Warehouse safety is moving beyond reacting to accidents and breakdowns, as connected equipment and predictive analytics give operators the ability to spot risks before they disrupt operations.
For decades, warehouse management has largely followed a reactive model. Equipment was repaired after a failure, operator coaching often followed an accident or near miss, and fleet decisions were based on past experience rather than live operational information.
Advances in connected material handling equipment, telematics and predictive analytics are now changing that approach.
Businesses can increasingly identify warning signs before equipment fails, safety risks escalate or productivity is affected. The shift is becoming more significant as operators face labour shortages, higher running costs and pressure to improve warehouse productivity.
Downtime also carries a growing financial impact, while changing workplace safety requirements are increasing the importance of proactive risk management.
Connected fleets can continuously generate operational information, giving warehouse managers greater visibility over equipment performance, utilisation and operator behaviour.
Fleet management platforms such as Toyota Material Handling UK's I_Site can provide data on how trucks are being operated and used, helping managers identify trends and make decisions based on actual activity rather than assumptions.
One area where this is having an impact is maintenance.
Unexpected truck breakdowns can disrupt warehouse operations, particularly during peak periods. Connected trucks can monitor information including battery health, charging behaviour, operating hours and fault codes.
Unusual patterns or signs of component wear can then be identified earlier, allowing maintenance to be planned before a failure takes equipment out of service.
It changes the question for warehouse operators from dealing with the cause of a stoppage to considering: "What can we do today to stop it failing tomorrow?"
Earlier intervention has the potential to improve equipment availability, control repair costs and reduce disruption to warehouse operations.
The same technology is also being used to address operator safety.
Telematics systems can record behaviours such as excessive speed, harsh braking, repeated impacts and equipment access. A single event may not necessarily indicate a serious problem, but recurring incidents can reveal wider patterns of risk.
Managers can use that information to focus operator coaching, reinforce safer working practices and identify where additional training may be needed.
Rather than relying solely on investigations after an incident, businesses can use operating data to identify potential risks earlier and take preventative action.
Fleet utilisation is another area where predictive analytics can expose inefficiencies.
Some warehouses may have trucks working heavily while other vehicles remain unused for significant periods. Operators can also maintain larger fleets than necessary when they do not have accurate information showing how individual trucks are being used.
Utilisation data can help identify underused or overworked equipment, support decisions about fleet size and allow trucks to be moved more effectively between locations.
It can also help businesses prepare for seasonal changes in demand and make better-informed decisions about future equipment investment.
Energy use is becoming another important source of operational data.
Monitoring charging cycles, idle time and vehicle utilisation can help businesses identify unnecessary energy consumption while making sure equipment remains available when it is required.
Combined with information about traffic movements and warehouse bottlenecks, that data can also highlight opportunities to improve productivity without simply adding more vehicles or employees.
The role of the forklift truck is consequently changing. Connected vehicles are increasingly becoming sources of operational information as well as pieces of material handling equipment.
As artificial intelligence, connected equipment and predictive analytics develop further, warehouses are expected to become better equipped to anticipate maintenance requirements, identify safety risks and make operational decisions using live information.
As the original analysis puts it: "The warehouse of the future won't simply react faster - it will anticipate challenges before they arise."
For operators looking to improve safety, increase equipment uptime and make better use of their fleets, predictive technology is increasingly moving from a future ambition to a practical part of day-to-day warehouse management.