5 Ways To Use Machine Learning In The Packaging Industry

Republished By Plato

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Supply chains are embracing digital transformation, and packaging industry technology must evolve to keep up and support this trend. One of the most versatile and widely beneficial of these technologies to invest in is machine learning. The rise of machine learning in the packaging industry could forever change the sector for the better.

Machine learning, a subset of artificial intelligence (AI), trains algorithms to think like humans, gradually improving over time. These pattern-recognizing, continuously self-optimizing AI models are starting to see use in many applications across the packaging industry. Here are five of the most promising of these use cases.

Reducing Material Use

One of the best applications of AI for the packaging industry is material reduction. Machine learning algorithms can simulate possible alternatives and find ways to package items with less material. Calculating and comparing all these possibilities would be slow by manual means, but AI can do it in mere minutes.

Amazon developed a packaging material reduction tool called PackOpt to do just that in 2018. Since its debut, PackOpt has saved the company roughly 60,000 tons of cardboard annually.

That massive drop in material usage comes from just a 7%-10% size reduction. This real-world use case highlights how even relatively modest improvements from machine learning can lead to considerable savings over time. Companies using these tools to reduce their material consumption will see their operating margins grow and sustainability improve.

Improving Packaging Sustainability

Reducing the amount of material in each package is just one way machine learning in the packaging industry can improve its sustainability. Similar models can analyze other materials’ costs, strengths and weaknesses to find more eco-friendly alternatives to plastic.

Sustainability is complex, so determining which materials are the most environmentally friendly requires balancing many disparate factors. Using machine learning lets companies tackle these complicated calculations faster and more accurately. Finding more easily recyclable or lower-carbon alternatives becomes less disruptive and more efficient.

The packaging industry will face rising pressure to adopt sustainable business practices as climate issues become increasingly prominent. Consequently, these machine-learning algorithms may become critical to a company’s ongoing success. Implementing them will protect the planet and the business’s reputation.

Matching Ideal Packages to Products

This packaging industry technology can also help companies find the ideal containers for each product. Damaged products have a significant financial impact from lost business and costly returns, but the safest packaging for one item may not be for another. Machine learning can help quickly identify the optimal solution for different things.

An AI algorithm may suggest boxes with thicker corners for products like TVs that need more edge protection. It could pair glass items with containers with internal locking mechanisms that minimize vibration. Companies can also use these algorithms to balance product protection with minimal material use to balance sustainability and safety.

Machine learning could design novel packaging to meet specific needs as companies develop new, uniquely shaped products. This bespoke packaging could help businesses stand out and create trust in consumers that the company cares about shipping its products safely.

Optimizing Quality Inspection

Another important use case of machine learning in the packaging industry is automated quality control. Mechanizing the most time-consuming or error-prone processes is one of the keys to effective automation, and for many packaging plants, product inspection meets that description.

AI can optimize these workflows through machine vision. These systems can scan packages for defects faster than a human eye could process. Unlike humans, they also deliver the same level of accuracy in every instance, eliminating errors from distraction, tiredness or boredom.

By automating quality control, machine learning lets packaging companies shorten lead times and avoid sending out defective products. Consequently, they can become more profitable and improve client satisfaction.

Driving Supply Chain Efficiency

Packaging companies can also use machine learning to drive broader supply chain improvements. AI can automate date labeling to ensure each package has an accurate label, preventing business-costing mistakes from human error and streamlining regulatory compliance. This automation is just the start of AI’s supply chain improvements.

Warehouses and factories can use machine learning to simulate workflow changes in digital replicas of their facilities. This analysis can reveal how they can remove inefficiencies or minimize errors, aiding ongoing improvements.

Machine learning algorithms can also assign each package unique RFID tags or other tracking technologies to improve visibility. Considering that some sectors have just a 65% inventory accuracy rate, these tracking systems could substantially improve efficiency and reliability throughout the supply chain.

It’s Time to Embrace Machine Learning in the Packaging Industry

Packaging industry technology has come a long way in just a few years. Supply chains that want to make the most of this innovation must start implementing machine learning across their processes.

These five ways to use machine learning are some of its most promising use cases, but new applications and benefits will emerge as the technology improves. AI could reshape the sector entirely if the industry capitalizes on that potential.

Author Bio:

Emily Newton

Emily Newton is the Editor-in-Chief of Revolutionized Magazine. She has over five years covering stories about warehousing, logistics and distribution.