Data science

Future in Maintenance: Will Machines And AI Replace Maintenance Workers?

A widespread narrative on work in the future is that machines will take care of everything. Robotics, artificial intelligence, and modern algorithms powered by new energy sources will replace the way this world works. People will be out of jobs as they will not be able to compete with machines powered by AI. Leading to widespread unemployment and dispossession of the masses. The state of affairs is, allegedly, no different for maintenance activities in the future and looks bleak for maintenance employees. 

Now, is this the bleak future we face, or is it ‘immanentizing the eschaton’ as Willian F. Buckley puts it? For that, we have to take a look at the current state of maintenance automation, the potential evolution of the same, and historical precedents for such radical changes.

Maintenance automation: A Swiss army knife?
Maintenance activities have become a lot simpler with the help of technology. Managing maintenance schedules to predictive maintenance can be accomplished with the aid of modern technology. Everything with some level of computerized decision-making is broadly termed automation. But there are varying degrees of automation depending upon the entity making the decisions in various processes.

All processes in an industrial environment are formed by one or more of the following functions.

1. Monitor function

2. Advice function

3. Decide function

4. Implement function

The control of each of these functions can be handled by a computer or a human. Based on this, there are ten levels of automation starting from complete manual control to full automation. In full automation, all the functions in the process are controlled by a computer. The different levels of automation and who controls different functions in each of those levels are illustrated in the table given below.

The aim of all automation advancements is to reach the level of full automation. Today in most automation instances, computers control only one or two of the functions that form the process. The common narrative is that technological improvements snowball and compound to an exponential degree to deliver fully automated systems in the not-so-distant future. This will lead to the take over of all maintenance activities by machines and AI replacing all maintenance workers.

But what the narrative misses out on is the law of diminishing returns. According to the definition from Investopedia, “The law of diminishing marginal returns is a theory in economics that predicts that after some optimal level of capacity is reached, adding an additional factor of production will actually result in smaller increases in output.”

Applying the law in maintenance automation, after a period of compounding a ceiling is reached from where incremental improvement requires a disproportionately high amount of time, resources, and effort. This follows the trajectory of an S-curve as shown above. The progress in automation will follow a snail’s pace after a critical limit is reached. 

The real-world impact of the S-curve can be seen everywhere in technological advancement. The capacity of semiconductor chips was supposed to grow exponentially to infinity. “Faster and faster processors every year” was the narrative pushed during the initial phases of semiconductor development. Today, semiconductor manufacturing is fast approaching the physical limitation and the cost of improving the tech is orders of magnitude higher than earlier.

A similar ceiling for innovation will also hit the march to full automation of maintenance activities in manufacturing facilities. The cost of implementing incremental automation will rise exponentially after reaching a critical limit. Till the critical point automation technology will rise exponentially at a minimal cost. The problem is that no one really knows what is the critical point for maintenance automation or for any other technological evolution.

In the future, there will be an exponential rise in the technology driving maintenance automation. But it will not completely eliminate the need for human workers in maintenance activities. Maintenance automation brings about improvement in processes, efficiency, and in turn bottom line. But after a critical limit, an incremental increase in efficiency comes at a huge cost.

Horse buggies were replaced by cars and taxis. A lot of coachmen lost their jobs due to the transition. In addition to that, horse merchants, workers taking care of horses, carriage makers, all lost their jobs. But plenty of new jobs were created in the process of transitioning into automobile-based transportation. Cars and taxis were unheard of before the existence of automobiles. Plenty of new jobs such as cab drivers, car salesmen, car dealers, mechanics, etc came into being. This is the sort of creative disruption that always happens in free-market capitalism and maintenance automation would be no different.

Creative disruption
The most plausible scenario, for maintenance automation, is where human workers work in conjunction with machines and artificial intelligence. Software and algorithmic tools will be used extensively for process automation intelligence. Robotic arms and other robotic devices that can be programmed to perform regular tasks would be created. But since there is a lot of variability in a lot of maintenance tasks creating custom programmed robots for each instance would be cumbersome. 

The tasks that require flexibility and dexterity will be exclusively carried out by human maintenance technicians. They will have assistance from cobots. ‘Cobot’ is an abbreviated form of ‘collaborative robot’. It is specially designed robots that assist human workers in accomplishing their tasks. This form of creative cooperation will be commonplace for maintenance activities of the future.

The bottom line is that machines and AI will take over a lot of mundane and repetitive tasks. This frees up human capital to deal with more creative and complex tasks. While on the one hand, a lot of traditional maintenance jobs will no longer exist. But on the other hand, plenty of never seen before jobs will be created. Machines and AI would be a net positive for all maintenance activities and jobs in a plant, in the long term.

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Bryan Christiansen is the founder and CEO of Limble CMMS. Limble is a modern, easy-to-use mobile CMMS software that takes the stress and chaos out of maintenance by helping managers organize, automate, and streamline their maintenance operations.

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