Quality control is a process that uses best practices, strict adherence to standards, and specifications to deliver products without defects. With tight quality control (QC) measures, companies can better manage costs, reduce waste, and protect themselves from brand damage and liability that often accompanies defective products.
The key to managing quality control programs is ensuring strict adherence to standardized processes. Any deviation from the process is likely to lead to products that are out of spec, and will also likely lead to other forms of waste.
Collecting data on part quality can be helpful for auditing production processes and ensuring that standardized work and work instructions are congruent with developing quality components while reducing waste.
MachineMetrics can collect data from both equipment and operators to ensure an accurate measure of cycle times, part counts, and reject reasons with the ability to drill in at the machine, operator, and shop floor level.
Quality managers can review the data to understand where bottlenecks and waste exist in the process while retaining the quality needed.
Many people confuse quality control with quality assurance (QA) or use them interchangeably, but they’re two different concepts. Quality control is the inspection aspect of a robust quality management system. It focuses on what is happening at the machine or assembly level and seeks to catch, record, and categorize defects.
Quality control systems may include the use of digital calipers, automated inspection systems, and other SPC tools.
QC is also reactive, meaning the verification of standards or specifications after production. Within connected factory ecosystems, this verification process may be digital; this moves it closer to the point of production and enables defects to be detected earlier.
A more complete program, 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; this enables quality managers to implement process changes for improvement.
QA systems may consist of formal quality best practices such as Total Quality Management (TQM). They may also be part of a Lean or Six Sigma deployment that incorporates QA into an overall improvement system.
Quality has a major impact on operational performance, with the impact being multiplied in certain high-tolerance industries such as aerospace and medical device manufacturing.
QC programs impact cost and on-time delivery significantly. Without proper QC, factories will produce higher scrap levels and incur increased labor costs from extra handling and rework. This oversight also throws off the performance and accuracy of the supply chain as planners must order more material to account for scrap loss.
QC also acts as the last line of defense for customer satisfaction. With accurate QC, customer returns are lower and brand perception is higher. This helps ensure a high rate of repeat customers.
Robust QC validates a company's processes and ensures that production systems work well. Detection of defects may indicate a broken process in the production chain. Changing the process using formal process improvement methodologies like Lean will help improve performance and lower defects.
There are several methods available for manufacturing quality control:
JIT is part of a broader philosophy that delivers material and parts to production as they’re needed. QC must be an integral part of the equation to ensure all parts arrive right before use with no disruption in production. This feat is accomplished by enacting strict QC standards with vendors and suppliers so that material arriving is guaranteed to meet required quality standards.
In industries like CNC machining, where a high degree of skill is required for workstation operation, companies may use workers to inspect parts before moving them downstream to the next workstation.
In Statistical QC, a sample of a batch of production is taken at a scheduled or random point in the production process. It’s a process used by many process manufacturers. Statistical QC relies on dependable production quality and past product history to ensure that sampling will identify when and where QC issues arise. The product must be consistent enough that sampling and statistical analysis will ensure that no defects get through.
Inspection is the most recognized and traditional method of QC and is still used in many industries. It relies on inspection of the finished product to ensure quality. It may also use some upstream inspection or sampling along the way, but the final inspection is the determining factor. Inspection-driven QC is heavily reliant upon sound manufacturing practices and documentation.
TQM is a philosophy-driven yet proven method for improving quality. It seeks to incorporate quality into the company's culture, train all staff to identify problems, and empower them to intervene when necessary. Today’s TQM programs are software-driven and become much more potent for controlling and ensuring quality when linked to a data management platform like MachineMetrics.
Learn more about the value of machine data for quality management systems.
Some of the best ways to improve QC in manufacturing include:
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 will also include benchmarking to ensure an accurate understanding of shop floor performance, from cycle times to scrap rates. This will give quality managers a better understanding of the processes around their machines.
Pareto charts are a helpful tool for understanding the most common reasons for rejected parts.
Data collection for best QC practices means gathering and using data from vendors, planners, inventory, production machinery, and final shipping. In manual data collection, this is a time-consuming and error-prone process that limits how much the QC effort can accomplish before it becomes cost prohibitive. With automated data collection, the data is available in real-time.
As manufacturers build out their connected factory technology stack, data collection and sharing capabilities will help to solve the issues presented by legacy solutions and manual data collection.
Regardless of the method of QC and the industry it’s applied in, the volume of data required for improvement is overwhelming for human analysis. A machine data platform like MachineMetrics empowers manufacturers with real-time production data that can be leveraged using actionable insights from advanced analytics.
In the MachineMetrics platform, data is immediately available to operators and managers in formats that allow them to understand current performance and make fast decisions. And because the platform integrates with other software such as ERP, MES, and QMS, it’s extensible to the entire enterprise.
To learn more about how the MachineMetrics Machine Data Platform can improve your QC by bringing the detection of defects closer to the point of production, book a demo with out team.
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