AI and quality management

AI - Artificial Intelligence and quality management, a new world of possibilities. AI, it seems so futuristic, but the practical possibilities for AI are already there. And that's just the beginning of AI...
The Metaware platform is built like 'Lego bricks' and one of them is the document management environment. The latter is in fact a knowledge database and AI can already be applied to it. To give the user faster and more accurate answers to his questions through machine learning.

Concrete example of AI. For webshop Bol and Teleperformance, a global business service provider, Bol's knowledge base BAS, a Metaware platform application, has been made accessible to Microsoft's Azure artificial intelligence. This provides a chatbot that supports the service experts to speak to customers faster and more effectively.

Another example. You want to have your quality documents available in another language. No problem. The source language of a quality document can be translated online into any target language of good quality. If necessary, this can be done in batches. The translation model LLM - Large Language Model can also be trained with your own (organizational) concepts. Please contact us for more details.

What does this mean for your own quality management system? First of all, the maturity stage is important. Start with AI when the maturity level is sufficiently high and see which functionalities can otherwise be applied first.

Quality management system - the maturity stages

In broad terms, the stages from a 'handbook' to a management system can be described in a number of steps:

1. Ad hoc
   The understanding of quality management is limited. The quality control of processes is fragmented and problems are widely ignored. There is a lot of ignorance in terms of quality and there is a belief that everything is good. Formally, there are no responsibilities and accountability is not given. Documentation of processes and practices is limited and often outdated. Communication by e-mail and access to quality data and documentation is difficult.
   Tooling: We have already passed this stage.
    
2. Reactive
   In addition to the quality manager, only a limited number of people are involved in quality management. Quality data is collected in a limited way, usually in separate spreadsheets. Users wait for problems to occur and only then react. Important quality problems are recorded, but not yet sufficiently analyzed to prevent recurrence. There is no integration yet.
   Tooling: A simple quality system for a limited number of people
    
3. Managed
   Quality management is important throughout the organization, not just for the quality manager. Audits and controls are carried out regularly. KPIs have been introduced and are being steered accordingly. Ownership and responsibilities have been established. 
   Tooling: Version management active, revision rolled out, navigation structures, audit system, registration, checklist ISO9001
    
4. Proactive
   Quality data is available and accessible throughout the organization. Working methods are up-to-date and laid down in a practical way and also accessible throughout the organization. Problems are recognized and analyzed. Actions are identified and implemented to prevent recurrence. 
   Tooling: Monitoring the use of the management system, reports; incident – > problem, 5W's / 8D, risk process, various workflowsfor checklists, assessments, approvals
    
5. Integrated and optimized.
   Quality management is a spearhead and a value within the organization. A full process integration supports proactive, risk-based quality decisions. Quality data is correlated with each other, if necessary with artificial intelligence. Collaboration is the key to success to drive positive business and customer outcomes. 
   Tooling: Integrated environment, knowledge base, risk analysis at relevant places/activities, risk carousel, quality calendar, FOBO  analyses, dashboards, APIs
    
6. 

Features

A management system is broad in scope: the description of the processes versus the operational implementation. In fact, the well-known slogan: 'Say what you do and do what you say.' A management system is therefore made up of various components: the descriptive part with document management and references to required standard items, the risk overview including risk treatment plan and then all related control processes (handling deviations, audits, complaints procedure, check operational excellence, ..).

An overview of 'standard' functionalities of a mature management system:

Want to know what a management system platform can do?
Just for fun, set up the Metaware platform in 60 seconds as a test, software tool for management systems: documentation, risk analysis, notifications, audits, supplier assessments, etc. And look for the differences with your current way of working ...