Experience Deficit: The Silent Thief of Knowledge Management
Larry M. Dooley, Ph.D.
Associate Professor and Chair
Human Resource Development
Educational Administration and Human Resource Development
College of Education and Human Development
TexasA&MUniversity
College Station, Texas77843-4226
USA
R. Brent Powell, Ph.D.
Senior Vice President
DPC Group, Inc.
1231 Greenway Drive, Suite 990
Irving, Texas75038
USA
Abstract
Knowledge Management has captured the idea of a knowledge as a resource, however, the increasing age of industrial and heavy industry workers, is leading to an experience deficit that is not being addressed by current use of knowledge management systems.
Key Words: experience, knowledge management, artificial experience.
Experience Deficit: The Silent Thief of Knowledge Management
Introduction
In the next five years, millions of years of irreplaceable experience will retire from critical manufacturing, industrial, defense sectors, and educational institutions. Many of the people will have been in place 30 years or more and know the ins and outs of their businesses and jobs at a level of mastery. The veteran knowledge and experience required to make operations run smoothly will retire as well. Without that knowledge and experience, error rate increases will be followed by cost increases as the masters retire. To prevent this new wave of challenges from becoming debilitating, knowledge and experience both must be captured and, as importantly, transferred rapidly and repeatedly to other personnel.
The focus for the last several years for both productivity and security has been high technology. Robots do more precise welding, security cameras never doze-off, and the saying; “he who controls the data controls the world” has become a bylaw. While our attention has been on improving hardware and software we seem to have forgotten about wetware: the people who glue it all together. The people who operate and fix the machines, the people who watch the cameras, and the people who enter the data may be in dire need of an upgrade. The upgrade requires increasing knowledge and experience (KE) and while knowledge is recognized as an important resource, the experience to use that knowledge will be in even shorter supply. Looking at the impact of the absence of KE on business and governmental processes, it can be seen manifesting itself several ways. The areas to look for are: an increase in common process mistakes such as missed steps, a shortage or abundance in stock materials due to unanticipated needs, a drop off in inspection quality and efficiency, and increased time in troubleshooting and decision making.
Since the mid ‘80s a knowledge and experience gap has been expanding. The growth has been slow and subtle and disguised by things like the dotcom boom and bust, but nonetheless, the gap is growing to critical proportions.
Statement of the Problem
To understand the full impact of the KE Gap we should first understand its business and industrial significance. Experience, by its very nature, takes a long time to acquire. In many of the heavy industries, personnel can take 15 to 25 years to really know, at a level of unconscious competency, how to anticipate problems or at least solve them quickly. Interviews with training and human resource professionals at international corporations like John Deere, Caterpillar, Freightliner, Halliburton and many of the defense contractors, have identified an aging workforce. The average age in that workforce is over 50 in the United States. In European countries, reports from Eurostat, UNESCO, and others provide information differently but point to a similar trend.
According to reports from the TOSCA D6 Symposium, compiled in September of 2002, in some countries as much as 99% of new job creation is in service industries. Those proceedings also report the average job stability in non service sectors at approximately five years. From these reports and the well-documented turnover of employees in the service industries, it may be inferred that the manufacturing and industrial segments of the economy of many European nations has an aging population as well.
Those industries that had a stable workforce are now faced with losing that workforce, the experience they acquired, and the stability that KE provided, due to retirement of the baby-boomer generation.
The dotcom era created an environment in the high-tech fields that encouraged people to jump from job to job to better their financial situation. Two or three companies in a year were not uncommon. The net result of the highly mobile high-tech workforce is a condition in the high-tech industries where there are few high-quality people who have been with the company long enough to know its technological quirks, its business processes, and how to anticipate the problems peculiar to those specific environments.
Not recognizing a symptom or knowing how to act, precludes the ability to know when to act. Even the use of a well maintained knowledge system will not help in an emergency if the individual actors have the knowledge but not the experience to act quickly and efficiently. If a small fire breaks out and a witness to the fire has never actually used the extinguisher before, the fire can spread beyond control while the witness familiarizes themselves with extinguisher operation. If a shortage of supply materials is not anticipated early enough, a manufacturing facility can face a slow down and added costs. A technician unfamiliar with the range of anomalous behavior in machinery or computer systems may act too soon, too late, or incorrectly because they had not experienced the patterns as they developed. They had been taught the signs of the problems, but did not have the feel established through experience.
On the kind of scale that predicts security, reliability, and efficiency, the workforce is about to retire a massive number of people who know how to get things done, who have internalized knowledge and experience not in the manual, and know when to use that knowledge through experience. They know how things are supposed to work; recognize when something is not working properly, and can act to prevent a problem or diagnose it quickly and correctly the first time.
Many of the stable long term workers have mastery at the unconsciously competent level, acquired through years of seeing and doing and exchanging information with their peers. Workforce reductions, low retention, and large scale retirement can all have similar impact on the acquired knowledge and experience base represented by workers in place for career length spans.
In the critical areas of: manufacturing and defense, high-tech, and security, there is a need to capture experience as well as knowledge, and press into service the new methodologies and technologies that produce artificial experience in the people who need to make things work. Artificial Experience (AE) can accelerate the speed of learning, but more importantly, ties the acquisition of fact and information to recognition and applicability. A knowledgeable person filled with facts may not be able to solve a problem, or if so, their solution arrives much more slowly than that of an experienced person.
Knowledge Management
Knowledge Management systems are being tried and applied in many areas. The issues involved in making the systems work were outlined and discussed clearly by Adam Brand of 3M in the United Kingdom in the Journal of Knowledge Management in September of 1998. He explains that knowledge and experience are crucial to corporate efficiency . He goes on to say that experience has impact on innovation, as the ability to step out of processes at the right time is a key to innovation
If a corporation or other entity has not maintained their knowledge base for any reason, and has lost there experience as well, they are faced with finding and hiring the right mix on a possibly large scale, or providing systems that not only capture and provide knowledge but the experience to use that knowledge. Artificial experience provides the means to dramatically reduce the frame of time required by traditional on-the-job learning.
Research Problem
Artificial Experience is a condensation of the typical trial and error learning methodology. It is composed of several parts:
First, the capture of knowledge and experience, in this case from an aging or disrupted workforce;
Second, the development of reference and support tools to bring that knowledge to people’s fingertips and internalizing that knowledge to create experience;
Third, the application of Accelerated Experiential Learning techniques designed to provide the information and applicability from which experience is derived.
Without actively pursuing these goals, the manufacturing sector will be dependent on high-technology instead of experience, the security industry will be dependent upon high-technology instead of experience, and high-technology industries will be dependent upon outside vendors.
For a productive and secure work environment there is no substitute for informed personnel, experienced with a corporation’s specific products, processes and systems. As the pendulum of business cycles swings, it is time to focus on the people behind the machines before their knowledge and experience is lost. That means moving aggressively to capture knowledge and experience and replace or instill it artificially.
References
‘Call Centres in Europe’ (2002). An ETUC-CGT Symposium on behalf of the TOSCA Project, September 24, 2002
Sviokla, J. (2001). Creating value in the economy. CIO Magazine. February 15, 2001.
Murphy, V. (2001). You’ve got expertise. Forbes. February 25, 2001
Ringle, B. (2000). What every business leader should know about knowledge management. Canada One Magazine. Fall.
Brand, A. (1998). Knowledge Management and Innovation at 3M. Journal of Knowledge Management, Volume 2, Number 1. September, 1998.
Skyrme, D. (1998). Developing a knowledge strategy. Strategy, January 1998.