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Data Analytics for Supply Chain Networks

ISBN-13: 9783031298226 / Angielski

Niamat Ullah Ibne Hossain
Data Analytics for Supply Chain Networks Niamat Ullah Ibn 9783031298226 Springer - książkaWidoczna okładka, to zdjęcie poglądowe, a rzeczywista szata graficzna może różnić się od prezentowanej.

Data Analytics for Supply Chain Networks

ISBN-13: 9783031298226 / Angielski

Niamat Ullah Ibne Hossain
cena 644,07
(netto: 613,40 VAT:  5%)

Najniższa cena z 30 dni: 578,30
Termin realizacji zamówienia:
ok. 16-18 dni roboczych.

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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.

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.

Kategorie:
Nauka, Ekonomia i biznes
Kategorie BISAC:
Business & Economics > Economics - General
Business & Economics > Production & Operations Management
Mathematics > Prawdopodobieństwo i statystyka
Wydawca:
Springer
Seria wydawnicza:
Greening of Industry Networks Studies
Język:
Angielski
ISBN-13:
9783031298226

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.

Niamat Ullah Ibne Hossain is an assistant professor in the Department of Engineering Management at Arkansas State University. Dr. Hossain received his PhD in Industrial and Systems Engineering in 2020 from Mississippi State University (MSU), Starkville, MS. He obtained his Bachelor’s degree in Mechanical Engineering in 2010 from Khulna University of Engineering and Technology (KUET) and his MBA in Management Information Systems in 2013 from the University of Dhaka, both in Bangladesh. His past academic appointments include adjunct assistant professor in the Department of Industrial and Systems Engineering at Temple University and post-doctoral research scientist at Raspet Flight Research Laboratory in affiliation with the Industrial and Systems Engineering Department at MSU. He has also worked on different projects affiliated with the Federal Aviation Agency (FAA) National Science Foundation (NSF), US Department of Defense (DOD), Industry, and other Research Laboratories. He has held industry positions as a senior engineer at Caterpillar, Bangladesh, and systems engineer at Energypac Power Generation Ltd. He has significant experience as a trainee for courses such as model-based systems engineering, simulation and modeling analysis using FlexSim, Six Sigma (Green and Yellow Belt), and the Graduate Teaching Assistant Certification Program at MSU. As a faculty member, he hopes to create a platform where students of different races and ethnicities can express themselves and exchange perspectives on teaching and research with their peers.

His main research interests include systems engineering, model-based systems engineering (MBSE)/SysML, systems thinking, systems dynamics simulation, systems resilience, as well as risk and sustainability management. Dr. Hossain is also the recipient of different prestigious awards at national and intra-university levels, such as the Outstanding PhD student award (2020) from ISE department at MSU, the Gulf Region Intelligent Transportation Society (GRITS) Scholarship, the Winner of Institute of Industrial and Systems Engineers (IISE) Innovation Design, the Best Paper Award in Doctoral Dissertation Category, the J.R. Ron Walsh Best Research Paper Award, the Best Poster Award at Graduate Student Council Annual Research Symposium, the National Science Foundation (NSF) Travel Grant, the Bagley College of Engineering Travel Grants, and the Graduate Student Travel Assistance Grants (TAGGS). He is also an active member of the International Student Advisory Board (ISAB) at MSU, INFORMS Student Chapter at MSU, American Society of Engineering and Management (ASEM), Institute of Industrial and Systems Engineers (IISE), and Habitat for Humanity at Starkville, Mississippi.

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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