From Limited Annotated Raw Material Data to Quality Production Data: A Case Study in the Milk Industry (Technical Report)

TL;DR

This paper presents a methodology using active learning to build predictive models with limited raw material data in Industry 4.0, demonstrated through a case study in the milk industry to improve data efficiency in production.

Contribution

It extends active learning techniques to regression problems and introduces qualitative measures for learner performance, addressing data scarcity in physical resource-constrained environments.

Findings

Effective active learning approach for regression tasks.

Qualitative performance measures for learners.

Successful application in milk industry case study.

Abstract

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.