Twelve depots. Twelve different ways to run a ticket.

What they asked for

"Give us a system for incidents, assets and field requests across our hubs and depots, with SLAs and visibility."

Not a ticketing problem at any one site, a standardization-and-visibility problem across all of them. Every hub ran its own way, so nothing could be compared, and head office was blind to the network.

One standard across every hub. The same process, SLAs and visibility at every hub, so head office runs one network instead of twelve islands.

The situation

A US logistics operator ran IT and field and maintenance operations across a dozen hubs and depots. Day to day, each site coped: incidents got logged somewhere, field and maintenance requests got actioned, work got done. But every hub did it its own way, on its own tool, to its own definition of urgent, and above the site level there was no shared standard and no live picture of the network.

Head office could not say, on any given morning, how the network as a whole was performing, which hub was slipping, or whether the same problem was quietly recurring across three depots. The sites were busy. The network was blind.

What we found

Our diagnostic surfaced three compounding issues.

Every hub ran its own way. Email at one depot, a spreadsheet at another, a local tool at a third, so a simple question like "how are we doing" returned a dozen different, non-comparable answers.

No SLAs, nothing escalated. Without shared priorities and response targets, urgent and trivial were treated alike, and work aged quietly until someone happened to chase it.

Assets and patterns were invisible across sites. The same equipment model failing at three depots looked like three unrelated tickets, because nobody could see the network, only their own patch of it.

The cost of this fragmentation is well quantified. Gartner estimates poor data quality costs the average organization around $12.9M a year, and names inconsistency across siloed sources as the single most challenging data-quality problem there is. That is exactly what a dozen hubs each keeping their own data their own way produces: not one messy system, but twelve inconsistent ones, and a leadership team making network decisions on numbers that do not line up. (Source: Gartner, Magic Quadrant for Data Quality Solutions, 2020.)

So we did not start by buying every depot a better ticket tool. We started by giving the whole network one standard and one view. It is a proven pattern on Power Platform: Microsoft is a multi-year Gartner Magic Quadrant Leader for low-code, and organizations run real multi-site operations on it. Call2Recycle, for instance, coordinates operations across 2,000-plus locations on Power Apps and Power Automate with real-time Power BI monitoring.

What we did

One front door, every hub. A Power Apps intake puts every incident, asset issue and field or maintenance request onto the same form and the same Dataverse data model, so a "ticket" finally means the same thing at every depot, and for the first time the data across sites is directly comparable.

One standard, enforced. Power Automate applies shared SLAs, routing and escalation automatically across the network, so priorities, response timers and hand-offs behave identically at every site and nothing ages unseen in a local inbox. The well-run hub and the struggling hub now run the same playbook.

One view of the network. A Power BI command centre shows SLA attainment, MTTR, backlog and asset health by hub, so head office can compare sites at a glance, see the pattern the individual depots could not (the same asset failing in three places) and act across the network instead of reacting locally, one site at a time.

How we rolled it out

We didn't boil the ocean. We rolled out at two hubs first, proved the standard held and that the network view told leadership something they could actually act on, then extended it site by site, keeping depot managers and field teams in the build, so the one standard reflected how the work is really done rather than being imposed from head office.

The shift

Before, every hub ran its own way and SLA attainment scattered from the low 50s to the low 90s. After one standard and one view, every hub converges near 95%.

Outcomes

• ~95% SLA attainment: every hub hits the same response and resolution targets, not just the well-run ones.
• ~45% faster MTTR: standardized routing and automatic escalation, network-wide.
• ~60% less reporting effort: a live Power BI network view replaces manual reconciliation across hubs.

The win wasn't a ticket tool at each depot. It was one standard, and one view, across the whole network.

An illustrative engagement. The scenario and figures are representative, drawn from outcomes across comparable Microsoft Power Platform deployments, not the audited results of a single named client.