Advanced Application of Artificial Intelligence (AI) in Automating Quality Assurance Processes

Quality leaders find their task cut out for them. On the one hand, customers are increasingly demanding high-quality products and services at low cost and timely delivery. On the other hand, supply chains have become global, needing greater visibility into procurement, manufacturing, and delivery processes.

Digitization has enabled greater transparency and empowered quality leaders with data that can help standardize operations across the supply chain while reducing wastage and improving process and resource efficiency. However, even handling the vast amounts of information generated from multiple sources within the organization can present a challenge for the quality assurance team. The data can be in multiple formats, incomplete, not of the required quality, and with several duplications. Though quality management systems do come with capabilities to manage such data, it require manual intervention that can be time-consuming and add to the existing errors. This can impact the quality processes, introducing new and unforeseen risks.

Data Quality Assurance with AI

Infusing AI technologies into quality management systems can help quality leaders deal with vast data sets, ensuring their quality and integrity for more accurate insights. Some of the AI technologies that enable this include:

• Machine Learning: They can help capture trends and deviations to identify nonconformances early. This helps take timely corrective action to prevent potential product quality issues.
• Deep Learning: Neural networks can be used for training and uncovering correlations that are not evident to human operators. This helps discover defective products or components by accurately classifying them.
• Computer Vision: AI can be used for textual data and images and videos to detect quality issues and automate visual QA tasks for greater accuracy and efficiency.

Areas Where Advanced Application of AI Can Improve Quality Assurance

With greater accuracy and actionable insights, some of the areas where AI can be applied to automate quality assurance processes include:

• Risk Management: A proactive approach to quality assurance requires identifying and managing risks to prevent quality events. With the advanced application of AI, quality assurance leaders can uncover risk patterns and implement corrective and preventive steps.
• Root Cause Analysis: Should a quality event occur, the root cause needs to be identified through investigations. This helps address the real issue responsible for the event to prevent its recurrence later.
• Predictive Maintenance: The machinery and tools used in the manufacturing process are assets that need periodic maintenance to work in optimal condition. Sometimes, external factors or regular wear and tear can lead to machine breakdown. This will impact production schedules and disrupt operations. AI can detect anomalies and perform predictive maintenance to identify and address potential issues and avoid unplanned downtimes.
• Training: Employee engagement in quality management is critical to improving compliance and creating a culture of quality. AI can be effectively deployed to create personalized training modules for different job functions, which will be more effective and empower workers with the right skills to contribute to quality.
• Visual Inspections: This is a critical part of quality assurance, where inspecting the product’s appearance and finish can help detect defects. AI can automate this process with computer vision and image recognition.
• Process Optimization: AI can help with continuous improvement, which further aids in optimizing process effectiveness, removing inefficiencies, and preventing potential bottlenecks. Production lines can be optimized, and wastage can be reduced while simultaneously increasing the quality and consistency of the products.
• Supply Chain Management: Advanced AI applications can benefit supplier inspections and inventory and shipment tracking. This can ensure the quality and timely delivery of raw materials, preventing disruptions to the supply chain.
• Customer Service: AI-based chatbots and robots are improving the quality of customer service and increasing the accuracy of customer-related information and feedback. This can be looped back to different functions to improve quality in those areas.
• Document: Documents and event recordings form a crucial part of quality management and compliance, but they often do not meet regulatory requirements due to employees’ inability to meet regulatory needs. Automating processes using AI will improve document management/ and compliance.

Best Practices for Implementing AI in QA

The implementation of AI has its challenges. From data quality to employee mindset, budgetary restrictions, and lack of awareness, there can be many hurdles to effective AI implementation in QA.

To overcome these challenges, businesses must improve the following best practices:

• Create and Communicate Vision: Having a clear vision for the AI project is important. This will help drill down the actions to be taken and allocate roles and responsibilities appropriately. To ensure its success, all employees should be made aware of the need for and their role in the AI application.
• Training and Tracking: Once the AI project is launched, the employees must be provided with appropriate training to enable them to leverage the new technology to improve their functioning. As people tend to revert to old ways of working after some time, tracking and monitoring progress and impact is important to ensure sustenance.
• Leadership Involvement: The managers must lead from the front to encourage and inspire their teams to use the new approach effectively and efficiently. They must also have a way to reward employees who achieve their goals and motivate and support those who need help to sustain.
• Right Fit: AI is a broad term and not an out-of-the-box solution. Therefore, it is crucial to work with the right partner to get the right solution that meets the organization’s needs. Due diligence and proof of concept before making it enterprise-wide will also be crucial.