Why predictive maintenance is a win-win for warehouses and OEMs
When a warehouse conveyor belt fails, thousands of packages are held up en route to their destinations.
When a distribution center’s automated forklift malfunctions, staff must step in to move pallets of goods by hand and keep order fulfillment on track.
When an automated sorting and packing machine responsible for fulfillment goes down, unprocessed packages pile up quickly and disrupt warehouse flow.
In material handling, every time a critical piece of equipment stops working, the result is diminished productivity, revenue and customer loyalty.
But how can you stop downtime before it happens? By recognizing the early warning signs of failure.
Because it proactively detects likely breakdowns before they occur, predictive maintenance keeps warehouse operations running smoothly.
A network of sensors monitors equipment health and collects data about operational parameters like vibration, temperature, speed and battery health. That information is then analyzed for patterns and variances to detect early signs of wear. When an anomaly is uncovered, maintenance teams are alerted so they can complete necessary repairs.
OEMs can gain an edge with predictive maintenance
Material handling teams often lack visibility into the performance of their critical systems, which prevents them from being able to recognize failure warning signs. Which machines are likely to break down next? Is efficiency being lost due to minor system delays that are going unnoticed? Are certain components wearing out faster than expected?
The OEMs and systems integrators that deliver material handling equipment and solutions can set themselves apart in a crowded market by offering real-time monitoring and predictive maintenance expertise. These capabilities give logistics teams the visibility they need to optimize operations and prevent unexpected disruptions.
This takes the partnership beyond machine design and delivery to incorporate ongoing maintenance and support for faster order processing and lower operating costs.
Predictive maintenance in the real world: empowering machines to talk to operators
In a recent and real example, a warehouse’s palletizing robot was failing often, which forced costly service engineer callouts and brought production to a halt every time. Each delay translated to lost revenue, idle workers and a rush to diagnose problems after operational disruption.
To reduce callouts, the warehouse team needed to see what was happening with their equipment. So, they worked with their OEM to integrate remote monitoring (to access real-time updates on equipment performance and potential issues) and predictive analytics (to detect subtle changes in conditions like vibration and motor efficiency).
Instead of waiting for an operator to experience downtime, the machines were able to tell operators there was a problem before failure occurred. Rather than reviewing machine data after the fact, real-time insights made data actionable on the spot.
For the warehouse, this change drastically cut down on the costs associated with emergency callouts. It also had a positive effect on uptime.
For the OEM, it unlocked a new revenue source—potentially one with recurring revenue. Because they can give their warehouse customers more information about machine uptime, overall equipment effectiveness (OEE) and upcoming maintenance needs, they bring more value to operations. This translates to stronger relationships and increased profits.
Finally, these capabilities enable the OEM to manufacture even better equipment. As their team monitors machine insights across multiple sites and customers, they get to see how certain system components perform in the real world, identify potential failure points and make improvements to new machines as they leave the production floor.
Transform material handling at your own pace
To stay ahead of equipment failure and protect uptime, warehouses and distribution centers must be able to capture and move their data. And that’s where we come in.
Belden’s complete connection solutions move information about machine performance, predictive maintenance and system alerts from equipment sensors to control rooms, cloud platforms and enterprise systems.
For example, Belden Horizon Data Operations (BHDO) allows for full connectivity of equipment to the network. It can pull data from PLCs and add it to vibration and temperature data gathered by sensors in typical predictive maintenance applications. By enabling fast, accurate data flow, Belden helps logistics teams connect to what’s possible in terms of operational efficiency. With onboard computing capabilities, data can be processed at the machine and reduce the amount of information going to the cloud, reducing costs and accelerating data-processing speeds.
Whether you’re relying on simple emails to alert team members to issues or feeding AI models to predict failure with precision, we can meet you wherever you — and your data — are. With a tailored digital roadmap, you can plan for transformation at a speed and investment level that works for you.
Learn about automating your warehouse space.
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About the Author
James Stokes joined Belden in 2021 and leads a team of Belden industrial networking professionals in the discrete automation space. He and his team use their experience to collaborate with customers, identifying pain points and gaps in their networks and applications to create targeted Belden solutions. He also draws from experience from previous roles in project management, software development and digital migrations, bringing a well-rounded approach to helping clients improve their use of technology.