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AGV IIoT Monitoring: Lean Six Sigma Monitoring
Manufacturers can now concretize data and make it actionable, predictive and fundamentally useful.
Mar 25, 2018—
All those sensors on automated guided vehicles (AGVs) which prevent collisions (with people, equipment or building structures) are constantly collecting data. Until recently, AGV manufacturers have been challenged to find a way to concretize this data and make it actionable, predictive and fundamentally useful.
This is a frequent complaint of Industry 4.0, big data, and the Industrial Internet of Things (IIoT): too much data, little of which can be accessed and used effectively. From tuggers, automated guided carts (AGCs) and AGVs, there is a real need to include IIoT monitoring. Asset management, asset tracking, predictive maintenance, remote monitoring of assets or conditions, and optimization are at the heart of IIoT initiatives.
The inclusion of IIoT monitoring ensures increases in operational efficiency. Eliminating the ripple effect caused by stopped vehicles is a metric of overall equipment effectiveness (OEE). The widely utilize data point, OEE, evaluates how effectively a manufacturing operation is utilized. The results are best used to identify scope for process performance improvement, and how to achieve the improvement. When the cycle time is reduced, the OEE will increase (more product is produced for less resource). More changeovers (setups) will lower the OEE, and this includes down-time from tuggers and AGVs not operating during production times. OEE measurement is used as a key performance indicator (KPI) in conjunction with lean-manufacturing efforts to provide a quantifiable measurement of success.
A single vehicle stopped in a high-traffic area has the potential to block all vehicles behind it. This means that a 15-minute stoppage for a single vehicle could mean 150 minutes of move time if there are 10 vehicles in the system. With an average move time of six minutes, that equals 25 moves within a 15-minute period—not to mention that many systems are not sized to catch up after a system error of that magnitude. Being able to utilize a remote management system allows for these types of errors to have an immediate corrective action implemented, and for the product to continue moving in a smooth and efficient manner.
IIoT Impacting Throughput in Manufacturing and Distribution
An example of how an IIOT asset-management system works is at Vecna Robotics; a live and continuous flow of information is being sent directly to a highly skilled team of support engineers. In the event of an issue, corrective actions can be executed and/or recommended through remote asset engagement before downtime occurs. This translates to bottom-line throughput improvement.
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