Let’s start with the cause. There have been acres of print and digital coverage devoted to the election result of November 8, so this blog will steer clear of that for the main part. But the election of Donald Trump, and along with it his policies of protectionism and immigration (or rather lack of it) bring an on-going challenge for US industry into sharp focus: access to low cost labour. Should the president-elect press ahead with his pre-election claim to deport 11 million unregistered immigrants, the agricultural, hospitality and fast food industries will face an immediate labour crisis. How will we address it?
Eyes will likely turn to automation and the tech industry, ironic given the strength of feeling against this industry from many of Trump’s supporters. But to be able to replace such a huge proportion of the labour market will be tremendously difficult unless a different approach is taken to automation. Here at Cambridge Consultants we’re looking not at how to replicate a human at any cost, but how to view automation in a way that we can generate low cost solutions to a number of menial, low paid tasks. Such an approach puts the emphasis on Development rather than Research. There is no shortage of well-funded, ambitious robotic start-ups out there (e.g. Rethink Robotics), but their products are all too often an order of magnitude too expensive to be considered as viable alternatives to the status quo. This is where pragmatic, world class engineering has a place to play, where determining what is “good enough” for a particular task takes precedent over precise human mimicry. It’s only by taking such an approach that we’ve been able to design, develop and deploy the world’s most sophisticated warehouse automation solution for Ocado; a great example of commercially viable automation in a low cost labour environment in action.
But what of John Deere, the third of my trio of names at the top of this blog? Even the best automation solutions may struggle for commercial viability in what is already a fantastically optimised sector – see the long lag between the first semi-autonomous tractors pioneered by Deere over a decade ago and the market appetite for this technology today. In cases such as the precision agriculture sector, a different business model is needed if automation is to be further embraced.
Traditionally the “Big Iron” sellers were reliant on outright purchases of their large machines by farmers. But as these devices have become ever more sophisticated, the capex required to buy them as become ever more difficult for farmers to justify, particularly as commodity prices have suffered. This combination has pushed companies like Deere to move to a leasing model, rather than outright ownership. One can see how this will continue to evolve in the future, where equipment providers will “sell” performance; say tons per acre of produce, rather like Rolls Royce sells airlines lbs of thrust hours. To do so requires great confidence in not only the precision of your equipment, but the management of factors outside of your control – e.g. weather – and adapting to them. That’s one big automation challenge, but one than can only be addressed with pragmatic, cost effective engineering if the produce picked today by immigrant labour is not to be left to rot in the fields of tomorrow. Donald Trump and his UK Brexit voting contemporaries may have inadvertently kick-started the Great Automation Race of the 21st century; how will your organisation rise to the challenge?