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Case study

How Dexory is transforming Menzies Aviation operations through AI and automation solutions

Menzies Aviation is an international aviation services business with operations worldwide, provides safe and efficient ground services, cargo, fueling, executive services and offline services. Operating at over 250 airports in 58 countries across six continents is supported by a global team of 25,000 highly trained people, and a delivery promise of providing safe and efficient services to every customer, every time.

At Heathrow, Menzies has the largest dedicated warehouse with a capacity that exceeds 7,000 square metres for handling passenger and air cargo.

The need for greater visibility

At the Heathrow cargo facility, the primary day to day operational challenges being faced included:

a) under-reported volumes and mis-declarations of air cargo due to human error in the manual processes. Inaccurate data resulted in incorrect storage charges, reducing loading efficiency and ultimately impacting profitability.

b) the availability of aircraft space, freight capacity, facility space, handling and delivery.

Menzies Aviation required greater visibility over the goods as they arrived, where they needed to go and where they actually were placed. The warehouse needed to optimise rack utilisation, get more accurate space forecasting and maximise the volume of cargo processed through the facility. Furthermore, it was necessary to identify lost, misplaced and non-compliant assets daily to recover revenue.

In order to keep the promise made to its customers, Menzies recognised that it needed an extremely reliable solution to automate its stock processes. They were highly impressed with the Dexory’s AI platform and fully autonomous mobile robot, Mimi, which can easily track and find goods within the facility by scanning and photographically capturing cargo leading to highly accurate, real-time data. Mimi operates within the warehouse without needing to change the facility and seamlessly moves around without interrupting people or workflows.

More efficient operations

Using the real-time access to the highly accurate data, Menzies Aviation now has efficient operations, providing a cost-effective alternative to refitting its warehouse at Heathrow.

The data insights gained provided far greater clarity on inbound and outbound loads. These loads are quickly scanned, allowing the warehouse-bonded inventory checks to be completed efficiently and accurately, with real-time comparisons against the data contained in warehouse management systems (WMS).

Saving valuable time

On average 30 minutes per pallet on mandatory automating bond checks, was saved, resulting in tens of hours per week saved. This allowed the warehouse employees now free to concentrate on more complex and less repetitive tasks. Mandatory bond checks, previously labour-intensive and only performed once to twice a week, are now carried out through Mimi’s automated perpetual checks,

The autonomous robot runs nightly audits for inventory checking and generating exception reports highlighting UTLs (unable to locate pallets), misplaced goods and rack occupancy levels, delivering far greater accuracy of the warehouse stock.

Optimising space

Menzies Aviation is also using the Dexory digital twin platform data to undertake very reliable space forecasting and maximizing the volume of cargo processed through their warehouses. Mimi is the only technology powered by autonomous robots that delivers this data and insights in real-time for inventory management.

Financial and reputation benefits

Financial benefits have been immediately visible as profitability has increased by reducing the number of misplaced and non-compliant assets.

Menzies Aviation enhanced its reputation, being at the forefront of logistics innovation, by being the first company in the industry to adopt an AI platform driven by robotics for real-time, complete visibility of warehouse operations.

What our customer has to say:

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This company has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement number 849938