The Improvement Paradox:
Designing Sustainable Quality Improvement Programs
John D. Sterman, Nelson P. Repenning, Rogelio Oliva,
Elizabeth Krahmer, Scott Rockart, Drew Jones
MIT Sloan School of Management, Cambridge MA 02142
For information, contact jsterman@mit.edu
See also: http://web.mit.edu/jsterman/www/
This paper is also available_on the virtual proceedings of the, 1996 System Dynamics Conference, at
http://web.mit.edu/jsterman/www/SD96/home.html
Abstract
Why are quality programs so successful in some firms but not in others? Some point to difficulties
in implementation or leadership. The problem is more fundamental, however. Quality programs
are tightly coupled with other functions, routines and structures. Product development, marketing,
accounting systems, human resource policies, employee morale, pricing policies, and financial
results are all affected by and in turn influence quality initiatives. We hypothesize that the
productivity gains from successful quality programs can interact unfavorably with existing routines
and structures. Unrecognized interdependencies among different functions can reduce the benefits
of process improvement. Under certain conditions these interactions may lead — or force — a firm to
take actions that ultimately cause the demise of an otherwise successful program. Field study and
formal models are used to test these hypotheses
Approach and methods: Designing sustainable quality programs has proven to be difficult, and
the evidence linking quality improvement to financial benefits is mixed. Even highly successful
quality programs can under certain conditions lead to significant short-run deterioration in financial
results and subsequent loss of commitment to the quality program (/). The cause appears to be
unanticipated consequences of successful improvement arising from feedbacks between quality
programs and other functions and organizational routines in the firm. Formal models of firm
behavior grounded in extensive field study can identify these unrecognized interdependencies and
relate them to the dynamics of improvement programs. Capturing complex interventions such as
quality improvement programs in formal models requires a methodology that can (i) represent the
physical and institutional structure of the firm and its markets; (ii) capture the decision processes of
the various actors in the system, including the role of soft variables such as work force
commitment, morale, and fear of job losses; and (iii) portray multiple functions and levels of
analysis (e.g., the shop floor, product development, competitor reactions, and the stock market).
We use the system dynamics method and related behavioral simulation techniques, drawing on
extensive field study, relevant theory in economics, operations management, quality, simulation
51
modeling, and organizational theory to formulate the models. We stress multiple data sources
including interviews with key participants throughout the firm and archival sources such as internal
data on the various metrics of quality, product histories, internal company materials, and financial
results. Our research involves detailed field study with four partner organizations to ground the
formal models in intensive longitudinal study of important improvement programs. The models
will be synthesized into a ‘management flight simulator’ — a simulation environment in which
managers and students will be able toexplore the tong-term dynamics-of improvement programs
and design more effective programs.
Research Partners and Initiatives Under Study: Each of the partner firms has made
significant improvements in quality and productivity. Each also faces continuing challenges as they
seek to maintain commitment to ongoing improvement at the same time they must respond to new
pressures. In some cases the challenges to continuous improvement are the result of past success.
* Lucent Technologies (formerly AT&T): Lucent is the former network systems division of
AT&T, winner of the Baldrige Award in 1992 and recently spun off into a separate company. We
are examining three initiatives that have been central to their success: A supplier quality program, a
program targeted at manufacturing quality, and one focused on reducing product development time.
+ Ford Motor Company: Working with the electronics divisions, we are developing case
histories of three improvement initiatives: a manufacturing cycle time reduction effort, a product
development time reduction effort, and an internal awards program similar to the Baldrige award.
* Harley-Davidson: As a result of significant quality improvement in the 1980s, Harley has
enjoyed robust demand growth, growth that has now outstripped capacity. Harley has launched
major programs to boost productivity in the manufacturing organization, reengineer workflow, and
speed product development. Our field study centers on the conflicts between the drive to boost
output and the commitment to quality improvement.
¢ National Semiconductor: In addition to ongoing improvement initiatives in manufacturing,
National is actively engaged in efforts to improve the product development process, a process
tightly coupled with marketing and manufacturing operations. We are working with the South
Portland, Maine facility, an integrated site responsible for new product development, wafer fab,
and marketing to explore the interactions among the many different improvement initiatives they
have undertaken.
Preliminary results: Though field work continues, preliminary results (/-8), suggest a number
of hypotheses:
* Improvement rates vary with the complexity of the process: The rate of defect
reduction varies across processes according to their technical and organizational complexity.
Sif
Technical complexity refers to the engineering involved in a process. Organizational complexity
refers to the number and type of different personnel and organizations that must be involved in a
quality improvement process. The greater the technical complexity and the more organizational
boundaries that must be crossed in the execution of an improvement program, the slower the
potential rate of improvement will be.
¢ Unbalanced Improvement can create excess capacity: Most quality improvement tools
were developed in manufacturing and are effective at the factory floor and operations level, where
technical and organizational complexity are comparatively low. When quality programs are
properly implemented, improvement rates for these processes are relatively high, leading to rapid
productivity gains. In contrast, organizationally and technically complex activities such as new
product development are likely to improve at slower rates. Processes where improvement is most
rapid tend to boost productivity and augment capacity, while demand-generating activities such as
learning to assess the voice of the customer, developing new products faster and more effectively,
and forging customer/supplier partnerships are intrinsically slower to improve due to greater
technical and particularly organizational complexity.
¢ Feedback to employee morale and commitment to quality programs: Thus success-
ful quality programs are likely to increase capacity faster than demand. Firms are then presented
with a short-term/long-term tradeoff: they can reduce costs by laying off the excess labor, or they
can commit themselves to job security and seek to utilize the excess labor elsewhere. If the firm
chooses to downsize, the resulting disruption to established work teams slows improvement,
erodes morale and reduces commitment to quality programs as employees fear that they will
‘improve themselves out of a job.’ Alternatively, if a firm resists downsizing, financial results
will, in the short-term, be worse. Unless management is aware of and prepared to accept this
‘worse-before-better’ tradeoff, they may abandon their initial commitment to job security,
destroying morale and participation in the improvement program, disrupting established
improvement teams, and halting progress.
* Interactions with accounting metrics and systems: Quality improvement can conflict
with measurement and incentive systems. For example, successful improvement programs can
dramatically alter a firm’s cost structure. Direct costs are driven by the relatively fast-improving
operations while the drivers of overhead and indirect costs are slower to improve. Successful
improvement can cause unit direct costs to fall faster than unit indirect costs. Unless pricing
policies adjust to these changes, gross margins and operating profit will fall. Management may
then seek to restore profit margins through cost cutting and layoffs, triggering the collapse of
commitment to the quality program. Even when a firm is aware of these dynamics, external capital
markets are likely to interpret the decline in profit margin as a signal of poor cost control, eroding
S14
the firm’s market value. The firm may then be forced to downsize to restore profitability or prevent
hostile acquisition. The resulting disruption and decline in morale then undercut worker
commitment to continued improvement efforts, reducing competitiveness.
Significance and Impact: The models and theory we are developing will provide firms with
tools to develop richer understanding of the management challenges associated with the design and
implementation of sustainable quality programs. A better understanding of the interaction of quality
programs with other key systems including accounting and information systems, incentives, and
other organizational routines will help practitioners design robust strategies for the implementation
of quality programs, programs consistent with theory and grounded in relevant experience.
Reports Available (see also web site):
1. Sterman, J., N. Repenning, F. Kofman. (1994) Unanticipated Side Effects of Successful
Quality Programs: Exploring a Paradox of Organizational Improvement. Forthcoming in
Management Science.
2. McPherson, A. (1995) Total Quality Management at AT&T. MS thesis, MIT Sloan School of
Management.
3. Repenning, N. (1995) Reducing Manufacturing Cycle Time at Ford Electronics. Case history
available from author, MIT Sloan School of Management, Cambridge, MA 02142.
4. Krahmer, E. & R. Oliva. (1995) Improving Product Development Interval at AT&T Merrimack
Valley Works. Case history available from author, MIT Sloan School of Management,
Cambridge, MA 02142.
5. Repenning, N. (1995) Reducing Product Development Time at Ford Electronics. Case history
available from author, MIT Sloan School of Management, Cambridge, MA 02142.
6. Johnsson, Fredrik. (1996) Sustainable Improvement Programs: Supplier Quality Excellence.
MS thesis, MIT Sloan School of Management.
7. Oliva, Rogelio and Rockart, Scott (1996) History of Improvement Programs: National
Semiconductor, South Portland Site. Case history available from author, MIT Sloan School of
Management, Cambridge, MA 02142.
8. Krahmer, E. (1996) Supplier Quality Initiatives at AT&T Merrimack Valley Works. Case
history available from author, MIT Sloan School of Management, Cambridge, MA 02142.
5 pe
