Most operations leaders don't lose sleep over a few missing cylinders. A cylinder goes missing, a tote never returns from a customer site, and the event gets written off, replaced, and absorbed into the cost of doing business. The math seems manageable. The problem is that this isn't really a shrink problem. It's a data problem. And data problems don't stay contained.
The Loss That Doesn't Look Like a Loss
A single unreturned cylinder doesn't trigger an alert. It disappears quietly — into a customer site, a transit hub, a truck that got rerouted. The asset record stays in your system, showing the cylinder as "out for delivery" or "at customer." The system has no way to distinguish it from an asset in transit. The record stays open, the count stays inflated, and the gap between physical reality and system state quietly widens.
That's the first distortion: your inventory count is off by one. Your utilization rate — the ratio of active assets to total fleet — is now slightly inflated because you're dividing by a denominator that includes an asset that will never come back.
One cylinder won't move the needle; but industrial gas and chemical operations run fleets measured in thousands. Consider a distributor managing 10,000 cylinders. At a 2% annual loss rate, roughly 200 units disappear over the course of a year. More significant than the headline number is the ongoing effect: hundreds of instances where systems indicate an asset is available even though it is not. If even 2–3% of assets are in this unseen state — not confirmed lost, not confirmed in service — the data you're relying on to run the operation is quietly wrong.
How Asset Data Distortion Compounds Across Operations
Asset data doesn't sit in isolation; rather, it feeds forecasting models, utilization reports, capital planning analyses, and billing cycles. When the input is corrupted, every downstream output inherits the error.
Cylinder forecasting illustrates how quickly the error propagates. When your fleet count is overstated by 3%, your planners see capacity that doesn't exist. Orders get committed. Delivery promises are made. The shortfall surfaces not in a report, but in a missed shipment, but by then, the root cause is invisible. The forecasting model didn't fail. The data it was fed was wrong.
Capital planning takes the same hit. When apparent utilization is depressed by phantom assets sitting in limbo, the business case for fleet expansion looks weaker than it is. You defer the purchase. The actual serviceable fleet continues to shrink relative to demand. Eventually you're covering with emergency buys at higher cost — solving a problem you didn't know you had because the reports told you something different.
Rental billing is the third exposure point. Assets that have gone dark — not confirmed returned, not confirmed in active service — fall out of billing cycles. Rental charges stop. The account looks clean. No dispute, no flag, no escalation. The revenue leakage is entirely silent. Argyle Welding Supply saw this pattern directly: after implementing TrackAbout, rental income increased by 6–8% — not because rates changed, but because assets that had been slipping through billing gaps were finally accounted for.
When Leaders Stop Trusting Their Own Data
This is where the issue becomes structural rather than operational. Over time, leaders start to discount their own data. They know the reports are imperfect. They add mental buffers to inventory numbers, apply gut-feel adjustments to utilization figures, and treat billing reconciliations as estimates rather than accounting. The system is still running — but decisions are being made around the data, not from it.
Teams compensate with workarounds: phone calls to confirm what reports should already show, physical spot-checks against inventory figures, shadow spreadsheets that run parallel to the system of record. These workarounds keep operations moving, but they slow decision-making and don't scale. Each one is also an acknowledgment that the operational data infrastructure has a reliability problem.
The technology ran exactly as designed but it ran on data that had already drifted from reality. The organizational costs accumulate from there: slower decisions, lower confidence in planning, more time spent reconciling before any number is trusted enough to act on.
Why Small Container Losses Get Underestimated
There are two reasons small losses get dismissed as operational noise rather than data risk:
- Replacement cost framing. When asset loss is measured in dollars per unit, individual events look trivial. A lost cylinder might represent $150–$400 in asset value and easy to absorb, but that framing ignores the downstream cost — the forecasting error, the missed rental revenue, the emergency replenishment, the hours spent on manual reconciliation that exists specifically because the system isn't trusted. Huber Supply Company recovered $30,000 in assets within their first year of using TrackAbout — not by buying new cylinders, but by finding ones the system had already written off.
- Visibility lag. By the time small losses have meaningfully distorted your data, months have passed and the connection between the original event and the current problem is hard to trace. Leaders investigating declining planning accuracy or unexplained operational friction rarely trace it back to asset tracking gaps. They're looking for the wrong kind of failure.
Scale Makes the Problem Harder to See
As operations expand, asset loss becomes harder to detect: more sites introduce more handoffs, and broader customer distribution spreads assets across wider geography. Manual reconciliation can bridge gaps early on, but it cannot keep pace indefinitely.
Empresas Gasco Colombia faced this at scale — managing over 1.5 million LPG cylinders across a distributed network. Without accurate, real-time traceability, losses were inevitable and largely invisible. The same dynamic plays out at mid-market scale: more transaction volume means more opportunities for assets to disappear into the gap between physical reality and system records.
Over time, loss becomes normalized and inconsistencies fade into background noise. Root causes are obscured by transaction volume. The business keeps moving — orders ship, customers are mostly satisfied — but the structural degradation continues below the threshold where anyone sounds an alarm.
What This Means for Analytics and AI Initiatives
Advanced analytics and AI-assisted planning tools assume accurate, timely, and consistent data. When asset records contain gaps from unrecorded losses, analytical outputs reflect those gaps. Models trained on inflated inventory counts propagate error rather than correcting it. An AI rental intelligence tool can surface which accounts are under-billed — but only if the in/out events that drive billing are actually being captured.
This matters increasingly as distributors invest in digital transformation. The value of any analytics layer is bound by the quality of the operational data feeding it. If an distributor’s operation relies on returnable containers like cylinders or totes to move product, fixing container tracking isn't a prerequisite for digital maturity, it's the foundation of it.
The Fix Isn't More Write-Offs
The instinct is often to sharpen the write-off process — identify losses faster, clear the dead weight from the system, and get the inventory count closer to reality. That improves the number and doesn't address the mechanism. Assets will keep disappearing into the same blind spots as long as the underlying tracking process has gaps.
What closes this loop is capturing asset movement at every handoff — not just delivery and return, but every point in between. When a container is scanned at load, at drop-off, at pickup, and at return, the system has enough events to distinguish "in active use" from "missing." The distinction matters operationally and financially.
Real-time visibility not only recovers lost assets, but it also stops the data corruption before it starts. Utilization rates reflect actual serviceable inventory, forecasting runs on real fleet capacity, and billing cycles close on time because the in/out events are captured, not estimated. The reports become trustworthy again, and decisions made from them become faster and more confident.
Indiana Oxygen increased its rental asset base by 6% after implementing TrackAbout — not because it bought more cylinders, but because it stopped losing track of the ones it already had.
The cost of small losses was never really about the containers.
See How TrackAbout Closes the Gap
If your operation depends on returnable containers, the blind spots described here are almost certainly present. The question is how much they're costing you — and whether your current data gives you any way to know.
Contact us to talk through what asset visibility looks like in practice for industrial gas, chemical, and LPG distribution operations.
