Creative Uses of Analysis to
Stimulate Productive Thinking

Tools for Analysis

For background context, here are three sections to read:
summary of

assigning a Quality Status by using Multiple Quality Checks ,
Competitive Solution-Options — Opportunities and Challenges

I.O.U. — Eventually, the rest of this page will be revised.

In a strategy for invention-by-revision, the homepage for Creative-and-Critical Productive Thinking says "it's useful to analyze an old Option into its different features, and think about ways to revise each feature to get a closer match with your goals.."  In the top table below, for a particular product-option (with Feature A,...) you can look at the column for Goal 1 and ask “which feature is causing a matching or mismatching of the option's properties with desired goal-properties, and how can I revise this feature to improve the partial matches and reduce any mismatches?” and then ask these questions for Goal 2,...

Two Perspectives when Using a Table:  As just described, you can focus on the column for a goal and ask “which features could be revised (in what ways) to achieve a better match with this goal?”  Or you can focus on the row for an feature and ask “how can I revise this feature to achieve a more optimal matching of all goals?”  Or you can do both, when you have mis-matches for multiple goals, and (in an effort to achieve better matches) you want to consider all possible options for revising features.

If you find a conflict between achieving different goals — for example, if revising Feature A to achieve a better match for Goal 1 makes it less satisfactory for Goal 2, ask “how can I modify Feature A to produce the best overall result with the least amounts of goal-mismatching?”, and when defining " best overall result" you can weight the goals by asking “which evaluation criteria are more important, those for Goal 1 or for Goal 2?”  Or you may find that the best overall result is produced by a combination of revisions;  for example, if a particular revision of Feature A makes the product-option worse for Goal 2, the amount of this goal-mismatch might be reduced by one of the possible revisions for Feature B, or C, D,...

The bottom table shows Competing Partial Solutions where "your best current options are a2 and b2" and you try to "revise a2 [into a3] so it will gain some of the unique benefits of b2... or revise b2 [into b3] so it also has what a2 offers... or try to invent an innovative new ‘ab’ option that is a hybrid of a-and-b, combining the best of both while minimizing their weaknesses."  This table summarizes information in a way that may help you make useful revisions and find the best overall solution.  If there is no clear winner, you can quantify your evaluation-rating for each cell (Option a2 for Goal 1,...) on a scale of 1-10, and weight each goal-criterion (e.g. if you think Goal 2 and Goal 3 are 1.5 times and .75 times as important as Goal 1, then multiply all ratings in Goal 2's column by 1.5, and in Goal 3's column by .75), then compare the total rating-scores for each option;  when doing this you may want to use a spreadsheet like Excel, which in a classroom will give students experience with a useful computer program.

Or instead of making a table of options-and-goals, you could make it with options-and-properties, and include an extra row that lists the goal-properties of your ideal solution.  These will be similar because you define goals for properties, but you may find one perspective more useful than the other.  With either perspective, the table can help you notice knowledge gaps.  If any table-cells are empty (with no information for predictions and/or observations) or have inadequate information (if it's imprecise or you suspect it might be inaccurate) you can decide whether it's worthwhile to gather additional information (old in 2A, or new in 2C or 2D) with predictions or observations, or both.

comment for reader:  Here, from a different page, are ideas about using table-grids for Experimental Design:

Sometimes a visual organization of information is useful.  For example, the analytical grid below can summarize information (in the 20 white cells) about five products (in top row) and four experiments (in left column):

    Scanning horizontally across a row shows the information that is generated, in one type of experiment, about five different products.  Or you can scan vertically down a column, to see how the properties of one product are revealed in different experimental contexts.
    This grid shows products that are both old and new, and experiments that are old and new.  Each cell can contain observations (if an experiment has been done already) or predictions, or both.  You could think of it as a "product and experiments" grid, but — since the result of experiments is information about product-properties, and properties are your focus during evaluation — it is probably more useful to view it as a product-and-properties grid.
    For some products it is useful to make several grids, one for each design decision.  For a car, decisions would include the type of body, trunk, seats, doors, colors, engine, and transmission.  One grid could show possibilities for car bodies (with different shapes, sizes, materials,...) and experiments (ways to test each body for aerodynamic efficiency, consumer appeal, manufacturing cost,...) along with observations and/or predictions.  Other grids could show information about trunks, seats,...
    A grid is a useful way to summarize, in a clearly organized way, knowledge about products and experiments.  By scanning horizontally or vertically, you can focus your attention on a particular experiment or product.  By thinking critically and creatively about what you see in one scan — or in several (horizontal, vertical, or mixed) — you can search for patterns and principles, and for ways to improve a product by imagining (in 2B) how to revise an old option to make a new option.

    Or you may notice a knowledge gap when you ask, "Do the experiments provide satisfactory information about all important properties of every product-option, or is there something else that we want to know but don't know?", and this will inspire the design of new experiments (testing, in 2C) to generate the desired knowledge.
    Noticing a knowledge gap is an example of an action decision.  Each cell in a product-and-experiment grid can be filled with predictions or observations, or neither, or both.  For each cell, for each combination of product and experiment, you can decide what is the best use of your time:  Should you invest the time that is needed to make a quick-and-rough prediction?  to make a careful prediction?  to use a computer simulation for making a careful prediction?  to collect observations by running a simple small-scale experiment, or an elaborate large-scale experiment?
    One important function of mental experiments is to let you explore, quickly and cheaply, a wide variety of experimental possibilities.  One objective of this exploration is to search for tests that seem capable of providing useful information, that may be worth doing as physical experiments in a TESTING mode of action, in 2C.