1.

Record Nr.

UNINA9910150332503321

Titolo

Supply chain management in the big data era / / Hing Kai Chan, Nachiappan Subramanian, Muhammad Dan-Asabe Abdulrahman, editors

Pubbl/distr/stampa

Hershey, Pennsylvania : , : IGI Global, , 2017

©2017

ISBN

9781522509578

9781522509561

Descrizione fisica

PDFs (298 pages) : illustrations

Collana

Advances in Logistics, Operations, and Management Science (ALOMS) Book Series, , 2327-3518

Disciplina

658.50285/57

Soggetti

Business logistics - Management

Business logistics - Data processing

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

Big data analytics: academic perspectives / Muhammad D. Abdulrahman [and 3 others] -- Big data analytics: service and manufacturing industries perspectives / Nachiappan Subramanian [and 3 others] -- How smart operations help better planning and replenishment?: empirical study - supply chain collaboration for smart operations / Usha Ramanathan -- Big data analytics for predictive maintenance strategies / C. K. M. Lee, Yi Cao, Kam Hung Ng -- Data-driven inventory management in the healthcare supply chain / Shuojiang Xu, Kim Hua Tan -- Role of operations strategy and big data: a study of transport company / Arvind Upadhyay [and 3 others] -- Big data and RFID in supply chain and logistics management: a review of the literature and applications for data driven research / Thanos Papadopoulos [and 3 others] -- Developing an integration framework for crowdsourcing and internet of things with applications for disaster response / Rameshwar Dubey -- Supply chain coordination based on web service / Kamalendu Pal -- Exploring the hidden pattern from tweets: investigation into Volkswagen emissions scandal / Ying Kei Tse [and 4 others] -- Swift Guanxi data analysis and its application to e-commerce retail strategies improvement / Ewelina Lacka -- Applying



big data with fuzzy DEMATEL to discover the critical factors for employee engagement in developing sustainability for the hospitality industry under uncertainty / Kuo-Jui Wu [and 4 others].

Sommario/riassunto

"This book is an authoritative reference source for the latest scholarly material on the implementation of big data analytics for improved operations and supply chain processes, highlighting emerging strategies from different industry perspectives"--Provided by publisher.

2.

Record Nr.

UNINA9910794380803321

Autore

Marston Daniel

Titolo

Autism and independence : assessments and interventions to prepare teens for adult life / / Daniel Marston, PhD, ABPP

Pubbl/distr/stampa

Eau Claire, WI : , : PESI Publishing & Media, , [2019]

©2019

ISBN

1-68373-197-2

Descrizione fisica

1 online resource (206 pages)

Disciplina

618.9285882

Soggetti

Autistic youth - Behavior modification

Autistic youth - Conduct of life

Autistic youth

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia



3.

Record Nr.

UNINA9910632470503321

Autore

Wüthrich Mario V.

Titolo

Statistical Foundations of Actuarial Learning and its Applications / / by Mario V. Wüthrich, Michael Merz

Pubbl/distr/stampa

Cham, : Springer Nature, 2023

Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023

ISBN

3-031-12409-X

Edizione

[1st ed. 2023.]

Descrizione fisica

1 online resource (XII, 605 p. 1 illus.)

Collana

Springer Actuarial, , 2523-3270

Classificazione

BUS061000COM004000COM031000MAT003000

Disciplina

368.01

Soggetti

Actuarial science

Statistics

Machine learning

Artificial intelligence—Data processing

Social sciences—Mathematics

Actuarial Mathematics

Statistics in Business, Management, Economics, Finance, Insurance

Machine Learning

Data Science

Mathematics in Business, Economics and Finance

Assegurances

Estadística

Llibres electrònics

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Sommario/riassunto

This open access book discusses the statistical modeling of insurance problems, a process which comprises data collection, data analysis and statistical model building to forecast insured events that may happen in the future. It presents the mathematical foundations behind these fundamental statistical concepts and how they can be applied in daily actuarial practice. Statistical modeling has a wide range of applications, and, depending on the application, the theoretical aspects may be



weighted differently: here the main focus is on prediction rather than explanation. Starting with a presentation of state-of-the-art actuarial models, such as generalized linear models, the book then dives into modern machine learning tools such as neural networks and text recognition to improve predictive modeling with complex features. Providing practitioners with detailed guidance on how to apply machine learning methods to real-world data sets, and how to interpret the results without losing sight of the mathematical assumptions on which these methods are based, the book can serve as a modern basis for an actuarial education syllabus.