Quality sounds like something that everyone will support on principle, unless you have a saboteur working in your midst. It’s probably safe to assume that no one is deliberately acting to produce a defective product or service. The problem is that everyone makes daily decisions that balance quality against other considerations, whether to save money, or meet a committed date, or keep the factory running. We tell ourselves that quality is free, but even in highly-evolved organizations it doesn’t happen without deliberate effort. The challenge to quality professionals is helping people understand how good quality contributes to the business and thereby provide a more useful basis for decision making.
Here’s a little not-so-secret secret: all decisions in for-profit businesses eventually come down to how to bring in more revenue while controlling expenses. If you want people to pay attention to quality, talk about money.
For better or worse, this is a lot easier after the cost has already been incurred. If you have to spend more money or time because of scrap or rework, or you have to repair or replace product at the customer, or you’re liable for warranty or other contractual post-sale costs, everyone will be interested to know how it happened and how it can be prevented in the future. After some investigation you may identify the cause or causes, and you can recommend actions to eliminate them. Of course those corrective actions will have a cost of their own, and you will have to determine if there’s a net gain.
All of that is based on the assumption that there’s a 100% probability of that bad thing happening again if you do not implement the corrective action, and a 0% probability if you do. If you want to get more analytical you can estimate those probabilities based on engineering analysis, historical trends, or just good old-fashioned judgment, and then apply a de-rating factor to the cost. This is where an FMEA analysis is useful, along with early prototyping and testing to check those assumptions about probability and impact.
Here it’s important to note that there are indirect costs of poor quality that are harder to factor in to this calculation. For example, even a single incident at a key customer could cause a significant decline in future revenue if it affects brand reputation. Low-probability yet high-severity events are also problematic.
Of course it’s generally harder to look ahead and assess the unknown probability and impact of a quality risk that has not yet been encountered. As long as the bad thing hasn’t happened yet, it’s easy to underestimate it. This is what causes organizations to reduce cost by using cheaper parts or removing design safeguards or eliminating quality checks. They’re saving real money today and implicitly accepting the uncertain risk (and cost) of a poor quality event in the future. Again, if you can say with 100% certainty that this bad thing will happen without specific actions being taken, then your choice is clear. Unfortunately there are many choices that are not clear, or even recognizable.
Are you really willing to spend whatever it takes to prevent any quality problem? Of course not. Managing quality is managing risk, and looking for ways to assess and minimize that risk while under pressure to reduce cost now. It’s not very satisfying to say “I told you so.”
