Determination of Business Intelligence and Analytics-Based Healthcare Facility Management Key Performance Indicators

The use of digital technologies such as Internet of Things (IoT) and smart meters induces a huge data stack in facility management (FM). However, the use of data analysis techniques has remained limited to converting available data into information within activities performed in FM. In this context, business intelligence and analytics (BI&A) techniques can provide a promising opportunity to elaborate facility performance and discover measurable new FM key performance indicators (KPIs) since existing KPIs are too crude to discover actual performance of facilities. Beside this, there is no comprehensive study that covers BI&A activities and their importance level for healthcare FM. Therefore, this study aims to identify healthcare FM KPIs and their importance levels for the Turkish healthcare FM industry with the use of the AHP integrated PROMETHEE method. As a result of the study, ninety-eight healthcare FM KPIs, which are categorized under six categories, were found. The comparison of the findings with the literature review showed that there are some similarities and differences between countries’ FM healthcare ranks. Within this context, differences between countries can be related to the consideration of limited FM KPIs in the existing studies. Therefore, the proposed FM KPIs under this study are very comprehensive and detailed to measure and discover healthcare FM performance. This study can help professionals perform more detailed building performance analyses in FM. Additionally, findings from this study will pave the way for new developments in FM software and effective use of available data to enable lean FM processes in healthcare facilities

Building energy performance monitoring through the lens of data quality: a review

Data quality is important across sectors to ensure that data meets the requirements of its users, but until now little attention has been given to how it is reported in the architecture, engineering and construction (AEC) sectors. The lack of visibility about data quality in building energy performance monitoring motivated a review of 162 articles published since 2017. This identified that data quality reporting was fragmented and limited, with a gap from best practice and a lack of standardisation around requirements specific to building performance, including data comparability and spatiotemporal granularity. Where data quality issues were diagnosed, this was in isolation and concerned individual data quality attributes rather than within a comprehensive data governance strategy. This powerfully evidences the need to build consensus across AEC about (1) the required levels of data quality from building energy performance data, (2) a consistent reporting vocabulary and (3) how data quality is achieved