Lessons Learned SP

REASON © Knowledge Management

A technology whose time has come

"Successful knowledge management treats knowledge as a resource by exercising selectivity, imposing order on information resources, adding structure to information to increase its value, and proactively capturing information that might be useful in the future." - PriceWaterhouseCoopers "Technology Forecast: 2002-2004,"

Can there be anything more important to an organization than the knowledge of what internally threatens its survival and success, and what can be done to control and prevent those threats? The concept of having corporate knowledge available at our fingertips to expand our perceptions and support our decisions is certainly a seductive one. Who can resist having the right information at the right time to make the right decision? This is the promise of knowledge management technology. The issue rests at the core of operations quality, and the future of continuous improvement activity.

Knowledge management seeks to place what we know about our organization into an integrated resource for decision-making. Data that have intrinsic value of their own, gain value through integration with other forms of data. New relationships are revealed to provide new perceptions for better decision-making. Knowledge management doctrine assumes that ordered data is knowledge, and not just addressable, indexed facts. In most cases, that is a daunting assumption. Today’s efforts to harness the potential of knowledge management still rely primarily upon integrating existing records into an addressable structure that can provide elevated perceptions and visibility. It is a logical first step, and in analogy the lowest hanging fruit on the knowledge management tree. One can anticipate the evolution of knowledge management going from this first phase to a systems environment in which new forms of data will be introduced, and new data methodologies will be applied both to produce a richer content, and to provide the means for better mining of the data resources.

Management’s Role

The manager has a critical role in the evolutionary process of knowledge management. It is management’s role to set the direction, provide the ground rules, and to allocate the resources. In order to succeed in their role, managers must take charge of the process. It is a role that requires understanding of concepts and current technologies. The goal of a knowledge management system is to provide the substance for continuously improving management decisions for planning, strategies, and goals. It requires an ordered and dynamic data resource, not just a storehouse of records. Fundamental criteria for the design of such a system must include stimulus and provision for continuous improvement of the resource information itself. Without management’s firm lead, the early goal of integrating all available existing data can itself become a fortress against change. It means that designers must accept the difficult challenge of a moving target in order to produce a system that will succeed in the long term. It means that management must keep its finger in the design pie in order to assure that criteria include the ingredients for growth of the system itself.

The Quest for New Knowledge

At DSI, the search for more meaningful information has long been an overriding priority. From DSI’s research recently came just the kind of advance that knowledge management seeks in order to expand the utility of data. For decades our clients have been applying REASON’s logic rules to guide the data gathering and analysis of problems. The REASON logic ordered the data into a verifiably accurate model of the problem. We reasoned that because each client was using the same objective standard procedure, all of the cases produced by the different clients should share data qualities in common. It was this premise that generated the research study of a large body of REASON models to uncover those common qualities. From the study came the discovery that systemic causes associated with the operations problems from many different industries and processes were combining into distinct causal patterns and in predictable sequences that were shared among the many different sources. We immediately perceived in application, that when a person knew these patterns, he could predict what types of causes combined to produce a particular pattern. With that ability he could then know exactly the right questions to ask in order to uncover those causes when gathering information about an operations problem. This is a classic example of how existing data can be mined to gain new insights and knowledge. From the new knowledge came perception of new avenues to pursue, and new practical applications that promise to be doorways to future advancements.

Our systems designers took all of the knowledge we had gained from over twenty years of field research and development, combined it with the new knowledge of causal patterns and ran with it. With the strict criterion for growth that we recognize as essential to knowledge management, we have developed the design of a seamless data system for operations improvement’s new horizon. The expert system software teams interactively with the user to discover systemic causes, watching input and responding with the important questions based upon the users input.

Focus upon systemic root causes

The system starts with a guided process for gathering and qualifying data about an operations problem. Based upon the recently discovered causal patterns, an expert system predicts what kinds of causes must combine to produce the problem, and asks the essential questions to accurately identify those causes.

Integrated into the data gathering process is the on-going validation of data. Data are ordered into sets of causes that combined to produce each step in the process that led to the problem. At each step, the individual sets of identified causes are tested for accuracy and completeness. This causal logic test assures that critical elements of the problem cannot fall through the cracks, as well as assuring that irrelevant facts are not included in the analysis process.

The information gathering process itself, automatically orders the data into a unique cause and effect model that supports objective quantification. Each avenue is pursued until either a root cause is identified, or the factor is found to be not correctable internally by the organization, or information is insufficient to continue the avenue of investigation. In effect, the process separates information much the same as do our minds naturally when we are solving our daily problems, into things we can do something about, things we cannot do anything about, and things for which we have no answers.

Once a root cause has been identified, the seamless process provides a systematic method for establishing where in the organization prevention can best be accomplished. This pinpoints exactly both what can be done in the action plan to gain immediate control over the internal system that produced the problem, and what steps are necessary to sustain ( click button on left to continue with article)