AI is already being implemented in many confectionery businesses to help analyze large amounts of data to improve R&D, plant management processes and packaging adaptability, as well as to extend equipment life and detect unforeseen events to prevent disruptions. Cloud computing has been created to process and evaluate the growing flow of data in manufacturing, with many of the artificial intelligence solutions advertised on the market being cloud-based. However, these solutions place significant demands on infrastructure and IT, and work with huge amounts of data that can be time-consuming to prepare and process.
The issue of added value often remains somewhat vague for suppliers, who cannot determine whether investments in AI will bring a return on investment. The fact that system designs for the manufacturing industry tend to be complex and unique is another contributing factor.
So, while the cloud is best suited for handling big data and managing massive long-term analytics, AI at the Edge is crucial for real-time applications if a confectionery supplier wants to integrate AI that creates tangible added value.
This approach provides greater flexibility and faster response times, so production environments can better utilize data analytics at the Edge. Instead of painstakingly searching for patterns in a huge amount of data, in addition to running processes, it is important to tackle things from the other side. What’s needed is a technology that integrates the necessary AI algorithms into the machine control system, thus creating the basis for real-time optimization truly at the edge – on the machine, for the machine.
A good example of this technology is Omron’s Sysmac AI Controller, an intelligent AI solution that collects, analyzes, and uses data on Edge devices in the controller to extend equipment life and detect faults to prevent failures. It combines production line and equipment control functions with real-time AI processing on the manufacturing floor.
Moving to the edge
Another example is Siemens Industrial Edge, a digitalization solution that adds machine-level data processing to automation devices, safely bringing edge computing intelligence and thus sophisticated analytics to the production level. Cloud connectivity is used in conjunction with Edge applications in an integrated ecosystem of hardware and software for automation components. According to the company, storage and transmission costs are reduced for users because large amounts of data are pre-processed and only relevant data is then transferred to the cloud or IT infrastructure.
But Siemens noted that Edge computing is not an end in itself, but a means to achieve specific goals based on the unique needs of the manufacturer. Cloud and edge computing are not mutually exclusive, but conditional, and when choosing one of the approaches or a hybrid solution, it is important to consider the framework conditions and business goals of the deployment. This is especially true for food and beverage companies, including confectionery, where production facilities are often outdated and investment funds are low. Find a technology provider that can provide a step-by-step approach to edge computing implementation and how it can benefit your business.