Predictive quality in manufacturing
What is predictive quality?
Predictive quality leverages machine learning and manufacturing data to detect defects in real time and perform faster root cause analysis.
Industry 4.0 has brought three key technologies that have allowed predictive quality to be developed:
- The maturity of industrial data collection
- Machine learning for industrial applications
- Affordable and available cloud computing
A predictive quality solution is a software interface built upon these three technologies and is actively integrated into the shop floor ecosystem. It performs complex analysis behind the scenes, presenting the analysis in familiar charts and terminology for manufacturing and quality engineers and managers to use. By putting machine learning in the hands of the domain experts, analysis can be put to use as fast as possible.
Predictive quality solutions are most commonly used in manufacturing environments that involve complex and precise production methods, such as automotive, electronics, or medical device manufacturing.
Leveraging manufacturing data
The manufacturing environment is rich in data that can enable predictive quality, spanning multiple sources:
- Process parameters and measurements (including direct machine readings)
- Product dimensions and attributes (serial numbers, part number, BOM, material, etc.)
- Test data (quantitative measure or indication of quality, including failure modes) as well as high fidelity test performance data
- Audit inspection and warranty information
Predictive quality software is designed to ingest multiple sources of data affecting quality—like the ones above—and find links between them that are difficult to see when the data is siloed. Even within a single dataset, predictive quality can offer improved value by analyzing multiple signals together, in order to better understand relationships between different machines or stations on the line.
Capabilities of predictive quality
Real-time data ingestion
Predictive quality is designed to work in manufacturing real time, which allows the insights to be viewed quickly and acted on as soon as possible. In this way, they can offer the most value to manufacturers who attach dollar signs directly to any inefficiencies.
To accomplish this, all data to be used in predictive quality analysis is sent from its origin to a cloud storage location, which can accommodate vast amounts of data. Once the initial data ingestion is set up, real time data will always be available in the platform for analysis.
Automated root cause analysis
When a product fails an in-process or end-of-line test, a predictive quality tool can be used to narrow down the cause of that failure based on patterns the algorithm can detect in the upstream data. The platform users have the ability to improve the results by applying their domain knowledge about relationships in the data.
By starting with a list of the most statistically likely signals to be related to the failure, a root cause analysis investigation can be completed much faster that relying on only traditional methods.
Defect prediction (anomaly detection)
Anomaly detection is the Industry 4.0 version of using SPC charts to determine whether a variable has moved beyond specification limits. With anomaly detection, machine learning algorithms designed for manufacturing are used to identify outliers in individual data variables. By comparing data points against historical patterns or statistical thresholds, the machine learning algorithm detects these anomalies (aka potential quality issues) in machine sensor data that could represent measurements such as temperature, pressure, or vibration.
Machine learning algorithms can also analyze multiple variables simultaneously, enabling manufacturers to uncover complex quality issues that involve interactions between different data points. By analyzing correlations and dependencies between multiple variables, manufacturers gain a holistic view of how quality deviations occur and can identify patterns that may be impossible to see otherwise.
The predictive quality solution generates alerts when an anomaly is detected, (which represents a potential defect) enabling manufacturers to act quickly. These alerts can be customized and sent to relevant stakeholders, such as quality engineers or production managers, to trigger immediate investigation.
Centralized quality dashboard
All sources of data are ingested into the platform and visualized from the same place. A centralized dashboard presents real-time and historical data visualizations, key performance indicators (KPIs), and quality metrics in one place. Manufacturers can monitor quality performance, track trends, and identify areas of concern.
Predictive quality: from manufacturing data to insights
For predictive quality to generate accurate quality predictions, a number of steps must take place. The actual implementation and intricacies of predictive quality may vary depending on the exact tools used.
Manufacturing data collection
Predictive quality starts right on the shop floor. A manufacturer collects relevant data from various sources such as machines, production systems, quality control checkpoints, and historical records. This data could include information about process parameters, machine performance, environmental conditions, and quality measurements, depending on the manufacturer’s unique goals. The data is often stored in a centralized database or data warehouse for further analysis.
Data preprocessing
Before analysis, the collected data undergoes preprocessing to clean and transform it into a usable format. This step may involve data normalization, handling missing values, and outlier detection. Feature engineering techniques are applied to extract meaningful information from the data, such as deriving statistical measures, creating derived variables, or aggregating data across time intervals.
Training and validation of machine learning models
Machine learning algorithms are trained using historical data that includes both good and defective product instances. The models learn patterns and relationships between input variables and quality outcomes. The training process involves splitting the data into training and validation sets, optimizing model parameters, and evaluating model performance. Various machine learning algorithms, such as decision trees, random forests, or neural networks, can be employed depending on the specific requirements of the predictive quality solution. Models will need to be monitored and re-trained periodically.
Making real-time quality predictions
Once the machine learning models are trained and validated, they can be deployed to make real-time predictions on new data as it becomes available during production. The predictive quality solution continuously monitors incoming data, compares it against the models, and generates alerts or warnings when outliers or anomalies are detected. Based on these insights, manufacturers can take immediate action to prevent quality issues, such as adjusting process parameters, implementing maintenance procedures, or inspecting products.
Leveraging advanced quality insights
Because the manufacturing data has been analyzed and visualized using the predictive quality solution, it can be shared among stakeholders and customers in order to provide transparency into the process and observe overall trends. Predictive analytics provides immediate, real-time and actionable insights that can greatly improve the ability to perform root cause analysis and even guide business decisions. Gone are the days of spending hours on single-use data science initiatives or time-consuming reactive analysis projects.
How predictive quality benefits manufacturers
Predictive quality offers a drastic step-up in ability to manage quality compared to familiar but limited Statistical Process Control (SPC) methods. It directly benefits manufacturers such as:
- Quality engineers: These are the most frequent users. Quality engineers and managers use predictive quality to monitor real-time quality metrics, detect anomalies, and perform root cause analysis.
- Production managers and supervisors use predictive quality to monitor production data, track quality trends, and automate reporting.
- Senior management can leverage predictive quality to gain a holistic view of quality performance and operational efficiency in their plant.
These manufacturers will see their work directly impacted when a predictive quality solution is directly impacted, including:
- A reduction in scrap and rework
- Improvement of first time through (FTT)
- Reduction of the risk of warranty claims
- Shortened testing time
- A reduction in material waste produced
- Lowered energy consumption
- Less manual effort spent on data gathering and analysis
- Shortened root cause analysis investigations
The benefits of predictive quality don’t just end on the shop floor. Implementing a predictive quality analytics solution ensures that products consistently meet or exceed quality expectations, which can result in improved customer satisfaction, repeat business, positive brand perception, and increased customer loyalty. It allows manufacturers to provide transparency to customers by sharing quality control data and reports, which demonstrates a commitment to quality.
Predictive quality in a nutshell
In summary, predictive quality is a Quality 4.0 method that uses machine learning and statistical modelling to analyze manufacturing data and generate predictions during the production process. As industrial data collection matures, predictive quality is widely becoming the modern standard for quality control among leading manufacturing facilities.
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