The Optimization Edge

I often hear analytics as just one pod, but I really think there are two types. One I call “analytics at rest” and the other “analytics in motion.”

Analytics at rest means using analytics to make strategic decisions, investments, and policy changes. Rather than going around a conference room, referencing PowerPoint decks, and polling executives, leadership uses analytics to make those decisions.

In analytics in motion, we optimize the continual decisions at an organization, both on the buy and sell side, that are made every day. At a trucking company, for example, that means where to place a load, whether to aggregate loads and move them via truckload, which mode to use, which customer to serve, which pilot or driver to assign, where to pick up next, when to hold, and when to go. That’s analytics in motion.

In 2001 I interviewed George Dantzig, the father of linear programming, as part of a project that ILOG was doing at the time to capture the reminiscences of the recipients of the Founders Award from the Mathematical Programming Society (see page 2 of Optima 65 for a picture).  Recently, the interviews were converted and transcribed for the Oral Histories project at INFORMS. You can view the complete video interview and transcript at the INFORMS webpage here.

Really Valuable Time

Optimization practitioners know it is not time that is valuable—it is contiguous time. Having a large chunk of time—a solid one-hour or two-hour block—is much more valuable than the same number of minutes in two-minute chunks.

A leading trucking company sought to improve a core business unit by integrating advanced analytics into planning and operations processes. The company retained Princeton Consultants to demonstrate how incorporating new data sources, improved algorithms and a corresponding shift in business processes will result in better economics, service, nimbleness, consistency, and transparency.

Princeton Consultants interviewed key personnel involved in the company’s operations, including business process owners, dispatchers, and information technology personnel. These discussions uncovered the key internal and external factors that determine the dispatch decision process, and revealed an inordinate amount of manual labor that was taxing the planning team and resulting in errors.

Optimizing Inventory Management

Some industries are really good at certain things. Maybe you [CPAs] can talk to client business executives and say, “You know, in the Grocery industry they have 20,000 SKUs and a profit margin of only 3%, so they better understand how to move product, a lot of which spoils, and be really on the money in terms of inventory management.”

In 2016 I conducted a survey at the INFORMS Analytics conference in Orlando on the organization of analytics groups, and on the potential impact of current trends in the analytics landscape. ORMS Today included the summary of the results and the editor Peter Horner, added the title, “O.R. and Data Science: a Complicated Relationship,” which encapsulates a longstanding challenge that is not going away.

Advanced analytics practitioners and business executives are always striving to understand the nature of the problems facing them, so they can hire and manage talent and develop the best solutions. Seems straightforward enough, but technologies, methodologies and terminologies are evolving.

The same professional can have a job title that includes either Operations Research (O.R.), Analytics, or Data Science. Conversely, these labels at times represent three very different communities.

The Jan de Wit Company, www.jandewit.com.br, is a wholesale producer of bulb flowers in Brazil. In 2000, when the company began to consider optimization to help its production planning, it had 18,745 square meters of greenhouses, 1,032 square meters of cold-storage rooms, a team of approximately 30 employees and US$80 million in annual sales.

Princeton Consultants performs a Quality Assurance service that helps clients understand if best practices are used in the deployment of their predictive analytics or optimization models. Based on years of experience deploying advanced analytics in operational systems that run 24/7, our analysis often uncovers areas for improvement by suggesting new modeling and algorithmic approaches. Practitioner executives gain an understanding of how well their team uses industry best practices. Business sponsors gain more confidence in the solutions provided by their teams of analytics practitioners, and those practitioners improve their skills for future projects.

“For an MBA student, it’s very important to understand how you will encounter a problem in the real world,” says Arnie Greenland, a professor at the University of Maryland’s Robert H. Smith School of Business.

“You have to excel at more than the technical side of optimization to succeed in business—you have to understand the human side and change management,” he says. “Often, the hardest part is getting the client to understand the value of a solution and use the results properly.”

Professor Greenland uses my book, The Optimization Edge: Reinventing Decision Making to Maximize All Your Company’s Assets (McGraw Hill), to present realistic experiences in applied optimization, challenges to implement solutions, and tips to overcome them.

Over a year ago, I discovered a wonderful article by Hadley Wickham introducing the concept of "Tidy Data." Here is a complete reference:

Hadley Wickham, "Tidy Data", Journal of Statistical Software, Volume 59(10), August 2014.