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Data analytics for supply chain networks

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data analytics for supply chain networksedited by Niamat Ullah Ibne Hossain.
other author:	Hossain, Niamat Ullah Ibne.
Published:	Cham :Springer International Publishing :2023.
Description:	vi, 261 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Business logisticsData processing.
Online resource:	https://doi.org/10.1007/978-3-031-29823-3
ISBN:	9783031298233$q(electronic bk.)

Data analytics for supply chain networks
Data analytics for supply chain networks[electronic resource] /edited by Niamat Ullah Ibne Hossain. - Cham :Springer International Publishing :2023. - vi, 261 p. :ill., digital ;24 cm. - Greening of industry networks studies,v. 112543-0254 ;. - Greening of industry networks studies,v. 11..

Chapter 1. The state of art of data analytics in resilience and sustainability management -- Chapter 2. Enhancing the viability of green supply chain management initiatives leveraging data fusion technique -- Chapter 3. Supply chain sustainability and supply chain resilience: A performance measurement framework with empirical validation -- Chapter 4. An assessment of decision-making in resilient and sustainable project between literature and practice -- Chapter 5. Barriers for Lean Supply Chain Management and their Overcoming Strategies in Context of the Indian Automobile Industry -- Chapter 6. Prioritizing Sustainability Criteria of Green Supply Chains using Best Worst Method -- Chapter 7. Economic performance analysis of resilient and sustainable supply chain: Adoption of electric vehicles as a sustainable logistics option -- Chapter 8. Integrating circular economy and reverse logistics for achieving sustainable dairy operations -- Chapter 9. The impact of big data analytics capabilities on the sustainability of maritime firms -- Chapter 10. Smart transportation logistics: Achieving supply chain efficiency with green initiatives.

The objective of the book is to adopt the application of data analytics to enhance the sustainability and resilience of the green supply chain networks. To demonstrate the applicability and usefulness of the method, the book adopts different data analytic models and approaches against the backdrop of case studies. In summary, this book attempts to address the question of methods, tools, and techniques that can be used to create resilient, anti-fragile, reliable, and invulnerable green supply chain networks.

ISBN: 9783031298233$q(electronic bk.)

Standard No.: 10.1007/978-3-031-29823-3doiSubjects--Topical Terms:

230874
Business logistics
--Data processing.

LC Class. No.: HD38.5

Dewey Class. No.: 658.5

Data analytics for supply chain networks
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505 0 $a Chapter 1. The state of art of data analytics in resilience and sustainability management -- Chapter 2. Enhancing the viability of green supply chain management initiatives leveraging data fusion technique -- Chapter 3. Supply chain sustainability and supply chain resilience: A performance measurement framework with empirical validation -- Chapter 4. An assessment of decision-making in resilient and sustainable project between literature and practice -- Chapter 5. Barriers for Lean Supply Chain Management and their Overcoming Strategies in Context of the Indian Automobile Industry -- Chapter 6. Prioritizing Sustainability Criteria of Green Supply Chains using Best Worst Method -- Chapter 7. Economic performance analysis of resilient and sustainable supply chain: Adoption of electric vehicles as a sustainable logistics option -- Chapter 8. Integrating circular economy and reverse logistics for achieving sustainable dairy operations -- Chapter 9. The impact of big data analytics capabilities on the sustainability of maritime firms -- Chapter 10. Smart transportation logistics: Achieving supply chain efficiency with green initiatives.
520 $a The objective of the book is to adopt the application of data analytics to enhance the sustainability and resilience of the green supply chain networks. To demonstrate the applicability and usefulness of the method, the book adopts different data analytic models and approaches against the backdrop of case studies. In summary, this book attempts to address the question of methods, tools, and techniques that can be used to create resilient, anti-fragile, reliable, and invulnerable green supply chain networks.
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