#176 from R&D Innovator Volume 4, Number 9          September 1995

Idea Evaluator:  An Expert Innovation Management System
by Thomas Abraham, Ph.D.

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 ideas.  Remember 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 forgotten.  DuPont, 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. 15-19).

Adding a New Idea

Employee 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.

Related Ideas

Another concern 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.

Ideally, everyone 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 reusable properties.

Then, the 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 file.

For example, 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 “Post-it Notes.”

My initial 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.

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