CHAPTER 11

Believe in Numbers—But Not Too Much

(What You Can Measure, You Might Manage)

Vignette: The Columbia Disaster

Numbers are not everything.

The following is a famous example in which the quantitative approach to decision making led to a faulty decision.

When the Physics Nobel laureate Richard Feynman was investigating the shuttle’s reliability following the Columbia disaster in 1986, he noticed that the probability of a failure was estimated to be 1 in 100,000 by management, but only 1 in 100 by the engineers.29 Instead of accepting the discrepancy of those estimates as a sign of weakness, the management preferred to rely on numbers, numbers that, in hindsight, made no sense. The management’s evaluation was wrong. It gave a false sense of security and, therefore, supported the decision to launch the shuttle, resulting in a disaster.

Feynman argued in his observations on the reliability of the Shuttle that he found “an enormous disparity between the management estimate and the judgment of engineers.”

He stated, “Officials behaved as if they understood it [the estimate variations], giving apparently logical arguments to each other often depending on the ‘success’ of previous flights.” He concluded with, “When using a mathematical model careful attention must be given to uncertainties in the model.”30

Vignette: The Capital Investment Decision

As the chief financial officer of a maritime services company (a shipyard serving mostly fishing fleets), I had to make capital investment decisions associated with new equipment and facilities improvement.

The company was often short of investment capital; therefore, any investment needed to meet stringent criteria, one of which was a payback periodi of 3 years or less.31

One investment opportunity consisted of upgrading aging machine-shop equipment composed of three old lathes. The machine-shop supervisors found new lathes that met the machine-shop needs and suggested that the company purchases those new machines. The capital required was around $500,000.

Most rules of thumb applied to capital investment decisions of this kind are based on straightforward calculations, usually applying a cost/benefits analysis (CBA).ii Although CBA has been used as a method for more than 100 years,iii using it may not necessarily result in optimal decisions. Yet, most small- and medium-sized company executives still use this simple and straightforward method.32

I informed the supervisors of the company’s rule regarding capital investment and advised them to submit a business case, with the help of the finance department, showing that this investment met the required cost/benefit criteria.

Taking into account various variables (such as maintenance costs of the old equipment, overtime, and improved productivity), the business case developed by the machine-shop supervisors and the finance team showed that investing in the new equipment as compared to continuing with the old one had the same financial consequences. This meant that, if I applied the standard cost/benefits rules, I could not justify this capital investment.

Moreover, I had to consider other rules of thumb, like

“Ensure that employees’ opinions are considered” and “maintain high employee morale.”

These rules of thumb were qualitative; they did not provide reliable and quantifiable data. Qualitative rules of thumb suggest that the decision maker needs to look beyond the numbers, beyond formal logic, and apply a different set of decision criteria based on the unquantifiable information that may apparently contradict the CBA payback period rule.

Believing that the acquisition of new equipment was an important motivational element for the machine-shop supervisors and machinists, I decided to apply the supposedly unreliable qualitative rule of thumb instead of the supposedly more reliable quantitative one. The company acquired the new equipment.

The results were surprising to me. The payback period was half of the originally predicted one. Indeed, the machinists were so enthusiastic in using the new equipment that their performance level was much higher than the one they predicted, resulting in increased productivity, work output, and revenue growth.

Beyond the immediate cost/benefits arising from the decision to acquire the new equipment, the machinists could now work on new products that were inaccessible to them before. One example was the production of a Shaft Brush Assembly that could be used as an alternative conduit for electricity in ships, reducing the electrolysis damage to the shaft (an expensive piece of equipment) and complementing the role of the sacrificial anodes (zinc) protecting the metal frame of the vessel.iv

When including the additional product-line production capability, the increased productivity, and the reduced maintenance costs, the new equipment expenditure was recovered within a year of the purchase.

Numbers are not all that counts.

Vignette: The Eternal Optimist

Charles, my older business partner, was an eternal optimist. He was a particular kind of genius. His vast experience of more than 40 years in the business was built on a strong customer base, and he respected his technical knowledge, a solid understanding of the relevant technology and its limitations, and a hands-on skill in the machine shop.

Charles was a doer.

Alas, not everyone in the company had this vast background and expertise. This meant that nobody could question the estimates Charles submitted.

He was consistently underestimating the time and cost to deliver new technological solutions addressing customer problems. This chronic project underestimation was partially explained by his overestimation of what others could do when contributing to the project.

It was also based on optimism. Nothing would ever go wrong. The equipment would perform to specifications (rarely the case), the suppliers would send the materials on time (delays were often the norm because we were on an island), and people (himself included) would not make mistakes (they did).

I ended up simply filling in the void.

If Charles suggested 3 weeks to deliver a new product, I would budget for six. If the investment required was $100,000, I would budget the double. This approach worked most of the time.

Other options might have also been possible, like more training, more precise estimating methods, input from other experts, and so on. I tried them. They did not work with Charles. I was out of options, and Charles was a key person in this department.

The challenge I was facing was managing the knowledge of a key person with limited or no access to that person’s knowledge.

Find an expert.v

The Challenge of Knowledge Transfer

One problem associated with knowledge transfer is in its ambiguity and lack of consistently accepted definitions. Unfortunately, knowledge transfer is also too easily associated with tools instead of processes or ways of thinking.

Organizations use knowledge transfer as a strategy to turn their intellectual assets or creative capital into greater productivity, new value, and increased competitiveness. Contrarily, small- and medium-sized businesses do not have the resources required to establish a stable base (supported either by technology or by experts) to deal with specific challenges they face in day-to-day operations.

Moreover, presently, most technology-based knowledge systems use tools like Data Mining, Data Warehousing, Business Intelligence, Executive Decision Support Systems, Enterprise Resource Systems, and Data Pattern Recognition systems (to name the most common ones), all of which require large repositories of data and information.

Some of the data collected in those repositories are text based, making their analysis and transfer even harder to perform. The results are high costs both in technology tools (hardware and software) and in human resources (knowledge management experts), resources small businesses often do not possess or can hardly afford.

Charles’s knowledge was individual. It was more of a process than a form of knowing. Knowledge of this kind is often labeled as tacit knowledge,vi a term coined by the scientist and philosopher Michael Polanyi.33

In small- and medium-sized business, the daily operations often depend on the availability of the personal contribution and experience of the owner-manager or some key personnel. This experience is not stored in any database, policy, or procedure manual. Thus, the firm depends on the owner-manager or some key people to continue to count on experiences, behaviors, attitudes, and abilities or competencies of those people to perpetuate the competitive advantage of the firm or just to ensure its survival: hence the challenge—how does one transfer this form of knowledge?

In the medieval ages, most apprenticeships took place in the artisan’s workshop. A carpenter apprentice would learn by watching his master work the wood, a stone carver apprentice would learn by following the advice of his trainer, and a tailor apprentice would mimic his instructor’s example. One-on-one teaching was the standard method in these situations. This kind of one-on-one knowledge transfer is cumbersome, time consuming, and often impossible in a small- or medium-sized business because of limited resources, time constraints, change, and the ever-present competition.

Research in knowledge transfer in small- or medium-sized firms is very limited. This lack of research is particularly evident when exploring the differences between the acquisition, transfer, dissemination, and maintenance of business knowledge transfer in the context of the owner-manager of a small- or medium-sized firm.

Often owner-managers need to share their knowledge (both tacit and explicit) with new key employees if they are to grow and ensure that a stable succession-planning process can take place. They do not have resources to set up knowledge management systems, and they are not aware of the latest scientific trends in this area.

Some researchers suggest that it may be more beneficial to transfer knowledge from the owner-manager to other employees through the process of socialization.34

For example, big companies enable knowledge transfer between individuals and groups by creating apprenticeship teams or using out-of-office social gatherings as a knowledge transfer catalyst.35

Another approach consists of associative thinking, which is a form of learning without being able to describe that knowledge was acquired and, therefore, difficult to automate. Zohar Danah, a management thought leader, physicist, philosopher, and author, states: “All of us must learn a skill in our own way, for ourselves. No two brains have the same set of neural connections.”36

Most small- and medium-sized firms do not codify their experiences or practices. People in these organizations just do things. In addition, small- and medium-sized companies usually have a strong corporate culture, primarily derived from the owner-manager’s vision for the firm. Thus, understanding fully how to transfer, disseminate, absorb, and manage this key-personnel knowledge is important for them.

Some work in codifying tacit knowledge for reference librarians was published by Dr. Mark Stover, from San José State University. Using terms such as “inarticulate intelligence,” “collective wisdom,” or “elusive knowledge,” Stover suggests a form of tacit knowledge transfer using two steps: from tacit to explicit and from explicit to codified.37

Sharing of tacit knowledge was the purpose of a study done by Megan Endres,vii in which he compared knowledge-sharing activities in the open source community with those of more traditional organizations. He concludes that, indeed, their “self-efficacy” model could serve as “a useful framework for better understanding the effects of context on tacit knowledge sharing.”38

Harold Harlow, a professor of management at the American University in Cairo, found significant relationships between the tacit knowledge level index (TKI) and innovation performance of firms. Although Harlow writes about an “operational definition” of TKI, he does not provide a formal definition of that index but rather the result of a series of correlations in financial and innovation performance measures.39 Similar results were identified in the work by Tamer Cavusgil,viii when studying the relationship between tacit knowledge transfer and a firm’s innovation capability.40

George Santayana reminded us of the aforementioned point when he wrote, “In imagination, not in perception, lies the substance of experience, while knowledge and reason are but its chastened and ultimate form.”41

Trust and shared values play an important role in the transfer of tacit knowledge.

Lessons Learned: The Knowledge Sausage Slicing Method

Theoretical approaches to knowledge transfer abound as can be deduced from the earlier references. But how does one address the particular situation of a very valuable and knowledgeable key person with a vast amount of “non-transferable” knowledge, like Charles’s?

My answer was using the sausage slicing approach: transferring one slice of knowledge at a time using different receptors.

Charles had three major knowledge slices that needed to be transferred to others. Those knowledge slices were part of his consolidated knowledge sausage, intertwined in a way that seemed un-sliceable.

First, in order to deal with a broad customer base, I hired a knowledgeable marketing manager with strong interpersonal skills. This person’s objective was to establish a contact with all the customers Charles knew (and expand that customer base) and update their profile by documenting their purchase history, the market segment they covered, and their current needs. The marketing manager would work closely with Charles whenever a technical challenge sprang up. This was a way of increasing the credibility of the new marketing manager and demonstrating his capability to understand and address customers’ needs.

The second step consisted of bringing on board a knowledgeable engineer conversant in the same technology area Charles was so experienced with—hydraulics. This person’s responsibility was to document all the equipment and machinery Charles designed and built, using the latest software and documentation tools. This facilitated a better support and maintenance of the manufacturing equipment, and the development of processes and methods, a prerequisite for ISO 9000 certification.ix The by-product of this step was an improvement in quality management.

Finally, another person with sound programming skills in control programming language was hired to assist in an upgrade of the control programs used on the various programmable control devices used in the manufacturing processes. The performance of the equipment was enhanced and debugging reduced operating hiccups.

One person was not enough. Three people met the knowledge transfer challenge partially. They were the knowledge receptors.

Of course, Charles was involved in both the recruitment and the mentoring of each individual. His motivation was more free time for research and development and less tied-up time for hands-on operations support.

This was a win–win situation. It took only a couple of years to achieve it.

Cemeteries are full of indispensable people.

iPayback period in capital budgeting refers to the period of time required to recoup the funds expended in an investment, or to reach the breakeven point.

iiCBA, sometimes called benefit–cost analysis, is a systematic approach to estimating the strengths and weaknesses of financial investment alternatives that satisfy transactions, activities, or functional requirements for a business.

iiiAccording to Hammond, the use of formal benefit–cost ratios goes back at least as far as the Rivers and Harbor Act of 1902, and was explicitly mandated in the amendment to the Act in 1920.

ivExplanatory note: Different metals in a conductive liquid, like seawater, create a type of battery. The resulting current removes metal from one of the metal pieces (electrolysis). The piece to protect is the propeller and the shaft it is attached to.

vFind an expert online at: http://www.findanexpertonline.com/

viTacit knowledge (as opposed to formal, codified, or explicit knowledge) is the kind of knowledge that is difficult to transfer to another person by means of writing it down or verbalizing it. For example, stating to someone that London is in the United Kingdom is a piece of explicit knowledge that can be written down, transmitted, and understood by a recipient. However, the ability to speak a language, knead dough, use algebra, or design and use complex equipment requires all sorts of knowledge that is not always known explicitly, even by expert practitioners, and which is difficult or impossible to explicitly transfer to other users. Although tacit knowledge appears to be simple, it has far-reaching consequences and is not widely understood. (Source: Wikipedia.)

viiDr. Megan Lee Endres is assistant professor of Management, Eastern Michigan University, Ypsilanti, Michigan, USA.

viiiDr. S. Tamer Cavusgil is Fuller E. Callaway Professorial Chair and Director, Institute of International Business, Robinson College of Business, Georgia State University.

ixThe ISO 9000 family of quality management systems standards is designed to help organizations ensure that they meet the needs of customers and other stakeholders while meeting statutory and regulatory requirements related to a product. ISO 9000 deals with the fundamentals of quality management systems. (Source: Wikipedia.)

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