How Applied AI Empowers Quality Management System (QMS) Users

Quality Management Systems (QMS) are key to maintaining desired product quality and providing excellent service delivery. QMS automate a wide range of business processes including product design, SOP (Standard Operating Procedure) development, management review, audits, training, complaints management, corrective & preventive actions (CAPA) etc. QMS users deal with vast amounts of data and a variety of documents in their day-to-day business. Manually processing such diverse information could result in human errors and put products and consumers at risk.

Artificial Intelligence (AI) and Machine Learning (ML) techniques are being widely adopted to mitigate such risks and significantly improve QMS user productivity. Process automation drives numerous benefits across all business functions. Some valuable advantages are outlined below.

Complaints Management

AI and ML tools are being used to automate complaints intake, triaging and classification of complaint data from unstructured sources such as emails and chats. The QMS uses machine intelligence to identify trends and surface issues with similar patterns. Risks are automatically categorized for instant assessment. Complaints that have already been resolved or those that are being addressed are analyzed and this data is collated to recommend product recalls or to take appropriate risk mitigation measures – all in an automated manner.

CAPA Effectiveness

AI-driven QMS facilitate automated root-cause analysis (RCA) by analyzing investigations of issues with similar recurrences. The system processes historical RCA data and recommends containment and corrective action plans eliminating duplicate effort. Risks are classified and prioritized to ensure adverse quality events do not recur in the future. CAPA effectiveness is thus verified in a data-driven way.

Risk Mitigation

Managing risks is a critical requirement in any organization. An AI-enabled QMS detects risks across Complaints, NCs, Inspections, Training, and Audit Findings. AI-technology not only detects risk patterns, but also recommends risk mitigation actions based on the type of risk. For any identified risk, the system presents the next steps such as mitigate, transfer, eliminate, accept. The system ingests complaint data from regulatory databases, social media, and other sources to recommend actions such as product recalls, process improvements etc.

Management Review

Based on analysis of current business performance, AI-infused QMS are predicting future trends for products, NCs, CAPAs (Corrective and Preventive Action), and audits etc. The systems also use performance data to recommend when it is suitable for a company to perform an internal audit or a supplier audit.

Training Talent & Skilling

Training needs analysis and skills management involves a lot of data crunching. QMS AI systems analyze the employees’ job functions and responsibilities and recommend what training they are required to take. Data from NCs, complaints, and audit findings are also factored in during recommendations. Specific upskilling opportunities and personalized programs are presented based on training paths, role assessments and organizational changes. Learning effectiveness data is used to measure and refine training plans.

Audits

Company performance data enables QMS to drive ad hoc supplier audits and schedule internal periodic audits based on findings and inspections.

Post-Market Surveillance

AI-augmented categorization of reportability and risk enables companies to prioritize high-risk issues. Faster signal detection using data from multiple channels including social media, online literature, health authority safety databases and internal data sources empower companies to process massive data in much less time.

SOP Development / Automation

QMS are using AI and ML to process structured, semi-structured, and unstructured documents for fast and accurate data extraction and routing and for recommending when documents should be changed / obsolesced.

Proactive Customer Service

AI analyzes historical service data to predict customer behavior and alert service reps to proactively reach out and resolve any customer complaints.

Design for Quality

AI improves design for quality initiatives by analyzing historical and market data and deriving reliability insight and quality performance information.

Process Improvement

AI bots are automating business processes for repetitive tasks empowering companies to identify opportunities to simplify, standardize, and automate as-is business processes through data-driven discovery.

The above are a few use cases where AI and ML are being effectively applied in QMS Systems. Machine intelligence competencies and natural language processing tools are driving the development of futuristic QMS. ComplianceQuest is at the forefront of advancing QMS technology research and supporting companies in their quality missions.

Power your enterprise with CQ.AI for clinical, quality, health and safety. CQ.AI applies natural language processing (NLP) and machine learning throughout the QMS to increase operational efficiency.

It also helps in predicting the risks and making recommendations, but the final decision is left to be made by subject matter experts giving them complete authority. This method adds value by optimizing processes and routing, allowing qualified professionals to focus on high-risk areas.

Request for a personalized demo to understand more about how CQ’s AI-based EQMS can be a game-changer for your enterprise: https://www.compliancequest.com/personalized-demo/