Research Areas

BPR, TQM, Six Sigma, Quality Circles, Juran, Deming, etc., etc.

All names for basically the same activity; systematic and objective viewpoint on quality and efforts leading to quality. The only difference is the terminology and which consultant you are writing the checks to.

The fundamental problem with quality management programs is their inabilty to be long term and sustainable. Too many times quality efforts lose steam after even a short life span of success, let alone be in existance for the long haul. Are all quality efforts doomed to fail?

Current research efforts in this area concentrate on uncovering the essentials of quality management programs and why they fail to be sustainable. The objective is to discover what features of quality management programs make them unable to become long term efforts that are acculturated into the organziation, and to identify those features that may lead to a truly sustainable quality management program.

Adaptive Belief Management

Risk and decision making, in general, are based on perceptive beliefs or measures of uncertainty. As situations increase in complexity, the need to manage these rapidly changing and dynamic uncertainties and beliefs becomes more and more critical to key decision makers.

Gone are the days of choosing a static distribution to describe your state of uncertainty; or of a single probability to quantify your belief. New information comes in constantly, and new knowledge is distilled from it at higher and higher levels, resulting in the need for a formalized management of these dynamically changing beliefs and the impacts they have on critical business decision

So how do we define a "rational decision" in this complex environment? What is the "right decision"? How can we (or even should we) adapt classical decision methodologies when the underlying assumptions that they rest on no longer exist?

State Based Reconstructabilty Analysis

Continuing on the work of B.Jones and of Johnson and Zwick in the area of State Based Reconstructabilty Analysis, Ike's current research in this area is looking at the underlying relationships and nature of noise in the signal that lend themselves more viable for this type of analysis. In addition, he is looking at using the technique to mine text for predictive purposes of continuous valued response variables.

Data Envelope Analysis [DEA] is an analytical technique to evaluate peer performance and establish internal benchmarking by looking at efficiency of decision making units [DMU] by examining the efficient utilization of resources.

However, DEA typically compares an inefficient DMU by showing one (or more) efficient ones that the inefficient one should be emulating. The issue in DEA that is the current area of Ike's research is what to do in the "zero-sum" situations where the production outputs or resource inputs are shared among DMUs.

For example if the output is market share, the only way for one DMU to improve is for one or more to degrade. So in these situations what is the true benchmark and does the methodology need to be modified to accommodate for shared resource constraints?

Classic conflict based game theory [Van Neumann, Morgenstern, Nash, etc] requires one to assume the opponent is a rational being with as least as much information and access to same methods of analysis as you do, and that they will chose the rationally best option. However, many times people do not act rationally and some times the irrationality is done for deceptive purposes. Knowing that people (and groups) are not required to act rationally and that one may have a sense of deception in their opponents behavior, how are they to respond? Do they need to (or should they?) adapt their strategies to accommodate for the irrationality, the deception, or both - for either protection or exploitation?