Energy spend with no attribution
The retailer was receiving 14 separate electricity bills each month and paying them, but had no structured way to compare stores, track trends, or identify which locations were performing well and which were outliers. Energy was treated as a fixed cost of operation — something that arrived in the mail and got paid — rather than a managed input. When bills started rising, there was no internal data to understand why.
Baseline monitoring across the fleet
Conexie specified and coordinated the installation of data loggers at each of the 14 locations, with sensors on the main switchboard and, where access allowed, on the HVAC distribution boards. Within two weeks of the first installations going live, consumption data was flowing into the Conexie platform and the retailer's operations team had a fleet dashboard for the first time — showing all 14 stores on a single screen, normalised by floor area to make meaningful comparisons possible.
Identifying the problem sites
The data told a clear story within the first billing cycle. Five stores were consuming significantly more energy than their floor area and trading hours would suggest. Drilling into the interval data for each of those stores, the pattern was consistent: HVAC systems were running at near-full capacity during the hours after closing — from 6pm through to midnight or later — with no occupancy to justify it. The cause, in each case, was a scheduling misconfiguration that had gone undetected because nobody had been looking at the numbers at that level of detail.
Fixing the schedule and measuring the outcome
The fix itself was straightforward: correcting the HVAC schedules at the five affected stores so that systems ramped down shortly after closing time rather than continuing to run. Conexie's platform was used to verify the change — comparing consumption profiles before and after for each store to confirm that the HVAC load had dropped at the expected time. The measurement approach meant the retailer could report the saving with precision rather than estimating it from bill comparisons.
A 23% reduction and an ongoing monitoring practice
Across the five stores where the scheduling changes were made, energy consumption fell sharply. Averaged across the full 14-store fleet, the overall reduction was 23% — achieved without capital expenditure beyond the monitoring hardware, and without any change to trading operations or customer experience. The retailer continues to monitor all stores through the Conexie platform, with alerts configured to flag any store whose after-hours consumption exceeds its historical baseline — catching future scheduling drift before it compounds over another billing cycle.