KPI-BASED ADAPTIVE SCORING MODEL OF MSME PERFORMANCE USING QUANTILE CLIPPING AND DATA-DRIVEN WEIGHTING
Abstract
Monitoring the performance of Micro, Small, and Medium Enterprises (MSMEs) is a challenge for local governments due to the large number of business actors, heterogeneity of business scale, and variations in financial indicators that often contain extreme values. These conditions cause Key Performance Indicators (KPI)-based evaluations to be susceptible to bias when using conventional normalization and weighting. This study aims to develop an adaptive scoring model for MSME performance based on KPIs that is robust to outliers and more objective in indicator weighting. The proposed method integrates quantile clipping at the P5–P95 percentiles to stabilize the KPI distribution, followed by min–max normalization to the range 0–100. Furthermore, KPI weights are determined in a data-driven manner using standard deviation (adaptive weighting) to represent the indicator's contribution based on actual data variations. Experiments were conducted on a dataset of 1,000 MSMEs in Serang City using three main KPIs, namely ROI, Profit Margin, and Growth Rate. The results show that the adaptive weights obtained are ROI 0.308, Profit Margin 0.353, and Growth Rate 0.339. A ranking comparison between fixed weighting and adaptive weighting yielded a Spearman correlation of 0.9879, and two entities changed in the Top 10. These findings indicate that the adaptive method maintains ranking stability while increasing evaluation objectivity. The proposed model is computationally efficient and has potential for application in multi-indicator-based performance monitoring systems.
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