So, by means of small design experiments, sub-systems of your use case can be tested in order to assure validity of the outcome based on high quality data. It is nearly impossible to do the systems engineering part first time right; testing and validating shows that, the majority of the time, things in practice don’t go the way you expected. In our example we found that at one line, basil leaves were added to the pizza, causing an error in the cheese detecting algorithm that was only focusing on yellow pixels in images. Our solution: have an agile approach, respond quickly, and test again.
3. Operationalize in production
The final step of solving your use case is to embed it in your daily processes. When you got all systems in place, your data is ingested and meets all requirements, your data scientists modeled the solution, and the output of the model is embedded into an app: then the result needs to be put into practice. Looking at the pizza-case, the final setup (camera, cloud, dashboard, PLC adjustment) had to be implemented at the pizza belt and the factory workers needed to be trained in a different, less manual way of working. Embedding the solution in your daily processes means tying everything together, making your solution part of the factory instead of being a tool on top of the factory. This step is critical for the success of your use case. After all, a solution has to be used in order to be useful – make things productional!
“Making your solution part of the factory instead of being a tool on top of the factory, that is the goal. After all, a solution has to be used in order to be useful.”
To become data-driven is, as mentioned before, not something that can be achieved overnight. Change management requires a shift in culture and ways of working; a process that takes time. There are many use cases to execute, requiring a multitude of competences. It is a long-term investment that can only be fully redeemed if your multidisciplinary team works well together and maintains the balanced focus of business, engineering, and operationalization.
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