Predictive and Prescriptive Analytics in Operations Management


Book Description

The recent surge in data availability and advances in hardware and software and the recent developments and democratization of analytics highlight the critical importance of prediction and prescription in harnessing the power of data to create value through optimal, data-driven decision making. This thesis proposes novel Machine Learning (ML) and optimization methods in (i) predictive analytics, (ii) prescriptive analytics, and (iii) their high-impact applications in operations management.













Simulating Business Processes for Descriptive, Predictive, and Prescriptive Analytics


Book Description

This book outlines the benefits and limitations of simulation, what is involved in setting up a simulation capability in an organization, the steps involved in developing a simulation model and how to ensure that model results are implemented. In addition, detailed example applications are provided to show where the tool is useful and what it can offer the decision maker. In Simulating Business Processes for Descriptive, Predictive, and Prescriptive Analytics, Andrew Greasley provides an in-depth discussion of Business process simulation and how it can enable business analytics How business process simulation can provide speed, cost, dependability, quality, and flexibility metrics Industrial case studies including improving service delivery while ensuring an efficient use of staff in public sector organizations such as the police service, testing the capacity of planned production facilities in manufacturing, and ensuring on-time delivery in logistics systems State-of-the-art developments in business process simulation regarding the generation of simulation analytics using process mining and modeling people’s behavior Managers and decision makers will learn how simulation provides a faster, cheaper and less risky way of observing the future performance of a real-world system. The book will also benefit personnel already involved in simulation development by providing a business perspective on managing the process of simulation, ensuring simulation results are implemented, and that performance is improved.




Applied Big Data Analytics in Operations Management


Book Description

Operations management is a tool by which companies can effectively meet customers’ needs using the least amount of resources necessary. With the emergence of sensors and smart metering, big data is becoming an intrinsic part of modern operations management. Applied Big Data Analytics in Operations Management enumerates the challenges and creative solutions and tools to apply when using big data in operations management. Outlining revolutionary concepts and applications that help businesses predict customer behavior along with applications of artificial neural networks, predictive analytics, and opinion mining on business management, this comprehensive publication is ideal for IT professionals, software engineers, business professionals, managers, and students of management.




Simulating Business Processes for Descriptive, Predictive, and Prescriptive Analytics


Book Description

This book outlines the benefits and limitations of simulation, what is involved in setting up a simulation capability in an organization, the steps involved in developing a simulation model and how to ensure that model results are implemented. In addition, detailed example applications are provided to show where the tool is useful and what it can offer the decision maker. In Simulating Business Processes for Descriptive, Predictive, and Prescriptive Analytics, Andrew Greasley provides an in-depth discussion of Business process simulation and how it can enable business analytics How business process simulation can provide speed, cost, dependability, quality, and flexibility metrics Industrial case studies including improving service delivery while ensuring an efficient use of staff in public sector organizations such as the police service, testing the capacity of planned production facilities in manufacturing, and ensuring on-time delivery in logistics systems State-of-the-art developments in business process simulation regarding the generation of simulation analytics using process mining and modeling people’s behavior Managers and decision makers will learn how simulation provides a faster, cheaper and less risky way of observing the future performance of a real-world system. The book will also benefit personnel already involved in simulation development by providing a business perspective on managing the process of simulation, ensuring simulation results are implemented, and that performance is improved.




Operations Research and Analytics in Latin America


Book Description

This book gathers a selection of peer-reviewed research papers presented at the joint IV ASOCIO/XIX IISE Region 16 Conference held in Chia and Bogota, Colombia. The conference was organized by the Universidad de La Sabana’s Research Group on Logistics Systems, in partnership with Chapters #782 (Universidad de La Sabana), #712 (Universidad Sergio Arboleda) and #988 (Universidad de Los Andes) of the Institute of Industrial and Systems Engineers (IISE). The main emphasis of the book is on modelling and solving business-related problems in operations research, and on applying descriptive, predictive and prescriptive analytics and the management sciences to actual decision-making in organizations. Both theoretical developments and algorithm implementation are presented. A special focus is given to business problems arising in emerging economies, particularly in Latin America and the Caribbean. This book is addressed to academics, practitioners, postgraduate students and researchers in operations research, analytics and industrial engineering, as well as to undergraduate students for educational purposes. In particular, the book will appeal to the academic and research community in Latin America and the Caribbean, as it presents projects developed and implemented there. Higher education engineering programs will benefit from the findings and insights shared in the fields of industrial engineering, operations research and analytics, applied mathematics, and computer science and engineering.




Operations Management and Data Analytics Modelling


Book Description

Operations Management and Data Analytics Modelling: Economic Crises Perspective addresses real operation management problems in thrust areas like the healthcare and energy management sectors and Industry 4.0. It discusses recent advances and trends in developing data-driven operation management-based methodologies, big data analysis, application of computers in industrial engineering, optimization techniques, development of decision support systems for industrial operation, the role of a multiple-criteria decision-making (MCDM) approach in operation management, fuzzy set theory-based operation management modelling and Lean Six Sigma. Features Discusses the importance of data analytics in industrial operations to improve economy Provides step-by-step implementation of operation management models to identify best practices Covers in-depth analysis using data-based operation management tools and techniques Discusses mathematical modelling for novel operation management models to solve industrial problems This book is aimed at graduate students and professionals in the field of industrial and production engineering, mechanical engineering and materials science.




Analytics in Operations/Supply Chain Management


Book Description

Efficient and effective operations/supply chain management is pivotal to an organisation's success in today's competitive global environment. This Symposium Proceedings focuses on the role of analytics in operations /supply chain management, particularly in the context of multi criteria decision making. It highlights emerging concepts and potential applications.