As of 2019, manufacturers in the United States accounted for 11.39% of the total output in the economy, employing 8.51% of the  national workforce. In 2022, however, with systemwide complications from high demand and the rising costs of supply chains, it’s increasingly critical for manufacturers to explore new processes that will help them navigate and adapt to turbulent market conditions. 

According to the 2022 Deloitte Manufacturing Industry Outlook, manufacturers looking to both grow and ensure profitability should embrace digital capabilities across the business, from corporate functions to the factory floor. The report finds artificial intelligence and machine learning to be key drivers of competitiveness for smart factories as manufacturing organizations continue to see results from more connected, reliable, efficient, and predictive processes.

A recent study from McKinsey found that, across manufacturing and various other markets, AI-enabled companies have witnessed cost savings and revenue growth. 16% of organizations surveyed reported a 10-19% decrease in costs, while 18% saw a 6-10% increase in overall revenue.

For many, the thought of advanced manufacturing processes conjures images of technology designed to mimic the human arm, safely assembling and handling products. However, some of the most exciting recent developments in the manufacturing space have to do with tech that mimics sight rather than touch. Rapid advancements in computer vision technology are transforming manufacturing by enabling organizations to derive powerful insights from their visual data. With the help of these insights, manufacturers can automate and improve processes with real-time operational analytics from images and video feeds.

PPE Detection 

Whatever the state of mask mandates, personal protective equipment (PPE) is essential to manufacturers. A range of personal protective equipment  – including helmets, gloves, goggles, and over-ear protection – keep workers and worksites as safe and efficient as possible. Object recognition models trained for PPE detection can confirm the presence of appropriate PPE and automate processes for both enforcing compliance and responding to non-compliance.

Product Counting and Tracking

Whether an organization ships out millions of products from a network of warehouses or manages just a single distribution center, inaccurate counts are a constant threat. Combining vision AI with video cameras delivers more accurate counting and tracking than traditional manual approaches can provide. JBS, for instance, one of the world’s largest food producers, improved its livestock counting and achieved greater than 99% accuracy by implementing computer vision solutions last year. 

Foreign Object Identification

Foreign objects can spell serious trouble, putting employees, consumers, and machinery at risk. Vision AI can not only automate and enhance the process of detecting foreign objects, but also expedite the response. Preventive models can keep track of equipment wear and tear, for example, and inspire action before broken-off pieces of machinery can become foreign objects and threaten the manufacturing process. By sending automatic alerts to the appropriate parties, AI can facilitate quick remediation.

Automated Inspection and Product Defect Detection

Delivering defective products can lead to costly recalls and dissatisfied customers. Organizations who rely on manual processes for identifying and addressing defects leave themselves vulnerable to production delays, damaged equipment, and additional ripple effects. Deep learning models deployed through vision AI platforms can automate quality control and assurance to reduce the risk of sending out subpar products or non-compliant packaging. They’ll even empower a proactive response to product and packaging defects.

More Computer Vision Use Cases

  • Self-driving vehicles: Perhaps the most challenging and inspiring application of vision AI, self-driving cars rely on massive datasets and highly complex real-time object recognition models to help them safely navigate the road.
  • Crop monitoring: Farms are hit especially hard by the effects of climate change. Vision AI enhances monitoring capabilities to help farmers more accurately  predict and quickly adapt to changing soil conditions and weather patterns while minimizing their own carbon outputs. 
  • Productivity Tracking and Enhancement: Rather than replacing human employees, computer vision solutions are helping people perform their jobs more effectively and efficiently. With enhanced operational analytics, organizations can determine where AI still needs a human helping hand and more effectively distribute operators throughout its facilities. Ultimately, vision AI provides insights into performance, time management, and teamwork that can prove invaluable for building a workforce that combines the best of what people and technology are capable of. 
  • Protecting forests: Vision AI can play an important role in fighting wildfires by using a combination of visual, topographical and weather data to train models capable of predicting and monitoring both the occurrences and likely paths of fires.

Visual data offers a wealth of untapped information, capable of helping businesses improve processes and address challenges of every size. Let Plainsight’s team show you how to streamline the computer vision lifecycle across the manufacturing value chain. We’ll provide the vision AI services and resources you need for creating, training, and deploying custom vision AI models to see value quickly. Schedule a conversation today.

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