Gamification has spread in our lives. A bracelet vibrates when 10,000 steps are taken during the day, the language learning app motivates daily vocabulary training and the car presents an “eco-score” when a journey is completed. The change in behavior through gamification can be observed in yourself — and has been proven in many studies. It has not yet spread in industry. That's about to change.
Gamification is the transfer of game elements to a non-game context. The aim is always to influence people's behavior in a desired direction. At the same time, this should create new incentives for people: simply fun or even an overarching sense, for example. In an industrial context, this can be behavior to reduce accidents at work or — as in the case of oee.ai — behavior to increase OEE. The psychological backgrounds are varied. A lot can be summed up by the fact that an inner motivation, the psychologist says intrinsic, is created to engage in a certain behavior.
oee.ai offers several gamification use cases. This article is dedicated to what is known as a progress bar. What is the background? Using AI algorithms to increase plant productivity requires an annotation of productivity losses in the time series as completely as possible in order to provide the algorithm with the cause of the loss of productivity. The control systems of modern systems can regularly provide only a limited proportion of the reasons for failure — older systems are often unable to do so 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 analysis. Machine learning or artificial intelligence algorithms are more accurate the more and the more complete data is made available to them. This is why it is necessary to annotate the losses in the time series as completely as possible, which requires humans due to the incompleteness of the machine data.
Psychologists regard “hunting,” i.e. the procurement of objects, as part of the human operating system. People also strive 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 or XING, for example, you are also asked to complete your profile via small animations.
oee.ai makes use of this effect to increase people's motivation to enter the reasons for the disturbance. On an Andon board, a progress indicator is shown in real time for everyone to see how complete the background input for the current shift is.
The icons used can be selected from a catalog. The length of the row that is to be built up can also be configured. At the start of the shift, the progress bar is empty — neither answered nor unanswered background inputs from the past are visualized. This is used to uniquely assign responsibility for data entry to the current shift.
In this way, only a few employees can evade the inner motivation to create completeness in the visualization, i.e. to annotate all reasons for interference, technically speaking. In this way, a complete series of data is generated for further use by machine learning or AI algorithms. In addition, the employee gets the feeling that they have done this with motivation and that they have achieved a goal somewhere else in addition to the actual production. All this is done voluntarily, without pressure and without extrinsic incentives, which in this case could possibly be a monetary payment for completeness.
Do you have any questions about gamifying the increase in plant productivity or do you have your own ideas about what you would like to gamify in this environment? Talk to us