How do you interpret the control limits and the process capability of a SPC chart?

The Upper Specification Limits (USL) and Lower Specification Limits (LSL) should be included to understand the limits of the customer.

Ao avaliar gráficos de Controle Estatístico de Processo (SPC), os limites de controle desempenham um papel vital na detec??o de varia??es normais e anormais no processo. Simultaneamente, a capacidade do processo, mensurada por índices como Cp e Cpk, fornece uma medida quantitativa da conformidade do processo com as especifica??es do cliente. A compreens?o aprofundada desses conceitos é essencial para garantir a estabilidade operacional e a qualidade consistente dos produtos, permitindo interven??es proativas para otimiza??o contínua.

I agree that USL and LSL are critical to controlling the process as these are your boundaries that signal a significant deviation which should trigger a countermeasure response while still ensuring the customer is protected from any possible nonconformance. Also, it is important to be objective and set your mean and targets according to your actual capabilities rather than what you “want” them to be or you feel they should be. A true objective approach to your current state and capability is paramount for success as it will allow you to set realistic and honest goals for continuous improvement.

?? Control Charts with Control Limits and Client Specifications with Limits ... The upper & lower limits in control charts are calculated based on your process data. When a data point appears outside your control limits, you investigate. The upper & lower limits in specification charts originate from your customers. Do not confuse the two & its recommended to keep them separate, as leaders commonly confuse them when both customer specifications & control limits are in the same chart. For example, the lower specification limit for a delivery service may be 5 minutes and the upper limit may be 30 minutes. This means a client is not willing to wait more than 30 minutes for their delivery. Faster is better! ?? Like to support this

Control limits are derived based on the previous actual performance. Control limits does not have any relation with Specification limits. However, Process capability (Cpk) is the indication of a process performance. i.e How good the process is producing output within the specification limits. Note: In Cp Process center is not considered where as in Cpk process center (X double bar) is also considered. So, Cpk is best to refer.

The concept of control limits in statistical process control says that control limits are not arbitrarily determined but are instead based on data and statistical analysis. These limits are initially set to reflect the process's historical performance, and they represent the typical variation in the process under stable conditions. Control limits can be influenced or adjusted when process improvements or changes occur. This practice ensures that control charts remain relevant and effective in monitoring the process's ongoing performance and aligning with the principles of continuous improvement.

When it comes to interpreting control limits, my experience has taught me to view them as the 'guardrails' of process stability. They define the acceptable range of variation. However, it's essential to strike a balance: being too rigid might lead to overreaction while being too lenient can mask issues. I've found that using statistical process control charts alongside control limits provides a dynamic view of process behavior. This helps in distinguishing between common cause variation (within limits) and special cause variation (outside limits). Additionally, continuous monitoring and trend analysis allow for proactive intervention when needed, ensuring sustained process excellence

In my experience, it is extremely important while plotting control charts to organize data into rational sub-groups, rather that plotting each data point on its own. This helps in quickly differentiating between common cause and special cause variation. To put it simply, the variation WITHIN a sub-group is inherent process variation and the variation BETWEEN sub-groups is usually due to special cause. A safe way to ensure this is to take samples for each hour then calculate the mean of each sub-groups and repeat this for each hour. The distribution of these sample means will always follow a normal distribution. This is explained by a theory in Statistics called The Central Limit Theorem. Higher the sample size (N-->30) the better.

Control limits are one, but you need to be able to read the out of control process in 7 ways. Divide your chart into 3 zones on both sides of the mean. For example, a process can be out of control when 14 consecutive data points that lie in one zone, or 2 consecutive points out of 3 lie in zone B and beyond.

In one of my recent projects, I utilized a Non-Normal Process Capability method which involved using Weibull distribution transformation of data. This gave me an existing process capability Ppk value of 0.96, indicating that the process performance is good but there is still room for improvement. The baseline process capability will be compared during project closure to validate any process improvement. When dealing with quality improvement projects, if we have attribute data such as the number of defects, we calculate the Defects Per Million Opportunities (DPMO) to determine the existing process capability in terms of process sigma value.

Interpreting process capability is like assessing a vehicle's performance. It involves evaluating whether the process can consistently produce outputs within desired specifications. Beyond traditional indices like Cp and Cpk, it's crucial to consider customer requirements. My experience highlights the value of understanding the entire capability spectrum. It helps identify opportunities for process refinement and guides efforts towards achieving not just statistical capability but real-world effectiveness. Moreover, continuous measurement and improvement are fundamental; they ensure that the process's capability evolves in tandem with changing customer expectations and industry standards

Entender la capacidad del proceso es clave para asegurar que lo que producimos cumple con las expectativas del cliente. Los índices como Cp y Cpk nos ofrecen una visión clara de cómo de ajustado está nuestro proceso a los límites de especificación establecidos. Me parece interesante cómo estos indicadores no solo nos muestran si estamos dentro de los rangos aceptables, sino también qué tan consistentemente podemos mantenernos allí.

I've found Design of Experiments (DOE) a helpful tool in determining which inputs have the greatest impact on process variation, and help me focus improvement efforts on a vital few number of inputs.

Don't forget to conduct your MSA’s before beginning any type of control or capability effort. You have to “prove” that you can measure what you are trying to control.

In SPC, we often use IMR charts and U charts to assess process stability in LSS projects and monitor defects. Additionally, monitoring TAT or defects per unit on a daily or weekly basis can facilitate discussions about immediate corrective actions with stakeholders or vendors at the onset of any issues, rather than waiting until month-end reporting. Team also uses frequently Cause & Effect diagram and Pareto chart for problem solving. Both give good visualization of possible causes and help to prioritize.

Using an SPC chart for continuous improvement is like a compass for process enhancement. Regular charting reveals early signs of non-random variations, going beyond just spotting outliers. Engage cross-functional teams for diverse insights and root cause analysis for targeted improvements. In my experience, integrating SPC into daily operations fosters a culture of ongoing refinement, turning every data point into a step toward excellence.

SPC can help to achieve lean manufacturing goals and objectives by using various tools, such as control charts, histograms, Pareto charts, scatter diagrams, capability analysis, process performance index (PPI), process capability index (Cpk), design of experiments (DOE), analysis of variance (ANOVA), regression analysis, etc. These tools can help to monitor and analyze the process, detect and eliminate the sources of variation, ensure that the process is capable of meeting the customer requirements and specifications, and optimize and enhance the process to achieve better quality and performance outcomes. SPC is a necessity for EV development as it can help to maintain a competitive edge in the rapidly evolving EV market.

It provides documented proof of process control, which is essential for regulatory compliance in the pharmaceutique industries.

I hate to be picky too, but I can't get PAST your use of "passed" when "past" is the grammatically correct word here. Check it out.

Interpreting an SPC chart involves understanding control limits - boundaries of normal process operation and process capability - a measure of process performance against specifications. Deviation from control limits signals an anomaly while the process capability provides insights on process efficiency and quality alignment. "Quality means doing it right when no one is looking." - Henry Ford. Keep striving for excellence through diligent interpretation and process control. #QualityControl #ContinuousImprovement #Leadership #Excellence

Negotiating requirements is probably a last resort in most cases. Speaking from a mechanical design perspective, there are a few things to consider: Is this dimension critical to the design / function of the part? If it's not, why are you measuring it? What type of tolerance analysis was done? Min/Max tolerancing is common, but also conservative. In reality it's statistically improbable all parts will be worst case simultaneously. Root Sum Squared (RSS) is more appropriate, or a Monte Carlo simulation can be used for processes that don't perform to a normal distribution. Has the designer used Geometric Dimensioning and Tolerancing (GD&T) to improve the allowance for conforming parts?