In the age of AI technology, numbers can be crunched to fit financial and shipping goals. The packaging of chocolate, biscuits and confectionery is no different in terms of modernizing supply chains to keep better track of product data. Here's a look at how automation and machine learning are playing an important role in food packaging.
Smart Packaging
In the past the food industry has been slow at adopting new technology but in the era of big data it's important for companies to analyze as much data as possible to make operations more efficient. Ultimately, AI technology can improve brand quality and cut costs. It gives managers more control in monitoring every step of the packaging and distribution processes. One of the greatest benefits of AI technology is its ability to predict and identify technical problems in manufacturing machinery.
The advent of radio frequency identification (RFID) embedded in packaging has helped brands track product data in numerous ways. Predictive tools and analytics can now be used to bridge gaps between supply and demand and develop closer relationships with customers. Factors such as weather can now be used to predict consumer demand. The use of robots in packaging by organizations such as Amazon and Ocado has helped streamline the shipping process. The packing process can be sped up by as much as four times the speed of a human by using a robot to pack products.
Data that can be collected in real-time on packaging includes unit counts, weight, and temperature. AI technology can even detect packages that are not filled to the proper content level. It's particularly useful in warehousing and determining logistics. Sensors are embedded in the packaging that communicates with cloud-based software that connects with supply chain members.
While many people may fear that robots are replacing jobs, industries using AI are focused on enhancing operations rather than replacing workers. Not only does AI speed up redundant tasks, it eliminates human error issues. The three main steps involved in AI processing are collecting data from sensors, analyzing the data and then automatically making operation adjustments to improve efficiency.
Why AI Has Become a Part of Packaging
The reason AI technology now plays a role in food packaging is a convergence of multiple factors. Big data has become a buzz term across several industries now that CEOs realize stakeholders want to know deeper details on shipments. The more access investors and suppliers have to shipping data, the more they can feel confident about global distribution. The enormous amounts of data collected can now be used to analyze historical patterns and strive toward more efficiency in deliveries.
Improvements in processing power now make it practical to deal with large volumes of data. Increased connectivity with an expansion in Internet of Things (IoT) devices is also making it possible to collect as much intelligence as possible about products in their journey from manufacturer to consumer. AI will be particularly useful in the future for creating "smart warehouses."
Operations that can be improved in the packaging process with AI include quality product sorting, control, testing for food safety, classifying items and real-time tracking. AI can further be used to evaluate overall equipment effectiveness (OEE). Automation software with machine vision systems can be used to detect packaging problems and notify managers.
Limitations of AI Technology
Even though there's currently an enormous amount of talk about AI taking over every industry, it's important to sort out the facts from the hype. Despite the machine learning abilities of AI it is far from replacing humans, especially when it comes to packaging. The key to AI's development will be improvements in the ways humans interact with machines that make sure machines are doing their jobs properly.
Realistically, the concept of computers developing better judgment than humans is still far away. Robots currently are able to identify deviations from standards in packaging and can replace them accordingly. One of the main challenges in the future will be to compress or reduce data so that it's not using up too much bandwidth. This challenge is already being resolved with edge computing. A practical way to integrate AI into manufacturing and supply chains is to partner with an IT firm with deep experience in industrial IoT.
In the end consumers may never know their food has been tested by a robot or analyzed by a computer to make sure the package contains everything they expect including taste and freshness. But there's a strong likelihood that robots and AI will become significantly more involved in the coming years in making sure customers are satisfied with their food purchases.
