Why AI ?
Drivers of Artificial Intelligence
Strategic Advantages of Artificial Intelligence
Accelerate Time to Market
Gain insights with your data
Improved Risk Management
Cost Reduction - Cost of rework, Failure, Launches
Improve Product Quality
Increase Team Efficiency
Knowledge Management
Increase Revenue
Drive Supply Chain Excellence
Answers to AI FAQs
EQMS.AI can automate repetitive tasks, analyze large sets of data to identify patterns and trends, predict potential quality issues, and provide recommendations for improvements. This leads to faster decision-making, reduced human error, and overall enhanced efficiency.
Common AI technologies include Machine Learning, Deep Learning, Natural Language Processing (NLP), and Computer Vision. These technologies are used for predictive analytics, automated inspections, root cause analysis, and intelligent document processing.
The key steps include:
- Assessment: Evaluating current systems and processes to identify areas where EQMS.AI can add value.
- Planning: Defining goals, timelines, and resource requirements.
- Configuration: Configuring, Customizing and integrating AI tools and algorithms. Training the model with your data.
- Testing: Running pilot tests to ensure the system works at accepted levels of accuracy with Omnex Subject Matter Experts (SMEs)
- Deployment: Rolling out the AI-powered system organization-wide.
- Training: Providing training and support to users.
- Monitor: Continuously monitoring and refining the AI Models.
While EQMS.AI can automate certain tasks, it is typically implemented to augment human capabilities, not replace them. AI can take over repetitive and mundane tasks, allowing employees to focus on higher-value activities such as strategic decision-making and problem-solving. Training and upskilling programs can help employees adapt to new roles.
The accuracy of EQMS.AI predictions depends on the quality and quantity of the data used for training the models. Continuous training and validation of the AI models using up-to-date and diverse datasets (70-30 rule) help improve accuracy. When an inaccurate prediction is identified, Human experts (Human in the Loop - HITL) review and train. The system learns from the mistake, adjusting the model parameters to improve future predictions. Omnex Subject Matter Experts (SME) will validate and train the model with your historical corrected data, and subsequently, this will be transferred to your organization's experts.