What Is the Cost of Quality?
“Cost of Quality” (CoQ) is a framework for assigning monetary value to quality in a finished product—not only for poor quality, but also for quality that is “too good.” It helps teams inform decision‑making and balance inspection, testing, prevention, and defect costs to manage budgets effectively
Defining Quality in Electronics
In electronics, quality isn’t visual. Three key dimensions matter:
- Test coverage – ensuring the product’s feature set is fully exercised (e.g. emergency‑call function even when network is down)
- Test recall – trust that a test failure means the product failed, not the test rig
- Test repeatability – consistent and reliable results even for intermittent or latent faults
The tolerance for imperfection varies by domain—medical devices, aerospace and others require very different thresholds than consumer gadgets.
When Too Much Testing Becomes Costly
Paradoxically, more testing isn’t always better. If final tests consume too much time per unit (e.g. longer than takt time), production cannot keep pace unless more resources are added—test benches, technicians, and tooling—all of which raise CoQ
The key is asking: does this test meaningfully reduce customer risk, or could the same risk be mitigated earlier in design, supply chain or simulation more cost‑effectively?
Traceability: When It Becomes a Burden
Traceability isn’t universally beneficial. For regulated sectors (ISO‑13485, AS‑9100), traceability may be mandatory. But outside those contexts, investing heavily in traceability without significant product risk or process variability may add unnecessary cost with little return .
Rework and Poor Yield
Low-volume or high‑value production often sees a high rate of rework or scrap. The author shares an example of building each unit more than twice to meet quality standards. This doubles capacity requirements at every stage—test, SOAK, repair—driving CoQ upward significantly
Testing Early in the Process
Rather than concentrating testing at end‑of‑line, effective quality management distributes test earlier—supplier inspection, in‑circuit checks, optical sampling—anchoring fault detection closer to the source. Importantly, if upstream tests reduce later failures, end‑line testing can be scaled back, reducing overall CoQ. It often requires patience for CoQ to increase before seeing a reduction later.
Diagnosis: Balance Thoroughness with Value
Root-cause analysis (RCA) is resource‑intensive, building fault trees and applying 5 Whys or fishbone diagrams. But it may be unnecessary if the cost of analysis exceeds the value of a single board. Engineers should ask whether time‑intensive RCA is justified given frequency and impact of the fault
Design for Test (DFT)
Embedding testing into product design (DFT) reduces reliance on later inspection or rework. A guiding rule: upstream testing investment should be comparable to costs of rework or replacement downstream. This alignment helps balance CoQ and maintain throughput without excessive overhead
Simulation and Testing in Development
Design-level tools—DFMEA, reliability models (FIDES), SPICE simulation—let teams validate risk early. Later, in production, formal testing (functional or in-circuit) takes over. JTAG boundary‑scan bridges both worlds, enabling test access from design to manufacturing with lower base cost
The Lean Perspective: Muda, Muri, Mura
Quality issues often disrupt lean workflows:
- Muda (waste): rejects and scrap reducing yield
- Muri (overburden): overworked equipment and operators
- Mura (unevenness): production buffers and uneven load—all increasing CoQ through inefficiency
Work‑in‑Progress (WIP) and Cash Flow
Higher testing and quality standards often result in large WIP. Holding high-value product in buffers—whether waiting for retest, repair, or process synchronization—ties up capital. An illustrative case: electronics boards valued at ~£200 k each, with ~1,000 in WIP, amounts to ~£200 M in tied‑up cash, severely affecting liquidity and investment capacity .
Proportionate Testing: Finding the Balance
The article concludes with indicators of where to invest more or less in testing. Invest more if customers complain, WIP is high, or rework is reducing throughput. Test less if end‑line tests are catching failures earlier tests should have found, if test repeatability is low, or traceability brings unnecessary complexity. It’s a nuanced, context-dependent balancing act .
In summary, the Cost of Quality framework in this article emphasizes proportionate, risk‑aligned testing across the lifecycle—from design and development to in‑line and end-of-line testing—balancing prevention, inspection, failure cost, cashflow, and lean principles to optimize quality and business performance.
