When dealing with complex processes carried out by complex organizations, we encounter problems. It is natural to seek solutions to those problems. Unfortunately, those placed in positions of influence in creating solutions may not be well-equipped to understand a solution’s cascading effects.
When I served in the Navy, it was common to hear suggestions for preventing problems. Sometimes these solutions were heavy-handed mechanisms of preventing an error from ever occurring again. Were these ideas usually effective? Yes. Were they usually excessively costly? Yes. In the Navy, when somebody is struck with an idea like this, we would say that the person making the suggestion was influenced by “The Good Idea Fairy.”
Consider this scenario. A specialty manufacturer produces very complex industrial widgets. These are low-rate production items and are costly to produce. Even though specialized, the margins are only about 20%. Each widget costs $800,000 to produce and has a sale price of $1,000,000. The manufacturer has experienced challenges in adjusting all the tooling and processes required to build these precision widgets, but after a year of development they were confident in their processes. They went to production, manufacturing 24 widgets per year.
After about two years of consistent, full-scale production, the manufacturing line experienced a damaging production issue. A technician, when entering a set of precision settings on the manufacturing floor, made an error. The error could have been identified and corrected at a later stage, except that an intermediate quality step did not include the right checks to identify the issue. Though the QA process did not explicitly include adequate steps to identify the error, managers agree that a good QA technician still would have caught it. In this case, though, the QA technician passed the widget onto later stages where the error became unrecoverable. The result - the widget made it through the entire production process and was found to be completely worthless during final testing. A labor and materials investment of $800,000 was now useless scrap, and $1,000,000 of revenue was lost.
Management convened a retrospective (or “critique”, or “after-action review,” or “incident review,” etc.) and determined that additional supervision and checks on both steps of the process (entering the settings and performing QA) would prevent such an error from occurring ever again. These checks also included some extensive documentation which required additional time on the part of technicians and managers. The result is that the manufacturing company incurs an extra $15,000 production cost on each widget and the facility now produces 23 widgets per year, instead of the previous 24 per year. Management, however, was confident that these actions would entirely preclude similar errors in the future. The Good Idea Fairy had made her contribution.
Here are the figures for the production facility before and after the process change:
Since the facility operated for about two years with only one major error, we could infer that the likelihood of an error like this one is less than about 2.0% (one in 48 widgets during those two years). That would mean that, even if we penalized annual profit based on the assumption of one lost widget in every 48, we would still be realizing annual profit of $4.3 million (48 units at a cost of $800k each with 47 sales at $1,000,000 each, over two years).
In this case, management implemented a correction that was effective, if evaluated only for its ability to stop the problem from recurring. They reduced the chance of error to essentially zero. But the cost of implementing it reduced the profit value of the facility to $4.26 million per year, whereas it had been somewhere between $4.3 million and $4.8 million beforehand (we don’t accurately know the chance of error, but we can guess at 2% because it was only experienced once). Admittedly most situations are more complex than this hypothetical example, but the truth remains that decisions are often made without analyzing their impacts fully. Seeking operational improvements may have been the right instinct, but the manufacturer’s leadership should have done the work to determine the cost of the improvement. An improvement only makes sense if it is worth the cost it takes to achieve.
“The Good Idea Fairy” generally produces effective mechanisms for improving operations, but she is totally immune to considerations of cost or business value. She steers leaders to narrowly focused tactical wins and they pay the price with resounding strategic defeats. The Good Idea Fairy is often subtle, but always dangerous. Every decision-maker should learn to identify her and shun her.