Blog: Reviewing AESQ RM13006 – Resistance to Statistical Process Control

“Low volume” typically refers to small lot sizes and shorter production runs — but this does not mean the process itself is inherently different. SPC monitors the process and the equipment, not just the product. A drilling operation, an assembly process, or a heat treatment cycle can all be statistically monitored regardless of which specific part is currently running.

By normalizing data — for example, monitoring deviation from nominal rather than absolute values — SPC can be effectively applied in high-mix, low-volume environments. DataLyzer supports short-run SPC and part-family approaches that make this practical. While the specific application of SPC will look different compared to a high-volume line, we have seen many examples where implementing SPC in low-volume production environments led to significant, measurable improvements in both quality and productivity.

3. “SPC is only suitable for simple products”

“Complex products tend to have large numbers of characteristics. One may argue against running SPC on all of these characteristics. Strategies can be employed that enable proper selection of ‘controlling’ characteristics (input or output) that give indication of the health of a process. These characteristics are included in the control strategy. Variation studies can be performed on feature groups collectively to reduce the burden of analysis.”

Managing SPC for complex products with hundreds of quality characteristics is genuinely challenging — but the challenge lies in the implementation approach, not in SPC itself. An Excel-based or manual approach will quickly become unmanageable at scale. However, when you select the right software solution — such as Datalyzer Qualis 4.0 SPC — and integrate it with your measurement equipment (CMMs, automated gauging, etc.), managing large numbers of characteristics becomes not only feasible but highly insightful.

The RM13006’s guidance on selecting controlling characteristics is valuable here: not every characteristic needs its own Control Chart. A well-designed control strategy identifies the key process indicators and uses them efficiently. Datalyzer’s platform supports this approach, enabling manufacturers of complex products to gain realtime visibility across their entire measurement landscape. Refer to this post to learn more about the automated (CMM) data collection possibilities: Automated CMM data and SPC integration.

4. “SPC is not suitable for high product mix situations”

“For high product mix situations, it is often useful to focus on characteristics that are common to the process rather than measure and monitor separate products by different mechanisms. Short run or part family approaches may be used in which the deviation from target is monitored (see 9.2 – Assessing Control and Capability of Variable Data by Process or Part Family). SPC analysis allows the manufacturer to see if differences between products are evident, thereby prioritizing improvement.”

This is one of the most frequently raised objections we hear. And the concern is understandable: when you produce dozens or hundreds of different part numbers, traditional SPC, with a separate Chart per characteristic per part, can seem impossibly complex to manage.

The solution, as the RM13006 correctly points out, is to shift focus from the product to the process. By monitoring the deviation from target (rather than absolute values), Short-run SPC, Part-family charts allow you to overlay multiple part numbers on a single chart and understand whether the process remains in control across different products. Datalyzer Qualis 4.0 directly supports these approaches, making it practical to implement meaningful SPC even in high-mix environments. The added benefit: when differences between products do become statistically visible, you have objective data to prioritize improvement efforts — rather than relying on gut feel.

5. “We have tried SPC before and failed”

“There are many pitfalls in SPC deployment and criticism of it is often based on historic issues and past experience of poor deployment. Causes of issues in deployment of SPC can be due to: Poor engagement of those recording and monitoring the data. Failure to do anything useful with the data (e.g., failure to investigate and correct special causes). Failure to develop an adequate control strategy (e.g., SPC not being ‘closed loop’ and timely). SPC done in isolation, with inadequate attention given to the ‘fundamentals’. Failing to develop the SPC approach as experience grows.”

A failed SPC deployment leaves a lasting negative impression, and organizations that have been through it are understandably reluctant to try again. But in nearly every case we investigate, the failure had less to do with SPC itself and more to do with how it was implemented.

The root causes identified by the RM13006 match closely with what we observe in the field. SPC only delivers value when it is closed loop: data must be collected, reviewed in real-time, and acted upon when signals appear. A chart that nobody reads, or where special causes are logged but never investigated, adds cost without benefit. Similarly, SPC implemented without adequate training, management commitment, or integration into the broader quality system is unlikely to sustain itself.

At Datalyzer, our implementation approach is designed specifically to avoid these pitfalls. Our implementation projects succeed because our practical approach is based on years of experience and lessons learned. Automated data collection reduces the burden on operators, built-in alerting ensures out-of-control conditions are escalated promptly, and our reporting tools make it easy for quality teams and management to review process capabilities and drive corrective actions and continuous improvement. If your previous SPC experience was built on spreadsheets and manual data entry, a modern integrated software platform is a fundamentally different proposition.

6. “SPC is only useful once we have 30 data points”

“It is true that confidence in the accuracy of control limits and capability indices is higher as more data is gathered, but to wait for an arbitrary number of points before review may result in a missed opportunity for improvement. This is not to say that process tampering (making unnecessary adjustments), is to be encouraged, but obvious issues may be seen with relatively few data points, e.g., a process that is running significantly off target may be corrected without initial need for control limits, but once on target control limits can be used to recognize when corrections are necessary, thus keeping the process stable. Initial assessment may be as simple as using a run chart or Pre-control chart in the early stages of production.”

The minimum of “30 data points” indeed makes sense as a guideline when reporting on Process Capability. Waiting passively for the 30 data points threshold to be reached means you are flying blind in the meantime. The RM13006’s point about Run charts and Pre-Control charts is right: even with very few data points, a visual representation of process output can reveal obvious problems that warrant immediate attention.

Several usefull features are implemented in Datalyzer Qualis 4.0, where the calculation of Control limits can start when a subgroup threshold is met, and the software clearly indicates when limits are based on limited data, giving users appropriate context to interpret results. Again, based on our experience our software implements SPC that actually works in practice.

For more information about Pre-Control and our view towards Pre-Control in modern Industry, refer to this blog: Pre-Control: A Critical Review and Its Place in Modern SPC

7. “SPC is only applicable to variable measurements”

“SPC can be used to monitor rate, frequency, proportion, and count for attribute type characteristics and defects. The benefit of monitoring these attributes through control charts is that change in the rate, frequency or incidence of the attributes can trigger positive (and prescriptive) action rather than relying on subjective ‘gut feel’ decisions or no action at all. Attributes that can be monitored statistically are for example: Proportion of defective parts – Number of attribute defects (either per batch or per item) – Rate of rare event type defects (similar to mean time between failure for machinery).”

Variable data, like dimensions, temperatures, torque values, is the most intuitive application of SPC, but it is definitely not the only one. Attribute data is everywhere in manufacturing: pass/fail inspection results, number of defects per unit, surface finish categories, checklists, visual inspection outcomes. The assumption that SPC cannot handle this type of data is simply incorrect.

Attribute control charts (p-charts, np-charts, c-charts, u-charts) bring the same discipline to attribute data that Xbar-R and Individuals charts bring to variable measurements. Applying SPC to attribute data leads to statistically grounded basis for deciding whether a change represents a genuine signal or normal variation.

Datalyzer Qualis 4.0 supports the full range of attribute chart types, enabling manufacturers to apply SPC across their entire quality data landscape. In an industry like Aerospace, where traceability and objective evidence of process control are essential, the ability to statistically monitor attribute characteristics alongside variable ones is a significant asset.

Conclusion

The resistance to SPC described in RM13006 Chapter 12 is real, and at Datalyzer we encounter it regularly. What is striking, however, is how consistently these objections dissolve when the right implementation approach is taken: the right software, the right integration with measurement systems, the right level of training, and a genuine commitment to closing the loop between data and action. SPC is not a reporting exercise; it is a quality management system to reduce process variation and increase proces capability. When implemented well, it delivers on every benefit the RM13006 describes, and the Aerospace industry’s long experience with SPC is testament to that.