AR Genie Work Assist: Empowering Industrial based Inspection, Training & Maintenance with Evidence based Workflows

AR Genie Work Assist: Empowering Industrial based Inspection, Training & Maintenance with Evidence based Workflows

Offering step-by-step, AR-powered evidence proof workflows to empower industrial-based inspection, maintenance & training with verifiable data

The factory floor is no longer a place of buzzing machines and workers struggling with bulky paper manuals. The rise of Industry 4.0 is transforming the industrial landscape with digitized workflows and operations. Technologies like Augmented Reality (AR), Artificial Intelligence (AI) or Generative AI are revolutionizing the way workers or industrial technician teams learn, operate on machinery, perform inspections, and accomplish their tasks with efficiency on their jobs. Here in this article, we will discover how AR Genie Work Assist software platform helps frontline workers in training, maintenance & inspection, enabling them to perform their tasks with evidence enriched workflows and greater efficiency.

Document Operations & Maintenance procedures & Digitize Industrial Training & Inspection with - AR Genie Work Assist

Traditionally, documenting maintenance and operational procedures had been the domain of desk based workers, but with augmented reality, this scenario has changed. Workers on the field have the most up to date and first hand understanding of how things actually work and possess invaluable understanding. AR Genie Work Assist allows frontline workers or technicians to document their maintenance and operational tasks digitally by capturing the images, video & text notes while performing their tasks in the form of evidence.

Conventional training methods often rely on bulky paper manuals, trainer’s lectures, and on-the-job shadowing, where a new technician closely observes the other skilled technician performing a task on machinery. These approaches are resource-intensive, very time-consuming, and lack practical real-world application. With Work Assist, trainees can view and follow step-by-step instructions with 3D models to train themselves on the task beforehand. Similarly, for inspections that are conducted frequently with photos & paper checklists, leading to risk of errors and lack of verifiable evidence can be improved by using step-by-step workflows.

AR Genie Work Assist is transforming these traditional approaches by offering a step-by-step, AR-powered evidence proof workflow system to empower industrial-based inspection, training and maintenance with verifiable data. Designed to seamlessly integrate with iPads and AR Glasses, it provides frontline workers or technician staff step-by-step AR instructions, 3D models, images, videos, or documents on their assigned jobs. It also features device management and job types such as training, inspection, operations, and maintenance, along with workflow instruction templates. 

Frontline workers can work hands-free on AR Glasses and access the workflow by viewing assets or devices, their classifications and select the respective templates they are assigned the work for. They can view their respective jobs on “My Jobs” & follow step-by-step instructions, leaving evidence notes ( video, audio, texts or more…) at the end of each step before completing their assigned task.

How AR Genie Work Assist helps workers to attain efficiency & precision in their jobs with evidence based workflows?

Imagine a scenario of a routine inspection of a complex offshore drilling site. Traditionally, inspectors rely on their experience and paper checklists to identify potential issues which can be prone to human error and time-consuming.  An inspector equipped with AR glasses can utilize AR Genie Work Assist to view his assigned job, select the job template & follow step-by-step instructions, highlighting specific areas such as: pipeline corrosion, leaked pipes etc in a drilling site, for inspection with relevant procedures & safety protocols. They can also capture evidence throughout the inspection process using AR interface via:

Video Notes: Inspectors can capture video footage of the specific task in each step while performing inspection procedures on the site.

Text Notes: Inspectors can leave text notes highlighting key points or any additional relevant information of the task.

Image Capture: Capturing images of completed steps, specific equipment gives clear information for future reference or reporting purposes. 

Similarly, trainees and maintenance personnel can also view their assigned training & maintenance tasks. They can follow step-by-step instructions and capture evidence notes after the completion of each step they perform. AR Genie Work Assist allows workers to leave evidence at the completion of each step of the assigned job workflow, creating a verifiable record of the specific job assignment. Supervisors can review these captured evidence to ensure if the proper procedures were followed and what corrective measures are necessary to be taken.

Why are augmented reality evidence-based Inspection & Training important? 

  • Detailed videos, photos and voice notes captured with AR facilitates clear communication for efficient planning based on accurate evidence.

  • It enables real-time supervision, allowing supervisors to remotely check whether standard procedures are followed and completed properly or not.

  • Captured evidence creates digital recordkeeping of the past inspections, ensuring historical analysis for future reference.

  • It helps to know the exact problem, leading to reduced downtime and increased productivity.

  • Identifying recurring issues through valuable data insights enable preventive maintenance for improved efficiency. 

AR Genie Work Assist goes beyond simple work instructions by providing step-by-step instructions with 3D models, images documents, videos, and evidence based workflows. It offers pre-built workflow templates for various tasks commonly encountered in industrial settings and can be customized according to the specific job requirements across diverse industries. 

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