AI in Enterprise Quality and EQMS.AI: Transforming Quality Management

AI in Enterprise Quality and EQMS.AI: Transforming Quality Management

Presented by Chad Kymal - CTO & Founder • Omnex Inc

As industries move toward digital transformation, the integration of Artificial Intelligence (AI) in enterprise quality management systems (EQMS) is becoming increasingly important. During our recent Customer Conference Chad Kymal, Omnex’s CTO, discussed how AI is revolutionizing quality management, making it smarter, faster, and more efficient.

The Challenges in Modern Quality Management

Modern product development is more complex than ever. With global supply chains, diverse teams, and ever-evolving regulatory requirements, ensuring quality across the board can be an overwhelming task. According to Chad, organizations face several challenges:

• Defects detected too late: Gartner reports that 50% of quality issues are identified after product design, leading to expensive rework and delays.
• Complex product development: A proliferation of standards and data generated from multiple software systems (images, documents, videos) makes managing quality increasingly difficult.
• Product launch delays: Around 35% of product launch delays are caused by production delays, further exacerbated by the growing complexity of the supply chain and design tools.

How AI is Reshaping Enterprise Quality

AI-powered platforms are transforming how organizations manage quality across various stages of product development. These platforms help address the challenges of modern quality management by integrating various processes into a seamless, automated workflow.

One such tool is Omnex Systems’s EQMS.AI suite, which leverages machine learning (ML) and natural language processing (NLP) to improve several aspects of quality management:

• Managing Requirements and Traceability: AI helps manage the Voice of Customer and ensures seamless traceability from system requirements down to component-level functions.
• Automated FMEAs and Risk Analysis: Advanced algorithms now conduct Failure Mode and Effect Analysis (FMEA), Hazard Analysis and Risk Assessment (HARA), and Cybersecurity Threat and Risk Assessment (TARA), ensuring compliance with industry standards like AIAG VDA.
• Supplier Quality Management: By analyzing supplier data, AI can pinpoint top-performing suppliers and help companies manage their supply chains more efficiently.

AI-Powered Review and Recommendations

As Chad emphasized, AI can now automate reviews and provide actionable recommendations across various quality management processes:

• FMEA Reviews: AI can automatically assess FMEA reports, predicting potential failures, causes, and severity, while offering recommendations on mitigation strategies.
• Audit Management: The system validates non-conformities during audits, ensuring corrective actions are timely and effective. It can even suggest improvements to the audit agenda based on historical data and failures from other sites.
• Document Management: AI streamlines document management by auto-linking related documents, summarizing content for reviewers, and ensuring compliance with ITAR and other regulatory standards.

AI’s Role in the Future of New Product Introduction (NPI)

AI is also significantly impacting new product development (NPD). Omnex Systems’s AI-powered tools dramatically reduce the time and cost associated with product launches by providing 24/7 digital reviews of NPI/Advanced Product Quality Planning (APQP) projects. AI enables organizations to:

• Track resources, timelines, and deliverables in real-time.
• Evaluate design changes and risks automatically, ensuring that projects stay on track and within budget.
• Eliminate the need for lengthy manual project reviews, saving both time and money.

The Power of Integration: Connecting Design, Safety, and Cybersecurity

In the world of eMobility, AI plays a crucial role in managing both functional safety (FuSa) and cybersecurity within integrated product design. AI tools can now integrate functional safety, ASPICE, and cybersecurity standards directly into the product design process, ensuring that quality is maintained from the initial concept to final production.

AI-Powered Predictive Maintenance and Problem Solving

Another area where AI shows immense potential is in predictive maintenance and problem-solving. AI analyzes data from product tests, customer complaints, and supplier issues to identify problems before they escalate. Continuous analysis helps organizations improve quality while also reducing downtime and cost.

Looking Ahead: The Future of AI in Enterprise Quality

The journey of integrating AI into quality management is just beginning. Omnex Systems’s innovative platform is already paving the way for the future of enterprise quality management, providing solutions across:

• Quality Management (QMS, HSE, and IT Security)
• New Product Introduction (NPI)
• Supply Chain Management
• eMobility and Functional Safety

With AI’s potential to automate processes, streamline workflows, and ensure compliance, the future of enterprise quality management looks smarter and more efficient than ever.

Speaker

Chad Kymal - CTO & Founder • Omnex Inc. Chad Kymal is the Chief Technology Officer and founder of Omnex Inc. , a global consulting, training, and software company based in the United States. He has a background in General Motors and KPMG before starting Omnex Inc. Chad has received awards for his quality achievements and has served on the Malcolm Baldrige Board of Examiners. As CEO of Omnex Systems, Chad has overseen the development of the EwQIMS Suite, which is utilized by major Automotive, Aerospace, and Semiconductor organizations worldwide, with over 250,000 users. This suite has evolved over the years, becoming AI and Machine Learning enabled in 2024. Chad is actively involved in various ISO committees related to Quality Management, Environmental Management, and Health and Safety Management Systems. He has contributed to the development of standards such as the Net Zero Standard IWA 42 and is an expert in Carbon Neutrality. Leading Omnex for three decades, Chad has made a significant impact on industries such as Automotive, Aerospace/Defense, Medical Devices, and High Tech/Semiconductor. He is dedicated to helping organizations improve and has a strong focus on innovation, including the integration of AI and Machine Learning. Chad has authored multiple books and papers on topics such as management systems distillation, Lean Six Sigma, and Net Zero. He holds degrees in industrial and operations engineering and an MBA cum laude, emphasizing his commitment to continuous learning and excellence.