#176 from R&D
Innovator Volume 4, Number 9
Evaluator: An Expert
Innovation Management System
Dr. Abraham is
assistant professor of management, St John’s University, Staten
Island, New York.
In the “garbage
can” model of decision making, organizations are essentially a
set of solutions searching for problems, mingled with a set of
problems seeking solutions. But
there's a missing link--the only mechanism suggested for matching
solutions and problems is serendipity.
There are ways your company can increase opportunities for
chance events that will lead to innovative advances.
A chance match
may involve data buried in your company’s files or in an
informal discussion that took place a month ago in a division you
have little to do with. Researchers
constantly reinvent the wheel because no one told them about the
stack of blueprints in the basement.
And technical or economic feasibility often lag behind
Charles Babbage and his wondrous analytical engine--which was not
feasible for more than 100 years?
Today, even though the lag may often be only a few years or
even months, the original idea may have been filed away and
for instance, believes that there’s gold in the hills of patents
they filed away years ago.
Since no company
can afford to misplace ideas or depend entirely on chance to solve
problems, I advocate a scheme which "mines" your
organization's past and current ideas, thus optimizing the
inherent creativity in your organization.
My group has
developed a computer-based expert system which stores, sorts,
evaluates, and presents ideas—to problem solvers—produced by
employee suggestion programs and other organizational sources.
For details of the computer programming, see Proceedings
of the Annual National Conference of the Association of Management by Abraham, Boone, Lyons & Massetti (August, 1992, pp.
a New Idea
suggestion systems should be designed to encourage the submission
of ideas no matter how "blue-sky".
An employee may be reluctant to submit an idea because it
requires technology which is unavailable, but another department
of the company may be working on just such a technology--or
someone in the company may know about an outside source.
If these ideas are submitted, the company can rapidly
capitalize on them.
Thus I propose a
change on the usual suggestion-box procedure.
When ideas are submitted, they receive a list of limiting
factors which make them impractical now.
The submitter also enumerates the probability (between 0.0
and 1.0) that the limiting factor will restrict the idea.
0.0 indicates that the limiting factor is so obstructive
that the idea is impractical until the limiting factor changes.
As the number approaches 1.0, the limiting factor becomes
less effective at restricting implementation.
Thus listing and evaluating the limiting factors makes
employees more inclined to submit ideas, especially daring ideas.
For example, the
idea might be: Create
an attachable bookmarker so readers needn’t worry about the
marker falling out of the book.
The limiting factor may be:
no known adhesive can be reused and not mark the page.
Probability of 0.8.
The employee who came up with the idea would now be more
likely to submit it with the attached caveat.
is the collection of potentially fruitful ideas related to
the new idea. For
example, to support an individual or group brainstorming session,
a researcher may wish to retrieve a group of related ideas in the
database, but only those with likelihoods of at least 0.8.
For this reason, the idea submitter should associate the
idea with different categories; for instance, the attachable
bookmarker idea may be associated with bulletin-board notes, or
reminder notes that can be stuck on the computer or desk lamp.
Then, when we want to consider a group of related ideas,
they can be retrieved by categories.
in the company has access to the database and anyone can include
new ideas, new associations, new (or updated) limiting factors,
and other comments on an idea.
In the example, a
computer search may show that someone else in the company added
this comment to the database:
some adhesives synthesized by the “X” method have
retrieved ideas can be restricted by likelihood. We could examine only ideas in a certain category with a
likelihood of 0.8 or better.
This type of retrieval helps in a dynamic environment,
where the likelihoods of the limiting factors are changing.
To make this scheme effective, everyone involved should
frequently search topics they’re interested in, and update the
suppose the likelihood of a certain limiting factor is initially
assessed at 0.1. This
idea will not appear in a retrieval if the desired likelihood is
higher than this, but if a recent advance in technology changes
the likelihood to 0.9, then the overall likelihood of any idea
associated with this limiting factor may also increase.
Once a limiting factor is removed (likelihood significantly
increases), another limiting factor will appear.
Thus a researcher
working on a reusable adhesive will spot the page-marker idea and
update it with the new likelihood of the limiting factor.
If the idea is now feasible, a marketing manager may review
the feasible ideas and consider marketing it under the brand name
finding is that the Idea Evaluator prototype demonstrates the
feasibility of using database and expert-system technology to
capture and process ideas. Such
a system should assist organizations to better capitalize on the
inherent creativity of their members.