Machine Learning – a kind of artificial intelligence – might seem like the stuff of science fiction or a cutting-edge technology used by blue-skies researchers like NASA scientists searching for new planets. Yet there are plenty of ways you can use Machine Learning (ML) in your manufacturing operations – today – to deliver real business benefits across the supply chain.
- Improve operational efficiency and lower costs, by using ML to optimise the factory floor. Take the example of Google, which was able to reduce electricity use in its data centres by 40% by using custom ML to control the air conditioning in its server farms. The improvement was achieved even though Google had already spent a lot of time manually optimising its processes.
- Reduce maintenance costs and improve reliability, by using ML to develop optimised maintenance schedules based on the way equipment is actually used. The same approach can be extended to customers to provide them with personalised maintenance schedules. For instance, a trucking company was able to save millions of dollars on parts each year by understanding when each truck would need servicing based on the actual routes and terrain it had driven.
- Reduce inventory levels and waste, by using ML to more accurately predict demand and optimise production schedules. One textile manufacturer was able to reduce stock levels by 30% as a result of using ML to predict customer demand more accurately, which let it move to just-in-time production.
- Improve quality control on the production line, by using ML to identify faulty products. Baby food manufacturer Kewpie is using ML – in the form of the Google Vision API – to pick out discoloured potato cubes that, while perfectly safe for babies to eat, may cause concern for parents. The ML system has replaced "human" inspection, which tedious and stressful for production line workers while also more error-prone and more expensive.
- Improve the design of new products, by using ML to understand how products are actually used, how they actually perform, and what causes them to fail. Those insights can be fed back to design teams, while ML can also be used to predict the performance of proposed design changes.
If you'd like to know more about how Machine Learning on the Google Cloud Platform can help your manufacturing business, whether you want to become more efficient and agile, improve product quality or deliver better customer service, come and talk to the ML experts on our GCP team.