Digital data diagnostic
3 Steps for a successful AI Deployment
As a first step, a Digital Data Diagnostic helps gauge the technical and economic feasibility of Artificial Intelligence projects in your plant. We evaluate your plant’s digital maturity, highlight data availability, identify roadblocks and how data can lead to process improvement opportunities and translate each opportunity into high-level economic potential.
Based on your different opportunities, a pilot project is chosen for Step 2.
1. PROCESS AND BUSINESS UNDERSTANDING
Which Key Performance Indicators (KPIs) measure my plant performances such as yield, energy efficiency, throughput, product quality, equipment availability?
The first phase of any analytics project allows our team to understand your process and business challenges.
2. FLASH ANALYSIS
What are the potential savings after implementing a pilot project?
The Flash Analysis is a preliminary analysis on historical production data to quickly quantify the potential improvements and main sources of variability that might explain low performances. The goal is to refine the expected savings, identify possible roadblocks and prepare the brainstorming sessions with plant staff.
3. WORKSHOP WITH OPERATORS
How can we involve operators to ensure a successful project?
At this stage, brainstorming workshops are organized with production and maintenance teams (operators, engineers, managers). These sessions are designed to pinpoint root causes underlying a particular performance problem and to understand operating constraints. Experience has shown that this phase plays a key role in the successful implementation and engagement of plant staff to trust AI based decision support tools.
4. ADVANCED ANALYTICS AND MODELING
Which parameters are key to maintain optimal performance?
In close collaboration with the production team and thanks to advanced analytics tools based on artificial intelligence, PEPITe’s team:
- Explores past operations variability and identify key parameters
- Understands conditions leading to best performances
- Predicts best performance depending on plant conditions
- Prescribes a range of manipulable parameters to maintain optimal performance
What tools, dashboards or reports are required to facilitate and support operators in maintaining optimal performance?
Models built with machine learning feed in real-time dashboards and reports to ensure the right information gets to the right person at the right time. The decision support tools are adapted specifically for each client (dashboards, daily/weekly/monthly reports, etc.) and typically use existing technology solutions already operational in your plant.
6. TRAINING AND REVIEW
How can I ensure long-term follow-up and sustainable results?
Following implementation, plant staff is trained to take the appropriate actions based on live dashboards and reports. A follow-up review period ensures that models are performing correctly and that the reports and dashboards are understood and well used.