Managing supply chain resources with Big Data Analytics: a systematic review

For AuthorsIf you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. Laszlo MonostoriResearch Laboratory of Engineering and Management Intelligence, Hungarian Academy of Sciences, Budapest, Hungary Abstract Purpose -The purpose of this paper is to examine challenges and potential of big data in heterogeneous business networks and relate these to an implemented logistics solution. Design/methodology/approach -The paper establishes an overview of challenges and opportunities of current significance in the area of big data, specifically in the context of transparency and processes in heterogeneous enterprise networks. Within this context, the paper presents how existing components and purpose-driven research were combined for a solution implemented in a nationwide network for less-than-truckload consignments. Findings -Aside from providing an extended overview of today's big data situation, the findings have shown that technical means and methods available today can comprise a feasible process transparency solution in a large heterogeneous network where legacy practices, reporting lags and incomplete data exist, yet processes are sensitive to inadequate policy changes. Practical implications -The means introduced in the paper were found to be of utility value in improving process efficiency, transparency and planning in logistics networks. The particular system design choices in the presented solution allow an incremental introduction or evolution of resource handling practices, incorporating existing fragmentary, unstructured or tacit knowledge of experienced personnel into the theoretically founded overall concept. Originality/value -The paper extends previous high-level view on the potential of big data, and presents new applied research and development results in a logistics application.

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“…• remaining (to be declared) at the same time on previous days over the past week R 1,2,3,5 ; and • day of the week.…”

Section: Results and Analysismentioning

confidence: 99%

Advanced predictive-analysis-based decision support for collaborative logistics networks

Ilie‐Zudor

Ekárt

Kemény

et al. 2015

Supply Chain Management: An International Journal

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“…The importance of using BDA techniques in SCM is true to an extent that organizations will not stand a chance of success in today's competitive markets. Since 2010, numerous articles have been published, which emphasized on the application of BDA in SCM and their major achievements [2,3,[10][11][12][13]. Since 2011 to 2015, Mishra et al identify the influential and prominent researchers and articles with most citations carried out a bibliographic analysis of big data.…”

Section: Supply Chain Analyticsmentioning

confidence: 99%

“…The results indicated that the number of articles in the field of BDA has increased [14]. Barbosa techniques usually use the predictive and prescriptive approaches rather than descriptive approach [10]. Dubey et al carried out a research in order to identify the effects of big data and predictive analysis on two aspects of sustainability, including environmental and social aspects.…”

Section: Supply Chain Analyticsmentioning

confidence: 99%

Big Data Analytics and Its Applications in Supply Chain Management

Darvazeh¹,

Vanani²,

Musolu³

2020

New Trends in the Use of Artificial Intelligence for the Industry 4.0

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In today's competitive marketplace, development of information technology, rising customer expectations, economic globalization, and the other modern competitive priorities have forced organizations to change. Therefore, competition among enterprises is replaced by competition among enterprises and their supply chains. In current competitive environment, supply chain professionals are struggling in handling the huge data in order to reach integrated, efficient, effective, and agile supply chain. Hence, explosive growth in volume and different types of data throughout the supply chain has created the need to develop technologies that can intelligently and rapidly analyze large volume of data. Big data analytics capability (BDA) is one of the best techniques, which can help organizations to overcome their problem. BDA provides a tool for extracting valuable patterns and information in large volume of data. So, the main purpose of this book chapter is to explore the application of BDA in supply chain management (SCM).

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“…Human capabilities are introduced as one of the BDAC by 72% of the studies [34,[49][50][51][52]. BDA highly depends on human skills and knowledge to utilise BD techniques and tools such as (data mining tools and analytical methods).…”

Section: Bda Human Capabilitiesmentioning

confidence: 99%

“…Further, analyst' interaction skills have influenced on managerial decision-making processes. As a result, the valuable analytic outputs of managers' perceptions have been improved [49]. Within the context of a firm's BD function, the intelligence gathered from the data may be of no use if the managers fail to understand which gathered insights could be useful.…”

Section: Skillsmentioning

confidence: 99%

Assessing Big Data Analytics Capability and Sustainability in Supply Chains

Çetindamar

Shdifat

Erfani

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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Big data analytics capability (BDAC) is a technologybased capability, which can influence sustainability performance of firms in supply chains. By using BDAC strategically, supply chains could improve their responses to social, environmental, and social changes taking place in uncertain business environments. This paper presents a detailed literature review on the two ends of the equation: BDACs and sustainability in supply chains performance (SSCP). The theoretical perspective of the dynamic capabilities helps us to understand BDAC holistically, a combination of non-human and human capabilities. Then, we adapt the three-bottom-line approach: economic, environmental, and social performance in order to offer a comprehensive measurement of SSCP Based on the overview of the literature, the paper offers metrics to be used in assessing both BDAC and SSCP that can advance the understanding of the relationship between them.

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Managing supply chain resources with Big Data Analytics: a systematic review

Cited by 110 publications

References 97 publications

Advanced predictive-analysis-based decision support for collaborative logistics networks

Advanced predictive-analysis-based decision support for collaborative logistics networks

Big Data Analytics and Its Applications in Supply Chain Management

Assessing Big Data Analytics Capability and Sustainability in Supply Chains

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