Abstract: 

Using Augmented Reality, Machine Vision & Deep Learning for solving Supply Chain Problems

The Fourth Industrial Revolution (Industry 4.0) is the ongoing automation of traditional manufacturing and industrial practices, using modern smart technology. Large-scale machine-to-machine communication (M2M), the internet of things (IoT), Augmented Reality, Machine Vision, and Deep Learning are integrated for increased automation, improved communication and self-monitoring, and the production of smart machines that can analyze and diagnose issues. Designing a humanless system sometimes poses the biggest risk if something goes wrong.
This is where Augmented Reality will add the human into the automation loop in the world of Automation.

There was never an exciting time to solve some of the complex supply chain problems using emerging technologies. Augmented Reality Will disrupt the Manufacturing Industry by delivering critical information to frontline workers—exactly when and where they need it on AR glasses. Industrial augmented reality offers a better way to create and provide easily consumable work instructions by overlaying digital content in real-world work environments such as manufacturing and warehousing.

Technicians are expected to wear AR glasses and walk around the outlet. IoT sensors embedded in product components will reveal whether the parts meet the quality constraints. This useful information will be displayed on the AR screen when the technician looks at a particular product, significantly reducing the inspection time. Besides, it will eliminate unintentional mistakes that were otherwise committed.

Machine vision is one of the founding technologies of industrial automation. It has helped improve product quality, speed production, and optimize manufacturing and logistics for decades. Now, this proven technology is merging with artificial intelligence and leading the transition to Industry 4.0.

Machine vision technology allows industrial equipment to “see” what it is doing and make rapid decisions based on what it sees. The most common uses of machine vision are visual inspection and defect detection, positioning and measuring parts, and identifying, sorting, and tracking products.

Using the massive amount of data generated by company operations, an organization can use Deep Learning solutions and teams of data scientists to transform supply chain operations: implementing factory automation, improving quality control, forecasting demand, predictive maintenance, and so much more.

By combing Augmented Reality, Machine Vision, and Deep Learning into one seamless solution, one can solve some of the complex supply chain problems.

I will be talking about the following scenarios of the Supply chain in the session.
Manufacturing
Quality Control
Inventory Management
Warehousing

Bio: 

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