Performance indicators for aquatic centres in Canada: Identification and selection using fuzzy based methods.

Aquatic centres (ACs) are becoming exceedingly popular in the urban agglomerations of cold climate countries like Canada but functioning without assessing the state of their sustainability performance. Previous studies examined health and safety, water and indoor air quality, and energy consumption aspects without aiming at the holistic sustainability performance assessment. The present research is the first systematic effort for benchmarking of ACs. A hierarchical-based framework arranged 81 performance indicators to appraise the key components, including water management, indoor environment, personnel, service quality, energy, social, and operations. Fuzzy AHP and fuzzy mean clustering methods evaluated the identified PIs based on the opinion of experts (from Canadian aquatic centres) on their importance, measurability, and understandability. Finally, the selection process ranked a set of 63 most suitable PIs under 14 sub-criteria. Fuzzy-based methods efficiently handled the subjective scoring process and the difference of opinion among the experts. The criteria performance indices inform the top-level management while the sub-indices stipulate the operations management for honing in the lacking indicators. Using the selected PIs, the AC's management can allocate the available resources for both the short-term (e.g., efficient response to complaints) and long-term (e.g., replacing failed manually operated fixtures with the sensor-operated ones) improvement actions. The selected PIs will enhance the sustainability of ACs in Canada and other cold regions around the globe through a structured benchmarking process. Unlabelled Image • Aquatic centres are facing issues in attaining sustainability objectives. • Seven performance criteria and 81 indicators were identified for assessment. • Expert opinion were gathered from five aquatic centres. • Weights were generated using fuzzy analytic hierarchy process. • Sixty-three performance indicators were selected using fuzzy c-means clustering. [ABSTRACT FROM AUTHOR]