The fourth industrial revolution has brought forth a wave of technological advancements, such as artificial intelligence (AI), industrial Internet of things (IIoT), and cloud storage and computing. These developments have paved the way for smart manufacturing (SM), revolutionizing the industry. However, the complexities of SM have posed challenges for traditional quality control techniques, leaving quality engineers struggling to innovate. In this blog post, we will explore the potential of AI in quality control and the importance of AI training for engineers, managers, and directors to successfully implement Quality 4.0 (Q4.0) practices.
The Slow Adoption of AI in Quality Control
Despite the potential benefits of AI in preventing or detecting defects in manufacturing processes, the adoption of AI in quality control has been slow. Many companies are still in the planning stage, with a lack of AI training among quality engineers being a significant challenge. To fully embrace Q4.0 practices, it is crucial for professionals in the manufacturing industry to receive AI training and understand its applications.
The Foundations of Quality 4.0
Q4.0 represents the next wave in the modern quality movement, leveraging industrial big data, IIoT, and AI to solve complex engineering problems. Real-time data generation, collection, analysis, and data analytics form the foundational pillars of Q4.0. Research focus areas in Q4.0 include rare quality event detection, predicting quality issues, eliminating manual inspections, augmenting human intelligence, and improving decision-making speed and quality.
Implementing Q4.0
Successful implementation of Q4.0 requires strategic planning, AI training, and team development. Manufacturers must prioritize Q4.0 in their AI strategic planning, while quality professionals should acquire a basic understanding of AI theory to effectively apply machine learning algorithms. Several critical steps, such as developing a vision and roadmap, training quality professionals in new technologies, and allocating budgets for new technologies, are essential for implementing Q4.0.
Building a Q4.0 Team
A Q4.0 team should consist of various members with different expertise, including a Master Black Belt, a Black Belt, a domain expert, a digital signal engineer, a data engineer, and MLOps personnel. Each member contributes unique skills to connect the business vision with the initiative, solve technical aspects, manipulate real-world signals, organize and prepare data, and automate and deploy ML solutions. The competencies of team members may vary depending on the project.
Q4.0 vs. Traditional Quality Control Methods
Q4.0 differs from traditional quality control methods like Six Sigma in several ways. ML models, such as Artificial Neural Networks (ANN), can learn complicated nonlinear relationships and perform better in predictive modeling. ML models can efficiently handle high-dimensional data, which may pose challenges for traditional statistical methods. However, ML models may lack explainability, an important aspect in some Q4.0 projects. Understanding the limitations and differences between traditional methods and ML-based approaches is crucial.
The Importance of Knowledge and Skills
Q4.0 offers new opportunities for innovation in quality control, especially with the application of AI technologies. Quality engineers need to acquire new knowledge and skills in ML, computer science, programming, databases, and optimization to fully leverage the potential of Q4.0. It is important to note that Q4.0 does not replace Six Sigma but complements it, as both approaches can work together to address different quality control challenges.
Embracing the New Era
In conclusion, Q4.0 is revolutionizing the manufacturing industry by incorporating AI and ML into quality control practices. The slow adoption of AI in quality control can be overcome through strategic planning, AI training, and team development. As professionals in the industry, it is essential to embrace the new era of Q4.0, acquire the necessary knowledge and skills, and explore new paradigms, problem-solving strategies, and tools to stay ahead in the rapidly evolving manufacturing landscape.
Reference
Escobar C.A.; Macias-Arregoyta D.; Morales-Menendez R. (2023). The decay of Six Sigma and the rise of Quality 4.0 in manufacturing innovation. Quality Engineering, (), -, DOI: 10.1080/08982112.2023.2206679.