MHI Solutions, which covers material handling and logistics, interviewed me for its current issue’s review of intermodal transportation trends. Many shippers used intermodal in the past, didn’t like it, and have been using truckload ever since. However, significant headwinds are now impacting truckload, such as the driver shortage. Truckload has not seen meaningful consolidation, so only a few companies are very large and profitable.
The Optimization Edge
A Blog for Business Executives and Advanced Analytics Practitioners
Technologies: Data Science, Big Data, Optimization, Machine Learning, Artificial Intelligence, Predictive Analytics, Forecasting
Applications: Operations, Supply Chain, Finance, Health Care, Workforce, Sales and Marketing
A revenue forecasting system we delivered for a publicly traded North American multimodal transportation company, summarized in a case study on this page, is noteworthy for many reasons. In this post, we focus on the collaboration throughout the project that helped the business team build confidence in changing forecasting processes from a manually intensive methodology to an automated system that provides insights into the underlying forecasting techniques. While many software vendors are promoting automated machine learning algorithms, we believe that the best predictive analytics blends business experience and insight with advanced analytics techniques.
An international airline sought to transform flight scheduling and crewing decisions through state-of-the-art optimization. The airline’s network is notably complex, with planning required for unscheduled pickup and delivery between 13,000 worldwide airports in more than 179 countries, with over 700 planes. There are variable transit times due to weather and potentials for grounding due to equipment failures.
The project sponsor recognizes the potential value of optimization and often initiates the contact with the optimization consultants to further define the right opportunity. While titles vary, this is generally a person near the top of the reporting chain of the people who will be using the software day in and out. The sponsor is usually a level or two above the SMEs: in most cases a vice president, director, or general manager.
The optimization solutions that we design and deliver typically consist of these components: an information technology (IT) system that consists of a database management system (DBMS), a data transport/communication layer to an optimization model, an optimization user interface, and some model diagnostics.
I recently had the great pleasure to be a guest on Road Dog Trucking, the Sirius XM radio program with 1.2 million daily listeners. The host, Mark Willis, asked me to discuss the disruptive technologies impacting the trucking industry: autonomous trucks, drones, the Uberization of freight, Big Data, and the Internet of Things. Based on their experiences with new technologies in the cab, on the road, and at the loading dock, callers asked a series of excellent questions. From a transcript, I have excerpted a few of our exchanges.
Mark Willis: Let’s go to the phone. First off, I’ve got Jeff coming up in Indiana. Do you think autonomous trucks will turn the industry on its ears? Is that going to be one of the disruptors? How do you feel about the technology?
A few years ago, I served on an INFORMS committee responsible for reviewing the questions that appear on the INFORMS Certified Analytics Practitioner exam. An interesting challenge for INFORMS is to take an item bank of questions, and choose a subset of the questions to appear on the exam that meet certain conditions related to the balance of topics and difficulties of the questions. This challenge doesn't just exist for INFORMS; it also occurs in many industries that use exam procedures to certify specialists. It even occurs for the SAT used in the college admissions process.
For time-sensitive Big Data research, renting processing capacity is often better than buying and maintaining farms of dedicated computers, and it helps organizations achieve tremendous leaps in productivity and ease of use.
Leaders of a High Frequency Trading (HFT) hedge fund retained Princeton Consultants to create a state-of-the-art environment for researching and simulating their alpha and execution ideas. In the typical cycle, proposed research ideas are discussed, approved, coded, and then back-tested against prior market and related data, using different combinations of parameters. In this never-ending quest, HFT funds face several classic challenges:
More Operations Research professors and students are building and deploying applications than they were two years ago, aligning them more with advanced analytics practitioners, according to our survey at the INFORMS Annual Meeting October 22-25 in Houston. The survey repeated with minor modifications our survey in 2015 at the same event, which found notable gaps between what students learn, what professors teach, and what practitioners need.
We can report the following:
- Gaps between practitioners and academia narrowed related to programming and application development, with more than 50% of students surveyed building applications.
- The O.R. community is very flexible when solving problems; there is no dominant software product or development tool.
- MATLAB is popular as a solver and as a programming language, and as an optimization tool, it is used more in academia than in industry.
- The use of modeling languages is especially varied.
We are proud that our colleague Irv Lustig received the INFORMS 2017 Volunteer Service Award for Distinguished Service. Irv has recently helped develop and improve programs and outreach to businesses and professional practitioners, a critical initiative for INFORMS. Irv points out how, since he began his career 30 years ago, the landscape for Operations Research has transformed on both the technology side and the human side.
Data availability is obviously much greater than it was before the Internet, but more exciting to Irv is the evolution of the associated tools to explore and understand data, making it easier to then choose the right data for an analytics application. Improvements in the performance of optimization software algorithms and implementations have far exceeded the improvements in hardware performance, making it possible to solve ever larger and more complex problems.