How Can Vision AI Predict and Prevent Supply Chain Disruptions?

Though links in the global supply chain can weaken and break under any conditions, the spread of COVID and, more recently, the war in Ukraine have made the supply chain a subject of daily conversation and concern. In the spring of 2020, shortages were exacerbated by panicked shopping, infamously resulting in empty shelves and online secondary marketplaces for goods like toilet paper and sanitizing wipes. More recently, Congress has been forced to respond to a nationwide infant formula shortage with a pair of bills aimed at accelerating production.

What’s Causing Supply Chain Shortages?

When the pandemic began, it hit manufacturing-rich regions in Asia and Europe especially hard. Facilities were forced to shut down or reduce capacity and shipping companies trimmed their schedules as they anticipated drops in demand. While certain types of spending all but disappeared, consumers rapidly purchased a range of other products, from exercise bikes to protective masks. Strained supply chains endured even more stress, and facilities that were accustomed to predictable schedules experienced unprecedented surges in demand. It has all resulted in volatility, delays, shortages, and shut-downs that continue to make regular headlines. 

The Formula Shortage: From Supply Chain Disruption to Nationwide Crisis

Even when COVID-19 is not directly responsible for a supply chain disruption, its effects can make a bad situation worse. The baby formula shortage, for example, started because of a voluntary recall due to potential bacterial contamination and a factory closure. Even before the FDA made the decision to temporarily shutter Abbott Nutrition’s manufacturing facility in Sturgis, Michigan, however, the supply chain for formula was experiencing strain and consumers were exhausted from years of COVID-19’s economic ravages. As a result, a supply chain shortage quickly evolved into a nationwide health and nutrition crisis with some states seeing as much as half of their available formula disappear. COVID-19 and its ripple effects not only created the worst possible conditions for such a shortage to occur, but potentially stymied attempts at corrective action. Abbott’s shuttered facility reopened on June 4th and parents should expect products to gradually return over the next several weeks and months. 

Introducing computer vision across the supply chain can help manufacturers predict and prevent the types of conditions that lead to these kinds of disruptions. From potential contaminants and foreign objects to defective products and packaging to unsafe or unsanitary behavior, computer vision is the key to recognizing supply chain obstacles early and stopping shortages in their tracks. AI solutions could prove especially useful in volatile, high-production periods where errors and disruptions are both especially likely and especially costly. 

Benefits of Computer Vision Along the Supply Chain

By empowering real-time insight generation and turning visual data into a nearly inexhaustible source of insights, computer vision can transform operations for manufacturers and organizations across the supply chain. 

  • Safer facilities and processes: With models for detecting personal protective equipment (PPE) and recognizing hazards like spills, custom vision AI can provide for safer operations and minimize risks and losses related to workplace injuries and stoppages.
  • Improved operational efficiency: Rather than replacing the human workforce (as some have long feared), AI helps human employees perform to the best of their ability while reducing their tedious, manual task workload. What’s more, automating operational analytics with vision can optimize processes from end to end.
  • More sustainable practices: Detection models help businesses recognize inefficiencies, regulatory violations, and waste in their practices, take corrective action, and ultimately mitigate their environmental impact. Providers in the oil and gas sector, for example, can deploy models for detecting unseen leaks and reducing unintended emissions.
  • Traceability and transparency: Connecting the computer vision lifecycle within enterprises across the supply chain offers a new level of visibility. In addition to paving the way for process improvement, this can lead to more transparent customer relations and regulations compliance. 

Manufacturing and Supply Chain Use Cases

  • Foreign Object Detection: Proactive models for foreign object detection and recognition prevent costly damage to machinery and reduce delays and shutdowns, and keep consumers safe.
  • Packaging and Product Compromise Detection: Vision AI detects subtle imperfections that the naked eye and even advanced cameras might miss to cut down on contamination risk and ensure a high-quality product.
  • PPE Detection: Custom models can detect gloves, helmets, reflective vests, and more to promote safer worksites and improve processes for enforcing compliance.
  • Precision Counting: Automated, vision AI-powered counting of products, actions, livestock, etc. prevents the lost time and money that can result from ineffective, inaccurate, manual processes.
  • Product Tracking: Manufacturers and companies see more when they trust vision AI models to follow products throughout the production process and identify signs of trouble like defects.
  • Space Optimization: From warehouses to retail locations, vision AI promotes efficient and safe use of space.

See the Value of Vision AI

The formula shortage will crawl to a close throughout June, but other global supply chains remain fraught with risk and vulnerable to potential disruptions. Deploying custom vision AI models today can detect potential problems early to avoid tomorrow’s supply chain concerns. To learn more about how Plainsight’s no-code computer vision platform and innovation services support enterprises across the supply chain, schedule a demo with our experts.

More Plainsight Blog Posts:

5 Ways Agribusinesses Can Prevent Recalls, Shutdowns, and Delays with Vision AI

5 Ways Agribusinesses Can Prevent Recalls, Shutdowns, and Delays with Vision AI

Investments in computer vision technology can help agribusinesses and food manufacturers of all types spot signs of trouble early and stop costly, potentially deadly recalls before they happen. Deployed across the production and manufacturing cycles, these models can detect hazards ranging from contaminants and foreign objects to defective equipment and non-compliant behavior. Organizations capture hundreds of thousands of hours of visual data in the form of video footage and imagery every day, and computer vision allows these businesses to put this data to work for process transformation.

Autonomous Vehicles Are Driving Computer Vision Into the Future

Autonomous Vehicles Are Driving Computer Vision Into the Future

Once the stuff of science fiction, self-driving vehicles are now merging onto roadways and into the news.Organizations ranging from car manufacturers to rideshare services to restaurants have launched ambitious initiatives and even started testing autonomous vehicles on select public roads.