The first robotics patent was filed in 1954 by George Devol (granted in 1961), and the first industrial robot was produced by his company, Unimation, in 1956. That robot was capable of moving material about a dozen feet or so.
It ushered in a technological evolution. Fast forward almost six decades, and now robotics has become a standard sight in the packaging industry, from product production to distribution channels. Robotics helped make assembly lines more efficient and precise.
Artificial Intelligence has proven to be a reliable tech to lead the way in the next evolution of the industry, from production to packaging to distribution. Increasing demand for eco-friendly packaging, consumer goods, circular economy are some of the critical drivers for the packaging industry to onboard AI. Artificial Intelligence and the underlying technology (machine learning) improves in minutes instead of decades.
But there's no one-size-fits-all approach to AI. Just like robotics, different solutions help in other parts of a business. Here are five use cases for AI in the packaging industry today.
AI-powered vision systems for inspection
AI has been quietly leading the way in sustainable packaging for a while. Despite what many might think of Amazon packaging, it uses an AI model that learns from real-world customer complaints data to reduce damage to products, choose the optimum package for a product.
This machine learning model has been applied to hundreds of thousands of Amazon packages, reducing shipment damage by 24% while cutting shipping costs by 5%.
The algorithms in this model can specify padded mailers instead of boxes for certain items or deliveries, making packages lighter. This means more packages can fit on every delivery truck, which reduces the amount of packaging that will eventually need to be recycled. In turn, this reduces the overall carbon footprint per item while cutting delivery costs, a triple bottom line success story.
Another case study is of a Packaging Distributors of America (PDA) client who developed a vision system that led to 100% accuracy with package inspection. The client owned a lighting systems company and customer complaints about orders being delivered with broken parts or missing items were on the rise.
After implementing the AI system, customer complaints reduced to zero.
Looking at how Machine Learning is used in business processes, it is easy to understand that it can be a game-changer by providing accurate data, analysis and insights on processes. The packaging industry also has benefited from adopting Machine Learning in its own way.
By deploying Machine Learning in processes like date labelling, companies benefit from better-standardised procedures and reduce customer dissatisfaction.
Wrongly labelled products can lead to inspection failures, customer dissatisfaction and likely cut into a company's profits. By using Machine Learning, date labelling can become a standard, guaranteeing lesser manual errors and greater efficiencies in processes.
Tesco uses data embedded barcodes made by a food tech company, OAL, on certain meat products, which it believes will quickly reduce food waste. The label and date code verification solution is the only system currently on the market that can read the information contained within the barcode inline via existing scanners, protecting Tesco's packaging line from errors, emergency product withdrawals (EPWs), and product recalls.
AI-based recycling systems
Both consumers and manufacturers are realising the ecological cost of not recycling correctly. Only a small fraction of the over 2.1 billion tons of the garbage the world produces each year gets recycled — about 16%.
AMP Robotics is an artificial intelligence and robotics company that aims to change the way we recycle. It is rolling out an AI-powered robot called "Cortex" that uses optical sensors to take in what rolls by and figures out what it should do with something — even if it looks different to anything its ever seen before.
At least four companies are rolling out similar models, in the hopes of turning a profit from the US' growing piles of hard-to-sort recyclables.
Artificially intelligent engineering (AIE) of products and packaging
The European Union estimated that over 80% of all product-related environmental impacts are determined during the design phase of a product.
An eco-friendly design impacts the entire supply chain in the packaging world, from manufacturing to distribution to consumers.
This is where, Monolith AI comes in as the only product engineering AI provider to work with all types of engineering data to help manufacturers create more sustainable products that still deliver the same quality that business buyers and end consumers expect.
L'Oreal, Aptar, other packaging manufacturers and retailers use monolith AI to not only develop more sustainable packaging for products but also to predict the performance of each design. Our AI solution can predict the performance and help you decide on the right design faster.
The future of AI and the packaging industry
As the only AI provider for product engineering, we believe so far we have only seen a glimpse of how machine learning can change the landscape of the packaging industry just like robotics did in 1956.
However, we think that AI exists to assist humankind and not entirely replace it. So, while errors can be decreased and processes can be streamlined, the future belongs to humans and AI working collaboratively together.