Most retail ICT models are built around a simple assumption: if you can fix a fault fast enough once it’s reported, the model works. In practice, the reporting and escalation chain is usually the slowest part of the whole process, and it’s rarely accounted for when support models are designed.
A ticket is logged. It’s triaged. It’s assigned. It’s escalated to a specialist, or a part is ordered, or a technician is dispatched from a regional hub two hours away. By the time anyone physically arrives at the till point, the queue has already backed up, the shift manager has already improvised a workaround, and the real cost of the fault has already been absorbed by the store, long before a resolution shows up in anyone’s uptime report.
The maths nobody puts in the SLA
Standard service-level agreements measure response time and resolution time as if they’re two ends of the same problem. They aren’t. Response time tells you how quickly someone acknowledged the issue exists. Resolution time tells you how long it took a person, on-site, with the right part and the right access, to actually fix it.
The gap between those two numbers is where a store bleeds. It’s the gap that determines whether a Friday afternoon POS failure costs a few minutes of inconvenience or a few hours of lost transactions. And it’s a gap that’s almost entirely a function of one variable: how far the nearest capable technician physically is from the store when the call comes in.
"The fastest fix isn't the one with the best process. It's the one with the shortest distance."
What proximity actually buys you
A proximity-based delivery model doesn’t try to make each individual repair faster, it reduces the number of steps between “something broke” and “someone capable is standing in front of it.” That has three compounding effects:
- Fewer handoffs. A technician already embedded in a region doesn’t need to be briefed, re-triaged or re-routed by a second-tier queue before they can act.
- Local stock, local context. Field teams who know a store’s environment carry the parts and history that matter, instead of arriving to diagnose from zero.
- Faster feedback into the system. Issues resolved close to the point of impact surface patterns, recurring faults, environmental causes, hardware ageing, much sooner than issues resolved from a distant hub.
Designing for where the problem is, not where the org chart is
Most support structures are drawn around internal specialisms, network, hardware, POS, connectivity, rather than around the physical footprint of the stores they serve. That makes sense on an org chart. It makes far less sense on a store floor, where a single fault rarely respects those boundaries.
The retailers seeing the biggest gains aren’t the ones with the most sophisticated ticketing software. They’re the ones who’ve rebuilt their delivery model around geography and response distance first, and layered specialism on top of that, rather than the other way around.
Proximity won’t fix a badly designed process. But no process, however well designed, can outrun the physical distance between a fault and the person who needs to stand in front of it to solve it.
