Quality control of industrial processes using manufacturing data

The quality control of industrial processes is a process that uses best practices, strict adherence to standards and specifications to deliver defect-free products. With stringent quality control (QC) measures in place, companies can better manage costs, reduce waste and protect themselves from the brand damage and liability that often accompany defective products.

The most important aspect of programme management in quality control of industrial processes is to ensure strict adherence to standardised processes. Any deviation from the process can lead to off-specification products and also to other forms of waste.

Collecting data on part quality can be useful for auditing production processes and ensuring that standardised work and work instructions are consistent with the development of quality components, while reducing waste. Therefore, there are many benefits of manufacturing data collection for industrial manufacturing companies.

Data can be collected from both equipment and operators to ensure accurate measurement of cycle times, part counts and scrap reasons with the ability to drill down to the machine, operator and shop floor level. Quality managers can then review that data to understand where bottlenecks and waste exist in the process, maintaining the necessary quality.

Quality control of industrial processes vs. quality assurance

The quality control of industrial processes and quality assurance (QA) are two different concepts. QA is the inspection aspect of a robust quality management system and focuses on what happens at the machine or assembly level to detect, record and classify defects. Quality control systems can include the use of digital gauges, automated inspection systems and other QMS tools.

The quality control of industrial processes is also reactive, i.e. verification of standards or specifications after production. Within connected factory ecosystems, this verification process can be digital, which brings it closer to the point of production and allows defects to be detected earlier.

A more comprehensive programme, quality assurance is a process dedicated to preventing defects before they occur. It uses tools such as histograms, control charts and Pareto charts to reveal trends for analysis, allowing quality managers to implement process changes for improvement.

Quality assurance systems can consist of formal quality best practices, such as Total Quality Management (TQM). They can also be part of a Lean Maintenance: Reviewing this modern predictive technology.

The importance of quality control of industrial processes

The programmes of quality control of industrial processes have a significant impact on costs and on-time deliveries. Without proper quality control, factories will produce higher levels of scrap and incur higher labour costs for additional handling and rework. This oversight also hurts supply chain performance and accuracy, as planners must order more material to account for waste losses.

Quality control also acts as the last line of defence for customer satisfaction. With accurate quality control, customer returns are lower and brand perception is higher. This helps ensure a high rate of repeat customers.

Sound quality control validates a company's processes and ensures the smooth running of production systems. The detection of defects can indicate a broken process in the production chain. Changing the process using formal process improvement methodologies such as Lean will help improve performance and reduce defects. Are you interested in knowing how to reduce waste in industrial manufacturing processes?

Methods and techniques for quality control of industrial processes

There are several methods available for the quality control of industrial processes:

• Just-in-time (JIT): JIT is part of a broader philosophy that delivers material and parts to production as they are needed. Quality control must be an integral part of the equation to ensure that all parts arrive safely before use without disrupting production. This is achieved by enacting strict quality control standards with vendors and suppliers to ensure that incoming material meets the required quality standards.
• Quality of the workstation: In industries such as CNC machining, where a high degree of dexterity is required for workstation operation, companies can use workers to inspect parts before moving them to the next workstation. Finally, don't miss out on how to improve the efficiency of CNC machining.
• Statistical quality control: In statistical quality control, a sample is taken from a production batch at a scheduled or random point in the production process. It is a process used by many process manufacturers. Statistical quality control relies on reliable production quality and a history of previous products to ensure that sampling identifies when and where quality control problems arise. The product must be consistent enough for sampling and statistical analysis to ensure that defects do not occur.
• Inspection: Inspection is the most recognised and traditional method of quality control and is still used in many industries. It is based on inspection of the finished product to ensure quality. Some pre-inspection or in-process sampling may also be used, but the final inspection is the determining factor. Inspection-based quality control relies heavily on sound manufacturing practices and documentation.
• Total Quality Management (TQM): Total quality management is a method that aims to embed quality into the culture of the company, train all staff to identify problems and empower them to intervene when necessary. Today's TQM programmes are software-based and are much more powerful in monitoring and assuring quality when linked to a data management platform.

How can quality control of industrial processes be improved?

Some of the best ways to optimise quality control in manufacturing are:

Auditing processes

Before any improvements can be made manually or digitally, processes must be automated. Manufacturers often use machinery from different OEMs and different generations. Auditing each process and how it applies to specific machines will help implement the best process for quality production.

This audit also includes benchmarking to ensure accurate knowledge of shop floor performance, from cycle times to scrap rates, giving quality managers a better understanding of the processes around the machines.

Data collection

Collecting data for quality control best practices means gathering and using data from suppliers, planners, inventory, production machinery and final shipment. Manual data collection is a time-consuming and error-prone process, limiting the quality control effort before it becomes cost prohibitive. In contrast, automated data collection makes it easier to make data available in real time.

Use of a machine data platform

Regardless of the quality control method and the industry in which it is applied, the volume of data required for improvement is overwhelming for human analysis. A machine data platform allows manufacturers to have real-time production data that can be leveraged through advanced analytical know-how.

On the platform, data is immediately available to operators and managers in formats that allow them to understand current performance and make quick decisions. And because the platform integrates with other software such as ERP, MES and QMS, it is extensible across the enterprise.