Some of the plants are so obsessive about tracking uptime on the production line. Sensors are there to keep an eye on motors, bearings, and drives and help in catching failure even before it turns into a shutdown. And if you walk even twenty feet away from the storage side of the same facility, there will be ignorance. The retrieval unit is actually doing inventory shift so that it gets a visual check during rounds and not much else.
This gap is something difficult to explain. Storage and retrieval equipment are actually operating continuously in facilities that lean on automation to keep pace with production. It carries the same moving parts that often fail on other machines, such as motors, chains, bearings, and drive belts. When any of these parts fail without warning, the retrieval process stops.
Part of the reason is visibility. A press or a robotic arm there in plain sight on the floor gets walked past by supervisors daily, and shows up on maintenance routines by default. A storage unit sandwich against a back wall doesn’t get the same attention, even when it’s cycling trays or totes hundreds of times a shift. Out of sight tends to mean out of the maintenance plan as well.
The next part is habit. The storage system has often been treated as static infrastructure, closer to shelving than to machinery. That framing made sense when storage is static racks bolted to the floor. It stops making sense once storage gets automated, motor-driven, and tied directly into everyday throughput. A unit that moves thousands of pounds vertically dozens of times an hour deserves the same evaluation as anything else with a drive motor.
There’s also a budgeting habit at play. Maintenance expense usually follows whatever equipment shows up on a criticality list first, and those lists were usually built before automated storage became common. Updating the list is a smaller project than most teams think, but it hardly gets prioritized until something breaks and forces the conversation.
Condition monitoring on any part of industrial equipment comes down to catching small shifts before they become failures. On a motor, that means vibration signatures, temperature, and current draw. On a mechanical lift or drive system, it is also described as cycle counts, load patterns, and how long each movement takes compared to baseline.
None of that is an exception. It’s the same logic plants already apply to pumps, fans, and production drives. The instrumentation and the data pipeline rarely change; what changes is deciding to point them at the storage equipment as well. Most facilities already have the analytics space in place. The missing piece is simply the sensor feed from a category of equipment that never got added.
On most systems, the good data comes from a small set of points:
Facilities running a VLM machine with conventional conveyors benefit the same way, since real-time condition data identify bearing wear or motor strain before it causes downtime. The equipment type changes; the background question doesn’t. Is this component behaving the way it did last month, or is something shifting?
Raw sensor data isn’t helpful on its own. A vibration reading only matters against a baseline for that specific unit under normal load, since two identical machines can operate at slightly different vibration levels based on installation, floor conditions, or age. Building that baseline takes a few weeks of routine checks before thresholds are set with any confidence.
Once a baseline is set, the useful signal is drift, not any single reading. A motor running a few degrees warmer than usual on a hot day isn’t an issue. That same motor running warmer every day for two weeks, regardless of ambient temperature, is worth a watch before it becomes a failure.
Unplanned downtime on a storage and retrieval system barely stays contained to storage. If choosing or replenishment depends on that unit, production and shipping feel it within the hour. Emergency repairs on mechanical lift systems also tend to run longer than scheduled maintenance, since parts usually need to be sourced instead of pulled from inventory, and a technician unfamiliar with that specific unit may need to be called in.
There’s a slower cost as well. Equipment that operates past the point of early wear without intervention degrades faster overall. A motor that keeps working under a mild vibration problem for months puts more strain on everything downstream of it, which shortens the service life of the whole assembly instead of just the failing part. What starts as a bearing replacement can turn into a full drive rebuild if the early signs go ignored long enough.
Labor costs factor in too. Facilities that depend on manual picking to work around a down storage unit often burn more labor hours in a single change than a planned maintenance window would have cost. That trade-off hardly shows up until it’s already been paid.
None of this needs ripping out existing equipment. Most modern storage systems already carry some built-in diagnostics from the manufacturer, and retrofitting sensors onto older units has become daily work for maintenance and automation teams.
A practical starting point looks like this:
The aim isn’t a dashboard for its own sake. It’s catching the version of an issue that costs a maintenance ticket rather than the version that costs a shift. Facilities that start with one or two high-cycle units tend to create the internal case for expanding coverage faster than facilities that try to track everything on day one and get buried in alerts nobody has time to review.
Getting budget approved for sensors on storage equipment is often easier when it’s framed around a specific unit instead of a general policy. A maintenance team that can point to one lift or shuttle with a history of unplanned stops has a much stronger case than an idea to monitor everything at once.
It also helps to tie the request to existing infrastructure instead of a new system. If the plant already has a condition monitoring platform for production tools, adding storage sensors is an extension of a proven tool, not a new line item that requires its own justification. Framing it that way tends to shorten the approval process considerably.
As more of the plant floor gets combined into shared analytics and asset management platforms, storage equipment stops making sense as a blind spot. The same systems already getting data from production machinery can often absorb storage and retrieval data without a separate platform, provided the equipment is instrumented in the first place.
Treating storage as production infrastructure, instead of as furniture that happens to move, is mostly a mindset shift. The technology to support it already exists in most plants. It just hasn’t been pointed at the right equipment yet. As storage systems keep taking on more of the daily workload, that gap becomes difficult to justify with each passing quarter.
Ans: It works on both. Newer units usually ship with built-in sensors, while older mechanical systems can often be retrofitted with vibration, temperature, and cycle-time sensors without replacing the equipment itself.
Ans: Cycle times drifting from their normal baseline are usually the earliest indicator, often showing up before any audible or visual sign of a problem.
Ans: No. Any motor-driven storage or retrieval equipment benefits, since the failure modes come from the same moving parts instead of how automated the rest of the facility is.
Ans: Most teams need a few weeks of normal operation to establish a good baseline before the data becomes meaningful for flagging anomalies.
