Food & beverage lines benefit from AI at the edge
27 September 2018
Remember when you had only a couple of flavours of your favourite snack and mainly the same package size? Well, those times are long gone. Consumer demands have evolved over time making food & beverage (F&B) manufacturers re-think their processes to be more flexible and adaptive to changes in those trends. To cope with that, the F&B industry has experienced a continuous increase in the automation content and in the amount of data taken from the factory floor known as “sensorisation”.
In addition to aiding with the analysis of large amounts of data, artificial intelligence can offer significant advantages at the machine level in packaging applications.
Thanks to this increased processing power and the availability of increasing volumes of data the discussion about "Artificial Intelligence" (AI) in manufacturing industries is gaining momentum. In the case of the advancements required for Industry 4.0, such as predictive maintenance, networking and efficient production, the use of adaptive algorithms offers enormous potential. Many F&B companies are realising that AI presents an opportunity to increase not only the Overall Equipment Effectiveness (OEE) – and therefore combine reduced costs with increased productivity – but also to improve the analysis of data to support continuous improvement programmes such as reducing waste or process operations variability.
However, there is still something of a chasm between the desired status and the reality of the situation: Many of the AI solutions advertised on the market, which are often cloud-based, have significant requirements in terms of infrastructure and IT; these solutions also work with an overwhelming amount of data that is laborious and time-consuming to prepare and process. The question of added value often remains somewhat murky for providers, who cannot determine whether and how the investment in AI will provide a return. The fact that system designs for the production industry are generally both complex and unique is another contributing factor.
Given these conditions, how do we go about designing and integrating AI that creates tangible added value in the production process? Instead of laboriously searching a huge volume of data for patterns, in addition to the processes that are running, Omron tackles things from the other direction: the required AI algorithms are integrated in the machine control system, thus creating the framework for real-time optimisation truly on the edge – at the machine, for the machine. In contrast to cloud computing, where individual manufacturing lines or sites are analysed using limited processing power at a high level, the AI controller used by Omron, which features adaptive intelligence, is closer to the action and learns to distinguish normal patterns from abnormal ones for the individual machine.
The AI controller integrated in Omron’s SYSMAC platform – a complete solution for factory automation featuring modules for control, motion and robotics, image processing and machine safety – is primarily used in the packaging and production process at the points where the customer is experiencing the greatest efficiency problems ("bottlenecks"). The processes gain intelligence based on previous findings and improvements that have been made and subsequently drive holistic optimisation of the entire manufacturing process.
According to a study by Aberdeen Group, although OEE values of 89 percent have been achieved by leaders of the F&B industry, many of the traditional systems currently in live usage by the followers have been generating figures of around 74 percent. But, what if you go beyond that and add in artificial intelligence solutions for automation? If quality is improved and predictive maintenance is used to prevent machine downtimes, it is possible to make much more significant efficiency gains. In any case, regardless of the number, what really matters about getting OEE information along the whole process, is what you do with it and how you tackle the identified pain points. The AI controller provides optimisation in exactly this area: what needs to be done to improve; it is driven by practical requirements and aims to noticeably improve the OEE – it is important to note that an improvement of just a few percentage points can result in significant efficiency gains and cost reductions. With its new AI solution, Omron hopes to drive added value and practical improvements, thus helping to create a smarter industry.
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