scholarly journals On relating big data analytics to supply chain planning: towards a research agenda

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jinou Xu ◽  
Margherita Emma Paola Pero ◽  
Federica Ciccullo ◽  
Andrea Sianesi
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Journal Articles ◽  
Supply Chain Planning ◽  
Research Directions ◽  
Content Type ◽  
Body Of Knowledge ◽  
Management Issues

PurposeThis paper aims to examine how the extant publication has related big data analytics (BDA) to supply chain planning (SCP). The paper presents a conceptual model based on the reviewed articles and the dominant research gaps and outlines the research directions for future advancement.Design/methodology/approachBased on a systematic literature review, this study analysed 72 journal articles and reported the descriptive and thematic analysis in assessing the established body of knowledge.FindingsThis study reveals the fact that literature on relating BDA to SCP has an ambiguous use of BDA-related terminologies and a siloed view on SCP processes that primarily focuses on the short-term. Looking at the big data sources, the objective of adopting BDA and changes to SCP, we identified three roles of big data and BDA for SCP: supportive facilitator, source of empowerment and game-changer. It bridges the conversation between BDA technology for SCP and its management issues in organisations and supply chains according to the technology-organisation-environmental framework.Research limitations/implicationsThis paper presents a comprehensive examination of existing literature on relating BDA to SCP. The resulted themes and research opportunities will help to advance the understanding of how BDA will reshape the future of SCP and how to manage BDA adoption towards a big data-driven SCP.Originality/valueThis study is unique in its discussion on how BDA will reshape SCP integrating the technical and managerial perspectives, which have not been discussed to date.

The role of big data analytics capabilities in bolstering supply chain resilience and firm performance: a dynamic capability view

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohamad Bahrami ◽  
Sajjad Shokouhyar
Keyword(s):  
Risk Management ◽  
Big Data ◽  
Supply Chain ◽  
Firm Performance ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Content Type ◽  
Supply Chain Resilience ◽  
Management Culture

PurposeBig data analytics capability (BDAC) can affect firm performance in several ways. The purpose of this paper is to understand how BDA capabilities affect firm performance through supply chain resilience in the presence of the risk management culture.Design/methodology/approachThe study adopted a cross-sectional approach to collect survey-based responses to examine the hypotheses. 167 responses were collected and analyzed using partial least squares in SmartPLS3. The respondents were generally senior IT executives with education and experience in data and business analytics.FindingsThe results show that BDA capabilities increase supply chain resilience as a mediator by enhancing innovative capabilities and information quality, ultimately leading to improved firm performance. In addition, the relationship between supply chain resilience and firm performance is influenced by risk management culture as a moderator.Originality/valueThe present study contributes to the relevant literature by demonstrating the mediating role of supply chain resilience between the BDA capabilities relationship and firm performance. In this context, some theoretical and managerial implications are proposed and discussed.

An examination of the importance of big data analytics in supply chain agility development

2018 ◽  
Vol 41 (10) ◽  
pp. 1201-1219 ◽  
Author(s):  
Santanu Mandal
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Technology Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Technical Knowledge ◽  
Content Type ◽  
Relational Knowledge ◽  
Management Knowledge ◽  
Business Knowledge

Purpose This paper aims to investigate the influence of big data analytics (BDA) personnel expertise capabilities in the development of supply chain (SC) agility. Based on extant literature, the study explores the role of BDA technical knowledge, BDA technology management knowledge, BDA business knowledge and BDA relational knowledge in SC agility development. Furthermore, the author also explores the inter-relationships among these four BDA personnel expertise capabilities. Design/methodology/approach An expert team consisting of IT practitioners (with a minimum experience of five years) were chosen to comment and modify the established scale items of the constructs used in the study. Subsequently, the measures were further pre-tested with 61 students specializing in computer science and information technology. The final survey was mailed to 651 IT professionals with a minimum experience of five years or more in an allied field. Repeated follow-ups and reminders resulted in 176 completed responses. The responses were analysed using partial least squares in SmartPLS 2.0.M3. Findings Findings suggested that BDA technology management knowledge, BDA business knowledge and BDA relational knowledge are prominent enablers of SC agility. Furthermore, BDA technology management knowledge is an essential precursor of BDA technical knowledge and BDA business knowledge. Originality/value The study is the foremost in addressing the importance of BDA personnel expertise capabilities in the development of SC agility. Furthermore, it is also the foremost in exploring the inter-relationships among the BDA personnel expertise capabilities.

Big Data Analytics Driven Supply Chain Transformation

2019 ◽  
pp. 106-124 ◽  
Author(s):  
Mondher Feki
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Research Directions ◽  
Digital Platforms ◽  
New Frontier ◽  
Future Research Directions ◽  
Chain Transformation

Big data has emerged as the new frontier in supply chain management; however, few firms know how to embrace big data and capitalize on its value. The non-stop production of massive amounts of data on various digital platforms has prompted academics and practitioners to focus on the data economy. Companies must rethink how to harness big data and take full advantage of its possibilities. Big data analytics can help them in giving valuable insights. This chapter provides an overview of big data analytics use in the supply chain field and underlines its potential role in the supply chain transformation. The results show that big data analytics techniques can be categorized into three types: descriptive, predictive, and prescriptive. These techniques influence supply chain processes and create business value. This study sets out future research directions.

The influence of big data analytics management capabilities on supply chain preparedness, alertness and agility

2019 ◽  
Vol 32 (2) ◽  
pp. 297-318 ◽  
Author(s):  
Santanu Mandal
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Investment Decision ◽  
Big Data Analytics ◽  
Content Type ◽  
Investment Decision Making ◽  
Management Capabilities ◽  
The Impact

Purpose The importance of big data analytics (BDA) on the development of supply chain (SC) resilience is not clearly understood. To address this, the purpose of this paper is to explore the impact of BDA management capabilities, namely, BDA planning, BDA investment decision making, BDA coordination and BDA control on SC resilience dimensions, namely, SC preparedness, SC alertness and SC agility. Design/methodology/approach The study relied on perceptual measures to test the proposed associations. Using extant measures, the scales for all the constructs were contextualized based on expert feedback. Using online survey, 249 complete responses were collected and were analyzed using partial least squares in SmartPLS 2.0.M3. The study targeted professionals with sufficient experience in analytics in different industry sectors for survey participation. Findings Results indicate BDA planning, BDA coordination and BDA control are critical enablers of SC preparedness, SC alertness and SC agility. BDA investment decision making did not have any prominent influence on any of the SC resilience dimensions. Originality/value The study is important as it addresses the contribution of BDA capabilities on the development of SC resilience, an important gap in the extant literature.

Big data analytics based enablers of supply chain capabilities and firm competitiveness: a fuzzy-TISM approach

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nisha Bamel ◽  
Umesh Bamel
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Structural Model ◽  
Big Data Analytics ◽  
Integrated Approach ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
People Skills ◽  
Interpretive Structural Model

PurposeThis paper aims to identify the big data analytics (BDAs) based enablers of supply chain capabilities (SCCs) and competitiveness of firms. This paper also models the interaction among identified enablers and thus projects the relationship strength of these enablers with SCC and a firm's competitiveness.Design/methodology/approachIn order to achieve the research objectives of this paper, we employed fuzzy total interpretive structural modeling (TISM), an integrated approach of an interpretive structural model and TISM.FindingsResults suggest that BDA-based enablers namely, IT infrastructure for BDA; leadership commitment; people skills for use of BDA and financial support for BDA significantly enable SCC and enhance firm competitiveness.Practical implicationsResults of the present study have implications for researchers and practitioners; the results will enable them to design policies around identified enablers of BDA initiatives.Originality/valueThe present paper is one of a few early efforts that address the role of BDA in augmenting SCC and subsequently a firm's competitiveness from a resource-dynamic capability perspective.

A systematic literature review with bibliometric analysis of big data analytics adoption from period 2014 to 2018

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zeeshan Inamdar ◽  
Rakesh Raut ◽  
Vaibhav S. Narwane ◽  
Bhaskar Gardas ◽  
Balkrishna Narkhede ◽  
...  
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Literature Review ◽  
Bibliometric Analysis ◽  
Systematic Literature Review ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Sustainable Supply Chain ◽  
Content Type ◽  
Future Direction

PurposeThe volume of data being generated by various sectors in recent years has increased exponentially. Consequently, professionals struggle to process essential data in the current competitive world. The purpose of the study is to explore and provide insights into the Big Data Analytics (BDA) studies in different sectors.Design/methodology/approachThis study performs a systematic literature review (SLR) with bibliometric analysis of BDA adoption (BDAA) in the supply chain and its applications in various sectors from 2014 to 2018. This paper focuses on BDAA studies have been carried out across different countries and sectors. Also, the paper explores different tools and techniques used in BDAA studies.FindingsThe benefits of adopting BDA, coupled with a lack of adequate research in the field, have motivated this study. This literature review categorizes paper into seven main areas and found that most of the studies were carried out in manufacturing and service.Practical implicationsThis research insight and observations can provide practitioners and academia with guidance on implementing BDA in different sustainable supply chain sectors. The article indicates a few remarkable gaps in the future direction and trends regarding the integration of BDA and sustainable supply chain development.Originality/valueThe study derives a new categorization of BDA, which investigates how data is generated, organized, captured, interpreted and evaluated to give valuable insights to manage the sustainable supply chain.

The contemporary state of big data analytics and artificial intelligence towards intelligent supply chain risk management: a comprehensive review

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Harsh M. Shah ◽  
Bhaskar B. Gardas ◽  
Vaibhav S. Narwane ◽  
Hitansh S. Mehta
Keyword(s):  
Artificial Intelligence ◽  
Supply Chain ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Supply Chain Risk Management ◽  
Supply Chain Risk ◽  
Research Directions ◽  
Content Type ◽  
Future Research Directions

PurposeThis paper aims to conduct a systematic literature review of the research in the field of Artificial Intelligence (AI) and Big Data Analytics (BDA) in Supply Chain Risk Management (SCRM). Finally, future research directions in this field have been suggested.Design/methodology/approachThe papers were searched using a set of keywords in the SCOPUS database. These papers were filtered using the Title abstract keywords principle. Further, more papers were found using the forward-backward referencing method. The finalized papers were then classified into eight categories.FindingsThe previous papers in AI and BDA in SCRM were studied. These papers emphasized various modelling and application techniques for AI and BDA in making the supply chain (SC) more resilient. It was found that more research has been done into conceptual modelling rather than real-life applications. It was seen that the use of AI-based techniques and structural equation modelling was prominent.Practical implicationsAI and BDA help build the risk profile, which will guide the decision-makers and risk managers make their decisions quickly and more effectively, reducing the risks on the SC and making it resilient. Other than this, they can predict the risks in disasters, epidemics and any further disruption. They also help select the suppliers and location of the various elements of the SC to reduce the lead times.Originality/valueThe paper suggests various future research directions that fellow researchers can explore. None of the previous research examined the role of BDA and AI in SCRM.

How big data analytics can help manufacturing companies strengthen supply chain resilience in the context of the COVID-19 pandemic

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Surajit Bag ◽  
Pavitra Dhamija ◽  
Sunil Luthra ◽  
Donald Huisingh
Keyword(s):  
Risk Management ◽  
Big Data ◽  
Supply Chain ◽  
Supply Chains ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Hypothetical Model ◽  
Content Type ◽  
Supply Chain Resilience ◽  
Management Capabilities

PurposeIn this paper, the authors emphasize that COVID-19 pandemic is a serious pandemic as it continues to cause deaths and long-term health effects, followed by the most prolonged crisis in the 21st century and has disrupted supply chains globally. This study questions “can technological inputs such as big data analytics help to restore strength and resilience to supply chains post COVID-19 pandemic?”; toward which authors identified risks associated with purchasing and supply chain management by using a hypothetical model to achieve supply chain resilience through big data analytics.Design/methodology/approachThe hypothetical model is tested by using the partial least squares structural equation modeling (PLS-SEM) technique on the primary data collected from the manufacturing industries.FindingsIt is found that big data analytics tools can be used to help to restore and to increase resilience to supply chains. Internal risk management capabilities were developed during the COVID-19 pandemic that increased the company's external risk management capabilities.Practical implicationsThe findings provide valuable insights in ways to achieve improved competitive advantage and to build internal and external capabilities and competencies for developing more resilient and viable supply chains.Originality/valueTo the best of authors' knowledge, the model is unique and this work advances literature on supply chain resilience.

Big Data Analytics Driven Supply Chain Transformation

2022 ◽  
pp. 1413-1432
Author(s):  
Mondher Feki
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Research Directions ◽  
Digital Platforms ◽  
New Frontier ◽  
Future Research Directions ◽  
Chain Transformation

Big data has emerged as the new frontier in supply chain management; however, few firms know how to embrace big data and capitalize on its value. The non-stop production of massive amounts of data on various digital platforms has prompted academics and practitioners to focus on the data economy. Companies must rethink how to harness big data and take full advantage of its possibilities. Big data analytics can help them in giving valuable insights. This chapter provides an overview of big data analytics use in the supply chain field and underlines its potential role in the supply chain transformation. The results show that big data analytics techniques can be categorized into three types: descriptive, predictive, and prescriptive. These techniques influence supply chain processes and create business value. This study sets out future research directions.

Sign in / Sign up

Export Citation Format

Share Document