Impact of big data and predictive analytics capability on supply chain sustainability

Jeble, Shirish; Dubey, Rameshwar; Childe, Stephen J.; Παπαδόπουλος, Θάνος; Roubaud, David; Prakash, Anand

doi:10.1108/ijlm-05-2017-0134

Cited by 212 publications

(217 citation statements)

References 108 publications

(215 reference statements)

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“…Therefore, the authors anticipate that applications within these categories will also constantly continue to grow. Furthermore, this research builds on existing B2B sustainability research Song et al, 2016;Song et al, 2017;Zhang et al, 2017), and answers the call of recent studies (Bughin et al, 2010;Dubey et al, 2017;Jeble et al, 2018) by offering insights into the B2B sustainability dyad through a stronger empirical and theoretical underpinning. This research has also mapped key big data analytical techniques from the extant literature and provided an conceptualization of B2B Social media data analytics process (Fig.…”

Section: Theoretical Contributionsmentioning

confidence: 59%

“…There is no denying the increasing role of big data analytics from several domains, Bughin et al (2010) highlight studies in big data and sustainability for firms in the auto industry. However, given the rise in studies which aim to provide information on the application of big data analytics to improve environmental sustainability (Song et al, 2017;Song, Fisher, Wang, & Cui, 2016;Zhang, Ren, Liu, & Si, 2017) and social sustainability Song et al, 2017), it is surprising that majority of these studies lack practical insights and merely offer conceptual and anecdotal evidences (Bughin et al, 2010;Dubey et al, 2017;Jeble et al, 2018). Additionally, while studies acknowledge big data analytics and its contributions to sustainability, there remains a lack of practical insights into the types of techniques which can be employed by organisations to leverage sustainability from their use of analytics.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Role of big data and social media analytics for business to business sustainability: A participatory web context

Sivarajah

Irani

Gupta

et al. 2020

Industrial Marketing Management

215

124

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Section: Theoretical Contributionsmentioning

confidence: 59%

Section: Introductionmentioning

confidence: 99%

Role of big data and social media analytics for business to business sustainability: A participatory web context

Sivarajah

Irani

Gupta

et al. 2020

Industrial Marketing Management

215

124

View full text Add to dashboard Cite

“…This growing combination of resources, tools, and applications has significant effects in the field of supply chain management. Some of the effects include improving forecasting accuracy, reducing costs and gaining better contextual intelligence across supply chain operations, which translate into lower costs and quicker response times to customers (Jeble et al, 2018;Waller & Fawcett, 2013).…”

Section: Predictive Risk Intelligence In Supply Chain Management (Scm)mentioning

confidence: 99%

Ethical Implications of Predictive Risk Intelligence

Jiya

2019

The ORBIT Journal

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This paper presents a case study on the ethical issues that relate to the use of Smart Information Systems (SIS) in predictive risk intelligence. The case study is based on a company that is using SIS to provide predictive risk intelligence in supply chain management (SCM), insurance, finance and sustainability. Thepaper covers an assessment of how the company recognises ethical concerns related to SIS and the ways it deals with them. Data was collected through a document review and two in-depth semi-structured interviews.Results from the case study indicate that the main ethical concerns with the use of SIS in predictive risk intelligence include protection of the data being used in predicting risk, data privacy and consent from those whose data has been collected from data providers such as social media sites. Also, there are issues relating to the transparency and accountability of processes used in predictive intelligence. The interviews highlighted the issue of bias in using the SIS for making predictions for specific target clients. The last ethical issue was related to trust and accuracy of the predictions of the SIS. In response to these issues,the company has put in place different mechanisms to ensure responsible innovation through what it calls Responsible Data Science. Under Responsible Data Science, the identified ethical issues are addressed by following a code of ethics, engaging with stakeholders and ethics committees.This paper is important because it provides lessons for the responsible implementation of SIS in industry, particularly for start-ups. The paper acknowledges ethical issues with the use of SIS in predictive risk intelligence and suggests that ethics should be a central consideration for companies and individuals developing SIS to create meaningful positive change for society.ORBIT Journal DOI: https://doi.org/10.29297/orbit.v2i2.112 2

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“…Technology management, control, and monitoring [21,22] Digital Systems Digital supply chains, data analytics, cyber physical systems [9,10,33,34] Big data analytics on environmental impacts [35].…”

Section: Management Systemsmentioning

confidence: 99%

Smart Systems Implementation in UK Food Manufacturing Companies: A Sustainability Perspective

et al. 2018

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The UK food industry faces significant challenges to remain sustainable. With major challenges, such as Brexit, on the horizon, companies can no longer rely on a low labour cost workforce to maintain low production costs and achieve economic sustainability. Smart Systems (SS) is being seen as an approach towards achieving significant improvements in both economic and environmental sustainability. However, there is little evidence to indicate whether UK food companies are prepared for the implementation of such systems. The purpose of this research is to explore the applicability of Smart Systems in UK food manufacturing companies, and to identify the key priority areas and improvement levers for the implementation of such systems. A triangulated primary research approach is adopted that includes a questionnaire, follow-up interviews, and visits to 32 food manufacturing companies in the UK. The questionnaire and interviews are guided by a unique measuring instrument that the authors developed that focusses upon SS technologies and systems. This paper makes an original contribution in that it is one of the few academic studies to explore the implementation of SS in the industry, and provides a new perspective on the key drivers and inhibitors of its implementation. The findings suggest that the current turbulence in the industry could be bringing food companies closer to the adoption of such systems; hence, it is a good time to define and develop the optimum SS implementation strategy.

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Impact of big data and predictive analytics capability on supply chain sustainability

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 212 publications

References 108 publications

Role of big data and social media analytics for business to business sustainability: A participatory web context

Role of big data and social media analytics for business to business sustainability: A participatory web context

Ethical Implications of Predictive Risk Intelligence

Smart Systems Implementation in UK Food Manufacturing Companies: A Sustainability Perspective

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