Margery Connor, CAP, Bill Klimack, and Yan Zhu
Chevron, 2015 INFORMS Prize Winner for excellence in analytics and operations research, will present their long and innovative history of applying analytics and operations research across their worldwide energy company. Highlighted projects include:
• Petro: Chevron’s refinery planning tool
• Workforce forecasting to ensure the right people on the right projects
• genOpt: Optimization model to maximize oil and gas production.
Chevron will also share their journey applying decision analysis.
Emory University, Georgia Tech, and Centers for Disease Control and Prevention
Eva K. Lee and Helder Nakaya
Machine Learning Framework for Predicting Vaccine Immunogenicity
The ability to better predict how different individuals will respond to vaccination and to understand what best protects individuals from infection greatly facilitates developing next-generation vaccines. We present a general-purpose, machine-learning framework for discovering gene signatures that can predict vaccine immunity and efficacy. Our models offer unique features not found in other models simultaneously. We will describe the implemented results for yellow fever and influenza vaccines, and highlight their implications for public health and precision medicine.
Yale University, School of Management
Meet the Member-in-Chief: Ed, Edelman, and Editor’s Cut
Ed Kaplan is the William N. and Marie A. Beach Professor of Operations Research at the Yale School of Management, and also Professor of Public Health and Professor of Engineering at Yale. He is in his 30th year on the faculty of Yale University. In 2014, Ed was elected to serve as President-Elect of INFORMS, which makes 2016 his presidential year. However, Ed prefers to be known as the Member-in-Chief. He contends that operations research is not a spectator sport, and as a rallying cry is known to exclaim “let’s do stuff!”
An expert in operations research, mathematical modeling and statistics, Ed’s research has focused on problems in public policy, with specific applications in public health and counterterrorism. It was on the basis of this work that Ed was elected to the National Academy of Engineering, and the National Academy of Medicine. More recently, he has developed an interest in sports analytics, having contributed a few inane papers in that area as well.
At the TIMS/ORSA spring meeting in April 1992, which just happened to take place right here in Orlando, a team from the New Haven Health Department with Ed as the lead analyst/modeler won the Edelman Award for Management Science Achievement for evaluating New Haven’s legal needle exchange program. Having not had the opportunity to recap his Edelman work like present-day prize winners, Ed will recreate his 1992 presentation using mostly original slides. This provides an interesting opportunity to see how things have changed in Edelman presentation land.
And while he has your attention, Ed will also introduce the newest edition of INFORMS Editor’s Cut titled Confronting Public Problems with Operations Research. INFORMS recognizes the importance of attacking public problems; indeed it is a stated goal of our society that “Operations research and analytics will advance society and make the world a better place.” With such spirit in mind, this issue presents a broad cross-section of applications to problems in public health, homeland security and counterterrorism, and public services. Whether you are looking for classroom examples, personal inspiration, or just interesting reads, the featured authors remind us that operations research is not a spectator sport. They have been “doing stuff” with operations research, and the results are impressive indeed.
Edward H. Kaplan is the author of more than 130 research articles. Kaplan received both the Lanchester Prize and the Edelman Award, two top honors in the operations research field, among many other awards. An elected member of the National Academy of Engineering and the National Academy of Medicine of the US National Academies, Kaplan’s current research focuses on the application of operations research to problems in counterterrorism and homeland security. He is presently the President of the Institute for Operations Research and the Management Sciences (INFORMS), the world’s largest society of operations research and management science professionals. You can learn more about Prof. Kaplan and his research by visiting http://faculty.som.yale.edu/EdKaplan/.
Quickstart Data Science: Learn How To Create Real World Models Using Data Science And Machine Learning Techniques
Learn how to apply data science and machine learning to your business analytics and operations research datasets via this 50 minute presentation. With data science and machine learning methods, your data can easily and quickly be converted into knowledge to yield more accurate and more actionable models. We will demonstrate these techniques, using four APPLIED real-world examples highlighting typical business analytics and operations research challenges and including: forecasting, ROI, optimization, risk analysis, scoring, segmentation, targeted marketing, analytics-based decision making, and scenario planning. Takeaway:
By the end of this presentation, our goal is that you will be able to build your own data science model, you will be able to replicate every step discussed in this presentation, both on the datasets we have used for illustrative purposes as well as on your own datasets, and you will understand the big ideas of data science and machine learning and how they apply to business analytics and operations research.
bring your computer and follow-along to create your own models.
Tom Davenport, Babson College
Julia Kirby, Harvard Business Review and Harvard University Press
Cognitive Technologies: The Next Step Up for Data and Analytics
This year’s version of the popular INFORMS/Harvard Business Review Panel features a special dialogue between HBR Contributing Editor Julia Kirby and Tom Davenport, author of the business best seller “Competing on Analytics.” Kirby and Davenport have just collaborated on “Only Humans Need Apply: Winners and Losers in the Age of Smart Machines,” which will be published by HarperBusiness this May. So-called “smart” technologies are everywhere, and the level of intelligence in smart machines is increasing over time. Analytics technology is evolving toward cognitive systems, capable of making basic decisions and performing rudimentary and repetitive tasks in data management. The authors will engage in a lively discussion about what is meant by cognitive intelligence and AI; cognitive tasks that are ripe for automation and those that still need the human touch; the business rules to make cognitive technology function in the workplace; and why the Robot Revolution only exists in the movies.
Thomas H. Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson College, cofounder of the International Institute for Analytics, Fellow at the MIT Initiative on the Digital Economy, and Senior Advisor to Deloitte Analytics. He teaches analytics/big data in executive programs at Babson, Harvard Business School and School of Public Health, and MIT Sloan School.
Davenport pioneered the concept of “competing on analytics” with his best-selling 2006 Harvard Business Review article and 2007 book. His most recent book (with Julia Kirby) is Only Humans Need Apply: Winners and Losers in the Age of Smart Machines. He wrote or edited seventeen other books and over 100 articles for Harvard Business Review, Sloan Management Review, The Financial Times, and many other publications. He is a regular contributor to the Wall Street Journal. He has been named one of the top 25 consultants by Consulting News, one of the 100 most influential people in the IT industry by Ziff-Davis, and one of the world’s top fifty business school professors by Fortune magazine.
Julia Kirby is a senior editor at Harvard University Press and contributing editor to Harvard Business Review. Prior to joining HBR in 2000, she worked in management consulting. At the Ernst & Young Center for Business Innovation, she edited the company’s quarterly journal of management research and also managed its conferences, website, book publishing program, and working paper series. At Accenture, she developed communication platforms for the Institute for Strategic Change, working with Tom Davenport.
She teamed up with Davenport most recently on Only Humans Need Apply: Winners and Losers in the Age of Smart Machines (Harper Collins, 2016). The book offers an optimistic game plan for knowledge workers as their employers increasingly rely on cognitive technologies. Previously, she coauthored Standing on the Sun: How the Explosion of Capitalism Abroad will Change Business Everywhere, with Christopher Meyer. The book was praised as one of the best business books of 2012 by the Financial Times.
The 2016 UPS Smith Prize Winner will be announced Monday, April 11 at the Edelman Gala. The competition is held Sunday, April 10 in Regency 5. The three programs competing for this year’s prize are:
• School of Information Systems & Management, and School of Public Policy and Management, H. John Heinz III College, Carnegie Mellon University
• Institute for Advanced Analytics, North Carolina State University
• Operations Research Program, United States Air Force Academy
Randall Davis and Cynthia Rudin, MIT;
Dana L. Penney, Lahey Hospital and Medical Center
Detecting Preclinical Cognitive Change
The Clock Drawing Test — a simple pencil and paper test — has been used for more than 50 years as a screening tool to differentiate healthy individuals from those with cognitive impairment, and has proven useful in helping to diagnose cognitive dysfunction associated with neurological disorders such as Alzheimer’s disease, Parkinson’s disease, and other dementias and conditions. For nearly a decade we have been administering the test using a digitizing ballpoint pen that reports its position with considerable spatial and temporal precision. We developed a methodology that analyzes the pen stroke data from these drawings and computed a large collection of features (statistics) of the drawings. We applied a variety of machine learning techniques to the data, producing interpretable predictive and prescriptive models. The resulting scoring systems were designed to be as easy to use as scoring systems currently used by clinicians, but more accurate. These new tests do not rely on doctors’ subjective judgment, unlike the current (non-digitized) clock drawing test. We evaluated our new models with a variety of techniques, including operationalizing a number of widely used manual scoring systems so that we could use them as benchmarks. We explored the tradeoffs in performance and interpretability in classifiers built using a number of different subsets of these features and a variety of different machine learning techniques. While more work is required for FDA approval, the work we have done offers the possibility of substantial improvement in detecting cognitive impairment earlier than currently possible. Given that the costs incurred for just a single form of dementia — Alzheimer’s Disease — are currently $200 billion per year, and are projected to reach one trillion dollars annually by 2050, early detection of impairment will have considerable medical, sociological, and economic impact.
Javier Aldrete and Lee Rehwinkel, Zilliant
Driving Organic Growth with Zilliant SalesMax
B2B industrial manufacturing and distribution companies face a unique challenge: how to enable large field sales forces to sell the right set of products to the accounts with the highest probability to purchase more. Prescriptive sales analytics is emerging as the means to enable sales teams to know where their time is best spent, maximize wallet share from existing customers, and preempt customer defection. In this talk, you’ll see how prescriptive sales analytics, that relies on advanced science and a company’s transaction data, enabled FleetPride, the leading supplier of aftermarket heavy-duty truck and trailer parts in the U.S., to meet double-digit revenue growth goals and deliver an incremental $1 million in revenue each month over an 8-month measurement period.
Aly Megahed and Mark Smith, IBM
Analytics for the Engagement Life Cycle of IBM’s Highly Valued IT Service Contracts
IBM competes to win multi-million IT service contracts. These large contracts typically involve composite services composed of several thousands of software, hardware, and services. Examples are data center consolidation, migration of IT services to the cloud, and help desk services management. In response to clients’ request for proposals (RFPs), IBM and other competing IT service providers submit proposals. Clients short list a number of providers and engage with them through intense negotiations to select a final winner for the bid. Service providers maintain and manage a pipeline of such deals. Each deal goes through a life cycle which begins with the identification and validation of the opportunity, qualifying it, receiving a RFP, pursuing it with a team of business and technical sellers until the contract is signed or the deal is not won.
Given the business value at stake, the conventional approach to taking these steps involve resource-intensive, and complex activities and decision making. This calls for a strong demand to bring in data-driven analytics to help manage the pipeline, make resource allocation decisions, strategize the winning of each deal, and forecast the revenue out of contract signings.
The team at IBM Almaden Research Center has partnered with stakeholders in IBM services organization and developed an analytical toolset that offers insights during various stages of this life cycle to assist different decisions. In this presentation, we will describe the objective and overview of the following tools, their key innovations, and resulting business impact:
• A requirement analysis tool that uses innovative NLP techniques and data mining to analyze clients’ RFP documents, extract requirements, and map them to relevant offerings.
• A pricing tool that consists of data mining of historical deals and market data in order to calculate pricing points, and a predictive model that provides the relative win probability of each price point.
• A work in progress resource allocation tool that optimizes the assignment of the sales personnel to the negotiation of different deals.
• A deal competitiveness assessment tool for what-if scenario studies, price re-assessment, and analyses of “in-flight” deals w.r.t. similar won historical deals.
• A win prediction tool that combines a quantitative predictive model and a text-based one that analyzes the comments written by the sales team during the pursuit.
• A predictive tool for the type and timing of the next milestone to be achieved in the engagement life cycle.
• An aggregated revenue prediction tool based on optimizing the weights on aggregated historical quarter revenues of deals at different stages.
As can be seen from the description, the tools span over the different types of analytics (descriptive, predictive, and prescriptive) as well as combinations across methods. The tools are innovative in their overall framework, detailed algorithmic design, or both. The developed tools have already been implemented, deployed, and are actively being used by the business. The impact of using these tools is significant to the business. The expected revenue gains are dramatic, projecting a multi-million dollar increase in revenues and more efficient management of the overall process.
Speakers organized by Track
2016 Franz Edelman Award Competition
Analytics Leadership & Soft Skills
Decision & Risk Analysis
Fraud Detection & Cyber Security
Health Care & Life Sciences
INFORMS Prizes & Special Sessions
Internet of Things
Revenue Management & Pricing
Sports & Entertainment
Supply Chain Analytics
Technology Workshops – Sunday