Automatic, for the people

Data driven robotics in the manufacturing sector

Robots’ role in manufacturing may have begun with basic tasks, but they have become a key component in today’s manufacturing and distribution processes. Whether manning automated production lines or working in support of human employees, the robots of tomorrow are getting smarter. But the challenge is on businesses to use their data to best utilise this technology.

Robots have been a growing staple in manufacturing since 1954, when George Devol designed a robot capable of moving objects from one point to another. Today, robots are ubiquitous in factories and warehouses. Entire production lines are manned by robot assemblers, like at Nissan's plant in Sunderland which has more than 1,000 robots assembling the Nissan Leaf.

In Amazon's warehouses, automated helpers work beside people to pick and pull goods from shelves, and Dyson's machine manufacturing facility relies on robots to make tens of thousands of motors each day. Robots' role in manufacturing may have started with the completion of basic tasks, but they have become a key component of today's manufacturing and distribution process.

Tomorrow's robots, which will be able to self-learn, will be even smarter still. However, computerised solutions are only as intelligent as the data that they are fed. While nearly 2 petabytes of new data are generated by the manufacturing sector every year (twice as much as in the banking sector), many manufacturers still struggle to categorise, contextualise and sort this information into anything resembling actionable data.

As a result, useful insights often sit underutilised in disconnected silos, while information piles up at a pace too rapid to ingest. Key business intelligence that organisations - and the robots which power them - could be applying goes unrecognised. This lack of visibility can hinder innovation, and efforts to automate and adapt to changing conditions not only within the factory, but across the entire enterprise as well.

Manufacturing in particular ranks among the industries most greatly impacted by these challenges, because of the sheer variety of data involved. This can result in not only reduced productivity, but also excessive energy consumption, system failures and downtime as well.

Solutions that work

New technology tools are helping bridge the gap, and turn data into operational intelligence. Our Optimum for manufacturing is an artificially intelligent energy platform that provides business insight for use in reducing both consumption and costs. It makes it possible to analyse and collect information more effectively, and can also optimise the use of robots across facilities, minimising equipment failures and making faster and better business decisions.

 

Our estimates show that some large international manufacturing organisations can reduce energy consumption by nearly 20%, with savings potentially in the millions. From chemical production and manufacturing to the creation of automotive parts and paper, providers operating in a growing range of industries can leverage these tools to re-imagine their operations.

From failure to success

A 1.1 million square-foot automotive plant at which 640,000 vehicles are assembled annually was experiencing catastrophic robot failure at times during production, which manual work crew inspections were needed to repair. With Optimum for manufacturing technology, data coming from 310 robots was pulled into the cloud, where simulations which mimicked how robots operated and behaved allowed for real-time analysis and identification of the problem. The plant's operators were able to scan for anomalies and recognise patterns that prefaced robot failure. With issues proactively spotted and addressed, there was a $1.3 million drop in unplanned downtime

 

While robotics remains a key area for hi-tech growth in the manufacturing sector, it's also important to remember that data-driven innovation is key to making the most of any investment in this area. The more information that you can feed artificially intelligent helpers, the smarter and more efficient both they and your overall enterprise will become.