Data and Decision Analytics for Business Operations: Principles, Problems, and Practice - Tapa blanda

Acerca del autor

Michelle L. F. Cheong is Professor of Information Systems (Education) and Associate Dean of Postgraduate Professional Education at the Singapore Management University (SMU), School of Computing & Information Systems (SCIS). She had 8 years of industry experience leading teams to develop complex enterprise-wide IT systems covering business functions from sales to engineering, inventory management, planning, production, and distribution, before she joined SMU in 2005. She teaches courses in business modeling, data analytics and decision analytics, has conducted executive and professional trainings in data and decision analytics topics for many public and private organizations, as well as individuals from open enrolment courses.

Ma Nang Laik is an Associate Professor at the School of Business at the Singapore University of Social Sciences (SUSS). She teaches courses on data analytics, logistics and supply chains, quantitative methods, business skills and management, and business analytics applications. Her research expertise lies in the simulation and modeling of large-scale real-world problems and the development of computationally efficient algorithms to enable sound and intelligent decision-making in the organization. She brings industry experience from work for Changi Airport Group, PSA – one of the largest container ports, and the Development Bank of Singapore.

De la contraportada

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.