The ability to ensure product quality is the cornerstone of a manufacturer’s success. By integrating quality into every step of the manufacturing process, companies can cut the production costs associated with rework and scrap, as well as lower the risk of recalls and potential litigation over defective products. Higher product quality also helps to grow the business by cementing loyalty among current customers while establishing a reputation as a trusted provider to attract new ones.
The most effective initiatives for ensuring product quality start with a data-driven quality management strategy. Real-time shop floor data captured through production and process monitoring enables manufacturers to Identify issues that can impact quality before products leave the plant—and often before an entire production run has to be scrapped or reworked. Meanwhile, the analysis of real-time and historical data provides insights into how to improve product quality over time.
Let’s look at how manufacturers can define a data-driven strategy for quality management and then review six techniques for identifying product, process, and quality gaps in order to ensure product quality.
Defining a Data-Driven Strategy for Quality Management
There are four essential steps to establishing a data-driven strategy for quality management that will improve product quality and help to reduce costs.
- Build quality management on a strong foundation of production and process monitoring. Quality management systems rely on real-time data to deliver various quality measurements, such as statistical process control (SPC) and analysis of variance (ANOVA), among others. Real-time production and process monitoring supports quality management systems by capturing data about products and the machines being used to produce them in real time. Using the information captured, quality managers can then track quality levels, from incoming inspections to finished product attributes, across the production process.
- Use real-time data for immediate feedback on product quality. With real-time data, manufacturers can closely monitor the production process to minimize defects and ensure consistency. For example, CIE Somaschini, a manufacturer of steel timing gears, relies on real-time production monitoring to conduct in-line quality inspections at multiple points during each production run. This allows the quality team to identify potential quality variations as they occur and take corrective actions.
- Integrate quality management with other enterprise systems. By integrating quality management with other solutions, such as enterprise resource planning (ERP) and manufacturing execution system (MES) software, manufacturers can gain broader insights into their business. These include the ability to compare supplier quality levels and understand the relationship between product quality and critical factors, such as costs, employee productivity, and profitability.
- Quantify quality success for every area of manufacturing operations. In manufacturing, the most common metric for evaluating production operations is overall equipment effectiveness (OEE). This is calculated by multiplying Performance x Quality x Availability. The Quality factor measures the percentage of products meeting standards without rework, calculated as the ratio of Good Count to Total Count.
Six Techniques to Ensure Product Quality
Once a data-driven quality management system is in place, there are six techniques that manufacturers can apply to improve their product quality by identifying product, process, and quality gaps.
- Root cause analysis (RCA) identifies the primary reason behind an issue in manufacturing. For example, if a machine consistently malfunctions, RCA can pinpoint the cause, like faulty wiring, so the company can make a permanent fix rather than a temporary repair.
- Automated SPC is a core part of a quality management system and detects anomalies in a production process. For example, if a product’s weight exceeds a set limit, the system can instantly flag it, ensuring consistent quality and reducing waste.
- Advanced product quality planning (APQP) is a structured approach to product and process control to ensure that a product meets a customer’s quality expectations.
- Corrective action requests (CAR) provide a systematic way to address product and process quality issues by identifying the root cause of the issue and then researching, verifying and implementing a solution.
- Automated corrective action/preventive action (CAPA) enhances the efficiency and effectiveness of managing corrective and preventive actions. For instance, an automated CAPA system can quickly track a faulty batch of products, limiting potential harm and recalls.
- Gage repeatability and reproducibility (R&R) and calibration help manufacturers assess the precision of measurement systems and ensure that measurements remain accurate. For example, a properly calibrated micrometer can prevent inaccurate readings that lead to product rejections.
Making Quality Part of Company Culture
Implementing a data-driven quality management system and the six troubleshooting techniques described here can go a long way to help manufacturing companies ensure product quality. However, for product quality to become a competitive differentiator, it has to become a core part of a manufacturer’s culture. This is achieved by establishing routines and metrics that embed quality into operations. Examples include conducting regular audits and tracking customer-centric data for insights on how to continuously improve, integrating compliance into systems and processes to sustain quality standards, and rigorously vetting and monitoring suppliers to establish accountability across the supply chain.
When quality becomes an organizational habit with ongoing measurement, analysis, and corrective actions, rather than an afterthought, it becomes a critical business discipline for delivering consistent, reliable products that exceed customer expectations and strengthen company performance.