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Data and decision analytics for business operationsprinciples, problems, and practice /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data and decision analytics for business operationsby Michelle L. F. Cheong, Ma Nang Laik.
Reminder of title:	principles, problems, and practice /
Author:	Cheong, Michelle L. F.
other author:	Ma Nang Laik.
Published:	Cham :Springer Nature Switzerland :2024.
Description:	xviii, 323 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Business logistics.
Online resource:	https://doi.org/10.1007/978-3-031-72255-4
ISBN:	9783031722554$q(electronic bk.)

Data and decision analytics for business operationsprinciples, problems, and practice /
Cheong, Michelle L. F.

Data and decision analytics for business operationsprinciples, problems, and practice /[electronic resource] :by Michelle L. F. Cheong, Ma Nang Laik. - Cham :Springer Nature Switzerland :2024. - xviii, 323 p. :ill. (some col.), digital ;24 cm.

1. Introduction -- 2. Demand Forecasting -- 3. Inventory Management -- 4. Distribution Management -- 5. Capacity Planning -- 6. Optimization Theory -- 7. Special Optimization Problems -- 8. Workforce Planning And Scheduling -- 9. Heuristic Algorithms -- 10. Queuing Theory -- 11. Simulation.

In this book, readers will be exposed to the Data and Decision Analytics Framework which helps a business analyst to first identify the root cause of business problems by collecting, preparing, and exploring data to gain business insights, before proposing what objectives and solutions should be developed to solve the problems. To guide the reader through the learning and application of this framework, several cases are included in the book to illustrate the typical operations management problems faced by businesses. These cases are based on experiences in business domains such as retail, healthcare, transportation and logistics operations, and banking, and they are related to demand forecasting, inventory management, distribution management, capacity planning, resource allocation, workforce scheduling, and service system management. For each case, a complete mapping of the case into the Data and Decision Analytics Framework was done to explain how the framework was applied to derive the data insights from data analytics, to define the business objectives, make the necessary assumptions, and then develop the solution to the business problem. This book aims at senior-year undergraduate or graduate students studying industrial engineering, business management with a focus on operations, or data science. They will learn how to use data analytics to first analyze problems to identify the root cause of problems, before developing the solutions supported by decision analytics.

ISBN: 9783031722554$q(electronic bk.)

Standard No.: 10.1007/978-3-031-72255-4doiSubjects--Topical Terms:

199351
Business logistics.

LC Class. No.: HD38.5

Dewey Class. No.: 658.5

Data and decision analytics for business operationsprinciples, problems, and practice /
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245 1 0 $a Data and decision analytics for business operations $h [electronic resource] : $b principles, problems, and practice / $c by Michelle L. F. Cheong, Ma Nang Laik.
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505 0 $a 1. Introduction -- 2. Demand Forecasting -- 3. Inventory Management -- 4. Distribution Management -- 5. Capacity Planning -- 6. Optimization Theory -- 7. Special Optimization Problems -- 8. Workforce Planning And Scheduling -- 9. Heuristic Algorithms -- 10. Queuing Theory -- 11. Simulation.
520 $a In this book, readers will be exposed to the Data and Decision Analytics Framework which helps a business analyst to first identify the root cause of business problems by collecting, preparing, and exploring data to gain business insights, before proposing what objectives and solutions should be developed to solve the problems. To guide the reader through the learning and application of this framework, several cases are included in the book to illustrate the typical operations management problems faced by businesses. These cases are based on experiences in business domains such as retail, healthcare, transportation and logistics operations, and banking, and they are related to demand forecasting, inventory management, distribution management, capacity planning, resource allocation, workforce scheduling, and service system management. For each case, a complete mapping of the case into the Data and Decision Analytics Framework was done to explain how the framework was applied to derive the data insights from data analytics, to define the business objectives, make the necessary assumptions, and then develop the solution to the business problem. This book aims at senior-year undergraduate or graduate students studying industrial engineering, business management with a focus on operations, or data science. They will learn how to use data analytics to first analyze problems to identify the root cause of problems, before developing the solutions supported by decision analytics.
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