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Transforming Supply Chains: Powering Circular Economy with Analytics, Integration and Flexibility Using Dual Theory and Deep Learning with PLS-SEM-ANN Analysis

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  • Muhammad Noman Shafique

    (CESAM—Centre for Environmental and Marine Studies, Department of Environment and Planning, University of Aveiro, 3810-193 Aveiro, Portugal
    Department of Management Sciences, University of Buner, Buner 19290, Pakistan)

  • Ammar Rashid

    (College of Engineering and IT, Ajman University, Ajman P.O. Box 346, United Arab Emirates)

  • Sook Fern Yeo

    (Faculty of Business, Multimedia University, Melaka 75450, Malaysia
    Department of Business Administration, Daffodil International University, Dhaka 1207, Bangladesh)

  • Umar Adeel

    (Department of Computer Science and Engineering, American University of Ras Al Khaimah, Ras Al-Khaimah P.O. Box 10021, United Arab Emirates)

Abstract

The Sustainable Development Goals and circular economy are two critical aspects of the 2030 Agenda for Sustainable Development. They both seek to reduce the waste of natural resources and enhance society’s social, economic, and environmental goals. This study aims to identify, develop, test, and verify the significant antecedents that affect the adoption of supply chain analytics and its consequences for achieving the circular economy. We have divided the conceptual framework into two parts. In the first part, the relationship among data integration and scalability, organizational readiness, and policies and regulations as Technological–Organizational–Environmental factors as antecedents in adopting supply chain analytics. In the second part, the dynamic capabilities view grounded the relationship among supply chain analytics, supply chain integration, and sustainable supply chain flexibility effect directly and indirectly on the circular economy. Data have been collected using the survey method from 231 respondents from the manufacturing industry in Pakistan. Data have been analyzed using (i) partial least square structure equation modeling (ii) and artificial neural network approaches. The empirical findings proved that antecedents (data integrity and scalability, organizational readiness, and policy and regulation) and consequences (supply chain integration and sustainable supply chain flexibility) of supply chain analytics adoption would improve the circular economy performance. Additionally, artificial neural networks have supported these relationships. The adoption of supply chain analytics will enable organizations to supply chain integration. Additionally, organizations with more integration and analytics in their operations tend to have more flexibility and a circular economy. Moreover, organizations and society will obtain social, economic, and environmental benefits and reduce wastage and negative environmental impacts.

Suggested Citation

  • Muhammad Noman Shafique & Ammar Rashid & Sook Fern Yeo & Umar Adeel, 2023. "Transforming Supply Chains: Powering Circular Economy with Analytics, Integration and Flexibility Using Dual Theory and Deep Learning with PLS-SEM-ANN Analysis," Sustainability, MDPI, vol. 15(15), pages 1-23, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:15:p:11979-:d:1210215
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    References listed on IDEAS

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    Cited by:

    1. Esam Salamah & Ahmad Alzubi & Azmiye Yinal, 2023. "Unveiling the Impact of Digitalization on Supply Chain Performance in the Post-COVID-19 Era: The Mediating Role of Supply Chain Integration and Efficiency," Sustainability, MDPI, vol. 16(1), pages 1-28, December.
    2. Xiaomei Li & Huchuan Deng & Xuanrui Yu & Yang Yu, 2024. "Cooperative Impact of the Digital Sector, Eco-Friendly Policies, and Sophisticated Economic Development: A Study Drawing from China’s Practices," Sustainability, MDPI, vol. 16(23), pages 1-26, November.
    3. Shafique, Muhammad Noman & Yeo, Sook Fern & Tan, Cheng Ling, 2024. "Roles of top management support and compatibility in big data predictive analytics for supply chain collaboration and supply chain performance," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    4. Xiaomei Li & Huchuan Deng & Xuanrui Yu & Jiehong Li & Yang Yu, 2024. "Research on the Coordinated Development of Digital Economy, Green Technology Innovation, and Ecological Environment Quality—A Case Study of China," Sustainability, MDPI, vol. 16(11), pages 1-27, June.
    5. Muhammad Noman Shafique & Umar Adeel & Ammar Rashid, 2024. "The Synergy Between Industry 5.0 and Circular Economy for Sustainable Performance in the Chinese Manufacturing Industry," Sustainability, MDPI, vol. 16(22), pages 1-17, November.

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