When one of Aston Martin’s international, high net worth customers orders a replacement wing mirror, they expect it to conform to the highest standards of craftsmanship and engineering and arrive immediately. Aston Martin, whose cars had been featured in eleven James Bond films going back as far as the 1965 film “Goldfinger,” had seen its business change considerably in recent years, with a growing clientele outside the UK including Middle East and Asia.
The new demands posed by its international client base prompted Aston Martin’s board in 2015 to raise targets for first time availability (FTA) by 2 percent without increasing inventory. For the first time, the board also wanted to achieve FTA parity across all three of its car categories: “Heritage (pre-1997),” “Recent Production (mid 90s forward, but no longer in production),” and “Current Production (today’s models)”.
Aston Martin called in ToolsGroup’s experts to tune its SO99+ engine, which had been at the heart of its spare parts operation for the past 10 years, to meet these new challenges. Nick Wilson, Senior Inventory Planner, Parts Operations, explains: “When our business was all UK-based, Heritage cars were set lower FTA targets. Culturally, British customers understood having to wait for bespoke parts for their classic cars. New international customers, however, expect to walk into a dealer and get these parts immediately. Since our board wouldn’t allow us to meet the new FTA targets by raising costly safety stock levels, we turned to our trusted partner ToolsGroup to review our situation.”
If Aston Martin’s forecasting story was a Bond film, “Q” would now rise up through the floorboards with the latest high-tech gizmo to help 007! In our story, ToolsGroup (“Q”) implemented an advanced machine learning engine. SO99+ is the first supply chain optimisation solution to embed advanced machine learning into daily demand and supply planning. Maybe not as dramatic as an exploding pen, but a lot more useful!
SO99+ was able to dip into the vast array of historical data collected by Aston Martin over the years and pick out 8 completely new categories of behaviour. Without any guidance from the humans
SO99+ uses these new categories to generate a more accurate forecast. Then, each day, SO99+ tunes the safety stock for all 80,000 SKUs, automatically reducing the inventory to take advantage of the improvement in forecast accuracy, before creating a replenishment plan to deliver the demanding new target service levels.
According to Wilson, the new focus on seasonality has been transformational: “The great thing about the eight categories is that people can see them. This has not only educated the purchasing team in important new skills, but has also really given them confidence in the planning system.”
In just two months of running the new machine learning system, Aston Martin reduced the inventory value of its safety stock on the clustered items by 18 percent while immediately improving FTA service levels to 97.1%, above its target. Outcomes are already trending towards significant further improvements in both service levels and reduced inventory value.
Beyond the hard outcomes, Wilson says “In a luxury business like ours, nothing affects team morale more than our ability to meet service requirements. Thanks to ToolsGroup applying its new machine learning technology to our problem in a creative way, we’re now much better geared up to serve our demanding client base without impacting our bottom line.”