A leading UK retailer saved £180,000 by optimising their energy and cut their reporting time by 90% with Analytics as a Service.
Our client was facing two major challenges:
Problem #1
The naming system for their devices was complex and unclear.
It was difficult to understand what each device was, what it powered, and its location within a store.
Problem #2
Producing reports was a lengthy process.
It required Energy Specialists to spend about 3 hours to identify the right devices, filter out irrelevant ones and time periods, and then format and combine the data into a final report.
Solution
We collaborated with client’s energy experts to automate categorisation of their devices.
We organised them by:
- device type - main incomers, distribution boards, subpanels
- location in the store - shop floor, clothing, admin area, car park
- what they powered - lighting, HVAC, refrigeration
Additionally, we created a custom widget that radically simplifies the reporting process.
Users simply select a time frame, choose devices by area or type across any number of stores, and hit a button to generate reports.
This report is automatically emailed to the user or a colleague, requiring no further formatting.
Impact #1
The device categorisation has revolutionised the team‘s understanding of energy consumption trends, putting them in the perfect position to take actions that are generating significant savings.
For example, the initiative uncovered 100s of previously unidentified heavy-usage devices.
Impact #2
The new report generator tool has reduced the time taken to create energy consumption reports by about 90% freeing up valuable time to be spent on proactive projects instead of labour-intensive admin tasks.
Iteration
Our next impact iteration is focusing on device reliability and data disconnects.
We are currently:
- building models to account for missing data until it is filled or completed,
- automating their issue notification and repair process with other supply chain partners.