Category: Business intelligence

Don Ratliff at IFORS

Mike Trick and elephant at IFORS 2008So what happened to my “live blogging” at IFORS 2008? Well, unfortunately I had an administrative role at the conference (nothing like the real work of Hans Ittmann and John Bartholdi, but a role none-the-less) and that took time. And I am always a believer in the “social networking” aspects (read: hanging out at the bar with friends), and that takes time. And I did go on one of the famous IFORS outings, where I got sucked at by an elephant. Finally, I spent 3 days at the end at a lodge with no internet connection (technically, there was an internet connection, but I pretended it didn’t exist). Put it all together, and I fell a bit behind. But here are some notes that I took along the way.

Don Ratliff at IFORS 2008The Tuesday plenary speaker was my co-adviser from 20 years ago, Don Ratliff from Georgia Tech. Don has had a great career, both in academia and in business. He was editor of Operations Research for a while, and published a number of interesting papers. He also founded CAPS Logistics, which was later bought out by Baan in a rather confusing and lawsuit-laden muddle. The title of his talk was “The Role of Operations Research in Lean Supply Chains”. He began by drawing an analogy between lean production and lean supply chains. In his view, lean production involves three main components:

  1. Eliminating waste. Waste can be in terms of inventory, or time, or anything else that is not needed to meet the ultimate end of production: producing the thing!
  2. Synchronizing flow. Getting things to where they need to be at exactly the right time.
  3. Continuous improvement. Production environments are not static, and there is the opportunity for improvement as processes change.

He suggested that there has been insufficient effort to adopt these precepts throughout the supply chain. Partially this is a result of the multiple actors within a supply chain. It is typically easier to say “do this” within a factory with a common boss; supply chains often have multiple people involved, including the customer, making it harder to do things like waste removal and synchronization. But there are significant savings possible, making things worthwhile.

I thought the most interesting point he made came under the area of “continuous improvement”. He pointed out that ERP (Enterprise Resource Planning, the SAPs and Manugistics of the world) have shut OR out. It is hard to improve part of the system, when that part is embedded in a larger whole. In essence, OR has missed out of ERP, so nothing much has changed in OR since the PC (which did have an effect on the OR world and thinking). But he identifies SOA (Service Oriented Architecture) as a great new opportunity for our field. Quoting from wikipedia:

Service-Oriented Architecture (SOA) is a software architecture where functionality is grouped around business processes and packaged as interoperable services.

It is through these services that OR can have its strongest effect. If you have a better routing system, you can embed that in the routing service of the SOA system, improving that aspect without affecting the rest of the system. This is exactly what is needed for continuous improvement, and exactly what OR is good for.

Within OR, we often don’t track IT concepts such as SOA or business intelligence, but we should: it can have a great effect on how our work is used in organizations.

I thought this was another very good plenary, and again the audience seemed quite engaged with in.

Business Intelligence and Operations Research

In the past couple of years, a field called “business intelligence” has sprung up. Based on the premise that businesses should get more out of data, business intelligence mixes data mining, algorithms, visualization and other approaches to help businesses make better decisions.

Of course, I thought that was the definition of operations research! Ever since I came across the area, I have included some of the blogs in my blogroll (see, for instance, James Taylor’s Decision Management and Smart (Enough) Systems). I find this area interesting, but I never can quite get my brain around what they are trying to say. It is like they are taking an area I know very well and translating it into a different language which I kind-of understand, but not quite well enough to grasp what they are saying.

Intelligent Enterprise (part of the group that publishes Information Week) has a short article “What BI Practitioners Can Learn from Operations Research”. It begins with the Netherlands Railway Edelman story, then continues to express confusion on the lack of interaction of the two fields:

It would be natural for BI practitioners to embrace OR, which has long focused on automating decision making, surely the goal of those who talk about closed-loop BI. “OR starts with the decision and works back to figuring out what math and data will help with devising a better solution, while BI tends to start with the data and see what can be done with it,” says James Taylor, co-author of Smart (Enough) Systems and one who believes that OR and BI are complementary but quite different. “OR folks tend to be focused on the nitty-gritty of day-to-day operations, and they use data from operational systems. BI tends to be focused on knowledge workers, data warehouses, and aggregation.”

It would be natural for the OR community to reach out to the BI world and its community of business-focused knowledge workers, who are increasingly looking to build out their analytical toolkits. “C-level decision makers are turning to analytics for help in the decision-making process,” writes Peter Horner, editor of Analytics, a new magazine published by the Institute for Operations Research and the Management Sciences (INFORMS). “When you see terms like operations research (OR), think analytics.” Many in the BI world, who are already supporting those executive decision makers, are saying close to the same things about BI and analytics.

Given the close kinship of BI and OR, one wonders why these two camps have long existed as separate communities?

SAS’s Mary Crissy (who I still think of as Major Crissy, though she left the military some years ago), has what I think is a pretty good explanation:

“Operations researchers don’t interact with the IT community as much as they ought to,” says Mary Crissey, an analytics marketing manager at SAS, a council officer of INFORMS, and, apparently, one of the few vendor executives with a foot in both the BI and OR camps.

“Academic mathematicians are not worried about what terms are buzzing about in the business world,” Crissey says. “They talk to each other in their mathematical language of equations and theory without getting entangled in terminology such as BI. Pure Intelligence for business or public service organizations all boils down to data analysis; they just don’t call it BI.”

Having gone through fights over “operations research”, “management science”, “decision engineering”, “analytical decision making” and countless others over the 50+ years of existence, the field is not particularly excited about embracing a new name for our field.

I guess I see BI’s relationship with OR to be similar to operations management’s relationship with our field. OM uses OR an awful lot, and OM would not be successful as a field without OR. But OM is not a subfield of OR: sometimes it uses approaches that are outside the range of OR (including organizational theory, case studies, or other methods). That is great! OM people are trying to solve problems, and they should be using whatever methods seem appropriate. Similarly, BI uses (or should use) OR extensively. And OR people should see the BI community as a great source of problems and inspiration (and should make an effort to learn their language). But BI will inevitably use non-OR methods for some of their issues, so is rightly not “the same as” OR. But we as a field should know more about what they are doing if we are going to be part of this business direction.