The youngest members of the generation known as Baby Boomers are approaching retirement age. If companies allow Baby Boomers’ years of expertise to walk out the door without capturing it, they are losing a valuable asset. Rebecca Brinkley, Director of Marketing and Communications at the American Gear Manufacturers Association, writes, “As the last of the baby boomers round their way up to 60, companies are slowly realizing that their retiring leadership is leaving gaps in intelligence and historical knowledge that threaten the future.”[1] Whenever a talented employee leaves a job or assumes a new role, a company risks losing valuable knowledge that he or she has gained while mastering their job. This knowledge often involves rules of thumb or tricks of the trade that have been learned through years of experience. Such knowledge is often referred to as Tribal Knowledge. Tribal knowledge is any unwritten information that is not commonly known by others within a company. People use tribal knowledge to help them make quick, but correct, decisions. In a corporate setting, note Leonard F Bertain and George Sibbald, “Tribal Knowledge or Know-How is the collective wisdom of the organization. It is the sum of all the knowledge and capabilities of all the people.”[2]

The importance of tribal knowledge

Norbert Najerus, a former Goodyear executive, writes, “Solid knowledge reuse practices prevent companies from wasting time and money reinventing knowledge that already exists.”[3] He laments, however, that companies capturing tribal knowledge are not the norm. He adds, “The average amount of reused knowledge in manufacturing remains low, reportedly about 30%. … When companies don’t properly identify and capture knowledge, they allow more and more key technical experts to leave with undocumented knowledge in their heads.” Of course, manufacturing is not the only business sector in which tribal knowledge is important. Jerry Marino, Chief Knowledge Engineer at k-based, writes, “Every day, as key employees leave — whether from retirement, lured away to the competition, or a just a desire for something new — companies worldwide experience the loss of capabilities, skills, and ‘tribal knowledge.’ As key employees depart, they take expertise and proprietary knowledge with them. This is especially true of supply chain professionals, where there is today a large talent gap.”[4]

Brinkley asserts, “When you have someone so good at their job, doing it for 30 years and not sharing information to the next person in line, you risk losing everything tied to that one ‘irreplaceable’ person. You risk your business.” Marino asks, “How do you deal with the loss of their personal contacts and customer vendor relationships built up over years? How about partner relationships and product and service sourcing knowledge? And, what about specialized process knowledge that enables faster turnaround time and in-depth troubleshooting of your unique processes?” He adds, “When experts leave, don’t let their critical knowledge walk out the door! Think about the time it takes to get new hires up to competence, not to mention high-performance; or the time and resources spent ‘reinventing’ or ‘rediscovering’ successful processes and products. And you experience a ‘double-whammy’ when expertise walks to competitors … expertise that your organization paid to develop! … When experts leave, they take their deep tacit knowledge with them — the knowledge that’s in their heads but not documented in manuals or other documentation.”

Capturing tribal knowledge

The challenge, of course, is how to capture and leverage tribal knowledge. A good cognitive computing system, like the Enterra Enterprise Cognitive System™ (Aila®), can help. Practically speaking, tribal knowledge is a collection of rules and rules can be written into code and leveraged by a computer. Although that sounds pretty straight forward in theory, in practice it is much more complicated. For example, it is common to have conflicting rules where there are multiple options or opinions. There must be a way to adjudicate truth in these situations. It is virtually impossible to have no contradictions in a large rule base. Most times, we do not want exceptions to invalidate the general case (e.g., we do not want the exception that chickens don’t fly to invalidate the general fact that birds fly). This method of reasoning is sometime called “true by default”; however, when needed, facts or relationships can be denoted as invariably true. Enterra’s cognitive computing system uses the world’s largest common sense ontology to perform its semantic reasoning and argumentation functionality. Common sense allows inference chaining to occur where other systems would halt because they are missing a fact that is obvious to a human but must be taught to a machine. For example, when a body moves, all their part (arms and legs) move with them. That means, when tribal knowledge rules are in conflict, the best rule will be used. Just as importantly, once tribal knowledge is captured it is retained, even if an employee leaves or assumes a new role.

Robert Handfield (@Robhandfield), Bank of America University Distinguished Professor of Supply Chain Management at North Carolina State, and Director of the Supply Chain Resource Cooperative, observes that capturing tribal knowledge is important even if the subject matter expert holding that knowledge is actively employed.[5] He explains, “Today, such experts often exist in a vacuum in a different geography or location within large organizations. And people are often hesitant to bother these experts, or in many cases don’t know who they are, nor how to ask the right question that helps to access the specific gray matter between the ears of these experts for help with a specific problem! In an ideal world, an individual would ‘ask the system’ how to solve a problem that is new to them, and be directed to the learning materials or individual to advise them on how to deal with that situation.” He adds, “A critical skill that future generations of supply chain managers will need to embrace is the ability to interact with a cognitive learning system.”

Concluding thoughts

Marino concludes, “The key to knowledge preservation and sharing across your enterprise is based on how well you capture and represent the knowledge of your expert(s). You want to not only mitigate losses if they leave but also share their knowledge with ‘less expert’ staff to bring their performance up to expert level. This means capturing the deep tacit knowledge acquired over years through study, experience, and practice. When you’ve captured that expertise, represent it in a high-fidelity format and then share it with appropriate workers.” A good cognitive computing system can help you achieve that goal.

Footnotes
[1] Rebecca Brinkley, “5 Clues You’re Falling Short on Succession Planning—and What to Do About It,” IndustryWeek, 2 August 2019.
[2] Leonard F Bertain, Ph.D. and George Sibbald, The Tribal Knowledge Paradigm (2012).
[3] Norbert Najerus, “Don’t Let Knowledge Walk Out the Door,” IndustryWeek, 30 October 2018.
[4] Jerry Marino, “Experts Leave: Don’t Lose Supply Chain Knowledge Too,” EBN, 1 February 2018.
[5] Robert Handfield, “Learning to Work with Machines That Learn What the Experts Know,” Supply Chain Resource Cooperative, 9 May 2017.