Gamifications have become widespread in our lives. A bracelet vibrates when 10,000 steps are taken in a day, the language-learning app motivates you to practice vocabulary every day, and the car presents an “eco-score” at the end of a journey. The behavior change through gamification can be observed in yourself – and has been proven in many studies. It has not yet spread to industry. That’s going to change.
Gamification is the transfer of game elements to a non-game context. The aim is always to influence human behavior in a desired direction. At the same time, new incentives should be created for people: Just fun or, for example, a higher purpose. In the industrial context, this can be, for example, behavior to reduce accidents at work or – as in the case of oee.ai – behavior to increase the OEE. The psychological background is diverse. Much can be summed up by the fact that an inner, the psychologist says intrinsic, motivation is created to display a certain behavior.
oee.ai offers several gamification use cases. This article is dedicated to a so-called progress visualization. What is the background? For the use of AI algorithms to increase equipment productivity, an annotation of the productivity losses in the time series that is as complete as possible is necessary in order to provide the algorithm with the cause of the productivity loss. The PLCs of modern systems can regularly only provide a limited proportion of the reasons for the losses – older systems are often not able to do this at all. In this case, the human steps in and tells oee.ai on a tablet what the actual cause of the loss of productivity was.
This information is of particular importance for the further course of automated data analytics. Machine learning or artificial intelligence algorithms are more accurate the more and the more complete the data is made available to them. Hence the need for an annotation of the losses in the time series that is as complete as possible, for which humans are required due to the incompleteness of the machine data.
Among psychologists, “hunting”, i.e. the acquisition of objects, is considered part of the human operating system. Man also strives for completeness. In this case, the human brain rewards itself, i.e. intrinsically. Anyone who remembers the rise of Pokémon Go will see clear parallels here. On LinkedIn, for example, small animations ask you to complete your profile.
oee.ai uses this effect to increase people’s motivation to enter the reasons for losses. On an Andon board, a progress visualization is shown in real time for everyone to see how complete the input of the loss reason for the current shift is.
The icons used can be selected from a catalog. The length of the row to be built is also configurable. At the start of the shift, the progress visualization is empty – neither answered nor unanswered loss reason entries from the past are visualized. This serves to clearly assign responsibility for data entry to the current shift.
In this way, only a few employees can escape the inner motivation to create completeness in the visualization, i.e., technically speaking, to annotate all causes of losses. In this way, a complete series of data is generated for further use by machine learning or AI algorithms. In addition, the employee acquires the feeling of having done this with motivation and of having achieved a goal at another point in addition to the actual production. All of this is done voluntarily, with no pressure and no extrinsic incentives, which in this case could possibly be a monetary payment for completeness.
Do you have questions about gamification of increasing equipment productivity or do you have your own ideas of what you would like to gamify in this environment? Contact us.