# In line quality and statistical process control

*Control charts, capability, and the difference between a process that is stable and one that is good.*

## Production summary

- Modules to record: 2
- Total script: 1580 words, about 11 minutes of finished audio
- Voices: Amara (host) and Nadia (practice educator)
- Level: Level 3 to 5. Quality inspectors, line leaders, process engineers and quality managers

## Accreditation wording that must appear in the description

- **The CPD Certification Service** (planned): Application scheduled.
- **BS EN ISO 9001 quality management systems** (aligned): Mapped to the monitoring, measurement and nonconforming output clauses of the published standard. Our own mapping, implying no certification or endorsement.

> Do not upgrade any of these words in a description or a thumbnail. Aligned is not accredited, and planned is not approved.


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## Two kinds of variation, and how a control chart tells them apart

**Runtime** about 5 minutes. **Words** 790. **Starts at** 00:00 in the full course recording.

### Learning outcomes to state on camera

- Distinguish common cause from special cause variation
- Explain who is responsible for acting on each kind
- Read a control chart using the beyond limits, run and trend rules
- Explain why control limits are not specification limits
- Recognise tampering and describe its effect on variation

### Script


`[CUE 1]` *A run of results building into a control chart, centre line and limits appearing*

**AMARA**  [00:00]
Start with the thing people get wrong on day one.

**NADIA**  [00:04]
That variation is a fault. It is not. Every process varies, and the only useful question is which of two kinds you are looking at, because the two have completely different owners.

**AMARA**  [00:16]
Name them.

**NADIA**  [00:17]
Common cause is the process being itself. Ambient temperature, material lot to material lot, the ordinary play in a mechanism. Always present, predictable within a range. Special cause is something new: a worn tool, a failed heater, a different operator, a delivery from a different supplier.

**AMARA**  [00:36]
Why does the distinction matter so much?

**NADIA**  [00:38]
Because it decides who acts. Special cause belongs to the person at the machine, now, and it can be found. Common cause belongs to management, because reducing it means changing the process itself. Asking an operator to fix common cause variation is asking them to do something the job does not permit.


`[CUE 2]` *The same chart with a run of seven points on one side highlighted*

**AMARA**  [00:59]
And the chart tells them apart.

**NADIA**  [01:01]
That is its entire purpose. Results in time order, a centre line at the average, limits at three standard deviations calculated from the process while it was behaving.

**AMARA**  [01:13]
Why three?

**NADIA**  [01:13]
It is a compromise, not a law. Tighter and the chart cries wolf and people hunt causes that are not there. Wider and real signals slip past. Three has held for a century because it balances those two costs about as well as anything does.

**AMARA**  [01:31]
Most people only know one rule.

**NADIA**  [01:34]
A point outside a limit, and on its own that catches large shifts only. A process can drift a very long way with every point still inside the lines.


`[CUE 3]` *A trend developing towards a limit, with the intervention point marked early*

**AMARA**  [01:45]
So what else are you looking for?

**NADIA**  [01:48]
Runs and trends. Seven consecutive points on one side of the centre line means the average has moved. Seven rising or falling means something is drifting progressively, and that is what tool wear looks like before it produces a single reject.

**AMARA**  [02:05]
That is the useful one.

**NADIA**  [02:07]
It is the whole prize. A trend caught at point five is a tool change scheduled at the end of the shift. The same trend caught when a point finally breaches the limit is a quarantined batch and a conversation with a customer.

**AMARA**  [02:24]
You also watch for points that are too neat.

**NADIA**  [02:27]
Data hugging the centre line unnaturally closely. Real processes are not that tidy. It usually means somebody is averaging, rounding, or writing down what they expect to see rather than what the gauge said.


`[CUE 4]` *Two charts side by side: in control and out of specification, then the reverse*

**AMARA**  [02:41]
Control limits and specification limits. Make the distinction hard to forget.

**NADIA**  [02:45]
Control limits come from the process and describe what it does. Specification limits come from the customer and describe what is acceptable. They are unrelated quantities that happen to be drawn in the same units.

**AMARA**  [02:59]
What goes wrong when they are confused?

**NADIA**  [03:02]
A process can be beautifully in control and outside specification on every unit. Stably producing rubbish. It can also be well inside specification and completely out of control, which means it is getting away with it today and you have no idea what it does tomorrow.

**AMARA**  [03:21]
And on the chart itself?

**NADIA**  [03:23]
If the tolerance is drawn on a control chart, that chart is training people to tamper. Every time a point wanders towards a line it was never meant to be compared with, somebody reaches for an adjustment.


`[CUE 5]` *A tampering simulation: an operator correcting noise and the spread widening*

**AMARA**  [03:38]
Define tampering.

**NADIA**  [03:38]
Adjusting in response to common cause variation. The part reads slightly high, the operator corrects, the next reads slightly low, they correct again. The output becomes measurably more variable than if nobody had touched the machine at all.

**AMARA**  [03:54]
That is counterintuitive enough that people will resist it.

**NADIA**  [03:57]
The arithmetic is simple. The natural swing is still there, and now your correction is added on top of it. Two sources of variation where there was one. It is not a failure of skill, it is what happens when you respond to noise.

**AMARA**  [04:15]
How do you stop it without telling people to care less?

**NADIA**  [04:19]
You never tell them to care less. You give them a rule that says when a reading is a signal and when it is not. That is precisely what the chart is for, and it is why an unread chart on a wall is worse than no chart, because it looks like control.

**AMARA**  [04:40]
Sampling. How much is enough?

**NADIA**  [04:42]
Take consecutive units so the sample reflects one moment of the process, and space the samples through the shift so the gaps between them reveal drift. A sample assembled from units picked at random across twelve hours blends the two and can hide a shift that happened at lunchtime.

**AMARA**  [05:02]
And frequency?

**NADIA**  [05:03]
Follow the rate of change. A process that drifts with tool wear needs sampling often enough to see the drift while correcting it still costs a tool change rather than a batch.

### Sources for the on screen credit

- BS EN ISO 7870 Control charts, British Standards Institution
- BS 5701 Guide to quality control and performance improvement using qualitative or attribute data, British Standards Institution
- Statistical process control published method literature, Established quality management literature

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## Capability, quality gates and what to do with a hold

**Runtime** about 5 minutes. **Words** 790. **Starts at** 05:16 in the full course recording.

### Learning outcomes to state on camera

- Calculate and interpret Cp and Cpk
- Explain what a gap between Cp and Cpk reveals about a process
- Explain first pass yield and the hidden factory of rework
- Apply a quality gate and describe when a gate should stop the line
- Disposition a held batch and justify the segregation of duties involved
- Describe the traceability needed to contain a defect

### Script


`[CUE 1]` *A distribution moving inside a fixed tolerance, with Cp constant and Cpk falling*

**AMARA**  [05:16]
A process is in control. Is it a good process?

**NADIA**  [05:20]
Unknown, and the two questions are genuinely separate. In control means stable and predictable. It says nothing at all about whether what comes out is acceptable. A process can be beautifully stable and consistently outside specification.

**AMARA**  [05:34]
So capability is the second question.

**NADIA**  [05:36]
And it only means anything once the first is answered. A capability figure calculated on an unstable process describes a fortnight that will not happen again.

**AMARA**  [05:47]
Cp and Cpk. Separate them.

**NADIA**  [05:49]
Cp asks whether the process would fit inside the tolerance if it were perfectly centred. Specification width over process spread. It takes no account of where the process actually sits. Cpk asks whether it fits where it really is: distance from the average to the nearer limit, over three standard deviations.


`[CUE 2]` *Four capability cases side by side against the diagnosis table*

**AMARA**  [06:09]
Why carry both?

**NADIA**  [06:10]
Because the gap between them is a diagnosis. If both are low the process is too variable and centring will not rescue it. If Cp is healthy and Cpk is much lower, the spread is fine and the process is simply off centre.

**AMARA**  [06:28]
Which is the good news.

**NADIA**  [06:30]
Much the better news. Off centre is frequently a setting and sometimes fixed the same morning. Too variable is a project. Two numbers tell you which conversation you are about to have, and that is why reporting only one of them wastes the method.

**AMARA**  [06:47]
What target should people work to?

**NADIA**  [06:50]
The convention is 1.33 for an established process and 1.67 where safety or a regulator is involved. Confirm what your customer actually specifies. Assuming the convention when the contract says something else is an expensive way to find out.


`[CUE 3]` *A line diagram with gates marked and a defect escaping past each in turn*

**AMARA**  [07:05]
First pass yield. Why insist on it when scrap is already counted?

**NADIA**  [07:10]
Because rework is not scrap and it is not free. It uses existing people, existing space, existing machines, and it produces a unit that eventually sells. So it appears in no report while consuming real capacity.

**AMARA**  [07:24]
The hidden factory.

**NADIA**  [07:26]
A second plant running inside the first. You can walk into it. There are people in it. It is on no organisation chart, and the first time a plant measures first pass yield the number is usually uncomfortable enough that somebody questions the measurement rather than the result.

**AMARA**  [07:45]
Quality gates. What makes one real?

**NADIA**  [07:47]
Whether it can stop something. A gate that records a result and lets the product continue is a measurement point with an intimidating name. The authority to halt is the entire content of the idea.


`[CUE 4]` *A held batch moving through release, rework and scrap paths*

**AMARA**  [08:01]
Where do you put them?

**NADIA**  [08:03]
Where the cost of continuing jumps. Before the constraint, because your slowest station must never spend its time on something already scrap. Before anything irreversible: sealing, curing, potting. Before packing, and before despatch.

**AMARA**  [08:16]
And the moment the lorry is waiting.

**NADIA**  [08:19]
That is the moment the gate either exists or does not. Which is why the authority to override cannot sit with the person who carries the despatch number. Not because they are dishonest, but because you should not build a control that depends on somebody resisting their own objective.

**AMARA**  [08:39]
A batch is held. Walk me through the outcomes.

**NADIA**  [08:42]
Three. Release, when investigation shows it conforms and you record the reasoning. Rework, by a defined and approved method, then reinspection against the original criteria. Scrap, when it cannot be brought into conformity.


`[CUE 5]` *A traceability tree from a finished unit back to material lots, and forward again*

**AMARA**  [08:56]
Against the original criteria. That will be argued.

**NADIA**  [08:59]
Constantly, and it is the line that must not move. Reinspecting reworked product against a relaxed standard is how a specification quietly becomes a suggestion. If a reworked batch fails again and is held again, that is the system working, not the system being awkward.

**AMARA**  [09:17]
What about use as is?

**NADIA**  [09:19]
It exists, and it is not a decision a plant may take on its own. It requires the customer or the design authority. A plant that grants itself concessions has stopped having a specification and has not told anybody.

**AMARA**  [09:34]
Who signs the scrap?

**NADIA**  [09:36]
Not the person carrying the output number. A common arrangement is that quality may release or rework any quantity, and scrap above a defined value needs a second, more senior authorisation. The threshold is arguable. The separation is not.

**AMARA**  [09:52]
Last one. Traceability.

**NADIA**  [09:53]
It sets the size of your worst day. With batch traceability a bad material lot means recalling the units that contain it. Without it, you recall everything made between two dates you are guessing at.

**AMARA**  [10:07]
How do you know yours works?

**NADIA**  [10:09]
Test it before you need it. Take a finished unit at random and time how long it takes to name every input. Then take an input lot and time how long it takes to list every unit containing it. Most plants have never run the second test, and it is the one a recall actually asks.

### Sources for the on screen credit

- BS EN ISO 9001 Quality management systems: requirements, British Standards Institution
- BS EN ISO 22514 Statistical methods in process management: capability and performance, British Standards Institution
- Product recall and traceability guidance for food businesses, Food Standards Agency

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