AutomatingMaintenanceWorkOrder Pro cessing in Facility Management - Classification, Responsibility Routing, and Data Readiness in Maintenance Workflows

Abstract

The Facility Management (FM) industry generates vast amounts of unstructured text data in the form of Maintenance Work Orders (MWOs). However, this data is frequently underutilised, often treated as an administrative burden rather than an analytical resource. Compared to hardware-dependent IoT (Internet-of-Things) and BMS (Building Management System) data, MWOs do not rely on modern building infrastructure and they can therefore provide valuable insights into building oper ations across diverse real-estate portfolios containing buildings of varying technical maturity. The Goal of this thesis is to investigate how MWOs can be leveraged to automate ad ministrative workflows and improve data quality within FM. Using a Design Science Research (DSR) approach in collaboration with Akademiska Hus, an exploratory data analysis established that traditional classification methods struggle with his torical noise, overlapping categories, and the under-specified nature of occupant text inputs. To address these bottlenecks, two artefacts, The Structurer and The De marcation Agent, were developed and evaluated. The results indicate that while full autonomy is restricted by non-deterministic model behavior and incomplete technical details in user texts, a Human-on-the Loop (HOTL) configuration provides a viable operational framework. Shifting the technician’s role from manual triage to validation lessens administrative burden without the risk of automated routing errors. Furthermore, the pipelines introduce a standardised classification logic that reduces the subjectivity and variance inher ent in manual logging, thereby improving data consistency for future maintenance analytics.