How AI is Making Quality Management a Leaner Process

What is Artificial Intelligence? It is simply the ability of machines (in most cases, software) to perform tasks that would otherwise require human intelligence.

Modern enterprises that are future-ready are already using AI in a wide range of scenarios; AI is being used for predicting consumer behavior, demand forecasting, failure analysis, spotting future trends, etc. Companies from across sectors including manufacturing, healthcare, pharmaceutical, medical devices, hi-tech, automotive, and oil & gas are working on focused initiatives to induce cutting-edge AI capabilities in their operations.

But one of the most important applications of AI is in analytics. AI has become a core part of the analytical pipelines that next-generation, data-driven enterprises will lose out on if they don’t use AI the right way. From a quality perspective, the implementation of a next-generation EQMS with world-class data and AI capabilities has become a no-brainer.

When your EQMS can offer a high level of data visibility and seamless flow of information across customers, suppliers and internal operations, using AI can take quality management to the next level. Specifically, it can help with early detection of quality issues, predictive analytics for non-conformance or failure, proactive risk management, and automated prioritization of complaints handling, to name a few use cases.

It can also help make quality management a leaner process. Lean, a concept based on continuous improvement, enables businesses to increase customer value, eliminate waste and optimize operations. It enables building a culture of quality, which is stressed on by regulatory bodies such as the FDA and ISO, where employees are engaged proactively in improving their areas of work collaboratively. With the use of AI, the process of integrated, enterprise-wide quality management becomes way more efficient.

Looking for a next-generation EQMS that brings to the fore cutting-edge data and AI capabilities? ComplianceQuest’s AI-powered EQMS offers one of the best solutions in the market.

Request for a demo of CQ.AI here

Holistic Quality Management using AI

Artificial intelligence enables implementing the lean concepts of time and cost efficiency through the following:

• Early identification of quality issues and operational inefficiencies
• Data-driven predictive analytics to spot failures and non-conformance ahead of time
• Better resource allocation by using AI models to automatically prioritize issues such as complaints, non-conformances, etc.
• Predictive analytics in various scenarios such as equipment maintenance
• Proactive approach towards risk management and planning risk mitigation efforts
• Time savings on audits and inspections
• AI-enabled tools to conduct better Management Reviews to drive quality improvement efforts

The overall process of using data and AI in the quality workflow is enabled by the following:

• Interactive Visualizations for Better Decision-Making Process: AI can help with offering better contextual understanding and awareness of the organization’s operations through rich data visualizations presented as intuitive user interfaces for faster and smarter decision-making.
• Transparency: A real-time view across the processes is made possible using AI-driven tools and methodologies. This helps to improve the management of supply chain risks, perform risk analysis and identify the most appropriate solutions to optimize existing work environments.
• Smarter Investigation: AI-based investigation tools enable delving deep into failure points that enable the effective mitigation of risks and preventing their recurrence. This has an impact on customer satisfaction while also lowering the cost of intervention.
• Shorter Process Cycles: AI facilitates the automation of processes and workflows, thereby reducing process cycles. It also helps spotting areas of improvement to make processes efficient, thereby further reducing process times and improving productivity, as personnel can focus on core business activities.
• Proactive Problem-Solving: AI tools can help organizations pre-empt quality issues before they occur by gathering and analyzing an enormous amount of data and identifying trends. This proactive problem-solving approach can improve quality statistics in their deliverables.
• Intelligent Analytics: AI technology empowers organizations to track KPIs and perform intelligent analysis. This can help identify potential problems and provide a timely and effective solution to improve the overall quality of products and processes.
• Reusable Templates and Checklists: Documentation, a time-consuming task, is simplified with the use of pre-defined templates for standard documents, checklists, and questionnaires. This saves the time and effort needed to create such materials from scratch every time.

Driving Efficiency into the Process of Gathering and Analyzing Data

At modern enterprises, data lies at the front and center of quality operations. The manufacturing industries today are being powered by Industrial Internet of Things (IIoT) technologies which generate ample data needed to improve visibility into operations.

It also provides real-time data to the QMS for identifying and triaging. Data from equipment and devices can be automatically “connected” to the EQMS to spot issues and challenges in real-time.

Using AI-based analytical tools, businesses can reduce the overall cost of poor quality. It can help with faster time to market of products by providing a contextualized understanding of workflows, reducing the time to review product batches while ensuring that both products and processes meet quality standards.

AI algorithms can be trained to classify data elements into different categories of issues and provide decision support for efficient problem-solving. With Natural Language Processing (NLP) capabilities, patterns and trends from data/information can be identified with ease. This can play a key role in categorizing risks based on their severity and prioritize corrective actions using data at the core.

AI is critical for predictive analytics that can help with anticipating future quality events, thereby ensuring the goals of product quality, patient safety, secure supply chain, and improved global regulatory compliance.

ComplianceQuest for a Leaner QMS

ComplianceQuest’s Enterprise-wide Quality Management System provides AI-based features that can help make your quality management efforts more time and cost-effective. Some of these include:

Natural Language Processing (NLP): The solution leverages NLP to detect, analyze and convert unstructured data into classifications for quality events to help drive consistency by removing bias.

Intelligent Chat Bots powered by Conversational CQ.AI: Highly interactive chatbots that help users engage with the system effortlessly as needed. Integrating CQ.AI in these chatbots helps users perform complex actions through conversational CQ.AI.

Machine Learning (ML), Trending Insights, and Signal Detection: Machine learning, trending, and insights enable monitoring, detecting, and predicting potential high-risk issues to automate the required actions to be taken to mitigate risks promptly through proven and consistent workflow steps.

Vision and Image Processing: Improve productivity with AI-powered vision and image processing, automatically monitor attachments and detect product information, lot info, issue dates, potential nonconformance, auto-create workflow records, and auto-populate record values as needed.

Predictions and Decision Making: With growing product and process complexity, quality and safety teams are pressed to stay on top of critical quality and safety issues and prioritize those which are most urgent. CQ.AI provides powerful, AI-assisted recommendations to automatically classify, categorize and streamline issues this helps with the triaging of the quality and safety event handling.

Email Discovery and Unstructured Data Processing: With the integrated email content discovery, intercept, process, analyze, and recognize issues and automatically trigger the creation of issue records. Auto-populate where possible with field values to drive increased productivity.

Next Best Action/Recommendation (NBA): Fully leverage the Next Best Action capability for recommendations to the users on what is the most logical next step in the workflow. NBA helps users take actions in a consistent manner by knowing what to do next.

Classification/Categorization and Prioritization: The ability to automatically, accurately, and systematically classify events early on in processes helps quality teams to quickly triage and ensure timely and appropriate action is taken to minimize recurring product issues and negative customer impact. The path to properly classify, evaluate, and identify the root cause and determine the necessary actions to prevent further occurrences involves sifting through tremendous amounts of data and is often not straightforward.

Similarity Identification: Avoid unnecessary investigations by improving “first-time issue” classification, reportability, and risk. Identify similar issues and re-use previous investigations to eliminate the same or similar issues repeatedly and share the lessons across the enterprise.

These AI features enhance the EQMS modules for the management of audit, CAPA, inspection, compliance, complaints-handling, nonconformance, change, suppliers, training, and documentation for an end-to-end quality assurance process using next-gen technologies.

To know how ComplianceQuest’s AI-based EQMS solution can help you implement a lean quality management program, visit: https://www.compliancequest.com/ai/