From Continuous Improvement to Continuous Innovation
Robert E. Cole, University of California–Berkeley.
In this paper, Robert E. Cole explores many concepts, including
continuous improvement, continuous innovation, discontinuous
innovation, incrementalism, exploitation, and exploration.
He reviews the many benefits of continuous improvement, as
it is defined in traditional quality programs. Cole discusses
organizational challenges arising from hypercompetition, working
at an accelerated pace, uncertainty, the infusion of new technology,
and the impact of software and information technology. Above
all, he focuses on explaining alternative ways to creatively
build quality improvement through continuous innovation into
the development process. His chosen vehicle is the probe-and-learn
process and how it can lead to higher quality and shorter
product development cycles.
Cole explains that if continuous improvement is conventionally
considered, then it is likely best in slow-moving industries
and in industries where firms are playing catch-up to a future
that is laid out before them. These are industries where exploitation
rather than exploration is required for success. However,
if one’s understanding of continuous improvement is widened
to think in terms of continuous innovation, then there is
a place for it in the process of exploration and discontinuous
innovation.
Whenever it occurs, continuous innovation is not a natural
process that automatically occurs in organizations. Continuous
innovation requires constant, active management and engagement
with workers in an effort to initiate and sustain momentum.
Probe and learn, insofar as it takes places in different parts
of the organization at different times, through multiple initiatives,
has the potential to serve as a sustained energizing force.
Cole notes that probe and learn, applied to the product development
process, captures the essence of continuous innovation. It
is a process well suited to fostering discontinuity and innovation.
He calls it “an experimental iterative process that operates
to successively solve problems in markets characterized by
turbulence, uncertainty, and complex interactions.” Probe
and learn teaches that the generation of error is part of
a productive learning process and should not always be avoided
or suppressed.
Cole notes that how firms learn to manage error in the new
economy will be an important indicator of their success. This
is a special challenge for the quality discipline—one
that has grown up viewing deviance and error as the enemy.
For if quality professionals don’t learn how to manage
error in a dual fashion, other disciplines will take up the
slack.
Three commentators further explore Cole’s theses. Finster
examines how continuous innovation is related to the creative
process. Melton discusses the role of learning in continuous
improvement and continuous innovation. And Weston explains
how both continuous improvement and continuous innovation
are necessary for business survival.
A New Modeling Framework for Organizational Self-assessment: Development and Application
C. H. R. Siow, J. B. Yang, and B. G. Dale, University of Manchester Institute of Science and Technology.
In this paper, Siow, Yang, and Dale report on the development
of a multiple-attribute decision-making (MADM) modeling framework
and methodology for application to the scoring process against
the criteria of the European Foundation for Quality Management
(EFQM) Model for Excellence.
The MADM model is based on the evidential reasoning approach
with the focus of the initial development and application
on the people management enabler. The model was tested with
real data from two award application documents from an electricity
distribution utility. The scores achieved using the methodology
proved the validity and reliability of the model.
The process of defining the evaluation was done as part of a three-stage process.
- Define the attributes with a precise list of checklist items.
- Decide on the evaluation grades.
- Define each checklist item in terms of the different grades.
Subjectivity was an inevitable problem because of the qualitative
nature of the people management enabler. However, the MADM
model was designed with precise definition for each qualitative
attribute and used a mathematical method via the evidential
reasoning approach. Thus, it has the potential to reduce the
subjectivity and biases of the scoring activity, and will
improve the validity and reliability of the scoring process.
There are a few limitations of the MADM modeling framework.
It was developed on the basis of the evidential reasoning
approach, which allows assessors to map evidence extracted
from self-assessment documents and site visits to the defined
evaluation grades. This mapping process provides a natural
way of interpreting a wide range of evidence, most of which
is of a qualitative nature. In this process, the assessors
are not forced to pre-aggregate qualitative evidence into
a single numerical score. However, the mapping process does
require the assessor to provide his or her assessments in
a different way from conventional scoring, and it is not straightforward
to map the extracted evidence to the set of evaluation grades.
Therefore, appropriate training is needed to implement the
MADM modeling framework.
Using checklists (or guidelines) can help assessors to improve
the accuracy and consistency of their self-assessment. It
has been recognized that it requires in-depth research and
takes time and resources to identify commonly used guidelines
for assessment criteria and areas to address.
The criteria aggregating process of the evidential reasoning
approach is not as simple as the additive weighting method
as used in the conventional scoring method. The implementation
of the proposed modeling framework and the methodology relies
on the availability of easy-to-use computer software packages.
The intelligent decision system mentioned in this paper provides
a window-based package to support self-assessment.
