IoT Predictive Maintenance

A large food retail chain with over 4,000 stores, identified maintenance related challenges across key equipment groups within their estate. Equipment groups such as: refrigeration cabinets, cold rooms and HVAC systems. The challenges revolved around poor visibility of faults that result in costly repeat work orders, spiralling call- out charges and differing work order volumes per site and equipment type.
Their current monitoring systems did not allow for an estate-wide analysis of the problem to be completed.

The project had a 5 key targets:
1) Understand what volume per type work orders were being raised and why (plant failure, cold room doors open, case over temperature)
2) Understand the frequency of repeat work orders
3) Benchmark sites in relation to volume and repeat work orders being raised
4) Identify problem equipment at a group and/or single unit level
5) Using the data collected make clear and reportable improvements in maintenance costs

The Solution

LoweConex was selected to address this project as its innovative IoT and automation principles, offer full visibility and control of the assets, their running conditions and the resultant actions. LoweConex uses the latest MQTT technology to continuously collect all the relevant data points and display them in a revolutionary data sphere
shown here.

The data sphere can receive up to 1 million data points per second and display data for 2700 sites in a single sphere. Once in the sphere, LoweConex uses AI driven automation to identify colorations between equipment running conditions, set points and store behaviours.
LoweConex continuously monitors real-time alarm telemetry and automatically push fixes directly to the equipment using rule-based logic. If the fault still remains the logic will then automatically generate a work order through the company’s pre-existing
system, checking and reporting if this is a repeat fault.

The Outcome

LoweConex has given the retailer both complete visibility, and control over the running conditions and alarm responses, including work order raising, of their entire connected estate. Alarm and running conditions have improved estate-wide, with problematic equipment and stores identified and corrective actions taken. Due to the data and the reporting of corrective actions, meaningful engineering SLAs can now be created, and monitored, to ensure improvements continue.

Return on Investment

After 12 months the retailer experienced a 32% reduction in work orders raised, a reduction of c19,000 in 2018 to c13,000 in 2019. These improvements have led the retailer to report that for every £1 spent on LoweConex the company have seen a reduction of £8 in maintenance costs alone.