Data and decision analytics for business operations: principles, problems, and practice

By:

Cheong, Michelle L. F

Contributor(s):

Ma Nang Laik

Material type: Text

TextPublication details: Springer Cham 2024Description: xviii, 323 pISBN:

9783031722547

Subject(s):

DDC classification:

658.403 CHE

Summary: 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. (https://link.springer.com/book/10.1007/978-3-031-72255-4)

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	Operations Management & Quantitative Techniques	658.403 CHE (Browse shelf(Opens below))	1	Available		007882

Table of contents:
Front Matter
Pages i-xviii
Download chapter PDF
Introduction
Michelle L. F. Cheong, Ma Nang Laik
Pages 1-9
Demand Forecasting
Michelle L. F. Cheong, Ma Nang Laik
Pages 11-68
Inventory Management
Michelle L. F. Cheong, Ma Nang Laik
Pages 69-107
Distribution Management
Michelle L. F. Cheong, Ma Nang Laik
Pages 109-129
Capacity Planning
Michelle L. F. Cheong, Ma Nang Laik
Pages 131-143
Optimization Theory
Michelle L. F. Cheong, Ma Nang Laik
Pages 145-189
Special Optimization Problems
Michelle L. F. Cheong, Ma Nang Laik
Pages 191-225
Workforce Planning and Scheduling
Michelle L. F. Cheong, Ma Nang Laik
Pages 227-245
Heuristic Algorithms
Michelle L. F. Cheong, Ma Nang Laik
Pages 247-266
Queuing Theory
Michelle L. F. Cheong, Ma Nang Laik
Pages 267-297
Simulation
Michelle L. F. Cheong, Ma Nang Laik
Pages 299-323
(https://link.springer.com/book/10.1007/978-3-031-72255-4)

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.

(https://link.springer.com/book/10.1007/978-3-031-72255-4)

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