Automated Safety Compliance

Accelerating Digital Transformation for Mobile Asset Intensive Enterprises
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8,219 workers were seriously injured or killed in forklift-related accidents in 2019. 

It is more than 30% increase since 2011 with an estimated total cost of $1,638,136,000.

$176,400 – Average Cost per serious injury
$2,240,000 – Average cost per fatal injury

Litigation risks for forklift-related accidents are also increasing:
10 jury awards ranging from $5 million to over $100 million in 7 states since 2017

OSHA estimated that 70% of forklift accidents can be avoided with proper safety training and oversight. However, ensuring compliance can be a significant challenge for companies that operate varied equipment from different manufacturers.

Safety Compliance Automation Enablers

 

Asset Profiles for tracking vehicle specifications, risks, and limitations – including maintenance, utilization, and impacts across different operators.
Location Profiles for defining work areas by risk level and automatically preventing dangerous activities.
Operator Profiles for tracking operator training and certifications, preventing unauthorized truck access, and monitoring operator safety and productivity across all types of vehicles.
Checklists for verifying vehicle safety before and after the operation, with real-time automatic reporting of issues.
Scorecards are driven by truck real-time vehicle metrics to benchmark operator safety, productivity, and asset utilization – combined with advanced visual analytics for managers to identify opportunities for improvement.
Real-Time Alerting that notifies operators and managers of high-risk conditions and behaviors – e.g., unsafe speed for an operating area/location or load, as well as excessive impacts and accidents.
Automated Data Collection for accident/injury record-keeping and financial reporting.

Connected Intelligence for Operational Assets

An innovative solution for rapidly boosting safety, efficiency and security.

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Flexible and Scalable

As most of the IoT markets are early, applications will need to be agile, adapting and changing as you learn more of what your customers value.  When you are successful and start to hit scale, it’s too late to learn your platform can not cope with the volume of data that can dramatically slow reporting, searches, analytics and the very real-time nature of the platform as data from devices sits on queues waiting to be processed.  Even worse the platform can become unstable and experience outages.  At this late stage re-architecting for scale are typically destabilizing and highly disruptive.

The ElasticM2M team have scaled applications to millions of devices, we have built our platform to horizontally scale and take full advantage of the elasticity in the cloud.  We auto sense increasing loads and dynamically create more workers to process the data and as the load drops we close down workers to save costs.  Our data ingest is based on software used to process billions of messages and modifying the logic is simple and flexible using our data flow visual interface.

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