A pragmatic approach to tracking the total cost of quality

Ask anyone in manufacturing about quality management and they’ll all agree that it’s important. Have them quantify quality efforts and successes, and you’ll likely see them struggle. Despite a clear evolution from error detection to prevention and using data that feeds continuous improvement, many quality leaders find it difficult to assess the quality management function. Even worse, because of the function’s unclear contribution, old beliefs – that quality is nothing more than a cost centre with no value other than detecting off-spec production – often resurface.

The most obvious way to change this perception is by highlighting financial value. Being able to justify how quality contributes to the bottom line will help get management commitment and drive quality innovations. This article outlines a solid financial model that helps quality leaders to systematically track and calculate the impact of good and poor quality.

Full picture or the tip of the iceberg?

Poor quality is not only felt by the quality team. It also affects the entire organisation. And, because quality is intertwined with most business functions, the extent of that impact is not easy to calculate. It becomes even more complicated when considering the invisible side of quality and less obvious costs.

Picture a large iceberg floating in the sea – a fragment of it rising above the water, while the greater part of the iceberg lies hidden in the deep blue. Just like in this famous metaphor, the costs of quality are partly visible and partly hidden. Aspects such as rework and quality control costs are easy to measure and understand, and they are visible to most people. But they are only the tip of the iceberg. On the other hand, less obvious costs hidden underwater represent the biggest share of our total cost of quality and can have a much bigger impact.

For example: all your rework consumes additional energy, leads to excessive wear on your equipment and might demotivate your team with an effect on your staff turnover. Similarly, it’s obvious that quality incidents can lead to customer claims. But the less visible impact on your customer satisfaction, brand image and lost sales is likely to be of a whole different magnitude than the immediate claims.

Good quality versus poor quality

To quantify our quality operation and be sure that we cover all costs of quality we need a more structured approach than this segmentation along the lines of visibility. Val Feigenbaum’s PAF model and his segmentation between good quality and poor quality are a good start for this.

Good quality represents everything you organise proactively for. Poor quality represents all quality incidents and their effects. The costs for both good and poor quality can also be segmented further. Good quality costs are those that go into prevention and appraisal, while poor quality costs can be divided into internal and external failures. You’ll want to track internal and external failure separately since the monetary impact increases drastically the moment your goods leave the factory grounds.

A balancing act

As quality management matures, manufacturers will invest more in prevention than fixing failure. Spending more on good quality will have them spend less on fixing poor quality. Conversely, by investing less in good quality, they’ll need to spend more time and resources on fixing failure. The sum of failure and prevention costs is the total cost of quality.