Try, learn, try again: Propelling productivity through experimentation
At Ocado Technology, we’ve spent the last 25 years developing AI and automation to improve the productivity of online grocery operations. And we’ve certainly learnt a lot along the way.
I first joined Ocado in 2006 as a graduate, working on our automated fulfilment proposition. Over the course of 19 years, I’ve built out our fulfilment, logistics and supply chain offerings. Some of my highlights include: developing our first goods-to-person picking system (a cubing system that optimises the placement of 50,000 SKUs in a customer order), building an AI orchestration system to navigate the movement of thousands of bots, and leading a range of cross-functional teams from hardware to software, data and product.
In that time, I’ve watched Ocado go from a UK online grocery retailer to a global technology platform provider. Today, our automated fulfilment centres are live in 8 countries and have captivated digital audiences en masse.
This rapid transformation didn’t happen by accident. In a complex logistical operation like online grocery, the only choice was to innovate and adapt. Success depended on efficiently executing a complex sequence of events, from inbounding to decanting, picking, packing and shipping, for thousands of items with varied temperature regimes and shelf lives.
When off-the-shelf technology couldn’t keep up, we developed specialised hardware and software to do the job. Experimentation came easier for us, as the consequences and costs associated with bruising bananas in the lab, for example, were far lower than making mistakes in industries like aerospace or automotive.
This allowed us to progress at a faster rate. Innovation was further propelled through a learn fast culture, where experimentation was prized and missteps reframed as a natural part of the learning process.
Derisking innovation with simulations
Bold innovation doesn't have to mean taking uncalculated risks. There are many ways to secure buy-in for your ideas before going all in.
Just take our first-generation Customer Fulfilment Centres (CFCs), which consisted of cranes, vertical storage and conveyor belts. These sites allowed Ocado to efficiently ship online grocery orders to a large number of customers via large centralised CFCs and a hub-and-spoke distribution model.
However, as we looked to scale our business, we knew we could do better. The conveyors were slow over long distances and bottlenecks in one area could impact the whole system.
We decided to make a disruptive move and after testing a range of new designs, we turned the concept ‘on its side’. Our new idea replaced a horizontal conveyor belt system with a vertical storage grid and retrieval bots for faster, scalable and more reliable picking.
The grid-and-bot system allowed for more direct access to inventory items, meaning that instead of waiting for items to move along a conveyor belt, bots could pick items directly from the grid in a fraction of the time.
How were we able to justify such a radical step? Highly accurate simulations, allowing us to test the viability of a new system in a virtual world before making any real-world builds.
Driving productivity through automation
And it paid off! Today our bespoke bot and grid system enables 50-item orders to be picked in under 5 minutes and hundreds of orders simultaneously.
Underneath every cell in the grid is a stack of totes up to 21 layers deep. Each tote contains a specific set of products and the job of the bots is to fetch and move these items. The magic behind the scenes is the AI orchestration system we’ve developed, which coordinates this army of bots for hyper-efficient fulfilment of customer orders.
Even higher performance, thanks to additive
Experimentation has been a key part of our bot development strategy. We currently have three series of bots in operation, and each iteration is lighter and more efficient.
For our latest generation, the 600 series, the engineers took a radically different approach to design. They identified the benefits that ‘lightweighting’ the bot would unlock and spun up a novel design process with additive manufacturing at its core in order to shave off as much weight as possible.
The product consists of 3D-printed nylon parts with carbon-fibre reinforcement to create a bot that’s three times lighter than its predecessors.
We achieve this weight reduction through an additive-first approach to design and topology optimisation techniques. This approach gives our designers complete freedom, as the printer can process complex designs which were not traditionally possible to create, at a lower cost.
The lightweighting of the bot also creates a circle of benefits, as lighter bots require smaller motors, less power and lighter batteries, all leading to enhanced efficiencies.
We consistently future-proof our business by keeping on top of the next wave of robotics innovation.
Developing an automated storage and retrieval system was the first level of transformation. Our robotic picking and packing arms take the productivity gains even further.
Grasping and handling items is an innately human skill; replicating it in a robot is a tough engineering challenge, but with big productivity gains at stake.
With AI and computer vision, the arm can now correctly identify the right way to pick each item. The algorithm identifies the optimal grasp points of a product so that the robot can adjust its grip, adding more pressure for a glass bottle versus a banana. Intelligent built-in sensors help along the way to reduce the risk of crushing or damaging products during picking and packing.
Every time the arm makes a mistake, this behaviour is learned in a self-improving system via reinforcement learning. This means as we scale, the same mistake will never be made twice. A lesson learnt by one arm, is a lesson learnt by the whole fleet.
Today, in our newest CFCs, retrieval bots drop containers of stock next to OGRP arms, which effortlessly pick and pack grocery items into customer shopping bags with no human touch.
Our automation journey over the past two decades shows why experimentation is such a crucial step in product development. Our first CFCs were impressive, until we completely disrupted our own model with the next-generation of automation. Embracing the unexpected, pivoting when necessary and extracting lessons from every iteration (even those that don't go as planned) has allowed us to push these technologies beyond their boundaries, introducing countless productivity benefits.
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Rory joined techUK in June 2023 after three years in the Civil Service on its Fast Stream leadership development programme.
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