In the last two years I was able to complete two master’s degrees that allowed me to gain a perspective and update on emerging trends in two specific areas: Quality Management and Big data.

In this article I will delve into some of the trends identified, either through benchmarking with other industries or even others that we are already taking advantage of in the industry I work in.

Artificial intelligence.

Some examples of trends to take advantage of AI; from visual recognition, language processing, chatbots, personal assistants.

• Automated Quality Inspections: Using computer vision and machine learning, AI systems can inspect products in real-time on the production line. These systems can detect defects such as surface imperfections, incorrect assembly, or color mismatches more accurately and quickly than human inspectors.
• Automated Root Cause Analysis: Using AI Chat Bots, teams can provide problem definitions and use the chatbots to complete the Root Cause Analysis.
• Predictive Maintenance: AI algorithms analyze historical data from machinery and production lines to predict when maintenance is needed.

Data Science

By bringing together different data sets from processes, analysis can be carried out for making predictions, performing classifications, finding patterns in large data sets. Some examples are:

• Voice of consumer analysis: customer reviews and feedback to identify common quality issues and areas for improvement by creating models to create clusters and key influencers that help teams focus on the right direction.
• Defect Prediction and prevention: Machine learning models analyze production data to predict the likelihood of defects occurring in future production runs. By understanding the factors that lead to defects, companies can take proactive measures to prevent them.
• Quality Data Management: Create systems that automate the collection, cleaning, and analysis of quality-related data from various sources. This ensures data integrity and provides a comprehensive view of quality metrics across the organization.

Enabling technologies.

Before analyzing the data and run most of the scenarios from above, you have to collect the data! This includes bringing affordable sensors, cloud computing, open-source software, augmented reality (AR), virtual reality (VR), data streaming, 5G networks and even Wi-Fi infrastructure, Internet of Things (IoT). Here are some examples:

• Low-code software development: Whether you’re using Google or Microsoft, both have strong elements on how to create Mobile Apps to gather data from production floor or different process, this allows you to have data available early.
• Internet of Things (IoT) for Real-Time Monitoring: sensors are deployed across the production line to continuously collect data on various parameters such as temperature, humidity, vibration, and machine performance. These sensors stream data in real-time to a cloud-based platform where AI algorithms analyze the data for anomalies and trends.
• 5G Networks or Wi-Fi Infrastructure for High-Speed Data Transmission: Implementing 5G networks or solid Wi-Fi Infrastructure within manufacturing facilities to support the rapid transmission of large volumes of data collected from IoT sensors, quality control machines, and other connected devices. This high-speed connectivity ensures that data can be analyzed near real time.

Finally, I would like to share the ecosystem of Quality 4.0 Tools to have a better perspective on how several tools are integrated in order to take advantage, excerpt from ASQ.

These are some examples and trends I have view regarding Quality 4.0, the transformation within industries is rapid. It is important to have an open mind and continuing learning attitude that help you upskilling & reskilling is crucial to jump and keep update on new trends.

EP