From Intuition to Insight: A Business Intelligence Case Study of Daily Sales and Food-Waste Performance in a Sri Lankan Café https://doi.org/10.34074/rere.00608
By: Senumi Amaya Rajasundara Waidyalankara Gamalathge Amasha Malindi Premarathne Sellapperumage Asheni Subhangi Fernando Dasanayake Mudiyanselage Yenuli Dulansa Gilmini Dantanarayan Dilki Shihara Muthumala Jayasooriya
Abstract
In Sri Lanka, small family-owned cafés and bakeries typically make decisions on production,staffing and menus on a day to day basis without having data on hand to assist in decision making. Such judgments are often sound, but they are off-the-record, unverifiable, unauditable and can represent unnoticed losses due to overproduction and mismatched staffing. This paper is an applied case-study of one month of operational data (1,890 records across 63 items over 30 days) collected from an operational café and analysed through a four page interactive Microsoft Power BI (MS-PB) dashboard that reports on sales overview, item analysis, day-wise sales pattern and wastage analysis. All references to “profit” are to gross contribution (sales revenue minus cost of goods sold and value of wastage) and not accounting net profit as this does not include business overheads, including rent, wages and utilities. It was observed that cake was the largest revenue category (40% of total revenue) and the largest source of food wastage (Rs. 363,045 of Rs. 806,133), making it the most financially impactful category to manage. Sales volume and contribution did not consistently align. Highest selling items were the low margin savoury snacks while the highest-margin items were moderately sold items like cake, this would not have been seen as important from a volume-based perspective. The revenue per day generated during weekend days was approximately 36% higher than during weekdays, and served as an evidence base to test weekend staffing. Overall wastage was 15.7% of production (almost one-third of monthly gross contribution), and the single best-selling product, Chicken Patty, was the most wasted, suggesting an overproduction pattern. This study demonstrates that just a small, structured, one-month dataset can reveal operational insight and that accessible BI tools makes the analysis possible for enterprises without a dedicated analytics team. The results are given as insights and testable proposals rather than demonstrated operational improvements, since the data are from one month.
Keywords:
Business Intelligence, Small and Medium Enterprises, Data Visualisation, Food Waste, Power BI, Foodservice Operations.