Humans Versus Robots: Not a Zero-Sum Game

Prognosticators often forecast dire consequences for human workers because of robots’ increasing penetration into jobs once performed by people. A 2014 Harvard Business Reviewpiece, for example, projected that 40 million jobs now performed by people could be done by robots as soon as 2025. Other estimates are that 60% to 80% of process jobs may become the province of robots and similar machines.

But, more recently, technology news indicates that humans will still be needed for 1) complex tasks and 2) managing the robots. In fact, a recent Forbes article posits that robotic workers and human workers will be corporate partners, not rivals.

Maximum Efficiency Not Achieved by Robots Alone

Forbes explores the auto industry – one of the chief examples of repetitive assembly line work – and finds that plants relying on robots on not necessarily the most efficient.

Why? Assembly lines today are not the assembly lines of yore. Customization and the relatively constant introduction of new models mean that vehicles are assembled from as many as 55,000 parts. Multiple options exist for each vehicle, from in-car vacuum cleaners to extra airbags.

Human workers follow the complexity of possible parts far more efficiently than robots. People dealing with innovations in available parts and models need some training, but not the same level of reprogramming as robots do.

There are also instances where maintaining robots require so many workers than efficiency is negative affected. An auto manufacturing facility in Europe sunk 10 million euros in technological methods of windshield installation, entirely replacing human workers for that function. However, unexpectedly, twice as many workers were needed to maintain the robots for that sophisticated technology.

Instances like this often result in plants that lead the pack in automation, but not in productivity. They fall into the bottom quartile in productivity.

Making Appropriate Determinations

This is not to say that robots don’t aid productivity. They do, but the tasks at which they provide the maximum boost to productivity must be determined by data and the appropriate business strategy, not by a belief that robots always equal maximum efficiency.

In the automotive industry, for example, body shops and the application of paint are almost entirely automated. Why? Because the processes are those of constant uniformity and repetition. The set-up is sometimes ergonomically inefficient for a human. For all these reasons, robots make sense.

But again, customization and innovation is the province of people. In one North American auto manufacturer, for example, the robots used in body shops and paint shops grew by over 1,000 in the 2005-2015 period. Yet, despite the fact that production dropped by nearly 100,000 units because of new model introductions, the number of human workers dropped only by roughly 8%.

Training people to do new jobs or standard jobs in newer ways is ultimately much more efficient than reprogramming robots for many factory positions. It all resides in making the best determination about who – or what – is best for the given job.