Schlagwort(e):
Environmental economics.
;
Business logistics.
;
Quantitative research.
;
Sustainability.
;
Environmental Economics.
;
Supply Chain Management.
;
Data Analysis and Big Data.
;
Sustainability.
Beschreibung / Inhaltsverzeichnis:
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.
Kurzfassung:
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.
Materialart:
Online-Ressource
Seiten:
VI, 261 p. 1 illus.
,
online resource.
Ausgabe:
1st ed. 2023.
ISBN:
9783031298233
Serie:
Greening of Industry Networks Studies, 11
URL:
https://doi.org/10.1007/978-3-031-29823-3
DOI:
10.1007/978-3-031-29823-3
DDC:
333.7
Sprache:
Englisch
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