Hotel Operations Intelligence

Objective

Determine which operational factors are associated with rooms being out of service, and how a manager could prioritize limited technicians and parts.

Data sources

Hybrid project. Public lodging performance data supplies occupancy, ADR and RevPAR context; the work-order dataset is clearly labelled synthetic training data. Synthetic results demonstrate method only and are never presented as real business outcomes. No employer supplied internal information.

Tools

SQL · Python (pandas) · Excel · Power BI at completion.

Analysis

Maintenance demand by category and time, response times, room availability, workload and staffing pressure, and the interaction between availability, occupancy and revenue per available room.

Key findings

Data preparation complete — findings publish after the analysis step.

Business impact

Documented alongside the verified findings.

A room out of order on a sold-out night cannot be recovered later, and a repeat failure in an occupied room adds a service recovery cost on top of the repair. Having run overnight engineering in large hotels, I know maintenance demand is not evenly distributed — which makes it a plannable problem rather than an unavoidable one.

• — Synthetic data can demonstrate method; it cannot describe any real property's performance.
• — Guest experience would be inferred from operational timing, not measured from survey data.
• — Cost figures need internal labor and parts rates that are not public.