SPC Blog

When we talk about Artificial Intelligence, do star Large Language Models (LLMs) like DeepSeek pop into our minds? They can not only engage in intelligent chat and write articles automatically, but even help us program efficiently. And when it comes to industrial production, we often focus on quality management tools like control charts and process capability analysis. General-purpose AI models (represented by large models such as DeepSeek, ChatGPT, and Gemini) represent the most cutting-edge intelligent technology of the information age, while Statistical Process Control (SPC) embodies the spirit of continuous improvement in product quality from the industrial age. Many people wonder how LLMs will replace SPC. Today, let’s discuss what general-purpose AI models (especially large models like DeepSeek) and SPC are all about. More importantly, let's explore whether we can leverage the “superpowers” of LLMs like DeepSeek to give traditional SPC analysis a major upgrade and usher in a new era of intelligent quality management!

First, we need to clarify the essence and respective focus of general-purpose AI models and SPC. Although both are important tools and methodologies, they have significant differences in application areas and core functions. Understanding their essential differences and possible connections will be very helpful for us to better utilize them in practical work.

General-purpose AI Models (Represented by DeepSeek, etc.): Powerful pre-trained language models, with natural language processing (NLP) at their core and particularly outstanding code generation capabilities.

General-purpose AI models might be more familiarly known by their “stage name” “GPT (Generative Pre-trained Transformer)”. Now, more representative examples might include a series of emerging powerful models like DeepSeek. These models are all “leaders” among pre-trained language models. General-purpose AI models are powerful pre-trained language models. Their core advantage lies in their expertise in understanding and generating natural language text – simply put, they are exceptionally good at “talking”. This makes them incredibly capable when handling various NLP tasks, demonstrating outstanding abilities in areas such as:

• Intelligent Question Answering: Able to understand questions posed by users and provide accurate and relevant answers, like having a "know-it-all" by your side.
• Writing Assistant: Can help users write articles, whether it's creating stories, drafting emails, or generating news reports, they can lend a hand and boost efficiency significantly.
• Code Generator (Especially DeepSeek, etc., with outstanding code generation capabilities): You heard it right, they can also write code! Especially large models like DeepSeek, which excel in code generation, and can even assist programmers in completing programming tasks, becoming a valuable assistant for programmers.
• Information Summarizer: When facing a large amount of textual information, they can quickly "scan" through it, extract key points, and generate concise summaries, saving time and effort.

SPC: The cornerstone of quality management, SPC is a quality management technique that utilizes statistical principles and methods to monitor and control production process variation, and it also needs to undergo "intelligent upgrades" by embracing large models like DeepSeek.

SPC, or Statistical Process Control, remains the “cornerstone” of quality management. It is a quality management technique that utilizes statistical principles and methods to monitor and control production process variation in order to ensure stable product quality and continuous improvement. Its core objective remains to “keep a close eye” on production process variation, ensuring our product quality is consistently reliable and can be continuously improved. The core methods of SPC include control charts, process capability analysis, and various statistical analysis tools. SPC's "combination punch" mainly consists of the following techniques:

• Control Charts: Like a "monitoring radar", they use graphical methods to visually display the fluctuations in production process data. Once an "abnormal signal" is detected, they immediately "alarm", reminding us to take timely measures.
• Process Capability Analysis: Like giving a "health check" to the production process, assessing its "physical fitness" to see if it can meet quality standards and identify areas for "improvement".
• Statistical Analysis Tools: Various statistical analysis methods, such as hypothesis testing, regression analysis, etc., are like "magnifying glasses" and "microscopes", helping us to deeply analyze process data and find the "hidden culprits" affecting quality.

The key to SPC lies in the collection, analysis, and interpretation of production process data, and taking corresponding control and improvement measures based on the analysis results. The key to SPC also lies in continuously collecting production process data, then conducting systematic analysis and professional interpretation, and taking corresponding control and improvement measures based on the analysis results. Simply put, it's about using data to speak and using statistical methods to guide quality improvement. However, in today's era of data explosion and increasingly complex production environments, traditional SPC analysis methods also face some "minor challenges":

• Slow Data Analysis: Traditional SPC analysis mainly relies on manual chart viewing and analysis, which is relatively inefficient and feels “overwhelmed” when facing massive real-time data.
• Over-reliance on "Veteran Experts": How to interpret SPC analysis results and determine improvement measures largely depends on the experience and expertise of quality engineers, and talent in this area is relatively “scarce”.
• Underutilization of Unstructured Data: Production processes generate a lot of unstructured data such as text records, images, and sounds, etc. Traditional SPC methods are not good at "dealing with" this information, which is somewhat wasteful.

Large Models (especially DeepSeek etc.) + SPC = "New Playbook" for Intelligent Quality Management? DeepSeek and other large models may become the key to intelligent upgrades of SPC.

Therefore, it's not about simply throwing a set of detection data at a large model and expecting it to automatically generate control charts and calculate process capability. Instead, it's about applying SPC tools, feeding the results data, such as control charts (out-of-control points) and process capability indices, into large models, and letting the models help us write SPC analysis reports, analyze root causes, and provide recommendations.

Large models like DeepSeek can become a “super plug-in” for SPC, enhancing the efficiency, depth, and intelligence level of SPC analysis in various aspects. How do large models like DeepSeek "buff up" SPC analysis?

Root Cause Analysis and Problem Diagnosis:

• Turning Unstructured Data into Valuable Assets: Large models like DeepSeek can "understand" and "see" various unstructured data generated in production processes, such as operator's text records, equipment maintenance logs, defect images, and even voice data. By analyzing these "edge-case" information, large models like DeepSeek can unearth deep-seated root causes of problems that traditional SPC methods "overlook".
• "Knowledge Graph" + "Expert Embodyment" + Intelligent Reasoning: Large models like DeepSeek can build a "knowledge graph" in the SPC domain, "loading" in knowledge of SPC principles, process knowledge, equipment information, historical cases, and so on. With this "knowledge base", large models like DeepSeek can perform intelligent reasoning and diagnosis, helping quality engineers quickly locate problems and even provide possible solutions as "suggestions".

Predictive Quality Management and Preventive Measures:

• "Early Warning" of Quality Trends: Large models like DeepSeek can analyze historical SPC data, learn the "temperament" and "patterns" of quality fluctuations, predict future quality trends, and provide "early warnings" of potential quality problems, giving companies ample time to "prepare for a rainy day".
• "Intelligent Optimization" Suggestions for Process Parameters: Based on a thorough "understanding" of SPC data and process knowledge, large models like DeepSeek can intelligently recommend optimal process parameter settings, making production processes more stable and product quality even better. This is much more powerful than the traditional SPC's "hindsight bias"; it directly "prevents problems before they happen"!

A More "Human-friendly" Human-Machine Collaborative SPC Analysis Platform:

• "Voice-activated" Natural Language Interaction Interface: Large models like DeepSeek can create a natural language interaction SPC analysis platform. Users can directly use "plain language" to issue commands to complete data queries, chart generation, anomaly analysis, and other operations, greatly lowering the threshold for using SPC tools and allowing more production personnel to participate in quality management.
• "Intelligent Assistant" + "Expert Empowerment": Large models like DeepSeek can become "intelligent assistants" for quality engineers, assisting experts in complex SPC analysis work, providing "one-stop service" such as data interpretation, report generation, and solution suggestions, improving expert work efficiency and decision-making levels, and also better "passing down" the expert’s knowledge and experience.

Looking ahead, the "marriage" of large models and SPC is definitely a major trend in intelligent quality management. The key to SPC lies in the collection, analysis, and interpretation of production process data, and taking corresponding control and improvement measures based on the analysis results. As large model technology becomes increasingly mature and widespread, we have reason to believe that large models will play an increasingly important role in SPC analysis, driving quality management from traditional models towards an intelligent, preventive, and efficient “fast lane”, ultimately helping companies achieve higher levels of quality excellence.

In the age of smart manufacturing, AI technology is profoundly transforming the field of quality management. This article introduces an artificial intelligence-based SPC analysis method (AI-SPC), which utilizes machine learning algorithms to predict future trends in detection data, achieving more accurate anomaly warnings. This helps enterprises improve product quality, reduce production costs, and advance towards a new stage of intelligent quality management.

This article is divided into four main sections:

1. Process Design
2. Core Concepts
3. Features and Advantages
4. Application Value

I. Process Design

Below is a flowchart of SPC integrated with AI prediction:

① Timed Detection of Detection Items Requiring Predictive Model Reconstruction:

To ensure the effectiveness and accuracy of the predictive model, it is necessary to set a model reconstruction cycle for detection items. The reconstruction cycle can be set based on time intervals (e.g., weekly, monthly) or data update volumes. Model reconstruction is triggered only when the new detection data volume of a detection item reaches a preset threshold or when the time since the last model reconstruction exceeds the set cycle.

② Model Reconstruction: Multi-Algorithm Model Training and Optimization:

For each detection item requiring predictive model reconstruction, the system automatically trains multiple machine learning algorithms and neural network models, such as time series models (ARIMA, Prophet), recurrent neural networks (RNN), long short-term memory networks (LSTM), gradient boosting decision trees (GBDT), random forests, and more than a dozen other prediction algorithms and their variants. The system evaluates the performance of the trained models through cross-validation, model evaluation metrics (e.g., root mean square error RMSE, mean absolute error MAE), and other methods, ultimately selecting the algorithm model with the best predictive performance as the optimal predictive model.

③ Record and Store Optimal Predictive Model:

The system records and stores the optimal predictive model information for each detection item, including key information such as the optimal algorithm name and version, model parameters, predictive performance evaluation metrics (e.g., RMSE, MAE), and feature variables used during model training. This information is stored in the database and associated with the corresponding detection items, facilitating subsequent model calls, performance tracking, and management, and providing a basis for subsequent model optimization and performance monitoring.

④ Predict Future N Points:

Predict future N detection points based on the optimal model: According to the user-set prediction step size N (e.g., predict the next 3, 5, 10 detection points), the system calls the stored optimal predictive model to predict the data of the next N detection points and record the prediction results. The setting of the prediction step size N can be determined according to actual production needs and warning lead time. The prediction results are stored in the form of data tables or charts for subsequent analysis and display, providing a data basis for drawing predictive SPC control charts.

⑤ Merge Predicted Values with Actual Values:

Merge predicted values with actual values to construct AI-SPC control charts: The system integrates the predicted values of the next N detection points with the existing actual detection data. On the basis of traditional SPC control charts, a predicted value curve is added to form an AI-SPC predictive control chart. For example, predicted values can be added to the X-bar control chart in the form of dashed lines or lines of different colors, so that the control chart not only displays historical data but also includes predictive information on future trends, helping users to more comprehensively grasp the process state.

⑥ Execute Eight Extended Rules for Determining Abnormalities:

Execute extended SPC eight rules for determining abnormalities (including predicted values): On the basis of the traditional SPC eight rules for determining abnormalities, for AI-SPC control charts that include predicted values, the system executes the set SPC rules for determining abnormalities, such as points exceeding control limits, consecutive points showing an upward or downward trend, consecutive points on one side of the centerline, and periodic fluctuations. The SPC eight rules for determining abnormalities are a set of statistical rules used to determine whether abnormal fluctuations or special causes occur in the production process. These rules for determining abnormalities can be flexibly configured according to the quality control requirements of the actual production process to meet the needs of different scenarios.

⑦ Anomaly Warning:

Anomaly warning and multi-channel notification: When the system detects an abnormality signal in the AI-SPC control chart (i.e., the set rules for determining abnormalities are triggered), it indicates that quality abnormalities or process fluctuations may occur in the predicted future. The system immediately activates the anomaly warning mechanism and sends real-time warning notifications to relevant personnel (e.g., quality management personnel, production line leaders) through preset interfaces (e.g., API interfaces), email, enterprise WeChat, SMS, and other channels. The notification content can include: the detection item where the anomaly occurred, the type of rules for determining abnormalities, the predicted anomaly trend, and recommended disposal measures.

⑧ New Detection Data Entered:

Data-driven continuous optimization: New data updates and model self-iteration: As new detection data is continuously generated, the system continuously monitors the new data volume of detection items. When the new data volume reaches a preset threshold, the system automatically triggers the model reconstruction process (return to step ①), uses the latest detection data to retrain the model, and performs model optimization, realizing self-iteration and continuous optimization of the predictive model, ensuring that the model can always capture the latest data features and provide the most accurate predictions. The entire process forms a data-driven closed-loop system that continuously learns and adapts to new data patterns, thereby achieving more intelligent and accurate SPC analysis and providing continuous value for quality management.

II. Core Concepts

Model Management and Reconstruction: The AI-SPC process demonstrates a comprehensive design concept in the model management and reconstruction stage. Its model reconstruction trigger mechanism takes into account both time cycles and data volume thresholds, ensuring that the model can be updated in a timely manner and avoid unnecessary reconstruction, reflecting efficiency and flexibility. The introduction of multi-algorithm model training covers multiple algorithms such as time series models and deep learning models, and model evaluation and optimization are performed through cross-validation and other methods, ensuring the scientific nature of model selection and prediction accuracy.

Prediction and Analysis: The core value of the AI-SPC process lies in its powerful prediction and analysis capabilities. Multi-step prediction based on the optimal model realizes effective prediction of future quality trends, transforming quality management from passive response to active prevention. The intelligent integration of predicted values and actual values constructs AI-SPC predictive control charts, which incorporate future trend information on the basis of traditional SPC, providing users with a more comprehensive view of the process state. The extended SPC eight rules for determining abnormalities fully utilize predictive information to achieve more intelligent and sensitive anomaly determination.

Warning and Optimization: The AI-SPC process reflects the foresight of intelligent quality management in terms of warning and optimization. The multi-channel warning mechanism ensures real-time access to anomaly information, giving enterprises valuable response time. More importantly, the data-driven model self-iteration update mechanism gives the system the ability to continuously learn and evolve, ensuring that the AI-SPC system can maintain optimal performance for a long time and continuously adapt to changes in the production process.

III. Features and Advantages

The AI-SPC system integrates the advantages of artificial intelligence and statistical process control, showing the following significant features and advantages:

• Intelligent Prediction Capability: Predict quality trends in advance. AI-SPC breaks through the limitations of traditional SPC and has the ability to predict future quality trends, helping enterprises to calmly respond to potential quality risks.
• Adaptive Optimization: Continuous learning and model updates. The system can continuously learn and optimize with the accumulation of new data, and the model performance continues to improve, ensuring the long-term effectiveness and intelligence level of the AI-SPC system.
• Multi-Algorithm Fusion: Improve prediction accuracy. The multi-algorithm fusion strategy fully explores the potential of data, selects the optimal predictive model, and significantly improves the accuracy and reliability of prediction.
• High Degree of Automation: Reduce manual intervention. The AI-SPC process has a high degree of automation, greatly reducing the need for manual intervention, improving the efficiency and consistency of SPC analysis, and reducing labor costs.
• Real-Time Warning: Provide longer problem response time. The real-time warning mechanism based on prediction results provides enterprises with longer response time, helping to take measures before problems occur and reduce quality losses.

IV. Application Value

The application of AI-SPC technology will bring significant value improvement to enterprises:

• Quality Improvement: Discover problems in advance through predictive analysis. The predictive capability of AI-SPC helps enterprises move the focus of quality management forward, discover potential quality problems in advance, prevent problems before they occur, and ultimately achieve continuous improvement of product quality.
• Cost Reduction: Reduce the generation of defective products. Through more timely anomaly warnings and faster problem response, AI-SPC helps reduce the generation of defective products, reduce quality costs such as rework and scrap, and improve enterprise profitability.
• Efficiency Improvement: Automated analysis replaces manual operations. The automation feature of AI-SPC frees quality management personnel from repetitive labor, allowing them to focus more on high-value work such as quality improvement and optimization, and improving overall quality management efficiency.

When we talk about artificial intelligence, don't images of star models like DeepSeek come to mind? They can not only chat intelligently and write articles automatically, but also help us program efficiently. And when it comes to industrial production, we often focus on quality management tools like control charts and process capability analysis. General-purpose AI models (represented by DeepSeek, ChatGPT, Gemini, etc.) represent the cutting edge of intelligent technology in the information age, while Statistical Process Control (SPC) embodies the spirit of continuous improvement in product quality from the industrial era. Many people wonder how large models can replace SPC. Today, let's discuss what general-purpose AI models (especially models like DeepSeek) and SPC are all about. More importantly, let's explore whether we can leverage the "superpowers" of DeepSeek and other large models to upgrade traditional SPC analysis and usher in a new era of intelligent quality management!

First, we need to clarify the essence and respective focuses of general-purpose AI models and SPC. Although both are important tools and methodologies, they have significant differences in application areas and core functions. Understanding their fundamental differences and potential connections will greatly help us apply them better in practical work.

• General-Purpose AI Models (Represented by DeepSeek, etc.):
  • Powerful pre-trained language models with a core focus on natural language processing and a prominent capability in code generation.
  • General-purpose AI models are more commonly known as "GPT (Generative Pre-trained Transformer)," and now, DeepSeek and other emerging powerful models are even more representative.
  • These models are leaders in pre-trained language models. Their core advantage lies in their ability to understand and generate natural language text, which is essentially being very good at "talking."
  • This makes them incredibly effective at handling various natural language tasks, demonstrating outstanding capabilities in areas such as question answering, writing, code generation, and information summarization. For example:
    • Intelligent Question Answering: Able to understand user questions and provide accurate and relevant answers, like having a "know-it-all" by your side.
    • Writing Assistant: Can help users write articles, whether it's creating stories, drafting emails, or generating news reports, they can assist you, greatly improving efficiency.
    • Code "Transporter" (especially models like DeepSeek, with outstanding code generation capabilities): That's right, they can also write code! Especially large models like DeepSeek excel in code generation, even assisting programmers in completing programming tasks, becoming a valuable assistant.
    • Information "Speed Reader": When faced with a large amount of text information, they can quickly "scan" it, extract key points, and generate concise summaries, saving time and effort.
• SPC (Statistical Process Control):
  • The cornerstone of quality management, a quality management technique that uses statistical principles and methods to monitor and control variations in the production process, ensuring stable and continuous improvement of product quality.
  • Its core goal is to "keep a close eye" on variations in the production process, ensuring stable and continuous improvement of product quality.
  • The core methods of SPC include control charts, process capability analysis, and various statistical analysis tools. The main techniques in this "combination punch" of SPC are:
    • Control Charts: Like a "monitoring radar," they visually display fluctuations in production process data, and immediately "alarm" when "abnormal signals" are detected, reminding us to take timely measures.
    • Process Capability Analysis: Like giving the production process a "physical examination," assessing its "physical fitness" and checking whether it can meet quality standards, as well as identifying areas for improvement.
    • Statistical Analysis Tools: Various statistical analysis methods, such as hypothesis testing, regression analysis, etc., act like "magnifying glasses" and "microscopes," helping us deeply analyze process data and identify the "hidden culprits" affecting quality.
  • The key to SPC lies in collecting, analyzing, and interpreting production process data, and taking corresponding control and improvement measures based on the analysis results. In essence, it's about using data to speak and using statistical methods to guide quality improvement. However, in today's era of data explosion and increasingly complex production environments, traditional SPC analysis methods also face some "minor challenges":
    • Data analysis is somewhat "slow": Traditional SPC analysis mainly relies on manual chart viewing and analysis, which is relatively inefficient. When faced with massive real-time data, it becomes somewhat "overwhelmed."
    • Too reliant on "experienced masters": How to interpret SPC analysis results and determine improvement measures largely depends on the experience and expertise of quality engineers, and there is a "shortage" of talent in this area.
    • Unstructured data is "underutilized": The production process generates a lot of unstructured data, such as text records, images, and audio, but traditional SPC methods don't pay much attention to this information, which is a bit of a waste.
• Large Models (Especially DeepSeek, etc.) + SPC = "New Ways" of Intelligent Quality Management? DeepSeek and Other Large Models May Become the Key to SPC's Intelligent Upgrade
  • Therefore, it's not about throwing a set of test data at a large model and having it create control charts and calculate process capabilities for you. Instead, we use SPC tools and feed the results data, such as control charts (outliers) and process capabilities, to the large model, allowing it to help us write SPC analysis reports, analyze causes, and provide suggestions.
  • DeepSeek and other large models can become "super plugins" for SPC, improving the efficiency, depth, and intelligence of SPC analysis in various ways. How can DeepSeek and other large models "buff" SPC analysis?
    • Root Cause Analysis and Problem Diagnosis:
      • Turning unstructured data into "treasure": DeepSeek and other large models can "understand" the various unstructured data generated in the production process, such as operator text records, equipment maintenance logs, defect images, and even voice data. By analyzing this "scrap" information, DeepSeek and other large models can uncover deep-seated root causes of problems that traditional SPC methods "overlook."
      • "Knowledge Graph" "Expert Embodiment" Intelligent Reasoning: DeepSeek and other large models can build a "knowledge graph" in the SPC field, "loading" knowledge such as SPC principles, process knowledge, equipment information, and historical cases. With this "knowledge base," DeepSeek and other large models can perform intelligent reasoning and diagnosis, helping quality engineers quickly locate problems and provide possible solutions for "reference."
    • Predictive Quality Management and Preventive Measures:
      • "Early Warning" of Quality Trends: DeepSeek and other large models can analyze historical SPC data, learn the "temperament" and "patterns" of quality fluctuations, predict future quality trends, and provide "early warnings" of potential quality problems, giving companies ample time to "prepare for a rainy day."
      • "Intelligent Optimization" Suggestions for Process Parameters: Based on a "thorough understanding" of SPC data and process knowledge, DeepSeek and other large models can intelligently recommend optimal process parameter settings, making the production process more stable and improving product quality. This is much better than the traditional SPC's "after-the-fact" approach, directly "preventing problems before they occur"!
    • A More "Humanized" Human-Machine Collaborative SPC Analysis Platform:
      • "Do it by talking" Natural Language Interaction Interface: DeepSeek and other large models can create natural language interaction SPC analysis platforms, where users can directly issue commands in "plain language" to complete data queries, chart generation, anomaly analysis, and other operations, greatly reducing the barrier to using SPC tools and allowing more production personnel to participate in quality management.
      • "Intelligent Assistant" "Expert Empowerment" Empowerment: DeepSeek and other large models can become "intelligent assistants" for quality engineers, assisting experts in complex SPC analysis work, providing "one-stop" services such as data interpretation, report generation, and solution recommendations, improving expert work efficiency and decision-making levels, and better "inheriting" expert knowledge and experience.
• Looking Ahead:
  • The "marriage" of large models and SPC is definitely a major trend in intelligent quality management. With the increasing maturity and popularity of large model technology, we have reason to believe that large models will play an increasingly important role in SPC analysis, driving quality management from traditional models to intelligent, preventive, and efficient "fast lanes," ultimately helping companies achieve higher levels of quality excellence.

With the rapid update and iteration of Simple SPC, we released Simple SPC 2.0 today. Let’s take a look at what features we have updated in 2.0!

1. Push SPC abnormal alarms to WeChat and DingTalk

In the SPC system, you can configure the appKey and alarm user group of WeChat and DingTalk, and push them directly to WeChat and DingTalk. The following figure shows the actual effect of the push.

2. Integrate the variance analysis tool into the SPC analysis report

When the factor of the independent variable contains multiple levels, the statistical method of testing whether the averages of each level are equal, we have integrated the variance analysis function into the SPC analysis report, making it easier for everyone to do variance analysis.

As shown below:

3. Add token authentication based on URL parameters to facilitate web system integration

Through something like http://xxx.com/access_token=xxxxxxxxxxm, any page of our SPC can be directly embedded through iframe. The actual effect is as follows

4. Added Spanish and Indian languages

Spanish

Hindi

5. Upgrade to Python 3.12, code optimization and performance improvement

The operating environment has been upgraded to the latest version of Python 3.12. At the same time, some major libraries such as sqlalchemy and pandas have also been upgraded to the latest version. During the upgrade process, some codes have been optimized, which has comprehensively improved the performance of the product.

6. Product documentation is synchronized and adapted to SPC2.0

We are serious about SPC and we are constantly innovating.

Our philosophy: extreme innovation, committed to making the best SPC products in China, and helping the quality of domestic manufacturing grow together.

In the realm of quality management, CPK (Process Capability Index) and PPK (Process Performance Index) are common interview questions and indispensable statistical indicators for quality professionals. They seem simple, yet often lead to confusion and debate.

Basic Definitions and Differences between CPK and PPK

CPK: Process Capability Index, reflects the capability of a process under controlled conditions, typically used to measure short-term process capability. PPK: Process Performance Index, reflects the actual performance of a process, typically used to measure long-term process capability. The calculation formulas for both are similar, but the estimation method for σ (standard deviation) differs: CPK: Uses within-subgroup standard deviation to estimate σ; the calculation method for within-subgroup standard deviation varies for different data types. PPK: Considers overall variation and uses overall standard deviation to estimate σ. CPK might overestimate process capability, while PPK is closer to the true capability.

Application Scenarios of CPK and PPK

CPK Calculation: Based on control charts (x̄-R chart or x̄-s chart), σ is calculated using the average range (R-bar) divided by d2, or the average sample standard deviation (S-bar) divided by c4. PPK Calculation: Includes all data within the control chart in the calculation, σ is calculated directly using the STDEV() function in Excel. Cpk reflects within-subgroup variation (short-term fluctuation), while Ppk includes both short-term within-subgroup variation and long-term between-subgroup variation, representing the overall quality indicator of the entire production process. In practical applications, some advocate using Ppk for control during new product trial production and switching to Cpk for control after mass production stabilizes. This is because the quality fluctuation is large during the trial production stage, and Cpk might not be effective for control; only Ppk can provide an understanding of the overall quality.

Doubts and Reflections: Are CPK/PPK Just a "Numbers Game"?

However, some people question the value of CPK and PPK. Some believe that Ppk has limited practicality because calculating overall quality means the product has already been produced, and it's impossible to prevent defective products in real-time. Moreover, the data might not come from actual measurements but rather be "fabricated." CPK and PPK seem to have become a "numbers game." Furthermore, there's also debate about whether CPK and PPK represent short-term or long-term capability. Some point out that short-term/long-term capability has nothing to do with CPK/PPK but is solely related to sampling. Short sampling time means short-term capability, and vice versa.

How to View CPK, PPK, and Sampling?

CPK and PPK, as important process capability indicators, play a significant role in quality management. However, we should also recognize their limitations and not blindly pursue indicators while neglecting the control and improvement of the actual process. Sampling plays a crucial role in quality management. The sampling method and sample size will both affect the assessment of process capability. Therefore, when using CPK and PPK, we need to pay attention to the rationality and representativeness of sampling.

CPK, PPK, and sampling are all very important tools in quality management. We need to deeply understand their connotations and limitations and apply them flexibly to truly realize their value and achieve effective quality control.