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We can show you Allen's pressure tank... or the more complex pressure tank model proposed by NASA... or perhaps an example of your own...

Want to Implement Visual Knowledge for Flawless Design in your organization?

We can help! Success Assured® was specifically designed to enable teams to do that 15+ years ago (when Allen originally wrote that material). And since then, based on our work with companies in a variety of industries, we have made many significant improvements on the foundation originally laid by Allen. This page will explain those improvements...

To teach Visual Knowledge, Allen uses an example of designing a pressurized tank that won't have weld ruptures. He fleshes out a causal diagram showing the different decisions to be made and the impacts those decisions may have.

Allen then identifies clusters within that causal diagram for which a trade-off curve can be developed....

He then derives a formulation, reworking it into unitless ratios, and then shows how that trade-off chart can be used to guide decision-making.

We teach very similar, but with some important refinements...

First, circling clusters that can be charted turns out to be visually awkward with even moderate complexity. Those clusters will necessarily overlap with the neighboring clusters, creating tremendous visual complexity and confusion.

Worse, as the real problem complexity rises, those clusters often get spread apart in the diagram, increasing that visual confusion – such that the diagrams become unusable.

To solve that, our Decision Maps use rectangular Relation shapes to represent the Trade-Off Charts that can be formulated between the Decisions.

 

For the Decisions we use circular shapes allowing us to attach information such as units of measure and characteristics such as being a targeted customer interest.

Second, the + and - notations on the lines in Allen’s causal diagrams indicating “tends to make better” or “tends to make worse” actually tend to create confusion because, for many of the intermediate and root decisions, what is “good” or “bad” is unclear.  That can result in unnecessary debate on the proper directions of the indicators.  

 

Similarly, the arrow heads indicating the causal direction also creates unnecessary debate because in reality, the cause-and-effect direction depends upon where you are in the decision-making process.  

 

Consider F=ma … is it the force on the mass causing the acceleration?  Or is the required acceleration causing you to apply the necessary force to the mass?  Or is the increase in mass due to other decisions causing your acceleration to go down or your force to go up?

 

Our problems are complex enough without adding extra things to debate or cause confusion.  We instead use a combination of arrows and +/- symbols to indicate how the values of the decisions actually move, without any implication of causal direction or any implication of good or bad.

Third, the Relation shapes between the Decision shapes simply capture what is known in whatever form it is known.

 

The person with the knowledge need not figure out how that knowledge will be best shown to the future decision-makers… that is left to the future decision-makers.

 

Rather, the person with the knowledge on that portion of the Causal Map can simply capture what is known however the experts in that space are most accustomed to seeing it… making it easy to review and improve over time.

 

The tools then help the future decision-makers reuse that knowledge by showing it to them the way they want to see it.

Causal Map showing a NASA-proposed model for optimizing Shuttle payload while minimizing the cost of the External Fuel Tank.

So, once a Decision Map has been constructed and the knowledge about the Relations captured from the individual experts, the decision makers can quickly use the Map to specify the multi-dimensional, multi-relational Trade-Off Charts that they want to see.

 

They can slice-and-dice the design space in numerous ways allowing them to gain a deeper understanding of the multi-dimensional design space than any two- or three-dimensional chart could give alone.

 

They can learn how best to make the decisions by visually exploring the multi-dimensional design space.

Once they work out the best way to make a set of decisions, they can capture the associated Trade-Off Charts and Trade-Off Solvers as a second layer of reusable knowledge built on the first layer of reusable knowledge (the Decision Map made up of Decision and Relation K-Briefs).

 

Those Maps, Charts, and Solvers can then be organized into a cohesive problem-solving or project-design K-Brief that tells the larger story, and forms the third layer of reusable knowledge.

 

Those three layers of reusable knowledge are a substantial upgrade to the visual Trade-Off Curve Sheets that Allen developed, and are far better able to handle the real-world complexity found in industries such as aerospace or medical devices.

Returning to Allen's Tank example, that K-Brief might look something like this: