Industrial Analytics Alerts for Machine and Operator Productivity
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Solution Overview
Problem
Current industrial management systems fail to effectively combine machine and operator productivity data to enhance overall process performance, lacking actionable insights for improving efficiency and productivity.
Innovation Solution
An augmented industrial management system that collects raw performance data from machines and context data through a machine user interface, aggregates and analyzes it using an analytics engine, and generates real-time alerts and prescriptive actions to improve process performance by comparing data against trigger definitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional industrial management systems are used to track equipment utilization, then basic OEE metrics can be obtained, but actionable insights for improving process performance are not provided
Solution Approach 1:
The patent introduces an analytics engine as an intermediary component that sits between the data collection layer (taps and sensors) and the user interface layer. This analytics engine processes raw performance data and context data to generate actionable insights, effectively mediating the transformation from raw data to decision-ready information without requiring direct complex interactions between all system components
Solution Approach 2:
The system enables self-service by automatically collecting performance data through taps, analyzing it through the analytics engine, and generating alerts without requiring manual intervention. The machine tap automatically captures raw performance data, the analytics engine autonomously processes this data against trigger definitions, and the alert engine automatically generates and delivers alerts, creating a self-serve analytics pipeline
2Productivity
If real-time data collection and analysis is implemented across all machines, then process performance can be improved, but system complexity and implementation cost increase
Solution Approach 1:
The patent creates a universal analytics platform that can be applied across multiple machines and manufacturing contexts. The machine tap, analytics engine, and alert engine form a multi-functional system that can handle various types of performance data (cycle times, counts, speeds, sensor data) and generate relevant insights for different machine types and processes, making the system scalable without proportionally increasing complexity
Solution Approach 2:
The system is segmented into independent, modular components: machine taps for data collection, context taps for additional context, analytics engines for processing, and alert engines for notification. Each component operates independently and can be deployed incrementally, allowing the system to scale from single-machine to multi-machine implementations without requiring complete system redesign
3Ease of manufacture
If existing machinery is monitored without technical upgrades, then implementation cost is reduced, but data quality and completeness may be insufficient
Solution Approach 1:
The machine tap acts as an intermediary layer that connects to existing machinery without requiring modification of the machinery itself. It captures performance data through non-invasive means and supplements it with context data from user interfaces, thereby maintaining data quality while avoiding the need for expensive technical upgrades to the actual manufacturing equipment
Solution Approach 2:
The system merges multiple data sources including raw performance data from machine taps, context data from user interfaces, and analytics data from the analytics engine. This combination of diverse data sources compensates for limitations in any single source, achieving comprehensive and high-quality data collection without requiring upgrades to the underlying machinery
Data Source
AI summary
A system and method for providing prescriptive analytics in an industrial process wherein a machine tap collects raw performance data from a machine, a machine user interface collects context data on operation of the machine, a server aggregates the performance data and context data, and an analytics engine analyzes the performance data and context data and generates analytics data. An alert engine compares the performance data, context data, and analytics data against a trigger definition, creates an alert if the trigger definition is satisfied, and sends the alert to a remote device to provide prescriptive guidance for improving process performance.