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and consumer products. Better information also allows for continuous improvement and process optimisations at a micro and macro level, generating multiple opportunities for increased efficiency and cost reduction.


Smart factories and the use of AI from an automation perspective


Factory automation has traditionally provided the food industry with faster, more reliable and cleaner production capacity - however, with the advent of the smart factory and commercial AI the factory of the very near future is starting to make production efficiency improvement decisions itself


T


he first stage of becoming a smart factory is digital network communication, having the right data infrastructure allows companies to create, move and use that data efficiently. With the swift movement and processing of data comes homogeneous control and fast responsive manufacturing, which in-turn justifies investment in the latest automation technology.


Getting the most from a Smart factory is then a case of being aware of the possibilities, this is where using state-of-the- art technology such as AI can already improve the performance and efficiency of factory equipment and human resources.


34 July/August 2020


The role of the Industrial Internet of Things (IIoT)


The role of the IIoT in today’s factory is to connect customer demand to a fast and flexible production facility. Once a purchase decision is made then any increase in the speed of response from the manufacturer is a competitive advantage. If the IIoT offers us one thing, then it is the ability to define customer demand instantly and adapt production to suit. Companies that can be flexible enough to move away from large batch production can also avoid the cost of large stock holding, both at the manufacturer and throughout the distribution chain. Customisation is already a unique selling point for a large number of consumer goods. The


Components in Electronics


food industry is following suit with individual printing and marking options being designed into many new products.


Customisation equals profitability in both cases. The transfer of data from a sales operation to a manufacturing site, out to the suppliers and then simultaneously back to the distribution and retail network is the key to responsive, flexible manufacturing. To achieve ‘batch size one’ profitably and efficiently we must have the connectivity that the IIoT offers.


The ability to generate, record, transfer and process a large amount of data reliably and efficiently has other benefits. It enables a higher degree of traceability for example, serialisation is already essential for many food, pharmaceutical


Artificial Intelligence (AI) in context AI is still at the beginning of its journey but we can expect it to have a substantial impact on the industrial environment over the next few years. AI is a perfect fit for manufacturing and leading companies are now integrating various AI functions into factory automation equipment. Advanced Analytics (AA) and Artificial Intelligence (AI) technologies are extending traditional machine control architectures with more advanced data processing, learning and decision-making capacity. The objective is to deliver increased productivity, efficiency, reliability and accuracy, as well as opening up new possibilities for machine control. AI can, for example, be a driver for increased productivity. Today, most machines are still built to work within defined margins of capability – perhaps to allow for different loads or speeds or safety ranges. AI technology using deep learning algorithms within the control system enables machines to be driven right up to and even beyond today’s margins, significantly boosting productivity without compromising reliability and quality. Applying AI principles to individual machine processes can already help to reduce auto- adjustment times, synchronise increasingly complex systems and offer helpful suggestions to operators. It can even enable autonomous decisions to be made based on measured data in real-time, further optimising the process. Making reliable predictions based on experience, evidence and guidelines is a fundamental function of human intelligence. AI is no different in this respect, it can contribute toward more effective predictive maintenance, monitoring the condition of components to enable replacement before damage occurs, so preventing unplanned downtime. We see this already but deep learning algorithms are pushing the boundaries further, calculating with more accuracy how long a component can run before replacement. Maybe even compensating for delivery times on replacement parts by slowing the machine down slightly to increase longevity rather than stopping the production line completely.


Similarly, the combination for AA and AI can be a driver for increased efficiencies right


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