Big data analytics in turbulent contexts: towards organizational change for enhanced agility

Barlette, Yves; Baillette, Paméla

doi:10.1080/09537287.2020.1810755

Cited by 77 publications

(68 citation statements)

References 131 publications

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“…Our research results are also in accordance with Phillips-Wren et al [11] that data-driven decision-making BI&A and BD can create competitive advantages and higher business value in IoT manufacturing identifying as a new research opportunity. However, our findings are not consistent with the results of a systematic literature review provided by Ardito et al [1], Saggi and Jain [2], Sheng et al [3], Barlette and Baillette [6], Günther et al [63], Mikalef et al [64], as well as a descriptive qualitative studies conducted by Zhou et al [66], Tao et al [67], Horváth and Szabó [68], considering different factors to be crucial within this topic in future research.…”

Section: Above) It Complies Partially With Qin [108]contrasting

confidence: 99%

“…There are a few recent research studies provided by a systematic literature review methodological approach [1]- [3], [6], [11], [25], [32], [59], [63]- [65]. We have found a systematic literature review that has recently been conducted by Mikalef et al helping to explain the mechanisms through which BDA lead to competitive performance gains [64].…”

Section: Big Data-driven Intelligent Iot Manufacturingmentioning

confidence: 99%

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Strategic Key Elements in Big Data Analytics as Driving Forces of IoT Manufacturing Value Creation: A Challenge for Research Framework

Rajnoha

Hadac

2024

IEEE Trans. Eng. Manage.

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Big Data (BD)-driven business intelligence (BI) is considered to be a new development stage within industrial engineering management to achieve higher business value creation. The article provides extensive research of BD-driven approaches to identify strategic knowledge and value-creating components within Internet of Things (IoT) manufacturing. Due to previous research studies focused mostly on partial solutions and lack a holistic view, this article topic is still far from being sufficiently explored and resolved. The article presents the results of an extensive research study of contemporary professional literature with a specific aim to provide a more holistic and systematical view on the current state and future trends in this issue. To solve this high complex research problem, both quantitative and qualitative research methodology approaches are used. The article is primarily based on a systematic literature review covering 187 essential and 36 additional current research papers within the Web of Science and Scopus database from 2012 to the present. Research findings identify the seven key strategic elements determining that Big Data analytics (BDA) can become a real and tremendous business value creation driver within IoT intelligent manufacturing. These research results provide important conclusions and implications for challenging future investigations into BDA and BI systems in the context of advancing the Industry 4.0 revolution. In conclusion, we propose a final suggestion in the form of a strategic future research chain from IoT through BDA to value creation based on BD intelligence within IoT manufacturing, whereas seven strategic elements might play a crucial.

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Section: Above) It Complies Partially With Qin [108]contrasting

confidence: 99%

Section: Big Data-driven Intelligent Iot Manufacturingmentioning

confidence: 99%

Strategic Key Elements in Big Data Analytics as Driving Forces of IoT Manufacturing Value Creation: A Challenge for Research Framework

Rajnoha

Hadac

2024

IEEE Trans. Eng. Manage.

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“…The analytics in big data offers companies to extract valuable insight from the data to support decision-making [104]. The studies found that one of the most important advantages the companies gain by using big data analytics is agility [105].…”

Section: Big Data and Analyticsmentioning

confidence: 99%

“…The big data supported by analytics brings accuracy in the predictive models of demand and supply [108]. Thus, the predictive accuracy in decisionmaking, more access to data about consumer and supply trends and internal resource reconfiguration develop a dynamic capability of organizations to increasingly face and address uncertainty in making themselves more agile [105].…”

Section: Big Data and Analyticsmentioning

confidence: 99%

Organizational Agility in Industry 4.0: A Systematic Literature Review

Mrugalska

Ahmed

2021

Sustainability

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Agility is the dynamic capability of an organization which helps it to manage a change and uncertainties in the environment. The purpose of this research is to review the literature from the perspective of agility in Industry 4.0. This paper systematically reviews 381 relevant articles from peer-reviewed academic journals in the period of the last five years. The results show that agility is important for an organization to adopt Industry 4.0 technologies as it helps companies to cope with the changes that arise along with the adoption of Industry 4.0 technologies. Further, it also indicates that by adopting Industry 4.0 technologies, companies can significantly enhance their agility capability into various aspects with different technologies. The technologies which enhance the agility are: smart manufacturing, internet of things, cyber-physical system, big data and analytics and cloud computing. On the other hand, important aspects of agility include supply chain, workforce, information system, facilities, management, manufacturing and technology agility.

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“…Support of management has proven to enhance the integration of technology with business, which in turn increases the level of technology adoption (Ooi et al, 2018). Managers play an important role as they provide the necessary resources for driving technological changes (Barlette and Baillette, 2020). Lack of support from the top management is considered as a barrier in many studies which involve the introduction of new technology into the existing process (Bag et al, 2018;Abed, 2020;Pech and Vrchota, 2020;Rouibah et al, 2020;Sony et al, 2020).…”

Section: Cultural Changementioning

confidence: 99%

Procurement 4.0 to the rescue: catalysing its adoption by modelling the challenges

Jerome

Saxena

Sonwaney

et al. 2021

BIJ

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PurposeThe pandemic crisis has resulted in global chaos that had caused massive disruption to the supply chain. The pharmaceutical industry, in particular, has been working tirelessly to ensure that they can cater to the people who need them. With restrictions being imposed to prevent the spread of the COVID-19 virus, the movement of raw materials required has been affected, thus creating the need for the procurement function to be innovative. This study proposes the application of Industry 4.0 concepts into the procurement activities of an organization to make it more resilient and efficient.Design/methodology/approachTo study the intensity of the challenges, Total Interpretive Structural Modelling is used alongside the “Matrice des Impacts Croises Multiplication Appliquee a un Classement” (MICMAC) technique.FindingsResilience can be achieved through the collaboration between the organization and its network of suppliers. This is however easier said than done. High and unclear investments have been identified as the challenge that is taking a toll on all technological investments in the pandemic era. The study also shows that organizational inertia which is present in established and structured firms are a deterrent as well.Originality/valueThis study is based on the application of procurement 4.0 to ensure that pharmaceutical supply chains stay least affected since they are essentials. This study using a multi-criteria decision-making approach to prioritize the challenges. This will help practitioners make decisions faster.

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Big data analytics in turbulent contexts: towards organizational change for enhanced agility

Cited by 77 publications

References 131 publications

Strategic Key Elements in Big Data Analytics as Driving Forces of IoT Manufacturing Value Creation: A Challenge for Research Framework

Strategic Key Elements in Big Data Analytics as Driving Forces of IoT Manufacturing Value Creation: A Challenge for Research Framework

Organizational Agility in Industry 4.0: A Systematic Literature Review

Procurement 4.0 to the rescue: catalysing its adoption by modelling the challenges

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