Quality & Continuous Improvement

Non-Quality Indicators: What to Measure and Why

By Claudia · · 6 min read

Non-quality indicators: what to measure and why

Non-quality indicators: what to measure and why

In the day-to-day of a food plant, it's easy to focus attention on what goes right: approved batches, passed audits, renewed certifications. Yet non-quality indicators are precisely the ones that reveal where money, efficiency, and customer trust are being lost. Measuring what fails — and doing it systematically — is one of the most powerful levers a quality manager has to demonstrate the real impact of their work on the organization.

What non-quality indicators are and how they differ from typical quality KPIs

Non-quality indicators are metrics that quantify the failures, deviations, and costs associated with not getting things right the first time. They differ from conventional quality KPIs in their focus: while a classic KPI measures compliance with a standard (percentage of analyses within limits, number of audits carried out), a non-quality indicator measures the cost and frequency of non-compliance.

In other words, quality KPIs tell you whether the system is working; non-quality indicators tell you how much it costs you when it isn't. Both are necessary, but in many food plants the latter are missing, or calculated only sporadically and without much rigor.

This distinction isn't just conceptual — it has practical consequences. When an operations director asks how much quality costs, the answer can't just be the department's budget. You also need to be able to answer how much non-quality costs, and that requires specific indicators that are well defined and kept up to date.

The indicators that really matter: complaints, waste, recurring issues, and rework

Not all indicators deserve the same level of attention. Below are the ones that provide the most useful information in the context of a food plant.

Customer complaints. Every complaint received is a symptom of a failure that has already reached the outside world. What matters isn't just counting them, but classifying them by type, origin, and product, and tracking how they evolve over time. A resolved complaint that recurs the following quarter is an issue that was closed poorly.

Waste and rejected product. The percentage of product rejected on the line or during final checks is one of the most direct indicators of production inefficiency. It's worth breaking it down by shift, line, and operator to spot patterns. Waste that looks small as a percentage can add up to thousands of euros a month once calculated against total volume produced.

Recurring issues. A one-off issue is an accident; a repeated issue is a systemic problem. Measuring the recurrence rate — that is, what percentage of logged issues had already happened before — lets you identify which corrective actions aren't working and where the management system needs reinforcement.

Rework. Rework is perhaps the most underrated indicator. It tends to be seen as a fix, when in reality it's a huge hidden cost: extra labor, energy consumption, risk of a new non-conformity, and delivery delays. Tracking how many units or batches go through rework, and how much time is spent on it, is essential to calculating the real cost of non-quality.

If you want to dig deeper into how to select and prioritize these metrics in your daily operations, check out our article on the 7 quality control KPIs that actually matter.

How to calculate the real cost of non-quality in a food plant

The cost of non-quality is typically structured into four categories: internal failures, external failures, appraisal, and prevention. In the food sector, internal and external failures account for most of the economic impact.

Internal failures are all the costs generated before the product reaches the customer: rejected product, rework, repeated analyses, downtime from issues, and waste management stemming from non-conformities. They're visible inside the plant, though not always tracked centrally.

External failures include complaints, returns, product recalls, and the associated reputational cost. These are the most expensive — not just in direct economic terms, but in their impact on the customer relationship and brand image. As set out in Regulation (EC) No 178/2002 on general food law (Regulation [EC] 178/2002, 2002), operators are responsible for ensuring that the food they place on the market is safe, meaning any external failure carries legal as well as economic consequences.

To calculate the cost of non-quality in practice, a simple starting formula is:

Cost of non-quality = (Cost of rejected product + Cost of rework + Cost of handling complaints + Cost of returns) / Total revenue × 100

This ratio, expressed as a percentage of revenue, lets you track progress over time and set improvement targets. In many food plants, this figure exceeds 3-5% of revenue without leadership teams even being aware of it — precisely because the data is scattered across different systems or departments.

How to automate tracking of these indicators without building a new spreadsheet every month

One of the biggest obstacles to measuring non-quality on an ongoing basis is the administrative burden of gathering, cross-referencing, and updating the data. When each indicator lives in a different spreadsheet — or worse, on paper — monthly tracking becomes a project in itself, with the added risk of transcription errors and stale data.

The solution is to centralize the logging of issues, complaints, rework, and rejections on a single digital platform that lets you check indicator status in real time. This doesn't require a massive technology rollout — what it does require is a system designed for how a food plant actually operates, not a generic environment.

Quality software built specifically for the food industry lets you, among other things, log an issue from the line in a few clicks, automatically link it to a batch, product, or process, calculate the estimated economic impact at the moment it's logged, and generate indicator-tracking reports with no extra manual work. The result is that the quality manager can spend their time analyzing and acting, instead of building spreadsheets.

Having well-structured historical data also makes it much easier to prepare for audits under standards such as ISO 22000 (ISO, n.d.), which require documented evidence of the effectiveness of the food safety management system, including tracking performance indicators and resolving non-conformities.

At Solved, we built our platform with exactly this logic in mind: measuring non-quality should be as simple as logging any other production data, and indicators should be available to the whole team without depending on a hand-built monthly report. Because only what's measured continuously can improve sustainably.

References