Ultimate Guide to Better Quality Management with AI & Automation

Challenges that Affect Quality Performance

• Challenge 1: Categorizing and Prioritizing Complaints– These processes reduced the overall productivity of quality teams as there was no refined process to identify which complaints warranted reporting to regulatory authorities.
• Challenge 2: Inefficiencies while Handling Large Complaints-related Data– All data is available in the CRM tool. However, quality leaders still need to go through the complaints within the system manually.
• Challenge 3: Complex Quality Workflow– Quality workflows are not always simple and are not easily followed by everyone involved in quality processes.
• Challenge 4: Limited Use of AI– Quality leaders wonder if AI can perform various tasks, such as identifying areas of poor quality, and performing management reviews, among others.

AI in QMS

Artificial Intelligence (AI) and Machine Learning (ML) techniques are widely adopted to mitigate risks and significantly improve QMS user productivity. Broadly, there are eleven areas where AI has proven to take quality management to the next level.

• Complaints Management – Automate complaints intake, triaging, and classification of complaint data from various unstructured sources, including chats and emails. Identify trends with similar patterns and categorize risks automatically. Resolved complaints are analyzed to recommend product recalls or to take appropriate risk mitigation measures.
• CAPA Effectiveness – Automate root-cause analysis (RCA) by analyzing investigations of issues with similar recurrences. The system uses AI to process historical RCA data and recommends the containment and corrective action plans. Risks are classified and prioritized to ensure adverse quality events do not recur in the future.
• Risk Mitigation -Detects risks across Complaints, NCs, Inspections, Training, and Audit Findings. Get insightful recommendations on risk mitigation actions based on the type of risk. Complaint data from regulatory databases, social media, and other sources can be used to recommend actions such as product recalls and process improvements.
• Management Review – Based on analysis of current business performance, AI-infused QMS Systems are predicting future trends for products, NCs, CAPAs, and audits. The systems also use performance data to recommend when it is suitable for a company to perform an internal or supplier audit.
• Training Talent & Skilling – Analyze employees’ job functions and responsibilities and recommend what training they must take. Specify upskilling opportunities and personalized programs based on training paths, role assessments, and organizational changes.
• Audits – Company performance data enables QMS systems to drive ad hoc supplier audits and schedule periodic internal audits based on findings and inspections.
• Post-Market Surveillance – Prioritize high-risk issues using AI-augmented categorization of reportability and risk. Process massive data from various sources using faster signal detection in less time.
• SOP Development/Automation – Process structured, semi-structured, and unstructured documents for fast and accurate data extraction and for recommending when documents should be changed/obsolesced.
• Proactive Customer Service – Analyze historical service data to predict customer behavior and alert service reps to reach out and resolve any customer complaints proactively.
• Design for Quality – AI improves design for quality initiatives by analyzing historical and market data and deriving insight into reliability and quality performance.
• Process Improvement – Automate business processes for repetitive tasks with AI bots to identify opportunities to simplify, standardize, and automate as-is business processes through data-driven discovery.

Quality Management with CQ.AI

CQ.AI helps business leaders implement hyper-automation in their quality and safety processes. Formally, hyper-automation can be defined as an end-to-end approach to using an ecosystem of automation-related technologies to automate all business processes that can or should be automated. It uses artificial intelligence (AI), machine learning (ML), robotic process automation (RPA), and other technologies to transform modern and legacy processes. Broadly, AI can analyze trends, develop data consistency, and provide forecasts to improve business decisions.

CQ’s Applied AI helps businesses with use cases across complaints management, audit management, nonconformance, CAPA, safety incidents, overall user experience, etc. ComplianceQuest’s AI framework is designed to implement three types of automation – task, process, and augmentation.

• Task Automation: Automating short-running, routine tasks such as automatically notifying a stakeholder once a CAPA is triggered or an audit is scheduled with an e-mail notification.
• Process Automation: Orchestrating and automating long-running processes, including eliminating redundant complaint records by detecting duplicates and automatically merging and closing them.
• Augmentation: Facilitating workers with intelligent capabilities such as spotting a non-conformance pattern and enabling customer self-service.