How Can Binary Decision Making Improve Your Problem Solving Effectiveness?
We challenge our problem solving classes to play “20 questions” – can we guess a randomly selected word using just 20 yes/no questions? Engineers often say it’s impossible. But, 99 out of 100 times, we find the word! How? There’s no magic: what’s on display is the power of binary decision making, what we call “splitting the dictionary”. And you can apply it to all sorts of real-world problem-solving scenarios.
“Splitting the dictionary” is a problem scoping technique of asking the right questions to eliminate as many incorrect possibilities as you can, thus focusing your root cause search in the right area. In the case of guessing a random word, it’s a binary search: “Does the word start with any of the letters A through M?” If yes, then, “Does the word start with any of the letters A through G?, etc. The quantity of possible starting letters is halved with each question. It’s certain to get the starting letter in 5 guesses (26 letters narrowed to 13, to 7, to 4,…). The word’s 2nd and 3rd letters are made easier to guess by supplementing the binary search with knowledge of the language (e.g., some letter combinations are never used, like bx).
Far too often, we’ve encountered issue resolution teams mired in complex testing or analysis. Do yourself a favor, before heading down a complicated, expensive route (e.g., a DOE, Monte Carlo Simulation, etc.) consider “What question could eliminate half (or at least a large percentage) of the potential causes?” “Is there a quick document review, analysis, or simple test that can reduce the scope of where we need to look?”
We have used this technique many times analyzing failed electrical assemblies. Consider an assembly that can be physically split between “input” and “output” sides. By taking the “input” side of the failed device and pairing it with a known-good “output” side (and vice versa), we can see in which mix-and-matched assembly the symptom persists and then focus attention there.
It’s worked for us in scoping the origination of defective parts in a manufacturing setting too, by asking, “Are there defective components coming in from our supplier?” A simple inspection can check the supply chain integrity. This separates supply chain concerns from internal manufacturing issues and allows the team to focus on one area and not both.
Effective scoping is key to efficient problem-solving. Once the problem is clearly defined, think of the root cause as a needle in a haystack. Before you start examining hay, first ask “Am I on the right farm?”, then, “Which haystack should I search?”, then “Which portion of the chosen haystack is most likely to contain the needle?” Scoping out the possibilities with a simple tool or test can radically accelerate progress toward identifying the root cause by eliminating complex, time-wasting activity.
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Over the years we’ve been exposed to Six Sigma, Juran, Deming PDCA, 8D, Dale Carnegie, A3, Shainin, and more. Each technique works pretty well, and has been demonstrated many times in a wide variety of industries and circumstances. At the core they are all essentially the same!
Each approach relies on an underlying logical flow that goes like this: [a] make sure the problem is clearly defined; [b] be open to all sources of information; [c] vet the information for relevance and accuracy; [d] use the process of elimination to narrow down all possible causes to the most likely few; [e] prove which of the suspects is really the cause of the issue; [f] generate a number of potential solutions; [g] evaluate the effectiveness, feasibility and risk of the potential solutions; [h] implement the winning solution(s); and [i] take steps to make sure your solution(s) don’t unravel in the future.
The differences between the paradigms resides in supplementary steps and toolkits. For example, 8D contains the important “In
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