How Real-Time IIOT Actionable Data Impacts Process Improvement of Warehouses and Manufacturers

8.31.17

All those sensors on AGVs (Automated Guided Vehicles) which prevent collisions (with people, equipment, or building structures) are collecting data. Until recently, no AGV manufacturer has found a way to concretise these data and make the 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, AGCs (Automated Guided Carts), and AGVs, there is a real need to include IIoT monitoring, says John Hayes, vice-president of sales and logistics at Vecna Robotics.

IIoT impacting throughput in manufacturing and distribution
Without IIoT monitoring, plant managers, operations managers, logistics coordinators throughout manufacturing and distribution sectors lack the data collected offering real-time continuous vehicle status and health information of the vehicle.

Vecna Robotics sends these data directly to a highly skilled team of support engineers; corrective actions are executed and/or recommended before down-time occurs. This translates into bottom-line throughput improvement. Manufacturing throughput time is the amount of time required for a product to pass through a manufacturing process, thereby being converted from raw materials into finished goods.

The concept also applies to the processing of raw materials into a component or sub-assembly. One important distinction of throughput in distribution centres is that a warehouse is usually used to store goods for longer periods of time, while distribution centres concentrate on faster turnover (or throughput) of goods.

IIoT data directly impacts safety
OSHA statistics indicate that there are roughly 85 forklift fatalities and 34,900 serious injuries each year, with 42% of the forklift fatalities from the operator being crushed by a tipping vehicle. The Industrial Truck Association reports that the average life of a forklift is nine years, and there is a 90% chance of a serious accident over the course of its life. 70 to 80% of the accidents impacts pedestrians around the truck.

Going fork-free immediately changes these dire and dangerous prognoses. Plants that implement fork truck free zones, or partially fork truck free implementation, realise significantly improved safety data, lower insurance rates, fewer workers’ compensation claims.

One of the unique differentiators and critical value propositions of IIoT monitoring includes a video feed from vehicles allowing for remote access to all vehicles and the ability to immediately address situations that are not safe. While other AGV vendors may have the data collected via sensors, it is the disruptive leadership of Vecna Robotics that determined the critical, even life-saving, need for IIoT monitoring with all their vehicles.

While there is absolutely an ROI (Return on Investment) calculation in this IIoT monitoring service, one qualifiable data point is peace of mind. Knowing that the operation of these vehicles and their interaction with the plant floor is monitored 24/7 by factory engineers represents of first-of-its-kind industry breakthrough in utilisation of data to produce the safest and most efficient operation.

IIoT and vehicle maintenance
Maintenance is not a sexy topic when it comes to AGVs or tuggers. Few AGV vendors want to discuss the costs associated with maintenance, yet it cannot be ignored or neglected. Maintenance issues often occur with competing systems rendering the vehicle inoperable; plant floor managers then must notify maintenance of the issue, locate the vehicle in the facility, travel to the vehicle, assess and address the issue.

This additional cost, combined with down-time, is never part of other vendor ROI calculations. Omitting this real cost is naïve at best and irresponsible at worst.
It is precisely why Vecna Robotics includes IIoT monitoring so the error can be assessed and addressed thereby reducing maintenance labor costs associated with nuisance errors and providing significant savings to the company.

IIoT drives operational efficiency and improved OEE
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 utilise data point, OEE, evaluates how effectively a manufacturing operation is utilised. The results are best used to identify scope for process performance improvement, and how to get the improvement.

When the cycle time is reduced, the OEE will increase (more product is produced for less resource). More changeovers (set-ups) 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 on a high traffic area has the potential to block ALL vehicles behind it. This means that a 15-minute stoppage for one vehicle could be 150 minutes of move time if there are ten vehicles in the system. With an average move time of six minutes, that equals 25 moves in a 15-minute period! Not to mention that many systems are NOT sized to catch up after system error of that magnitude.