The client
The client is a nationwide retail chain with stores in numerous locations.
Each store runs heating, cooling, lighting and other equipment that shoppers depend on. Facility costs were high, and how well a store was maintained varied from one location to the next, which affected the shopping experience.
The challenge
Facility costs were rising across the chain, driven by energy use and uneven maintenance. Without good data, managers could not see where the money was going.
- Energy consumption across stores was inefficient.
- Maintenance practices differed from store to store.
- Store assets were poorly recorded and managed.
- Decisions lacked analytics and reporting.
- These problems raised costs and affected the shopping experience.
What they set up in Asset Infinity
The chain moved its store assets, maintenance and energy data into Asset Infinity and added sensors that watch conditions in each store.
Energy by store
Utility meters and readings are recorded for every store, so energy consumption and cost can be compared by site and over time. (Energy)
Sensors in the stores
IoT sensors watch temperature and lighting conditions, and raise a work order when a reading crosses its limit. (Connected)
Preventive maintenance by schedule
Plans by date or runtime create work orders for HVAC, lighting and other equipment before it breaks down. (Maintenance)
Automatic reports
Scheduled reports on energy use, maintenance costs and other measures go to managers without anyone compiling them. (Insight)
How it works day to day
Here is how store facilities are now run across the chain.
- System: records energy readings. Meter readings for each store are recorded against that site, building a record of consumption and cost.
- System: flags a reading out of range. When a store’s temperature or lighting sensor crosses its limit, a work order is raised for that store and asset.
- System: schedules preventive work. Preventive plans create work orders for heating, cooling and lighting equipment when service is due.
- Technician: does the work. The technician receives the job on the mobile app, records what was done and closes it, adding to the asset’s history.
- Facilities manager: compares stores. Dashboards show energy cost and maintenance spend by store, so outliers stand out.
- Head office: receives scheduled reports. Reports on energy, maintenance costs and other measures arrive on schedule to support decisions.
Before and after
| Before | With Asset Infinity | |
|---|---|---|
| Energy use | Inefficient, not tracked by store | Readings and cost by store |
| Store conditions | No live view | Sensors raise work when out of limits |
| Maintenance | Inconsistent between stores | Preventive plans for the whole chain |
| Asset records | Poorly managed | One central register |
| Reporting | Little data for decisions | Automatic scheduled reports |
How it rolled out
- Store assets recorded. Each store’s equipment was set up in one central asset register.
- Sensors and meters connected. IoT sensors and energy readings were linked to the stores and assets they cover.
- Plans and reports configured. Preventive plans and scheduled reports were set up for the whole chain.
The results
- 20% lower energy costs chain-wide
- 15% lower overall operational costs
- Heating, cooling and lighting schedules were adjusted to actual usage patterns.
- Less downtime of critical equipment through preventive maintenance.
- Longer asset life as equipment is serviced before it fails.
- Consistent maintenance practices across every store.
Why it worked
Energy and maintenance together
Seeing energy use next to equipment and its maintenance shows where costs come from and what to fix.
Sensors that create work
A reading out of range becomes a job for a technician, so problems are acted on, not just noticed.
One standard for every store
The same plans and reports apply chain-wide, so every store is maintained the same way.
Looking ahead
By putting store assets, maintenance and energy data in one place, the chain cut energy costs by 20% and overall operational costs by 15%.
The chain plans to use more of its data to plan store operations around peak hours, building on the energy and maintenance records it now keeps for every location.