Static Spreadsheet Reporting
Existing reports lacked analytical depth, dynamic filtering, and driver-based explanations.
Multi-Branch QSR Sales & Operational Performance Intelligence Platform
Developed a multi-layer Power BI analytics solution for a UAE-based fast-food business, transforming Oracle ERP sales data into executive, branch, seasonality, revenue-driver, channel, staffing, and inventory intelligence.
QSR Performance Analytics
Multi-Branch Intelligence
Sales Driver Analysis
Peak-Hour Optimization
Power BI Decision Support
1.
Business Challenge
Leadership relied on static spreadsheets that reported outcomes but could not explain sales drivers, branch efficiency, seasonality, channel dependence, or the operational causes behind revenue changes.
Unexplained Revenue Changes
Leadership could not determine whether month-over-month changes were caused by seasonality, order volume, or pricing.
Uneven Branch Performance
The business could not distinguish high-volume, low-efficiency branches from locations with stronger average checks and premium-sales potential.
Channel Dependency Risk
Management lacked a clear view of in-store and delivery dependence, transaction volume, and channel profitability patterns.
Staffing & Inventory Inefficiency
Store teams lacked hourly demand intelligence for aligning labor schedules and inventory budgets with peak and low-demand periods.
2.
Solution
A centralized relational model and five-layer Power BI suite converted point-of-sale data into diagnostic and operational intelligence.
Centralized Oracle Data Model
Cleaned and unified point-of-sale datasets from Oracle ERP and Oracle Database into a low-latency star schema.
Dynamic Time Intelligence
Advanced DAX automated month-over-month growth and year-to-date cumulative performance benchmarks.
Seasonality Index
A mathematical seasonality model separated recurring demand patterns from branch-specific operational issues.
Revenue Driver Decomposition
A Waterfall model split revenue movement into Orders Impact and Average Check Impact.
Branch Performance Intelligence
Scatter analysis compared sales, order volume, average check, contribution, and monthly trends across locations.
Hourly Operational Heat Maps
Daily and hourly matrices revealed peak demand, low-demand windows, and branch-specific operating patterns.
Channel Performance Analysis
In-store and delivery channels were compared across revenue, order volume, and average check.
Five-Layer Power BI Suite
Delivered executive overview, branch intelligence, trends and seasonality, driver analysis, and operational intelligence.
3.
Business Impact
Analytical Layers Delivered
Executive, branch, trend, driver, and operational views supported strategic and tactical decision-making.
Peak-Month Uplift Identified
Seasonality analysis revealed months performing materially above average, supporting targeted marketing, staffing, and inventory planning.
In-Store Channel Share Identified
The analysis quantified dependence on in-store revenue and highlighted the need for a more balanced channel strategy.
Primary Revenue Drivers Isolated
Revenue movement was separated into order-volume impact and average-check impact.
Volume-Driven Growth Strategy Clarified
Revenue growth was shown to depend primarily on customer traffic and order volume rather than price increases, guiding marketing toward customer acquisition.
Branch-Specific Strategies Enabled
High-volume branches could focus on throughput and efficiency, while high-average-check branches such as NAH and APR could prioritize premium upselling.
Labor & Inventory Optimization Opportunities
Peak-hour and low-demand windows supported improved staff scheduling, reduced idle labor, and better inventory allocation.
Channel Risk Visibility Improved
Management gained clearer insight into in-store dependence and delivery performance across volume and profitability dimensions.
Quantification note: The supplied documentation did not include measured cost savings or margin improvement. The quantified impact above reflects documented dashboard findings and analytical scope only.
4.
Solution Architecture
QSR Sales & Operational Intelligence Architecture
Point-of-Sale DataSales, Orders, Tickets
Oracle ERPOperational Source
Branch & Channel DataLocation and Service Mode
↓↓↓
Star SchemaCentralized Analytical Model
→
DAX IntelligenceMoM, YTD, Seasonality
→
Driver AnalysisOrders vs. Average Check
↓
Power BI QSR Performance & Operations Suite
Oracle sales data is modeled and enriched with time intelligence, seasonality, and driver decomposition before delivery through interactive Power BI dashboards.
5.
Analytics Delivery Flow
Extract
Oracle ERP and point-of-sale data
Prepare
Clean, standardize, and unify
Model
Star schema and branch relationships
Calculate
MoM, YTD, seasonality, drivers
Optimize
Growth, branches, channels, staffing, inventory
The analytical flow moves from raw sales records to decision-ready commercial and operational intelligence.
6.
Technology Stack
Oracle ERPOracle DatabaseOracle SQLStar SchemaPower BI DesktopPower BI ServiceDAXTime IntelligenceSeasonality IndexWaterfall AnalysisDecomposition TreeScatter PlotsExcel
7.
Dashboard Gallery
8.
Project Showcase
Live Power BI Dashboard
Replace this placeholder with the public Power BI embed URL after publishing the report.
Video Walkthrough
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9.
GitHub Repository
Explore the QSR Analytics Project
Explore the complete project documentation, Oracle SQL data model, Power BI dashboards, DAX calculations, business insights, and supporting assets used to build the Multi-Branch QSR Sales & Operational Performance Intelligence Platform.
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