  Industry 4.0 offers opportunities to combine multiple sensor data sources
using IoT technologies for better utilization of raw material in production
lines. A common belief that data is readily available (the big data
phenomenon), is oftentimes challenged by the need to effectively acquire
quality data under severe constraints. In this paper we propose a design
methodology, using active learning to enhance learning capabilities, for
building a model of production outcome using a constrained amount of raw
material training data. The proposed methodology extends existing active
learning methods to effectively solve regression-based learning problems and
may serve settings where data acquisition requires excessive resources in the
physical world. We further suggest a set of qualitative measures to analyze
learners performance. The proposed methodology is demonstrated using an actual
application in the milk industry, where milk is gathered from multiple small
milk farms and brought to a dairy production plant to be processed into cottage
cheese.
