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"Evaporating Conditions – Physical and/or Technical" examined the equivalence of different types of contradiction. This article focuses on how the many contradictions inevitably present in any system are connected to one another and how solving one contradiction impacts other contradictions in the system. Figure 1 shows how one aspect of a problem – relating to the design of an unmanned air vehicle (UAV) – can be framed in a way that creates multiple different solution generation opportunities.

Each of the links in Figure 1 model represents a conflict. The yellow boxes show how the conflicts can be mapped onto the contradiction matrix. If we can solve any one of the six identified conflicts we will automatically "evaporate" or "dissolve" each of the other five. The word "evaporation" comes from Eli Goldratt and the Evaporating Cloud model that acts as the foundation of the framework. (1) The word "dissolving" comes from Russell Ackoff. (2) Although Ackoff never develops such a structured way of looking at the world, he has thought about a hierarchy of problem solution strategies, the highest level of which is what he describes as "dissolving" the problem. Dissolving a problem means "solving a contradiction." Whether it is called dissolving or evaporating, the basic idea is the same: solve one problem and we (tend to) get rid of the other connections.

This idea is far from obvious. What does this mean in practice? In Figure 1, there are six defined conflicts, any of which could be thought of as the start point. Figure 2 shows the conflict between noise and altitude.

 Figure 2: Noise vs. Altitude Conflict

The specific problem here is that the UAV makes a noise that makes it easy to detect when the altitude (a linear dimension) is low. The contradiction matrix provides the strategies used by others facing their own noise-versus-length problems. The first inventive principle recommended by the matrix for this pair is number 3, local quality. The instructions for principle 3 say to look for things that are homogenous in the current system and then make them non-homogenous.

So what is homogenous? Altitude? Noise? The UAV? How can they be made non-homogenous? How about turning the engine down? Or off? How about turning the engine off when the UAV is flying over its target area, so that it can over-fly at as low an altitude as it wants without being heard? The UAV, in other words, would turn into a glider when it is over a potential threat environment.

By turning the engine down (or off) and gliding over the target area the noise-versus-altitude conflict is solved. Looking back at Figure 1 and the other five conflicts, it becomes evident that this solution has solved or made irrelevant the other conflicts. The conflict between image quality and altitude, for example, becomes irrelevant because with the ability to remove the noise, the UAV can fly at whatever altitude desired. And if the UAV can fly over the target zone at any altitude, without being detected, the mission will be successful.

(This gliding solution is a strategy already used by UAV pilots so this is not a discovery. Again, the point is not to invent new UAVs but to demonstrate important points about the innovation process.)

From One Conflict to Two

Figure 3 shows a new conflict configured using the same six-link framework as used in Figure 1. The problem this time concerns how noise, endurance and type of power plant interact with each other.

 Figure 3: Second UAV Conflict Scenario

This time the conflict is not expressed in terms of altitude and image quality, but in terms of the endurance of the UAV. The physical contradiction part of this endurance-versus-noise conflict is best mapped to the type of propulsion system we employ; by using a battery and electric motor, the noise is very low compared to a petrol engine, but unfortunately, the battery has less power density than petrol and therefore the endurance is not so good. As with the noise-altitude-image clarity model, we can repeat the same contradiction matrix mapping process for each of the six links.

 Figure 4: Combining the Two Six-Link Conflict  Interaction Models

Figure 4 illustrates one way of combining Figures 1 and 3, given that both problems feature "noise." Another connector is that the ultimate aim in both problems is for the UAV to successfully complete the intended mission. When these two common factors are connected, a conjoined pair of conflict interaction models, with the link between noise and successful mission in common. The fact that such a conjoined model can be constructed is indicative of the fact that the two conflict families defined in the individual models are coupled to one another.

Why might this be useful? Previously, a solution to one of the six conflicts present in the noise-image-altitude model "dissolved" the other five. Going further, solving that same single conflict not only dissolves the other five conflicts, it also dissolves the conflicts that are coupled.

In the noise-endurance-propulsion type model, solving the noise problem dissolves the endurance versus propulsion type conflict since there is now found a way to achieve low noise using a noisy but high energy density (and, therefore, higher endurance) petrol engine. The battery versus petrol debate becomes an irrelevant discussion – at least as far as the conflicts coupled together in this cluster are concerned. Dissolving the noise-versus-altitude conflict turns out to also dissolve not just the other five links of the first model, but also the five additional links of the coupled model.

From Two Conflicts to Many

Now think about all of the other conflicts that are present in the UAV. "All," as is often the case in TRIZ, is a dangerous word. "All" is difficult to prove. Nevertheless this is precisely what the Ideal Final Result and Attribute template (illustrated in Figure 5) is trying to achieve. The attributes of the UAV that are considered important and identified, and then what different customers might define as ideal for each, how each of the attributes present in the system might conflict with each of the others can be systematically mapped. The shaded boxes represent all the attribute conflicts identifiable for the listed attributes. There may, of course, be other attributes not included, but the purpose here is to discuss general concepts rather than the specifics of UAV design.

 Figure 5: Ideal-Attribute Conflict Matrix Template (copies of this template can be downloaded at Reference 5)

In Figure 4, two of the attribute conflicts identified in the left hand-side were examined. These two conflicts are identified in Figure 6.

 Figure 6: Ideal-Attribute Conflict Matrix Template and Dissolution of Noise Related Conflicts

Also shown (in different colors in Figure 6) are each of the other attribute conflicts where noise forms one of the pair. In looking at any pair of these attribute conflicts, dissolving one conflict will also dissolve a coupled conflict. In other words, the solution to the noise versus image quality will also solve or dissolve any other conflict involving noise.

The implication of this statement is potentially quite profound. It implies that if there are "n" attributes under consideration that if (n-1) coupled conflict clusters are solved then all (there's that word again!) of the attribute conflicts will be dissolved.

So Close and Yet So Far

It is important to be cautious with statements like the above. First, the attribute conflicts identified in the triangle at the side of the template represent just one type of conflict. The two other main types involve the columns on the right hand side of the template; conflicts where different customers have a different definition of ideal (see the speed attribute – where, depending on the mission, the user may want a range of capabilities that ideally spans from zero to infinite). Second, conflicts resulting from the differences between what the customer wants and what the provider or manufacturer wants.

The resolution of any of these types of conflict may result in a breakthrough that un-dissolves our noise problem. Similarly, the resolution of one attribute conflict may also un-dissolve another uncoupled conflict.

Take the conflict between endurance and speed. Or the customer desire for both high speed and zero speed (hover) capability. As soon as a simultaneous desire for high endurance at zero speed is selected – i.e., a not unreasonable desire to hover over a given position – then suddenly the turn-the-engine-off-and-glide solution to the initial noise problem ceases to be an effective solution. Just because a noise problem for one scenario is resolved does not necessarily mean all future noise problems are resolved. This same idea applies to any other of the attributes present in the system; solving today's problems with attribute ‘x' will dissolve other coupled conflicts. But solving a different, uncoupled conflict may cause problems with ‘x' to arise again.

It is not necessarily something to worry about this since resolving contradictions moves us in the overall direction of the Ideal Final Result endpoint (see Figure 7). The trick from a commercial perspective is "stay(ing) at least one contradiction ahead of our competitors." (6)

 Figure 7: Contrdiction Discovery and the Route to  the Ideal Final Results

Conclusion

1. Every technical contradiction has a parallel physical contradiction. By using a modified version of the Evaporating Cloud template, any technical or physical contradiction can be expressed in terms of six conflict resolution opportunities.
2. By resolving any one of these six conflict definitions, we also resolve or dissolve the other five.
3. Whenever we achieve this resolution, we also dissolve any other coupled conflicts.
4. Dissolving a contradiction moves the system a step closer to its Ideal Final Result end-state.
5. Even though we may be able to dissolve all of the conflicts relating to a given attribute of the system, the resolution of other uncoupled contradictions may result in a re-emergence of conflicts involving this attribute, even though, in net terms, we should expect to be closer to the overall Ideal Final Result evolution endpoint.

References

1. Scheinkopf, L., Thinking for a Change: Putting the TOC Thinking Processes to Use, St Lucie Press, 1999.
2. Ackoff, R.L., Magidson, J., Addison, H.J., Idealized Design: Creating An Organization's Future, Wharton School Publishing, 2006.
3. Systematic Innovation E-Zine, "Turning the House of Quality on Its Side," Issue 57, December 2006.
4. Mann, D.L., Hands-On Systematic Innovation, 2nd edition, IFR Press, 2007.