How much does your organization know? The vital impact of organizational knowledge on performance is now widely recognized, but the study of how to manage such knowledge is still in its infancy. The author defines technical knowledge and gives a framework for mapping and evaluating levels of knowledge. He shows how to apply the framework to measure how much your organization knows and doesn’t know about its production processes, to learn where knowledge resides in your company, and to make better use of what you know. He shows why automation without adequate knowledge leads to disaster and how to manage knowledge in a world of continual organization learning.
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).
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.