Defects are expensive.
When identified early, defects require manufacturers to remake components or products, increasing material waste and causing unexpected delivery delays. If defects make it through quality control and products are shipped to customers, both revenue and reputation are at stake.
In manufacturing, a small shift in pressure, temperature, or vibration can quietly introduce defects long before they’re visible at final inspection. Without predictive insight, these issues often surface only after scrap, rework or customer complaints begin to rise.
Predictive quality offers a proactive approach to quality management. Instead of using a reactive inspection process, predictive quality analysis used data to anticipate potential defects.
As manufacturers make the shift to connected, complex and data-rich environments that support Industry 4.0 aims, predictive quality processes are essential.
What is predictive quality?
Predictive quality uses data and analytics to anticipate defects before they occur. Instead of using visual observations or issue checklists, predictive quality processes rely on patterns, trends and correlations across process and performance data.
In practice, predictive quality focuses on identifying underlying patterns that lead to defects, so teams can prevent issues before they occur.
The data-driven nature of predictive quality means that it can be applied across products, processes and production systems. For example, teams can use predictive quality to track processes that lead to possible product defects before items are produced, allowing them to solve underlying issues. This approach can also be used to track equipment performance and pinpoint operations that could lead to item or component defects.
Predictive quality vs. traditional quality control
Traditional quality control uses an inspection-based approach. Once products are complete, they are physically and visually evaluated for defects. Many manufacturers now use a combination of human expertise and digital technologies to assess product quality, such as resistance to stress and heat and the presence of any cracks, warping or other defects.
Because inspection happens at the end of the line, defects are only discovered once items are produced. This delays the process of identifying root causes; occasional defects may not prompt machine health monitoring, leading to increasing defect volumes over time. Once problems are detected, teams must then carry out root cause failure analysis (RCFA) to find and eliminate the source of defects.
Predictive quality identifies risk earlier in the process by anticipating where and when it will occur. As a result, predictive quality addresses root causes rather than product symptoms—helping reduce scrap, rework and warranty costs.
Aspect | Predictive | Traditional |
Approach | Proactive: Uses data and AI to forecast potential defects before they happen | Reactive: Detects defects after they occur through inspections and testing |
Timing | Real-time monitoring during production | End-of-line or post-production checks |
Methods | AI, machine learning, IoT sensors, IoT data analysis and predictive analytics | Manual sampling, statistical process control, visual inspections and end-product testing |
Advantages | Reduces waste, minimizes downtime, improves yield and enables early interventions | Established, straightforward, lower initial tech investment |
Challenges | Requires data infrastructure, upfront investment in tech and expertise | Higher rework costs, potential for defective products that impact customer satisfaction and less efficient |
Cost impact | Lower overall and long-term costs through prevention and optimization | Higher long-term costs due to defects and recalls |
Best suited for | High-volume, complex manufacturing processes aiming for zero defects and Industry 4.0 | Low-volume or simple processes with stable conditions |
Key data sources that enable predictive quality
Predictive quality control depends on data. Common data sources include:
- Process parameters: Equipment process parameters relate directly to operational conditions. Examples include temperature, pressure, speed, friction and vibration. These parameters are often captured using predictive maintenance sensors that allow immediate analysis.
- Equipment performance and condition data: Equipment performance data may include uptime, throughput, demand forecasting, total operational time and typical cycle time. Condition data, meanwhile, speaks to issues such as wear-and-tear and the impact of environmental conditions such as temperature and humidity.
- Quality inspection and test results: Product quality inspections and test results also inform predictive quality. This is because predictive frameworks are not a replacement for traditional manufacturing quality control. Products should still be tested and evaluated by trained staff before they are packaged and delivered.
- Environmental and material data: Environmental data, such as changes in local conditions, can impact machine performance. For example, unseasonable heat waves or cold snaps can have a negative effect on equipment. Material data, meanwhile, speaks to the quality of raw materials and other supplies. If materials have inherent defects, output quality naturally suffers.
- Maintenance and downtime history: Maintenance helps pinpoint recurring problems and sets the stage for root cause failure analysis. Downtime history is useful for tracking larger-scale issues that can create production bottlenecks and lead to reduced product quality.
- Operator and technician reports: People are a key part of the predictive quality process. Reports from operators and technicians about equipment performance, wear-and-tear and overall operating condition help inform proactive maintenance that reduces defect risk.
These data sources are typically collected by computerized maintenance management systems (CMMS), manufacturing execution systems (MES) or enterprise asset management (EAM) software. Once collected, data is used by analytics software and platforms to identify common patterns and suggest resolutions.
Consider a piece of production line equipment that produces extremely precise automotive parts that must withstand significant temperature and vibration. Historically, defect rates for these parts have been extremely low, but over the past six months, 3-5% of all parts have been failing quality checks.
Using predictive quality analytics, teams discover that unstable line pressures in the machine are creating occasional defects, with the number of defects increasing over time. Equipped with this information, maintenance staff can take action to resolve the root problem.
The role of equipment reliability in predictive quality
Equipment reliability and condition play key roles in predictive quality.
This is because all parts and products depend on equipment. While low-quality materials may be the cause of quality issues, companies must be able to verify the performance of production line machines before seeking other defect sources.
Two common factors that influence equipment reliability are environmental conditions and repair schedules. Machines operating in extremely hot, wet or dirty conditions are more prone to failures, which in turn create defects. By the same token, equipment that is not regularly evaluated and serviced is more likely to fail due to component wear-and-tear or because regular services such as cleaning, lubrication and calibration have not been performed.
By capturing and analyzing maintenance data, predictive quality frameworks can detect early signs of abnormal operating conditions. The impact of this data also speaks to the need for predictive maintenance solutions. If companies can anticipate and address possible equipment issues before they occur, they can improve overall product quality and reduce the risk of defects.
Predictive quality and Manufacturing 4.0
Manufacturing 4.0 takes a connected and collaborative approach to production line performance. Gone are the days of siloed processes. Now, IIoT trends see organizations relying on interconnected operations to provide complete visibility from supply chains to finished products.
Predictive quality aligns with this approach. Shared characteristics include:
- Use of advanced analytics and AI
- Real-time monitoring and feedback loops
- Continuous learning and process optimization
Best practices for getting started with predictive quality
Considering a predictive quality approach, but not sure where to begin? Start with five best practices:
1. Prioritize high-impact processes or products: Core products and essential production line processes are first on the list for predictive quality. To find these products and processes, assess their defect impact: Higher impacts mean higher priorities.
2. Focus on data quality and consistency: Predictive frameworks depend on data. Before taking major corrective action, ensure that teams have access to high-quality, consistent and relevant data.
3. Involve quality, maintenance and operations teams: Reducing defects is a team effort that combines quality, maintenance and operations. All three departments should be a part of any predictive process.
4. Use pilot programs before scaling: Start small and scale up. Predictive quality isn’t a perfect process; missteps and mistakes are common, and it’s better they happen when the stakes are low.
5. Align initiatives with business objectives: Use business objectives to inform predictive quality initiatives. For example, if higher output volumes are the top priority for your business, build predictive processes that target high-volume operations with the highest defect rates.
Predictive quality as part of continuous improvement
Predictive quality processes aren’t one-and-done. Instead, they are ongoing capabilities that underpin continuous improvements.
This starts with the continuous refinement of predictive models. Using a combination of real-time and historical data paired with analysis tools that leverage AI in predictive maintenance and machine learning (ML) algorithms, organizations can continually discover new connections and identify new outcomes. This helps improve production process stability over time by allowing companies to discover root causes and create predictive maintenance frameworks that address and fully remediate these causes.
This is only possible, however, if manufacturers embed predictive quality into daily operations. By connecting predictive processes to maintenance, operations, planning and production strategies, companies can set the stage for incremental improvements that eliminate outliers and reduce defect rates.
Predictive quality is shifting manufacturing
Predictive quality is changing the way manufacturers identify, track and manage defects. It aligns with the adoption of Manufacturing 4.0, which relies on accurate and timely data to address issues, improve performance and optimize outputs. Predictive processes are a competitive advantage for modern manufacturers.
Enable predictive quality processes with predictive maintenance frameworks. See how ATS helps manufacturers improve reliability, reduce defects and keep production on track.
