Laundry Performance Analysis Using KPI and Linear Regression Dashboard

Abstract

This study develops an integrated approach for measuring and forecasting the revenue performance of a small laundry business using Key Performance Indicators (KPIs), simple linear regression, and a dashboard prototype. The dataset consists of operational records from one Indonesian laundry enterprise covering January–June 2024. Daily records were validated, anonymized, and aggregated into six monthly observations containing transaction volume, customer count, laundry weight, revenue targets, and actual revenue. The KPIs comprised total revenue, month-to-month revenue growth, average revenue per transaction, and target achievement. A chronological holdout was used to evaluate the regression model: January–April formed the training set, whereas May–June formed the test set. The holdout evaluation produced MAE of Rp1,287,500, RMSE of Rp1,319,209, and MAPE of 6.31%. After validation, the model was refitted to all six observations, resulting in Ŷ = 13,216,666.67 + 1,235,714.29X with R² = 0.9198. Total observed revenue was Rp105,250,000; the highest monthly revenue was Rp21,000,000 in June; average revenue per transaction remained Rp50,000; and overall target achievement was 105.25%. The model forecast revenues of Rp21.87 million, Rp23.10 million, and Rp24.34 million for July, August, and September, respectively. The dashboard consolidates the KPI and forecast results into a concise decision-support view. Because the analysis uses one business and only six monthly observations, the forecasts should be interpreted as an exploratory trend estimate rather than a generalizable long-term model.