An integrated multi-stage fuzzy inference performance measurement scheme in humanitarian relief operations

Abstract

Performance based measurement models and systems in humanitarian supply chains require inputs derived from decision-makers’ and practitioners’ experience. Past knowledge related to performance indicator are generally qualitative and imprecise. Existing humanitarian performance measurement systems are largely theoretical and do not consider qualitative and imprecise information in their assessment of humanitarian relief operations. This paper aims to address a practical approach to the performance measurement of humanitarian operations by recognizing the need for multi-dimensional performance evaluation while dealing with imprecision and uncertainty in evaluation processes. A hierarchical multi-stage Fuzzy Inference System (FIS) was developed to address imprecise and uncertain performance indicators. Performance indicators were clustered into four Balanced Scorecard (BSC) type categories. A prototype was developed using MATLAB toolbox. The FIS scheme was tested and validated by practitioners from leading international humanitarian organizations in Malaysia. The scheme facilitated performance analysis and provided insights into operational areas that need further improvements. The proposed FIS scheme enable decision-makers to address imprecise and fuzzy information more objectively.