The intralogistics sector is under a lot of pressure: same-day delivery is testing machinery, fuel costs are soaring and fewer skilled workers are on-hand to fulfil orders.
Customers who were once satisfied with the service they received are asking for more products at a faster rate - and they are not prepared to compromise on order accuracy or quality of service.
Unfortunately, downtime threatens the ‘well-oiled machine’ that customers have come to expect. And wherever downtime exists, so do losses – both in time and money.
The current response to increasing demand is to push existing machinery harder.
However, this often leads to its own set of issues, such as more frequent machine faults, complex repairs and costly maintenance. The outcome? Costly downtime.
In these cases, traditional reactive methods, such as keeping engineers on standby and maintaining a large reserve of spare parts is hugely costly and inefficient.
For engineers, this is the constant burden of downtime: just as machines can take no more, they are needed more than ever – and they cannot be fixed quickly enough.
Planned maintenance would be far more cost-effective and operationally efficient so that engineers could react before downtime occurs.
After all, no machine failure has ever appeared at random. There is always a reason – perhaps the health of a conveyor motor has deteriorated. If engineers had the means to understand not just where errors have occurred, but why, they might be able to finally predict issues and provide solutions before they become a problem.
The focus, therefore, becomes more than just ‘how far can we possibly push our machines?’, but ‘how much can we potentially learn from them?’
Digitalising the drive train
Just think of the wealth of data that runs through a typical intralogistics operation. Machine data is generated but because it isn’t captured, stored or analysed, the value it could have created is lost. Engineers, therefore, only learn about their machines from historical data, which offers few valuable insights into real-time machine performance.
When they digitalise the drive train, however, real-time data becomes available. By connecting drive components to control technology, data can be analysed locally, on the edge or in the cloud so that they can diagnose an issue before it fails and have the foresight to know what spare parts to bring in advance. At the same time the equipment manufacturers receive diagnostics data about the health of the machines so that they can provide value-add services throughout the life cycle of the machine - and even guarantee uptime for their end user.
As an industry that looks for proof before investing in anything new, predictive maintenance is a welcome solution. Engineers who were once at the mercy of their machines are in a position to finally take back control.
Siemens are exhibiting at IMHX 2019, 24-27 September 2019 in Birmingham. Visit stand A6 in the AMHSA Pavilion or stand 6C202 in Hall 6 (café) to learn how Siemens can help your business work smarter, not harder.