Orquestr8 Pick started with the simple objective of delivering manual picking process improvement in warehouses without relying on expensive automation or long ROI timelines. Software Development Director, Phil Rowlands talks through the journey of Orquestr8 Pick.
The idea came from a real customer challenge. A third party logistics (3PL) customer needed to improve pick performance mid-contract. They wanted a low-risk, fast-return solution—and that got us thinking.
The first version of Orquestr8 Pick was a simulation environment. It helped us test transaction data and calculate walking distance savings.
One of our main challenges was to build our digital twin components, allowing us to accurately replicate warehouse layouts and Pick walking routes. We also had to develop an optimisation engine that could handle complex, fast-moving environments in real time.
We teamed up with Manchester Metropolitan University to explore the latest in route optimisation—and their insight helped shape our algorithms. Being based at Sci-Tech Daresbury, a UK hub for science and innovation, also gave us access to expert advice in artificial intelligence (AI) and machine learning (ML).
Orquestr8 Pick uses nature-inspired AI algorithms, an application of artificial intelligence in warehouse operations, to find the most efficient pick paths, fast. The latest feature allows us to understand the execution time of optimised pick routes, and train an ML model to pick the most efficient route from a collection of routes with a similar distance.
This means your routes improve over time—adapting to the quirks of your warehouse, your people, and your workflow. It provides human-friendly, routes that improve efficiency naturally e.g. reduced need to change direction.
Pickers have told us they’re spending less time walking and more time picking and our data shows this too —unit-per-hour picking rates have risen consistently post-implementation.
One standout win was a cartonisation logic update that reduced carton use by 5%, cutting both consumables and shipping costs and also saving CO2 emissions.
Read more of our customer feedback for Orquestr8 Pick.
With our Orquestr8 platform we’re already helping our customers improve both Picking and Packing, helping to reduce their dependency on labour. Logically the next areas to target are Replenishment or Putaway, with dynamic allocation able to choose the best location for a SKU based on historical and forward order requirements.
Phil Rowlands, Software Delivery Director