Measuring and Managing Technological Knowledge

“Knowledge is power.” — Francis Bacon

As we move from the industrial age into the information age, knowledge is becoming an ever more central force behind the competitive success of firms and even nations. Nonaka has commented, “In an economy where the only certainty is uncertainty, the one sure source of lasting competitive advantage is knowledge.”1 Philosophers have analyzed the nature of knowledge for millennia; in the past half-century, cognitive and computer scientists have pursued it with increased vigor. But it has turned out that information is much easier to store, describe, and manipulate than is knowledge. One consequence is that, although an organization’s knowledge base may be its single most important asset, its very intangibility makes it difficult to manage systematically.2

The goal of this paper is to present a framework for measuring and understanding one particular type of knowledge: technological knowledge, i.e., knowledge about how to produce goods and services. We can use this framework to more precisely map, evaluate, and compare levels of knowledge. The level of knowledge that a process has reached determines how a process should be controlled, whether and how it can be automated, the key tasks of the workforce, and other major aspects of its management. Better knowledge of key variables leads to better performance without incremental physical investment.

Two examples illustrate the importance of technological knowledge in the form of detailed process understanding. Chaparral Steel, a minimill, was able to double output from its original electric furnace and caster. Semiconductor companies routinely increase yields on their chip fabrication lines from below 40 percent to above 80 percent during a period of several years. In these cases, the incremental capital investments are minimal. The improvements are instead due to multiple changes in the manufacturing process, including different procedures, adjustments of controls, changes in raw material recipes, etc. Why weren’t these changes implemented at startup? The reason is that the knowledge about the process and how to run it is incomplete and develops gradually through various kinds of learning.

Many authors have noted that there is a difference between data and information. A few have also noted that there is a difference between information and knowledge.3 Although not always clear-cut, the distinction among the three in production processes is very important. Data are what come directly from sensors, reporting on the measured level of some variable.

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References

1. I. Nonaka, “The Knowledge-Creating Company,”Harvard Business Review , November–December 1991, pp. 96–104.

2. Peter Drucker has commented, “In fact, knowledge is the only meaningful resource today. The traditional ‘factors of production’ have not disappeared, but they have become secondary.” See:

P.F. Drucker, Post-Capitalist Society (New York: Harper Business, 1993), p. 42.

3. Harlan Cleveland distinguishes data, information, knowledge, and wisdom. However, he then intermixes the four concepts. See:

H. Cleveland, “The Knowledge Dynamic,” The Knowledge Executive (New York: Human Valley Books, 1985).

4. R. Glazer, “Marketing in an Information-Intensive Environment: Strategic Implications of Knowledge as an Asset,”Journal of Marketing 55 (1991): 1–19.

5. R. Jaikumar, “From Filing and Fitting to Flexible Manufacturing: A Study in the Evolution of Process Control” (Boston: Harvard Business School, working paper, 1988); and

A.S. Mukherjee, “The Effective Management of Organizational Learning and Process Control” (Boston: Harvard Business School, doctoral dissertation, 1992).

6. R.E. Bohn and R. Jaikumar, “The Structure of Technological Knowledge in Manufacturing” (Boston: Harvard Business School, working paper 93–035, 1992); and

R.E. Bohn and R. Jaikumar, “The Development of Intelligent Systems for Industrial Use: An Empirical Investigation,” in Research on Technological Innovation, Management and Policy, ed. R.S. Rosenbloom (London and Greenwich, Connecticut: JAI Press, 1986), pp. 213–262.

7. This formalism is pursued in Bohn and Jaikumar (1992).

8. ∂/f∂xi in a local region.

9. Glazer (1991); and

N.R. Kleinfield, “Targeting the Grocery Shopper,” New York Times, 26 May 1991.

10. J.A. Seeger, “Reversing the Images of BCG’s Growth/Share Matrix,” Strategic Management Journal 5 (1984): 93–97.

11. J. Dutton and A. Thomas, “Treating Progress Functions as a Managerial Opportunity,” Academy of Management Review 9 (1984): 235–247.

12. P.S. Adler and K.B. Clark, “Behind the Learning Curve: A Sketch of the Learning Process,” Management Science 37 (1991): 267–281.

13. R. Jaikumar and R.E. Bohn, “A Dynamic Approach to Operations Management: An Alternative to Static Optimization,” International Journal of Production Economics 27 (1992): 265–282.

14. J.M Juran and F.M. Gryna, eds., Juran’s Quality Control Handbook (New York: McGraw-Hill, 1988), Chapter 22.

15. These methods include Pareto charts, use of analogies to similar but better understood processes, screening experiments, and other methods discussed in the quality control literature. Notice that screening experiments are possible only if the variable is already at stage four or higher.

16. G.V. Shirley and R. Jaikumar, “Turing Machines and Gutenberg Technologies: The Post-Industrial Marriage,”ASME Manufacturing Review 1 (1988): 36–43.

17. J. Flanagan, “GM Saga a Lesson for America,” Los Angeles Times, 27 October 1992, p. A1.

18. Bohn and Jaikumar (1992).

19. K.E. Weick, “Organizational Culture as a Source of High Reliability,”California Management Review, Winter 1987, pp. 112–127.

20. Experienced bakers will realize that the following account is highly simplified. A case simulation of some of the following issues is provided in:

R.E. Bohn, “Kristen’s Cookie Company (B)” (Boston: Harvard Business School, Case 9-686-015, 1986).

21. For example, eggs, flour, and chocolate are relatively complex agricultural products, of imperfect consistency over time.

22. P. Waldman, “Change of Pace: New RJR Chief Faces a Daunting Challenge at Debt-Heavy Firm,” Wall Street Journal, 14 March 1989.

23. J.P. Walsh and G.R. Ungson, “Organizational Memory,”Academy of Management Review 16 (1991): 57–91.

24. Bohn and Jaikumar (1992).

25. R. Jaikumar, “Postindustrial Manufacturing,”Harvard Business Review, November–December 1986, pp. 69–76.

26. W.B. Chew, D. Leonard-Barton, and R.E. Bohn, “Beating Murphy’s Law,”Sloan Management Review, Spring 1991, pp. 5–16.

27. Learning is obviously of central importance in knowledge-based competition, but detailed analysis is beyond the scope of this paper. A very interesting study of how machine developers become aware of new variables (stage two) through field use is provided by:

E. von Hippel and M. Tyre, “How Learning by Doing Is Done: Problem Identification in Novel Process Equipment,” Research Policy, forthcoming.

For a description of how one company manages learning as an integral part of the manufacturing process, see:

D. Leonard-Barton, “The Factory as a Learning Laboratory,” Sloan Management Review, Fall 1992, pp. 23–38.

For a discussion of the characteristics of organizations that learn successfully, see:

D.A. Garvin, “Building a Learning Organization,” Harvard Business Review, July–August 1993, pp. 78–91.

For a general typology of methods of technological learning, see:

R.E. Bohn, “Learning by Experimentation in Manufacturing” (Boston: Harvard Business School, working paper 88–001, 1987).

Acknowledgments

My thanks to Jim Cook, Thérèse Flaherty, and two reviewers for especially helpful comments on earlier drafts of this article; to Steve Furbush and Liz Bohn for research assistance; and to hundreds of managers and Harvard Business School students for allowing me to test these ideas on them. Remaining errors of omission and commission are my responsibility.