Context-Aware Analytics in MOM Applications

TL;DR

This paper discusses the importance of context-aware analytics in Manufacturing Operations Management systems, emphasizing data integration challenges and the benefits of semantic context data for predictive models.

Contribution

It introduces a framework for applying knowledge extraction techniques to enhance context-aware analytics in MOM, addressing data integration and change tracking issues.

Findings

Semantic context models improve data comparability in MOM analytics

Knowledge extraction techniques facilitate context-aware data integration

Effective change tracking of context information is essential for predictive maintenance

Abstract

Manufacturing Operations Management (MOM) systems are complex in the sense that they integrate data from heterogeneous systems inside the automation pyramid. The need for context-aware analytics arises from the dynamics of these systems that influence data generation and hamper comparability of analytics, especially predictive models (e.g. predictive maintenance), where concept drift affects application of these models in the future. Recently, an increasing amount of research has been directed towards data integration using semantic context models. Manual construction of such context models is an elaborate and error-prone task. Therefore, we pose the challenge to apply combinations of knowledge extraction techniques in the domain of analytics in MOM, which comprises the scope of data integration within Product Life-cycle Management (PLM), Enterprise Resource Planning (ERP), and Manufacturing Execution Systems (MES). We describe motivations, technological challenges and show benefits of context-aware analytics, which leverage from and regard the interconnectedness of semantic context data. Our example scenario shows the need for distribution and effective change tracking of context information.