Impact of big data analytics capabilities on supply chain sustainability

2020 ◽  
Vol 17 (1) ◽  
pp. 33-57 ◽  
Author(s):  
Sajjad Shokouhyar ◽  
Mohammad Reza Seddigh ◽  
Farhad Panahifar
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Sustainable Supply Chain ◽  
Self Assessment ◽  
Pharmaceutical Supply Chain ◽  
Content Type ◽  
Different Dimensions ◽  
The Relationship

Purpose The purpose of this paper is to develop a theoretical model to explain the impact of big data analytics capabilities (BDAC) on company’s supply chain sustainability (CSCS). The secondary objective of the study is to assess the relationship between different dimensions of supply chain sustainability and companies’ BDAC. Design/methodology/approach This research was carried out by conducting a survey among 234 pharmaceutical companies in Iran (a case study of Iran), using a standard questionnaire of BDAC and United Nations (UN) online self-assessment on supply chain sustainability. However, the respond of managers of 188 companies were usable in this research. Smart PLS3 was used to employ partial least squares method to examine the validity and reliability of the measurement and structural model. Findings The results of this study demonstrate that BDAC have a strong impact on both pharmaceutical supply chain sustainability, and the dimensions including vision, engage and internal. It is found that the relationships between BDAC and the other dimensions of supply chain sustainability including expect, scope and goals are not significant but positive. Research limitations/implications Research on the relationship between BDAC and CSCS, especially in the pharmaceutical supply chain, is scanty, and this gap is highlighted in developing countries and the pharmaceutical supply chain that plays a prominent role in public health. This paper discusses several important barriers to forming a sustainable supply chain and strong BDA capabilities. Practical implications This paper could be a guide to managers and consultants who are involved in big data analytics and sustainable development. Since UN urges companies do the online self-assessment, the results of this paper would be attractive and useful for UN global compact specialists. Originality/value No study has directly measured the relation between BDAC and CSCS and different dimensions of CSCS, using a comprehensive survey throughout all pharmaceutical companies in Iran. Moreover, this research assesses the different dimensions of BDA capabilities and supply chain sustainability. This paper represents the facts about situation of sustainability of pharmaceutical supply chain and BDAC in these companies, and discloses several related issues that are serious barriers to forming a sustainable supply chain and strong BDAC. In addition, this paper provided academic support for UN questionnaire about CSCS and used it in the survey.

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.

Big data analytics in supply chain and logistics: an empirical approach

2018 ◽  
Vol 29 (2) ◽  
pp. 767-783 ◽  
Author(s):  
Maciel Manoel Queiroz ◽  
Renato Telles
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Research Field ◽  
Content Type ◽  
Additional Tool ◽  
Current State ◽  
The Impact ◽  
The Relationship

Purpose The purpose of this paper is to recognise the current state of big data analytics (BDA) on different organisational and supply chain management (SCM) levels in Brazilian firms. Specifically, the paper focuses on understanding BDA awareness in Brazilian firms and proposes a framework to analyse firms’ maturity in implementing BDA projects in logistics/SCM. Design/methodology/approach A survey on SCM levels of 1,000 firms was conducted via questionnaires. Of the 272 questionnaires received, 155 were considered valid, representing a 15.5 per cent response rate. Findings The knowledge of Brazilian firms regarding BDA, the difficulties and barriers to BDA project adoption, and the relationship between supply chain levels and BDA knowledge were identified. A framework was proposed for the adoption of BDA projects in SCM. Research limitations/implications This study does not offer external validity due to restrictions for the generalisation of the results even in the Brazilian context, which stems from the conducted sampling. Future studies should improve the comprehension in this research field and focus on the impact of big data on supply chains or networks in emerging world regions, such as Latin America. Practical implications This paper provides insights for practitioners to develop activities involving big data and SCM, and proposes functional and consistent guidance through the BDA-SCM triangle framework as an additional tool in the implementation of BDA projects in the SCM context. Originality/value This study is the first to analyse BDA on different organisational and SCM levels in emerging countries, offering instrumentalisation for BDA-SCM projects.

Dave Snowden on KM and big data/analytics: interview with David J. Pauleen

2017 ◽  
Vol 21 (1) ◽  
pp. 12-17 ◽  
Author(s):  
David J. Pauleen
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Theory And Practice ◽  
Content Type ◽  
Face To Face ◽  
The Relationship ◽  
A Company ◽  
Practical Implications ◽  
Scientific Officer

Purpose Dave Snowden has been an important voice in knowledge management over the years. As the founder and chief scientific officer of Cognitive Edge, a company focused on the development of the theory and practice of social complexity, he offers informative views on the relationship between big data/analytics and KM. Design/methodology/approach A face-to-face interview was held with Dave Snowden in May 2015 in Auckland, New Zealand. Findings According to Snowden, analytics in the form of algorithms are imperfect and can only to a small extent capture the reasoning and analytical capabilities of people. For this reason, while big data/analytics can be useful, they are limited and must be used in conjunction with human knowledge and reasoning. Practical implications Snowden offers his views on big data/analytics and how they can be used effectively in real world situations in combination with human reasoning and input, for example in fields from resource management to individual health care. Originality/value Snowden is an innovative thinker. He combines knowledge and experience from many fields and offers original views and understanding of big data/analytics, knowledge and management.

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.

Big data analytics capabilities and organizational performance: the mediating effect of dual innovations

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaofeng Su ◽  
Weipeng Zeng ◽  
Manhua Zheng ◽  
Xiaoli Jiang ◽  
Wenhe Lin ◽  
...  
Keyword(s):  
Big Data ◽  
Organizational Performance ◽  
Empirical Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Mediating Effect ◽  
Content Type ◽  
Mediating Role ◽  
The Relationship

PurposeFollowing the rapid expansion of data volume, velocity and variety, techniques and technologies, big data analytics have achieved substantial development and a surge of companies make investments in big data. Academics and practitioners have been considering the mechanism through which big data analytics capabilities can transform into their improved organizational performance. This paper aims to examine how big data analytics capabilities influence organizational performance through the mediating role of dual innovations.Design/methodology/approachDrawing on the resource-based view and recent literature on big data analytics, this paper aims to examine the direct effects of big data analytics capabilities (BDAC) on organizational performance, as well as the mediating role of dual innovations on the relationship between (BDAC) and organizational performance. The study extends existing research by making a distinction of BDACs' effect on their outcomes and proposing that BDACs help organizations to generate insights that can help strengthen their dual innovations, which in turn have a positive impact on organizational performance. To test our proposed research model, this study conducts empirical analysis based on questionnaire-base survey data collected from 309 respondents working in Chinese manufacturing firms.FindingsThe results support the proposed hypotheses regarding the direct and indirect effect that BDACs have on organizational performance. Specifically, this paper finds that dual innovations positively mediate BDACs' effect on organizational performance.Originality/valueThe conclusions on the relationship between big data analytics capabilities and organizational performance in previous research are controversial due to lack of theoretical foundation and empirical testing. This study resolves the issue by provides empirical analysis, which makes the research conclusions more scientific and credible. In addition, previous literature mainly focused on BDACs' direct impact on organizational performance without making a distinction of BDAC's three dimensions. This study contributes to the literature by thoroughly introducing the notions of BDAC's three core constituents and fully analyzing their relationships with organizational performance. What's more, empirical research on the mechanism of big data analytics' influence on organizational performance is still at a rudimentary stage. The authors address this critical gap by exploring the mediation of dual innovations in the relationship through survey-based research. The research conclusions of this paper provide new perspective for understanding the impact of big data analytics capabilities on organizational performance, and enrich the theoretical research connotation of big data analysis capabilities and dual innovation behavior.

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.

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.

Quality dominant logic in big data analytics and firm performance

2019 ◽  
Vol 25 (3) ◽  
pp. 512-532 ◽  
Author(s):  
Samuel Fosso Wamba ◽  
Shahriar Akter ◽  
Marc de Bourmont
Keyword(s):  
Big Data ◽  
Firm Performance ◽  
Data Analytics ◽  
Online Survey ◽  
Information Quality ◽  
Big Data Analytics ◽  
Firm Strategy ◽  
Theoretical Understanding ◽  
Content Type ◽  
The Relationship

Purpose Big data analytics (BDA) gets all the attention these days, but as important—and perhaps even more important—is big data analytics quality (BDAQ). Although many companies realize a full return from BDA, others clearly struggle. It appears that quality dynamics and their holistic impact on firm performance are unresolved in data economy. The purpose of this paper is to draw on the resource-based view and information systems quality to develop a BDAQ model and measure its impact on firm performance. Design/methodology/approach The study uses an online survey to collect data from 150 panel members in France from a leading market research firm. The participants in the study were business analysts and IT managers with analytics experience. Findings The study confirms that perceived technology, talent and information quality are significant determinants of BDAQ. It also identifies that alignment between analytics quality and firm strategy moderates the relationship between BDAQ and firm performance. Practical implications The findings inform practitioners that BDAQ is a hierarchical, multi-dimensional and context-specific model. Originality/value The study advances theoretical understanding of the relationship between BDAQ and firm performance under the influence of firm strategy alignment.

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.

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.

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