Industrial quality is the set of processes, methods, controls, and indicators used to ensure that products and manufacturing processes meet the requirements defined by the company, its customers, and applicable regulations.
In practice, managing industrial quality involves planning controls, verifying processes, measuring results, detecting deviations, managing non-conformities, analyzing their causes, and implementing improvement actions.
Industrial quality is not limited to inspecting the finished product: it must be integrated throughout the entire production process and increasingly supported by data, traceability, and digital systems.
In this guide, you will learn what industrial quality is, how it is managed, which tools are used, which indicators can be used to measure it, and how digitalization with MES can improve quality control in production.
What is industrial quality?
Industrial quality is an organization’s ability to consistently manufacture products that meet established requirements while simultaneously controlling the processes involved in their production.
According to the terminology of ISO 9000:2026, quality is defined as the degree to which a set of inherent characteristics of an object fulfills requirements. ISO 9000 also establishes the fundamentals and vocabulary for quality management systems.
Therefore, when talking about industrial quality, it is useful to distinguish between several related concepts:
| Concept | What it means |
|---|---|
| Quality | The degree to which a product, process, or object meets established requirements. |
| Quality management | The set of activities used to plan, control, and improve quality. |
| Quality assurance (QA) | Activities intended to provide confidence that quality requirements will be met. |
| Quality control (QC) | Activities aimed at verifying that quality requirements are met. |
| Quality improvement | Actions aimed at increasing the ability to meet requirements and improve performance. |
Why is quality important in industry?
Proper industrial quality management helps reduce defects, scrap, and rework, detect deviations before they become major problems, improve traceability, and meet customer and regulatory requirements.
In addition, quality is directly related to productivity and manufacturing costs.
A quality problem can lead to:
- Non-conforming product.
- Scrap and material waste.
- Reprocessing and rework.
- Production stoppages or interruptions.
- Customer complaints.
- Returns.
- Costs associated with poor quality.
- Loss of traceability.
- Additional resource consumption.
- Loss of production capacity.
That is why it is important to understand and control the cost of poor quality, as well as identify the main sources of waste and reduce scrap generated during manufacturing.
How is industrial quality managed?
Industrial quality management can be structured as a continuous cycle: plan → control → measure → detect → analyze → correct → verify → improve.
This approach makes it possible to move from a model based exclusively on detecting defects to a system focused on preventing them and continuously improving processes.
1. Plan quality
The industrial quality control plan defines which requirements the product must meet and how they will be checked during manufacturing.
At this stage, the following can be established:
- Critical quality characteristics.
- Product specifications.
- Inspection points.
- Sampling frequencies.
- Measurement methods.
- Acceptance limits.
- Responsibilities.
- Measuring equipment and measuring instruments.
- Required records and evidence.
2. Control processes
Quality control in production involves verifying that the relevant process variables and characteristics remain within the established parameters.
It may include:
- Incoming inspections.
- In-process controls.
- Final inspections.
- Dimensional measurements.
- Visual inspections.
- Checklists.
- Sampling.
- Statistical controls such as SPC.
- Verification of process parameters.
In sampling-based inspection processes, AQL (Acceptable Quality Limit) can also be used to establish lot acceptance and rejection criteria based on sample size and the defined quality level.
3. Measure performance
Detecting defects is not enough. An industrial organization needs to measure how quality is evolving and whether the implemented actions are delivering results.
Some commonly used indicators include:
- Defect rate.
- Scrap.
- Rework.
- First Pass Yield (FPY).
- First Time Quality (FTQ).
- Number of non-conformities.
- Customer complaints.
- Control plan compliance.
- Cp and Cpk.
- Cost of poor quality.
To explore this topic in greater depth, see our guide to quality KPIs, which explains the main indicators that can be used to measure the performance of quality processes.
4. Detect and manage deviations
When a result does not meet the established requirements, it is necessary to identify the deviation, contain its effects, and determine the appropriate action.
Depending on the case, it may be necessary to:
- Identify the affected product.
- Segregate the material.
- Record the incident.
- Analyze the scope.
- Identify the cause.
- Define corrective actions.
- Verify their effectiveness.
- Prevent recurrence.
Proper management of non-conformities helps prevent affected products from continuing through the process or reaching the customer while also generating useful information for analyzing the root causes of the problem.
In organizations with more advanced quality management systems, this process can be integrated with CAPA (Corrective and Preventive Actions), root cause analysis, and structured problem-solving methodologies.
5. Analyze and solve problems
When a deviation is recurring or has a significant impact, it is not enough to correct the specific defect. The goal should be to identify and eliminate the root cause.
Industrial quality tools help collect information, analyze problems, control processes, and make data-driven decisions.
Some of the most widely used tools include:
| Tool | What it is used for |
|---|---|
| Pareto chart | Prioritize problems based on their impact. |
| Ishikawa diagram | Analyze possible causes of a problem. |
| 5 Whys | Dig deeper until root causes are identified. |
| Check sheets | Collect data in a structured way. |
| Histograms | Analyze data distribution. |
| Control charts | Detect variations and changes in a process. |
| Scatter diagram | Analyze potential relationships between variables. |
These tools should not be viewed as isolated elements. Their value increases when they are integrated into a management system in which quality data is linked to production orders, products, batches, machines, and processes.
For more information about each of these tools, see our article on the 7 quality tools.
6. Verify and monitor
A corrective action is only effective if it solves the problem and prevents it from recurring.
For this reason, quality management should incorporate verification and monitoring mechanisms.
Audits make it possible to check whether processes are being carried out according to established standards. These include layered process audits, which enable the systematic verification of compliance with specific processes and standards directly on the production floor.
The results obtained feed back into the improvement cycle: detect → analyze → act → verify → standardize → measure again. In this way, industrial quality ceases to be a static process and becomes a continuous improvement system.
How to improve the quality of industrial processes?
Improving the quality of industrial processes requires taking action across the entire manufacturing cycle: preventing defects, controlling processes, detecting deviations, analyzing their causes, and verifying that the implemented actions are effective.
To achieve this, industrial companies can focus on seven key areas:
1. Standardize processes
Process standardization makes it possible to define how each operation should be performed, which parameters should be controlled, and which requirements the product must meet in order to reduce variability and establish a common reference for production and quality.
2. Control quality during the process
Performing inspections at different points in the process makes it possible to detect deviations before they affect large quantities of product.
Control plans can establish what should be checked, how often, which method should be used, and what the acceptance limits are.
3. Use data to identify trends
Historical quality data makes it possible to identify patterns, recurring deviations, and processes with abnormal behavior.
Analyzing this data together with production information provides a more comprehensive view of the root causes of problems.
4. Analyze root causes
When a defect occurs, it is not enough to correct the affected product. It is necessary to determine why it occurred and address the root cause to prevent it from happening again.
As mentioned above, tools such as Pareto, Ishikawa, 5 Whys, and 8D help structure this analysis.
5. Integrate quality, production, and traceability
Quality data becomes much more valuable when it can be linked to the context in which the product was manufactured.
This relationship makes it possible to investigate deviations more quickly, identify potentially affected products, and make decisions based on contextualized information.
6. Digitize and automate data collection
Replacing manual records and isolated systems with digital tools makes it easier to have up-to-date, structured, and accessible quality information.
In addition, automating data collection reduces manual data entry and facilitates integration with production systems.
7. Measure the impact of actions
An improvement should be measurable through relevant indicators.
Comparing results before and after implementing an action makes it possible to determine whether the defect rate, scrap, rework, non-conformities, or other relevant indicators have actually been reduced.
What role does an MES play in improving industrial quality?
An MES system connects quality management with production execution and centralizes the data generated during manufacturing.
Instead of managing quality controls as an independent process, an MES can integrate information from production management software, quality management software, and industrial traceability software within a single workflow:
- Control plans.
- In-process inspections and controls.
- Quality measurements and results.
- Checklists.
- Non-conformity management.
- Corrective actions.
- KPIs and dashboards.
- Statistical Process Control (SPC).
- Audits.
- Quality history.
Below, we show the benefits of managing quality with an MES through four real-world examples.
Embutidos Monells
At Embutidos Monells, automating real-time data collection and centralizing production, quality, and traceability information on a single platform has provided a more connected view of the production process.
This digitalization has resulted in a 4% reduction in product loss, with total savings of €370,000 over one year on a production line.
The case shows how connecting production, quality, and traceability data provides a more comprehensive view of the process and contextualized information to identify improvement opportunities and take action on them.
Flex-N-Gate
At Flex-N-Gate, more precise, real-time process control has facilitated the immediate detection and correction of errors, as well as the implementation of action plans focused on continuous quality improvement.
In just one year, the company reduced its scrap rate by 26%.
This result shows how having real-time quality information can enable a faster response to deviations and help reduce their impact on the production process.
In this way, an MES does not replace quality methodologies and tools; instead, it provides an environment for applying them in a more integrated, traceable way that is better connected to the reality of the production floor.
Galletas Gullón
Galletas Gullón has more than 3,000 signals connected to Mapex, enabling advanced sensorization to automatically capture process data and variables.
This information is complemented by alert management and real-time monitoring, enabling more comprehensive and efficient control of the variables involved in manufacturing.
The Gullón case shows how connecting sensors, process data, and management systems makes it possible to have quality information captured automatically and available in real time to monitor production performance.
This speeds up the response to deviations while also generating valuable information for continuous process improvement.
Congelados de Navarra
At Congelados de Navarra, standardizing quality plans across its six plants made it possible to digitize controls in Mapex and roll them out extensively across all processes.
The system enables up to 400,000 quality checks to be performed daily through in-plant self-checks, eliminating paper-based processes and ensuring that documentation and quality criteria are centralized on a single platform.
This type of digitalization makes it possible to implement a standardized quality management model across different plants and processes, maintaining common criteria and facilitating the monitoring of completed inspections.
In this regard, an MES can become a key component in the shift from quality control based primarily on inspection to industrial quality management based on data, prevention, traceability, standardization, and continuous improvement.
For more information, contact our team and request a demo of our MES platform.
