15 May 2008 by Sanjay Saigal.

In a recent article, I examined the need for the practice of Operations Research to be driven by a formal methodology. The primary motivating factor is the increasing mainstreaming of OR (or more general Advanced Analytics) techniques in Enterprise IT. This in turn is forcing system developers and consultants to proactively manage scalability and management of risk by “industrializing” OR delivery.

The typical use of this word in OR-related discussion concerns specific techniques – usually mathematical or statistical in nature – for solving reasonably well-posed problems. (Fairly typical of this usage is the paper titled “A methodology for integrating cell formation and production planning in cellular manufacturing”, published in Annals of Operations Research.) This OR is replete with (so-called) methodologies to solve problems fitting into structured classes. However, in most cases, “methodology” is just a nickel cigar encased in a five dollar label. What is under discussion is essentially a “method” or “technique” (or perhaps a class of such methods.). When speaking of an OR Practice Methodology, we’re interested not in the tools but in the practical principles and artifacts governing the deployment of OR techniques and methods.

The focus of this series of articles is on embedding OR in Enterprise IT. (While OR value is often delivered through one-off analyses, the role of a practice methodology in that purely consultative model is less clear.) In the Enterprise IT context, software engineering provides a sound basis for exploration. Methodologies such as Agile Programming, eXtreme Programming (XP), CMMI and Rational Unified Process (RUP) provide the IT project manager with a variety of risk control frameworks and usable artifacts for standard software development. However, they do not directly address the needs of the OR practitioner. Neither are they well-understood or used in the OR community.

Attempts on OR-specific methodologies have failed to gain traction in the practice community. The CHIC methodology for constraint programming was extended in the late nineties to large-scale combinatorial optimization problems and named CHIC2. However, I do not believe that CHIC2 was ever taught or used anywhere outside its academic seedbed in Europe.

More recently, French software vendor Ilog has extended its proprietary methodology for rule-based systems – ISIS – to optimization. Being a proprietary system that is viewed as a competitive advantage by Ilog, a list of OR-specific ISIS features has not been released. I do believe that ISIS has not, in any meaningful way, been used to develop a large-scale OR application. Ilog’s view of OR is limited to optimization, and thus ISIS is unlikely to be applicable to OR as a whole. But if opened to public view, it is the most promising methodological effort that I am aware of.

In the next article in this series, I will discuss practice methodology from the perspective of INFORMS, the leading professional society for OR.

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19 April 2008 by Sanjay Saigal.

At the recently concluded INFORMS 2008 Practice Meeting, multiple colleagues asked about our vision for Intechné. Quite simply, our vision for the company is to reliably deliver smart decision-making capability to our clients.

It goes without saying that smart business decision-making involves advanced analytical techniques from the fields of Operations Research, Statistics, and Artificial Intelligence. These include Predictive Analytics and Data Mining (to detect correlative, possibly causal, relationships in historical data), Monte Carlo Simulation and Decision Analysis (to simulate the impact of such relationships and tease out key sensitivities in anticipative decision-making) and various flavors of Optimization (both mathematically-driven algorithms and less-structured, heuristic approaches). But the application of a wide spectrum of techiques does not necessarily guarantee smart decisions. Intechné differentiates itself by explicitly focusing on an often overlooked issue in applying advanced analytics in the enterprise: Risk.

Viewed in the context of applying advanced analytics to business improvement, risk is like the weather: everybody talks about it, but nobody does anything about it. Or, nothing systematic at any rate. At the purely technical level, approaches such as mathematical optimization produce “brittle” decisions; very small changes in input can produce dramatically different recommendations. Perhaps that’s not of concern in the few situations where human operators are not in the associated decision chain. But in general, analytics are used to support decision, not execute them. Unexplainable, non-intuitive, or volatile decisions often force operators to work around their decision-support systems, or even completely ignore them. For instance, we found a sophisticated SAP/APO installation essentially ignored by its users (demand planners at a Food & Beverage company) because it couldn’t auto-profile different product types. While the overall MAPE was ok, sales forecasts for individual products diverged from reality in unexpected ways.

When it comes to delivering decision-support technology based on advanced analytics, a host of implementation risks arise beyond standard IT development risks. For instance, the response times of constraint programming models can decay exponentially with input size. (This is quite different from, as an example, rule-based decision engines.) Encountered unexpectedly, such non-responsiveness leads to expensive and disruptive modeling/algorithmic rework at an advanced project stage.

All in all, the business of delivering smart business decision-making is characterized by these, and many other risks. In informal feedback from colleagues and clients, we find that project mortality in this area is unacceptably high: about one in three advanced analytics projects fails to perform to expectation.

An active risk management orientation lies at the core of our vision for Intechné. In forthcoming communications we describe how this orientation is incorporated into our practice culture, and how it has been shown to improve client results.

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