The Heat Map
Key Points:
- More complexity does not always create more understanding.
- Strip away what matters less to expose what must be true.
- A simple analysis can clearly frame a difficult technical decision.
The development and progression of simulators with increasing levels of detail, informed by real-world data, can be a powerful way to converge on the recipe for techno-economic viability, with learning and course correction along the way.
But developing the recipe for techno-economic viability is often not the only program need. Sometimes there is a need to take a step back and assess core decisions being made.
Consider a technology whose performance and economics depend on many intertwined effects: design, operation, inputs, costs, customer value, and so forth. While those closest to the work may develop a strong sense of what matters most, other stakeholders remain separated from the details or anchored to weak or erroneous assumptions. How can the subject matter experts confirm or at least bound their intuitions and communicate them to the decision makers?
The natural course would be to further refine the simulation effort, to press to make it more real, more comprehensive, and more productive. The challenge is not just that key information may not yet be known, or that more time is needed for development and analysis, but that the complexity itself obscures the message. Sometimes the most valuable analysis comes from taking a step backward: stripping away detail to clearly expose a core requirement for success.
In one case it was becoming clear to us that a key component was not fit for purpose, but the message was not gaining traction with key decision makers for a variety of reasons. As with the Titanic, stakeholder dynamics had us moving forward full steam ahead. None of the available simulations were able to generate in them a clear vision or acceptance of where this technical decision would take us. Present testing was rooted in feasibility, not commercial viability, and focused on staying on schedule. It was time for a jugular analysis.
A heat map of performance was created in MS Excel to keep it simple and accessible to all. In this analysis, all peripheral complexity was stripped out: all costs were assumed to be free, save only for the cost of this component. All controls and dynamics complexity was left by the wayside. The heat map was formatted to calculate the system’s lifetime production value divided by the cost of the component, versus the two key performance measures of that product—hence the map.
The map basically says this: Assuming everything about this operation is free except for the cost of that component, how many dollars do you make for every dollar spent?
On our map, we look in the broader region where our component performance lay, and we see, in full Excel conditional formatting, deep red. The value out per cost in is below breakeven. Even if all else was free, even if time did not matter, we would still lose money on this choice, hand over fist.
The calculation was almost embarrassingly simple. The conclusion was not. Our concern was laid bare, now open for discussion. That component would have to perform at levels far better than we could reasonably achieve, or we needed to pivot. There was no hiding behind simulation complexity and incompleteness. The program strategy had to turn or we would hit the iceberg head on.
Of course there is a preference to produce results showing convergence on commercialization. Sometimes reality dictates some soul searching and hard decisions. Sometimes that process needs to be motivated by an analysis that is simple, not complex, to make the issue impossible to ignore.
— Cassandra
Joseph Wright of Derby - A Philosopher Lecturing on the Orrery (1766)
