Integration of Spark framework in Supply Chain Management

Contributors

Supply chain managers face a variety of obstacles when preparing for the future, as change is bound to happen. The increase in the importance of “big data” and also the use of “analytics” to analyze this data are two significant changes in the past few years. The analysis of big data is extremely important because it has the potential to yield significant value, and it is essential for companies to make use of the wide range of information sources by carrying out a comprehensive and accurate examination. Goal: The purpose of this article is to showcase the constantly changing nature of supply chain management practices, predict the future impact of big data and analytics in SCM, emphasize the potential benefits of these trends, and offer guidance to leaders in the field of SCM. Approach/technique/procedure: It is emphasized how crucial it is to derive value from the vast quantity of data accessible in the field of supply chain management. Definition of "big data" and analytics, with explanation of how they affect SCM applications. Outcomes: Instances demonstrate how the supply chain management domain can be influenced by these recent trends and advancements. These examples have effectively adopted, utilized, and put into practice analytics that rely on large volumes of data. The existence of big data is undeniable, and utilizing analytics to derive valuable insights from this information has the power to create a significant influence. In summary, it can be stated that... It is important for supply chain managers to closely monitor these 2 trends because effectively incorporating "big data" analytics are able to keep them updated on advancements and alterations, ultimately enhancing their competitiveness.

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Changes in the structure of 20th century international industrial organisation that have incited research interest among economists have also driven a significant body of work in the business literature. Indeed, many of the factors driving the changing industrial structure are derived from business. Examples include the innovation and implementation of assembly lines, scientific management, modularisation, lean manufacturing, and just-intime production. While the economics literature has produced terminology such as "task trade", "vertical specialisation", and "production sharing", the business literature tends to emphasise "supply chains". This is in conjunction with terms from political economists and development theorists that include "value chains", "global commodity chains", and "global production networks". Of these, the supply chain provides the most relevant perspective for the business practitioner. Networks of firms are viewed from a focal firm perspective, and the supply chain ontology adopts various dimensions to orient a firm with its network surroundings (for example, direct-extended-ultimate supply chains, horizontal tiers or degrees of separation, and vertical structures within each tier). Further functionalising the supply chain concept is the field of supply chain management (SCM). Born from multidisciplinary roots that include logistics, marketing, management, and sociology, SCM has developed into a distinct field of study over the past fifty years. SCM theory has only recently reached a state of maturation where it produces operationalisable concepts and tools, but progress is being made in advancing both the overarching field of SCM and the specific issues that fall under the SCM umbrella. This chapter will review the overarching field, while Part II and its chapters will address the specific issues.

Few topics have generated more discourse in recent years than big data analytics. Given their knowledge of analytical and mathematical methods, operations research (OR) scholars would seem well poised to take a lead role in this discussion. Unfortunately, some have suggested there is a misalignment between the work of OR scholars and the needs of practicing managers, especially those in the field of operations and supply chain management where data-driven decision-making is a key component of most job descriptions. In this paper, we attempt to address this misalignment. We examine both applied and scholarly applications of OR-based big data analytical tools and techniques within an operations and supply chain management context to highlight their future potential in this domain. This paper contributes by providing suggestions for scholars, educators, and practitioners that aid to illustrate how OR can be instrumental in solving big data analytics problems in support of operations and supply chain management.

Over the period of last twenty years or so, field of supply chain management has evolved to such an extent that it has become backbone of today's businesses. Yet mere acceptance of the fact that supply chains can act as business levers/drivers, which can help propel organizational performance is not sufficient. There needs to be a performance measurement mechanism which can link supply chain management with overall organizational performance. This paper tries to provide brief literature review on supply chain management with a specific reference to its relationship with organizational performance. The paper studies the various approaches/methodologies used by researchers for exploring relationships between supply chain management and organizational performance. The focus of this review is to study empirical studies in particular. The review study covers articles from the field of operations, logistics and supply chain domain. Findings reveal a need for empirical research to establish link between supply chain performance & organizational performance in Indian context. Also revealed is a need to focus on softer aspects of management under supply chain management which can have significant impact on organizational performance.

With an increasing number of disasters disrupting commerce and community life around the world, it is timely to position humanitarian and disaster relief supply chains (HDRSC) within the broad field of supply chain management. This article presents a framework to that end. It distinguishes attributes of the environment that illustrate the difficulties encountered in supply chain management. Although considerable research has been conducted in logistics issues affecting HDRSCs, very little management research speaks to the complicating attributes. Thus, this article describes activities such as demand determination, supply chain coordination, recognizing when to move along the life cycle and post‐disaster reconstruction that differentiates supply chain concerns from logistics concerns. From this backdrop, some of the areas where research into HDRSCs can inform supply chain management in general are presented. The article concludes by discussing critical areas of research need as identified by experienced practitioners. Research in these areas will provide insights for supply chain managers facing similar issues in other environments.

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.

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.

Evaluation of green and sustainable supply chain management using structural equation modelling: A systematic review of the state of the art literature and recommendations for future research

Reducing the bullwhip effect is of the main challenges in the field of supply chain management (SCM). One of the solutions is to implement SCM software for collaborating within every upstream and downstream supply chain members effectively. However, there is a lack of measurement models to show the relationship between changes of the bullwhip effect and the implementation of SCM software. Therefore, this study proposed an innovative model for measuring the bullwhip effect under the implementation of supply chain management software. Moreover, an empirical four-stage supply chain case in Taiwan is taken for numerical analysis. The results show that a higher implementation rate of supply chain management software in the supply chain, the more improvements of the bullwhip effect will make. Furthermore, it also found that under the same software implementation rate in better performed and worse performed supply chains, the worse one can make more notable improvements for the bullwhip effect.

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.

• The objectives of this study match the CIE content as it provides academcians and practioners with innovative and helpful information on advanced computing technology in the industry. • Foundation models are large-scale machine learning models that have been trained on large amounts of data to perform a variety of tasks. • FMs are used to support management and operations in supply chain and logistics management and operations. • The paper is based on research conducted by the authors with several Italian and European organizations under a grant from the European Union's Next GenerationUE initiative. • The paper concludes with a real-world industrial case study to further substantiate these claims. The organizational environment is subject to constant change, driven by rapid technological progress and the advent of Industry 5.0. This dynamic scenario compels authors and practitioners in the fields of logistics and supply chain management and operations (LSCM) to explore the transformative impact of foundation models (FMs) and artificial intelligence (AI) on these areas. The integration of AI FMs, coupled with digital twins (DT) and cyber-physical systems (CPS), will not only optimize current processes but also fundamentally reshape the future of digital engineering (DE) in LSCM. Understanding the opportunities and challenges with FMs is critical to the strategic positioning in an ever-evolving marketplace where Industry 5.0 technologies drive innovation and efficiency. Organizations can gain a competitive advantage by analyzing the opportunities and risks of integrating FMs into LSCM operations and leveraging their ability to improve decision making and enhance predictive analytics and real-time monitoring through DT and CPS. This paper serves as a comprehensive and informative guide for evaluating the transformative potential of FMs in redesigning LSCM operations in the context of Industry 5.0, highlighting critical applications, exploring potential benefits and challenges, and pointing to upcoming innovations such as AI-driven DT and smart cyber-physical systems that are revolutionizing supply chain (SC) processes. The detailed analysis based on the Supply Chain Operations Reference (SCOR) components shows that FM-based solutions, supported by AI and Industry 5.0 technologies, can significantly improve LSCM business processes. This paper presents an integrated functional framework and technical architecture for implementing FM in LSCM, emphasizing the role of DT and CPS in enabling smart, adaptive, and resilient SC. The paper concludes with an industrial case study that further substantiates these claims and shows the practical application of FM in a shipbuilding organization. The case study describes successful approaches to implementing and effectively utilizing AI and DT. It provides a practical, functional, and technical framework for applying the transformative impact of FMs on LSCM within the Industry 5.0 paradigm. This integration highlights the synergy between FM, AI, DT, and CPS and paves the way. It paves the way for a more innovative, sustainable, and competitive management of supply networks and their operations.

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In today's world, competition is not only restricted between the marketing aspects of two companies or big firms. The competition has expanded between the supply chain management perspectives of two firms. In order to sustain within the current competitive environment supply chain professionals of a firm are struggling hard to handle large-scale data, that is important to reach an integrated, efficient, effective as well as agile supply chain process within their business. As a result, explosive growth of data volume and amount of data within business, made it mandatory for the supply chain processionals to use an effective data analytic tool in order to manage these data. The main aim of this study is to identify use of various data analytic tools in order to manage large data of supply process and inventory management process with several advantages and disadvantages. As per the literature findings, it has been observed that use of data analytic tools within an organisation can be helpful to investigate new insights of the supply chain process, which can be able to detect different parts and elements of that process. A mixed method approach has been incorporated within this research paper by collecting primary and secondary both data collection process. Total 50 employees engaged with inventory and supply chain management of different firm are chosen to conduct a survey. Secondary data are collected from secondary sources. As per the findings, it can be stated that big data analytics plays a positive role in organization for managing large data.