The process

In the first phase, Data Capture, we ‘train’ DIDO using a variety of data to achieve two critical objectives. Firstly, a “big” operational dataset provides raw information that allows DIDO to understand the operation in detail. Secondly, answers to strategic questions are converted into a numerical system that prioritises and ‘weights’ the strategic objectives of the warehouse.

In phase two, Conceptual Design Iteration, DIDO orchestrates three daughter AI platforms to analyse, design and evaluate thousands of potential warehouse layouts.

The first platform, ELSA, uses machine learning to classify every SKU into six bands of products that have similar picking location needs. You may think of this as a very sophisticated ABC analysis that ensures that we place fast-moving products into larger apertures and slow-moving ones into smaller apertures.

DIDO then passes the data to CLARA, an AI design bot trained to quickly and accurately design warehouses. CLARA develops a layout appropriate to ELSA’s analysis before giving that design to ELISA, who simulates it.

Once the simulation is complete, ELISA calculates performance metrics and combines this with strategic objective ‘weightings’ completed earlier to create a “Design Score” out of 100, indicating this system’s overall success.

Then the process starts again, albeit this time a different layout is created by CLARA, which generates a new score, and so on, until the highest scoring system – the optimal design – has been identified. This optimal design is then turned into a precise virtual twin, which we can visualise in Virtual, Augmented or Rendered Reality using SEC XR, our Extended Reality engine.

Ultimately, through the combination of Machine Learning, AI Simulation and Extended Reality technology, we can ensure that we deliver THE optimal design, ideally suited to an operation’s specific strategic and operational requirements.