MIT Sloan Management Review

Operations Management and Research, Service and Quality

 

Make Your Service Fail-Safe

By Richard B. Chase and Douglas M. Stewart

April 15, 1994

ONE OF THE MOST USEFUL CONCEPTS OF THE TQM MOVEMENT IN MANUFACTURING IS THE APPLICATION OF POKA-YOKE, OR FAIL-SAFE, methods to prevent human errors from becoming defects in the end product. Here the authors argue that these methods apply equally well to services and provide a framework for systematically applying poka-yokes to service encounters. They suggest that actions of the system, the server, and the customer can be fail-safed, and provide numerous examples to stimulate service managers to think in fail-safe terms.

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Total quality management (TQM) has become accepted practice in services. Concepts from TQM in manufacturing, such as benchmarking, diagnostic tools (fishbone diagrams, Pareto charts, and so on), and customer-driven design (through quality function deployment), have joined with such concepts as service guarantees and service recovery planning to drive the quality philosophies of many service firms. Nevertheless, there remains the monumental challenge of quality assurance when the goal is to achieve zero defects in the day-to-day provision of services. Our objective here is to suggest how another concept with proven success in manufacturing, fail-safing, can and should be applied systematically to services to achieve this goal.1

The Nature of Fail-Safing

The idea of fail-safing is to prevent the inevitable mistake from turning into a defect. The late Shigeo Shingo (known as “Mr. Improvement” in Japan) articulated this basic concept. In his writings, Shingo gave examples of how manufacturing companies have set up their equipment and manual processes to prevent errors in parts counts, sequence of work performance, and product configurations. Shingo’s concepts are seen as particularly appropriate where full-scale automation is too costly or is otherwise impractical. According to Hall, “Simple fail-safe methods are the low-cost route to parts-per-million error rates.”2

The objective of fail-safing is similar to what Taguchi methods have attempted in creating robust products and processes — that is, ensuring that they can withstand the effects of factors beyond the producer’s control. We see two main differences between fail-safing and these methods. The first is the relative complexity of Taguchi methods compared to fail-safing: Taguchi methods rely heavily on sophisticated statistical techniques to set optimal product and process parameters. Fail-safing, by contrast, does not require that a specific value be put on process parameters. It requires only the ability to discriminate good from bad. As such, it is easier to apply to intangible service processes.

The second difference is more subtle, concerning the strategy taken by each approach in controlling... To read the complete article, login or sign-up using the form below.

 
 

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