Methodology combining Advanced Analytics and people
Published by Pepite


  • Digital data diagnostic
  • Pilot project
  • Knowledge transfer

Digital data diagnostic

3 Steps for a successful AI Deployment

  • Digital data diagnostic
  • Pilot project
  • Knowledge transfer
  • 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.

    Digital data diagnostic

    Pilot project

    This second step can be split into 6 phases
    Pilot project
    • 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.

    • 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.

    • 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.

    • 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.

    • 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.

    Knowledge transfer

    The objective is that our clients have the tools and skills to deploy AI projects using DATAmaestro Advanced Analytics software and OPTImaestro methodology. PEPITe, or a certified partner, will continuously coach your teams on the OPTImaestro methodology and help you to take full advantage of DATAmaestro features. Learn how to implement your own AI projects!
    Knowledge transfer
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