Data-driven insights play a vital role in optimising performance, maintenance, and sustainability within warehouse automation.
Current materials handling and intralogistics equipment is amazingly reliable. Nonetheless, there is a lot to go wrong – all those mechanical parts like rollers, bearings, motors, belts, not to mention switches, sensors and the rest of the electronics. For many businesses this equipment is fundamental – if it’s offline, everything stops.
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Common Failures in Intralogistics Equipment
Today’s materials handling and intralogistics equipment are remarkably reliable. However, with so many components – from rollers, bearings, and motors to belts, switches, and sensors – plenty can go wrong. For businesses relying on automation, a single offline component can halt operations entirely. Unexpected failures, and unplanned maintenance and repair, don’t just increase costs and impair customer service, they have direct and significant environmental and sustainability impacts. But by implementing data driven maintenance strategies these cost, performance, and environmental impacts can be greatly reduced.
The Risk of Minimalist Approaches to Maintenance
Some companies, especially those with limited in-house capabilities, operate on a ‘don’t fix it unless it’s broken’ basis. This approach may appear to reduce downtime and costs but can backfire at critical moments, such as during peak seasons or holiday weekends when support options may be limited. A slightly more advanced approach is scheduled maintenance, where wear-prone parts are replaced at regular intervals. While more effective, it still has limitations.
The typical lifespan of a component is a statistical average; some parts fail early, while others outlast expectations. Scheduled maintenance often replaces parts based on time rather than actual use, sending viable parts to scrap while ignoring other components that may degrade sooner than expected. This practice leads to increased waste and unnecessary use of energy and consumables, ultimately impacting sustainability efforts.
Adopting a Smart & Data-Driven Maintenance Approach
Maintenance in warehouse automation does not have to be arbitrary. Most automation systems collect large amounts of data that can be utilised to develop a proactive, preventative maintenance strategy. Key performance parameters, such as motor energy consumption or bearing temperatures, can be monitored to issue alerts when components operate outside optimal conditions.
With intelligent analytics, maintenance goes beyond reactive measures, using real-time and historical data from SCADA and other systems to identify failure-prone areas, average failure times, and necessary downtime. By analysing data on actual usage rather than elapsed time, businesses can predict component replacement needs accurately, ensuring components are used to their full life span. Software solutions help analyse data, informing strategic maintenance decisions and supporting continuous improvements in system performance and longevity.
Analytics also reveal when investments in new equipment, upgrades, or operator training may be beneficial. Data-driven maintenance enables equipment to operate longer at maximum capacity, minimising minor disruptions and enabling companies to schedule maintenance during low-demand periods. This approach optimises engineering resources, ensures that necessary parts are available, and helps avoid wasted downtime.
Supporting Sustainability through Intelligent Maintenance
Data-driven insights on warehouse automation systems also contribute to broader sustainability efforts. By planning where and when maintenance resources are needed, companies can reduce labour demands, training needs, and overall costs. Effective maintenance minimises waste by reducing the frequency of part replacements, allowing parts to be reconditioned rather than scrapped.
Proactive maintenance also ensures efficient energy and resource use; for example, a worn conveyor belt can use two to six times more energy than one in good condition. Analytics can adjust automated systems for energy-efficient operation, reducing overall environmental impact. By maintaining peak equipment performance, companies reduce the need for polluting forms of materials handling and cut down on packaging waste due to fewer product handling issues.
Warehouse automation also helps mitigate health and safety risks associated with manual labour, making automation safer for employees. Importantly, analytics provide insights into the sustainability of parts from various suppliers, helping shape environmentally conscious procurement practices. Routine inspections that necessitate line stops are also reduced, as data reveals potential issues in advance, avoiding unnecessary shutdowns and further contributing to sustainability.
Optimising Warehouse Automation with Predictive Analytics
A data-driven approach to warehouse automation maintenance enables companies to reduce downtime, improve service, and lower environmental impacts. Through intelligent data analysis, predictive maintenance can enhance the efficiency and sustainability of operations while offering insights for continuous improvement. Embracing a data-led maintenance strategy is an effective way to support warehouse automation and the overall sustainability of warehouse operations.
