How would you like to automate the Knowledge Graphing of your Manufacturing Industry Uptime and Downtime historical data and provide predictive analytics to your Project Teams with Uptime/Downtime probabilities roadmapped out for them?
How often do you manually collect and distribute these manufacturing Uptime-Downtime data from $100s of millions of production products to your Executive Team and Operating Teams - CFO, VP Supply Chain, VP of Manufacturing, IT, Business Analysts, Plant Managers etc?
Like Habla AI Project Team Knowledge Graphs, we have Manufacturing Industry Knowledge Graphs that are based upon Uptime - Downtime Use Cases with objectives of increased factory performance and utilization. How do we do this? We provide our Manufacturing Industry Customers (CPG, Electronics, Retail) with performance insights at very detailed Knowledge Graph levels:
Plant Uptime and Downtime by date, SKUs, Shift,
Comparisons across multiple plants of Uptime-Downtime
Downtime Super-reasons and sub-level reasons down to the //Plant/Line/String levels and Plant Manager, Shift Supervisor, and Line Operator levels associated with end-customer product SKUs
Semantic relationships between Plant, Line, String, Line Operator, SKUs, Machine Type
Downtime sub-level reasons Knowledge Graphed for Sanitation, Maintenance, Festooner Failures, Equipment Breakdown, Process Failures, Minor Stops, Changeovers, and Quality Checks
Graph and view manufacturing plant data in real time from MES systems and gain insights into Uptime and Downtime for manufacturing like never before. Take production and operations decisions based upon these insights to improve plant performance through process improvements. For each 1% increase in plant utilization, an increase in production revenue, faster shipping, and higher gross profits occur.