Big data analytics as an operational excellence approach to enhance sustainable supply chain performance

BAG, S, WOOD, LC, XU, L, DHAMIJA, P and KAYIKCI, Yasanur (2020). Big data analytics as an operational excellence approach to enhance sustainable supply chain performance. Resources, Conservation and Recycling, 153: 104559.

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Link to published version:: https://doi.org/10.1016/j.resconrec.2019.104559
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    Abstract

    Operations management is a core organizational function involved in the management of activities to produce and deliver products and services. Appropriate operations decisions rely on assessing and using information; a task made more challenging in the Big Data era. Effective management of data (big data analytics; BDA), along with staff capabilities (the talent capability in the use of big data) support firms to leverage big data analytics and organizational learning in support of sustainable supply chain management outcomes. The current study uses dynamic capability theory as a foundation for evaluating the role of BDA capability as an operational excellence approach in improving sustainable supply chain performance. We surveyed mining executives in the emerging economy of South Africa and received 520 valid responses (47% response rate). We used Partial Least Squares Structural Equation Modelling (PLS-SEM) to analyze the data. The findings show that big data analytics management capabilities have a strong and significant effect on innovative green product development and sustainable supply chain outcomes. Big data analytics talent capabilities have a weaker but still significant effect on employee development and sustainable supply chain outcomes. Innovation and learning performance affect sustainable supply chain performance, and supply chain innovativeness has an important moderating role. A contribution of the study is identifying two pathways that managers can use to improve sustainable supply chain outcomes in the mining industry, based on big data analytics capabilities.

    Item Type: Article
    Uncontrolled Keywords: Big data analytics; Operational excellence; Dynamic capability view; Supply chain sustainability; Learning performance; Environmental Sciences; 05 Environmental Sciences; 09 Engineering; 12 Built Environment and Design
    Identification Number: https://doi.org/10.1016/j.resconrec.2019.104559
    SWORD Depositor: Symplectic Elements
    Depositing User: Symplectic Elements
    Date Deposited: 16 May 2022 10:50
    Last Modified: 16 May 2022 10:50
    URI: http://shura.shu.ac.uk/id/eprint/29905

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