Distinguished McKnight University Professor and Professor and Incoming Head of Industrial and Systems Engineering; Director, Initiative on the Sharing Economy; Fellow of the Institute on the Environment, University of Minnesota
Models for the Sharing Economy
The sharing economy is used to denote emerging business models that enable the access to products and services on an on-demand basis. In some cases, this access is mediated by a digital platform that connects suppliers (often single individuals who are willing to share assets or provide a service) with buyers. Some of these platforms have been successful in overcoming the inefficiencies of peer-to-peer interactions by reducing transaction and search costs, facilitating payments, reducing moral hazard, and enabling trust among strangers. Others have been successful in harnessing economies of scale by tapping into idle assets or leveraging the crowd. These platforms present unique operational challenges regarding how best to match supply and demand, including pricing, wage setting, real time matching of buyers and suppliers, and the management of inventory and workforce, among others. They also raise important questions regarding the impact on consumers, incumbent firms, and society (e.g., environmental impact and impact on labor welfare). In this tutorial, we describe recent efforts at developing models for the design, analysis, and optimization of these systems. In particular, we draw on three papers that consider (1) peer-to-peer product sharing, (2) labor platforms for on-demand services, and (3) product rental networks. We also discuss the many outstanding opportunities for operations and management science research in this area. (Based on joint work with Costas Courcoubetis, Jian-Ya Ding, Xiang Li, Xiaobo Li, Guangwen Kong, and Terry Taylor.)
Professor, Stewart School of Industrial & Systems Engineering, Georgia Tech
Applications of Custom 3D Printed Physiological Heart Valve Models for Reducing Heart Surgery Risks
3D printing is finding more and more applications in medical fields, particularly personalized healthcare, due to its capabilities of producing patient-specific products, devices, and prototypes/models. This tutorial presents an innovative method of producing patient-specific, tissue-mimicking heart valves with sensing capability by integrating metamaterial design, multi-material 3D printing and printed electronics techniques. The physiological models can help surgeons or cardiologists make informed diagnosis, optimize surgery planning, and practice the operation prior to the surgery. In addition, the tissue-mimicking heart valves can provide effective means for surgeon training and patient education. This method is demonstrated through an application case of predicting post-surgery paravalvular leak of transcatheter aortic valve replacement (TAVR) procedure for treating aortic stenosis conditions. The 3D printed heart valve models were found to be effective in mimicking the valves of real patients for surgery outcome prediction. This tutorial will also present the ongoing research of this project: to explore the application of the virtual physiological models to augment the limited real patients data for developing machine learning-based predictive models to improve medical devices design, and diagnosis and treatment of certain medical conditions.
The H. John Heinz III College of Information Systems and Public Policy, Carnegie Mellon University
Healthcare Informatics and Analytics
The significant advances in clinical and consumer health technologies combined with the rapid developments in advanced analytics of high dimensional, high volume, and complex healthcare data is powering a transformation of healthcare delivery worldwide. Innovative analytical techniques are being developed to support a range of decisions that include predicting responses to different treatment regimens, individual and population level risk assessments, detecting anomalies, and preventing deterioration in the health status of the patient. Supporting patient – provider communication and shared decision making via intelligent reminders, notifications and informed guidance, and providing smart healthcare delivery operations to increase satisfaction, efficiency and quality of care are further capabilities being architected using informatics tools. Learning current and potential best practices from data using quantitative methodologies, such as statistical machine learning and operations research, and translating the new evidence to the frontlines of care via efficient software implementations and institutional deployments, offer both major challenges and opportunities for researchers and practitioners alike. This tutorial will highlight some of the methods and tools to address these issues with illustrative examples drawn from clinical, consumer self-care and public health domains.
Nicholas G. Hall
INFORMS President and Professor, Fisher College of Business, Ohio State University
Research and Teaching Opportunities in Project Management
One-quarter of the world’s economic activity, with an annual value of $18 trillion, is organized using the business process of project management. This process has exhibited dramatic growth in business interest in recent years, with a more than 1000% increase in Project Management Institute membership since 1996. Contributing to this growth are new applications, for example IT implementations, research and development, software development, corporate change management, and new product and service development. However, the very different characteristics of modern projects present new challenges. The partial resolution of these challenges by industry over the last 15 years presents numerous interesting opportunities for academic researchers. These research opportunities make use of a remarkably broad range of methodologies, including robust optimization, cooperative and non-cooperative game theory, predictive analytics, and behavioral modeling. Furthermore, the $4.5 trillion that is annually at risk from a shortage of skilled project managers, and the 15.7 million new jobs in project management expected by 2020, provide great opportunities for contributions to project management education. These educational opportunities include the integration of case studies, analytics challenges, online simulations, in-class games, self-assessment exercises, videos, and guest speaker presentations, which form an appealing course for both business and engineering schools.
President, AMPL Optimization Inc.
Professor Emeritus, Northwestern University
Model-Based Optimization for Operations Research: Best Practices and Current Trends
As optimization (or prescriptive analytics) has grown as a tool for business decision-making, a key factor in its success has been the adoption of model-based optimization. Using this approach, an analyst’s major work is to describe a problem of interest by means of an algebraic model, while the computation of a solution is left to general-purpose, off-the-shelf software. Powerful modeling systems manage the difficulties of translating between the human modeler’s ideas and the computer software’s needs. This tutorial introduces model-based optimization and offers a guide to its effective use.
Eva K. Lee
Virginia C. and Joseph C. Mello Chair and Professor, School of Industrial and Systems Engineering
Machine Learning and Big Data Analytics
The effect of big data is being felt everywhere, from business to science, from government to the arts. Information has gone from scarce to overabundant. This makes it possible to do many things that previously could not be done: uncover business trends, prevent diseases, combat crime, plus a multitude of other possibilities. Harnessing the data well may bring huge and innovative benefits, unlock new sources of economic value, provide fresh insights into science and provide policy makers with solid and convincing evidence to support their stands. Yet critical challenges lie ahead, including data security, privacy, and yet-to-be-discovered technology to effectively and efficiently analyze the data for business innovation. Multi-source data system modeling, machine learning and big data analytics play an increasingly important role in modern business enterprise. Many problems arising from multi-source data can be formulated into mathematical models and can be analyzed using sophisticated optimization, decision analysis, and computational techniques. In this tutorial, we will discuss various machine learning technologies, and share some of our successes in healthcare, defense, and service sector applications through innovation in predictive and big data analytics.
Analytics and Data Science Leader, LinkedIn
Business Analytics Manager, LinkedIn
How to Leverage Big Data Analytics to Grow Business
In this tutorial, we will illustrate the big data analytics lifecycle and share our practices leveraging advanced big data analytics and machine learning techniques to grow business at LinkedIn. You’ll learn how to empower business partners to access insights whenever needed, how to optimize business performance by leveraging unique data, and how to innovate for sustainable business growth.
Full Professor of Supply Chain and Operations Strategy, Head of Logistics Department, Kühne Logistics University
Analytics for the Supply Chain 4.0
Supply chain management has always been technology oriented and data intensive. New technologies like robots in warehousing, self-driving trucks or IoT solutions offer many interesting possibilities for supply chain research. However, the on-going explosion of data available along the supply chain has also attracted a lot of attention from practice and academia. Until recently, the supply chain management community has focused primarily on complex mathematical models and operations research methods. However, now there is a strong push by many scholars to leverage new data sources and the breadth of data for creating new insights and to improve decision making. In this tutorial, we have a look at the analytics opportunities along the different stages of the supply chain and discuss some detailed applications.
Lessons from the Crypt: Lessons Learned in Implementing Optimization Systems
Though the field of operations research was founded in practice, today there exists a substantial gap between its theoretical capabilities and real world impact. The field of operations research/management science/advanced analytics has never been more relevant than it is today. Our profession is in a unique position to solve the myriad problems that face our societies. The estimated $250 billion benefits generated by the by the 266 Edelman finalists since 1976, while very impressive, only represent the tip of the real savings produced by the operations research projects. Yet, a large number of systems are either never completed or fail to provide the full benefits. The never ending need for increased efficiency, availability of abundant data, and relatively inexpensive computing power makes it a golden age for the profession … provided we make use of the opportunity and deliver real world benefits. This interactive session will draw on the author’s more than 25 years of experience on implementing operations research systems and discuss the factors that contribute to the successful implementation and deployment of operations research systems.
Jayashankar M. Swaminathan
Kenan-Flagler Business School, University of North Carolina
Responsible Operations: Models, Relevance and Impact
There is a growing movement across various industries around developing and optimizing business models that not only focus on financial goals but also impact the society and the environment in a positive manner. These topics focus on a wide range of for-profit and non-profit operations on environmental issues such as remanufacturing, alternative energy and carbon emissions as well as social issues such as child labor, humanitarian operations, agriculture and healthcare. Research models on responsible operations that incorporate optimization and data can have an influential role in positively impacting the society and world at large. In this tutorial, I will provide an overview of the types of problems in this domain, the unique dimensions that need special attention and discuss currently available methods and identify opportunities for future research in this area.
Richard E. Dauch Chair in Manufacturing and Operations Management, Purdue University
Data Integrated Stochastics: Models and Methods
This tutorial will review the current data integrated approaches for predictive and prescriptive analysis of stochastic systems. In particularly we will review: 1) approaches such as Multi-Armed Bandit, Regularization in Sample Average Approximation and Data Driven Robust Optimization for generating prescriptive solutions to stochastic systems, and 2) some of the Machine Learning approaches used for predictive analysis of stochastic systems. We will then provide a framework for data integrated methodology for predictive and prescriptive analytics for stochastic systems. Specific attention will be paid to overcoming structural and statistical errors. This is achieved through Operational Statistics and Objective Operational Learning which are built on the basis of data integration and cross validation. We will illustrate how, 1) regularization in sample approximation approaches and data driven robust optimization with cross validation relates to Operational Statistics, and 2) multi-armed bandit and machine learning approaches compares to Objectives Operational Learning. Applications in pricing and revenue management, inventory control, queueing systems performance evaluation and staffing in service systems will be demonstrated.
Indian Institute of Management, Ahmedabad
The Evolution of Supply Chain Function in the Context of India
Based on a set of illustrative examples, in this tutorial we attempt to capture the evolution of supply chain function in the Indian context. These examples span areas related to (a) supply chain with diffused priority (b) AAA supply chain ( c) cultural impact on supply chain design (d) mass customization ( e ) postponement strategy (f) optimization models to improve supply chain efficiency (g) model based response to strategic questions (h) Business Process Re-engineering (i) value chain reconfiguration and (j) managing Bull-whip effect.
The evolution of supply chain function in the Indian context is characterized by sporadic scientific applications in select industries. The under managed sectors’ performance in supply chain is surprisingly better. This situation is the consequence of the combined effect of lower bargaining power of consumers and modest intensity of competition. The supply chain in the Indian context is driven by availability of material rather than efficiency or effectiveness of the supply chain. The inefficiency of the supply chain is subsidized by the consumer. With the increase in the trend to integrate the Indian economy with the global economy, we expect the emphasis on supply chain and its performance would improve in the Indian context..