When we look at cost curves, often we notice they have a U shape which is the result of two opposite effects on the same driver. For example, the quality cost curve and the logistic cost curve.
Quality Cost Curve
If we put the quality level on the x-axis, from 100% defect to 100% perfect, say 1 to 10 in terms of quality level, the cost of achieving the different quality levels increases as we approach perfection. Something like this:
But there is another cost related to quality and that is the failure cost: the lower the quality, the more cost goes on fixing defects, something like this:
Putting these together (simplifying assumption that the two curves have similar slopes), we get a U-shape of the total cost of quality. The implication is that there is a minimum product cost which gives the optimal spending on rectifying defects and prevention, respectively. Sure, there are other dimensions such as pricing and branding which may dictate a quality level different from the minimum, say something like a 9 or so, which would then give a different ratio between failure vs. prevention spending.
Logistic Cost Curve
The logistic cost curve is also a U-shape, driven by decreasing transport costs with increased number of locations and flip-side of it which is increased warehousing costs. Something like this:
Something like this:
Such an understanding is important when designing metrics and KPIs for optimization because it means the metric should be a pair of opposite indicators. Another exercise is to figure out how these curves actually look like and whether the optimization efforts are aligned with the strategy. An example of misalignment would be when costly resources are invested in prevention to achieve higher and higher levels of quality while the failure cost is low, and the pricing cannot follow accordingly. Over time, profitability will get lower and lower while the quality KPI will get greener and greener.