Beating Murphy’s Law

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IN THE PRESS, managers trumpet the success of new technologies. But all too often the private answer to “How is the new equipment working?” is “Badly.” Managers lament that their experience is living proof of Murphy’s famous Law: “Whatever can go wrong, will.” Yet the introduction of new technology is essential for long-term survival. Several examples illustrate how Murphy can intrude.

A southern furniture manufacturer decided to implement computer-controlled fabric cutting equipment. The head of the project, a top engineer, visited firms that had already successfully installed the equipment, developed a detailed action plan, and projected the expected efficiency gains (see Figure 1).

Nine months later the expected gains had yet to materialize. The problems could be traced to a multitude of unforeseen circumstances. The software to support the cutting equipment was not prepared on time. The changes in production scheduling, which were made to keep the equipment’s utilization high, required more indirect labor than originally planned. New cutting tables were too wide. The cutting department found that their old jack-of-all-trades approach resulted in too much waiting time and that they had to work as specialized teams. Downstream from the cutting department, the sewing staff found their productivity reduced—the fabric was poorly cut, primarily because the suppliers sent nonstandard widths. Upstream and downstream, direct and indirect, cost and quality; everything that could go wrong, did. Murphy’s Law had taken over the project.

In a large aluminum processing plant, the “JRC” electromechanical pump, designed to stir molten metal in an electrical remelt furnace, was expected to improve furnace efficiency, reduce fuel and labor costs, and lengthen furnace life. Although the new pump had been successfully used in more than a dozen steel operations and in at least one other aluminum foundry, the innovation ran into multiple problems. The silicon materials in the nose piece turned brittle in the extremely high temperature baths. The vendor (which had licensed the original technology from this same aluminum company’s laboratories) was undergoing a major personnel re-structuring and was inattentive. Spare parts were very slow to arrive. Previously, the operators had fed the furnace infrequently; they prided themselves on pushing the furnaces past their designed limits by loading in large quantities of metal every few hours. The JRC required more “baby-sitting”—the operators had to load in smaller quantities of scrap more frequently so that the pump did not choke.


1. A. Majchrzak, The Human Side of Factory Automation (San Francisco: Jossey-Bass, 1988).

2. R.H. Hayes and K.B. Clark, “Why Some Factories Are More Productive Than Others” Harvard Business Review, September–October 1986, pp. 66–73.

3. F.R. Lichtenberg, “Estimation of the Internal Adjustment Costs Model Using Longitudinal Establishment Data,” Review of Economics and Statistics, August 1988, pp. 421–430.

4. On auto components, see:

B.E. Ichniowski, “How Do Labor Relations Matter? A Study of Productivity in Eleven Manufacturing Plants” (Cambridge, Massachusetts: MIT Sloan School of Management, Ph.D. Diss., 1983).

On paper mills, see:

W.B. Chew, “Productivity and Change: Short-Term Effects of Investments on Factory Level Productivity” (Cambridge, Massachusetts: Harvard University, Ph.D. Diss., 1986).

On commercial kitchens, see:

W.B. Chew, T.F. Bresnahan, and K.B. Clark, “Measurement, Coordination, and Learning in a Multiplant Network,” in Measures for Manufacturing Excellence, ed. R.S. Kaplan (Boston: Harvard Business School Press, 1990).

5. R.H. Hayes and K.B. Clark, “Exploring the Sources of Productivity Differences at the Factory Level,” in The Uneasy Alliance, eds. K.B. Clark et al. (Boston: Harvard Business School Press, 1985).

6. D. Leonard-Barton, “Implementation as Mutual Adaptation of Technology and Organization,” Research Policy 17 (1988): 251–267.

7. R. Jaikumar and R.E. Bohn, “The Development of Intelligent Systems for Industrial Use: A Conceptual Framework,” in Research on Technological Innovation, Management, and Policy, Vol. 3, ed. R. Rosenbloom (Greenwich, Connecticut: JAI Press, 1986) pp. 169–211.

8. See D. Leonard-Barton, “Implementing New Production Technologies: Exercises in Corporate Learning,” in Managing Complexity in High Technology Industries: Systems and People, eds. M. Von Glinow and S. Mohrman (London: Oxford Press, 1989); and

Chew (1986).

9. M.J. Tyre and O. Hauptman, “Effectiveness of Organizational Response Mechanisms to Technological Change in the Production Process,” in Organizational Science, forthcoming. This study of forty-eight new process introduction projects in one company showed value for both interfunctional and interorganizational coordination mechanisms for problem solving.

10. Hayes and Clark (1985).

11. Chew et al. (1990).

12. R. Bohn, “Learning by Experimentation in Manufacturing” (Boston: Harvard Business School, Working Paper No. 88-001, 1988).

13. Bohn (1988).

14. D. Leonard-Barton, “The Case for Integrative Innovation: An Expert System at Digital,” Sloan Management Review, Fall 1987, pp. 7–19.

15. R.E. Bohn and R. Jaikumar, “The Dynamic Approach: An Alternative Paradigm for Operations Management,” Proceedings of the ASME Conference, Atlanta, Georgia, 1988.

16. See R.S. Kaplan, “Must CIM Be Justified by Faith Alone?” Harvard Business Review, March–April 1986, pp. 87–95.

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