Towards Active Learning Based Smart Assistant for Manufacturing
This work addresses the need for interactive decision support in manufacturing, but it appears incremental as it builds on existing active learning and forecasting concepts without claiming major breakthroughs.
The paper tackles the problem of guiding users through decision-making steps based on machine learning forecasts in manufacturing, specifically demonstrating it on a demand forecasting use case with a methodology that can be extended to other scenarios.
A general approach for building a smart assistant that guides a user from a forecast generated by a machine learning model through a sequence of decision-making steps is presented. We develop a methodology to build such a system. The system is demonstrated on a demand forecasting use case in manufacturing. The methodology can be extended to several use cases in manufacturing. The system provides means for knowledge acquisition, gathering data from users. We envision active learning can be used to get data labels where labeled data is scarce.