This article is for quality managers, production managers, plant managers and continuous improvement leads who run QRQC sessions or short problem solving meetings at the line. It answers one practical question: which machine downtime and scrap data should you bring to a QRQC so that the analysis starts from facts rather than memories, and how do you organize it without adding work for the team?
- QRQC means reacting fast, on the spot, with facts.
- Memory, manual logs and end of shift counts leave gaps.
- Stops and scrap per machine on one timeline guide containment.
- The same data shows at the next QRQC whether the action worked.
What Edvance Industrial School says: quality as a cross-cutting pillar
Edvance Industrial School, a Barcelona-based training provider for industry professionals, published the article La Calidad Industrial: un pilar transversal en toda la fábrica (Industrial quality: a cross-cutting pillar across the whole factory). Its starting point is clear: “La gestión de la calidad industrial ya no puede ser responsabilidad exclusiva del departamento de Calidad.” (Industrial quality management can no longer be the sole responsibility of the Quality department.)
The article organizes quality into three stages (planning, control and improvement) and reviews the tools that belong to each, from the Core Tools to Lean Six Sigma. Among them, it presents QRQC (Quick Response Quality Control) with this definition: “Sistema de reacción rápida en planta para tratar incidencias en el momento.” (A rapid response system on the shop floor to deal with incidents as they happen.) The response combines basic analysis, immediate containment and escalation when needed. Edvance brings the method and trains the teams. This article focuses on a complementary piece: the line data that lets teams apply that method with facts.
Why so many QRQC sessions run without data
QRQC rests on a simple principle: react fast, at the place where the problem occurs, with facts. In practice, the third element is the weakest. When the team gathers on the shop floor, the description of the problem usually comes from three sources, each with its limits.
- Shift memory. The operator remembers the long breakdown during the night, much less the short stops that came before it. If the shift has already changed, the information arrives second hand.
- Manual logs. Incident sheets capture what someone had time to write down. Durations are rounded, stops of a few minutes rarely appear and the exact time gets lost.
- End of shift scrap counts. They show how many parts were rejected, but not when it started, on which machine, or whether it followed a restart or a changeover.
With these sources, the QRQC spends much of its time reconstructing what happened instead of analyzing why it happened. Containment is decided on an incomplete picture and escalation depends on the impression of whoever leads the meeting. This is where the Edvance idea makes full sense on the shop floor: if quality is everyone’s responsibility, production, maintenance and quality need to look at the same data, at the same time, machine by machine.
Which downtime and scrap data to bring to the QRQC
The goal is not a full report but answers to the questions a QRQC asks about every incident. The table below maps each question to the machine data that answers it.
| QRQC question | Useful machine data | What it helps decide |
|---|---|---|
| What exactly happened? | Start time, duration and reason for each stop | Describe the problem with facts, not memories |
| Is it new or recurring? | Number of stops with the same reason over the last few days | Separate an isolated incident from a recurring problem |
| Does it affect quality? | Scrap by machine and by time slot | See whether scrap clusters after a stop, a restart or a changeover |
| Where to contain? | Machine and shift where stops and scrap concentrate | Place containment at the right point on the line |
| Should we escalate? | Cumulative duration and frequency against the threshold set by the team | Escalate on an objective criterion, not on the mood of the moment |
An example shows the difference. For example, take a line where the night shift reports “problems on the packaging machine”: with machine level data, the morning QRQC sees whether it was one forty minute breakdown or some twenty two minute micro-stops, and whether scrap appears right after each restart. These are two different problems, with different containment actions and different owners.
A data-driven QRQC, step by step
- Define what counts as a stop and as scrap. Set a shared duration threshold (for example, any stop longer than one minute) and a short list of reasons that operators, maintenance and quality understand the same way.
- Record stops automatically, machine by machine. Start time and duration must come from the machine, not from memory. The operator only adds the reason, on the spot.
- Put scrap and stops on the same timeline. This shows whether scrap appears after a restart, an adjustment or a changeover.
- Prepare the QRQC view before the meeting. Stops and scrap from the last 24 hours, sorted by machine and by cumulative duration, on a single screen on the shop floor.
- Decide and verify. Containment, owner and deadline for each incident. At the next QRQC, the same data shows whether the problem has come back.
This last step closes the loop. A containment or corrective action is only effective if the stop or the scrap stops recurring. With machine data, that check happens at the next meeting, not weeks later. When the problem calls for deeper analysis (8D, A3 or a Lean Six Sigma project), the team starts from a history that is already timestamped and quantified.
This is where TEEPTRAK fits. The solution measures OEE, downtime and micro-stops on each machine in real time, using a module wired into the electrical cabinet and a sensor at the edge of the line, with no PLC connection. Installation takes about one hour per machine and OEE is available within 48 hours. Every stop is timestamped and the operator enters its reason at the workstation, so the daily meeting and the QRQC work from the same information. TEEPTRAK does not replace the method or the training: it provides the measurement that makes them workable. The solution runs in more than 700 plants across more than 30 countries, where teams gain on average 10 to 15 OEE points and 30% fewer unplanned stops.
Further reading
- La Calidad Industrial: un pilar transversal en toda la fábrica (Edvance Industrial School, in Spanish)
- Nonconformance reports: linking each scrap part to machine history
- Kaizen event results: proving the gain machine by machine with stop data
FAQ
What is QRQC? QRQC stands for Quick Response Quality Control. It is a rapid response system on the shop floor that deals with each incident as it happens, through basic analysis, immediate containment and escalation if the problem requires it.
How is a QRQC different from the daily production meeting? The daily meeting reviews the previous day’s performance and sets priorities. The QRQC deals with a specific incident, at the place and at the time it occurs. Both work better when they use the same machine downtime and scrap data.
Do you need a PLC connection to get downtime data by machine? No. A module in the electrical cabinet and a sensor at the edge of the line are enough to detect run and stop states. The operator adds the reason and the data is available without an integration project.
0 Comments