strategies. The impact of cutting-edge technologies such as Big Data, artificial intelligence, the Internet of Things, and blockchain on optimizing supply chain processes was analyzed. The effectiveness of ERP systems in the context of business process integration and risk management was evaluated. The study identified key issues in supply chain management: insufficient integration between systems, low supply chain transparency, inefficient inventory management, and high logistics costs. To address these issues, the following solutions were proposed: Implementing ERP systems and enhancing their functionality for business process integration; Using Big Data analytics for demand forecasting; Developing risk management strategies, particularly through supplier and transport route diversification; Optimizing inventory and reducing logistics costs by automating and applying flexible supply models. Improving supply chain management efficiency requires active use of modern technologies and innovative optimization methods. Integration of Big Data, artificial intelligence, and the Internet of Things helps reduce costs, shorten delivery times, increase process transparency, and ensure stability in order fulfillment. The proposed solutions will contribute to enhancing the competitiveness of enterprises in dynamic economic conditions.

In today’s world, the supply chain management sector plays a vital role in everyone’s life. During the recent times, the number of people or customers buying or ordering goods online has increased enormously. The management process of transformation of raw materials to finished goods can be termed as Supply Chain Management (SCM). The actors involved in the supply chain are the vendors or suppliers, distributors and customers. At every stage of this chain, large volumes of data get generated. These data are a collection of information from a variety of domains such as goods, clothing, accessories and so on. This big data need be used wisely to improve the supply chain management. The big data is a more than just internal data from Enterprise Resource Planning (ERP) and SCM. The statistical analysis methods such as regression, hypothesis testing or sample size determination are used to analyse the internal as well and newly created data that provide new outcomes which in turn help to improve the decision making involved in the supply chain. Decision making choices might be which operating model to choose, who should be the vendor for a particular item and so on. This chapter aims to explain the role of Big data and its analysis in supply chain management.KeywordsEnterprise resource planning (ERP)Supply chain management (SCM)Big dataStatisticsBig data analytics