From Continuous Improvement to Continuous Innovation
Robert E. Cole, University of CaliforniaBerkeley.
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 ones 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 disciplineone that has grown up viewing deviance and error as the enemy. For if quality professionals dont learn how to manage error in a dual fashion, other disciplines will take up the slack.
Three commentators further explore Coles 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.
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.