hospitality-expert

Hospitality Expert

Expert guidance for hotel management, reservation systems, property management systems (PMS), guest services, revenue management, and hospitality technology solutions.

Core Concepts

Hotel Management Systems

• Property Management System (PMS)
• Central Reservation System (CRS)
• Revenue Management System (RMS)
• Channel Manager
• Point of Sale (POS)
• Guest Relationship Management (GRM)
• Housekeeping management

Technologies

• Mobile check-in/check-out
• Digital key systems
• Guest messaging platforms
• IoT for room automation
• AI chatbots for customer service
• Contactless payments
• Energy management systems

Standards and Protocols

• HTNG (Hotel Technology Next Generation)
• OpenTravel Alliance standards
• PCI-DSS for payment security
• ADA compliance for accessibility
• Brand standards (if franchise)
• OTA integrations (Booking.com, Expedia)

Revenue Management System

import numpy as np

class RevenueManagementSystem:
    """Hotel revenue management and dynamic pricing"""

    def __init__(self):
        self.pricing_rules = []
        self.demand_forecast = {}

    def calculate_dynamic_rate(self,
                              room_type: RoomType,
                              check_in_date: date,
                              days_until_arrival: int,
                              current_occupancy: float,
                              historical_data: dict) -> Decimal:
        """Calculate dynamic room rate"""
        # Base rate
        base_rates = {
            RoomType.STANDARD: Decimal('150'),
            RoomType.DELUXE: Decimal('200'),
            RoomType.SUITE: Decimal('350'),
            RoomType.EXECUTIVE: Decimal('450')
        }

        base_rate = base_rates.get(room_type, Decimal('150'))

        # Demand multiplier based on occupancy
        if current_occupancy > 0.85:
            demand_multiplier = Decimal('1.30')  # High demand
        elif current_occupancy > 0.70:
            demand_multiplier = Decimal('1.15')  # Moderate demand
        elif current_occupancy > 0.50:
            demand_multiplier = Decimal('1.00')  # Normal
        else:
            demand_multiplier = Decimal('0.85')  # Low demand

        # Booking window multiplier
        if days_until_arrival < 7:
            window_multiplier = Decimal('1.20')  # Last minute
        elif days_until_arrival < 14:
            window_multiplier = Decimal('1.10')
        elif days_until_arrival > 60:
            window_multiplier = Decimal('0.90')  # Early bird
        else:
            window_multiplier = Decimal('1.00')

        # Day of week adjustment
        if check_in_date.weekday() in [4, 5]:  # Friday, Saturday
            day_multiplier = Decimal('1.25')
        elif check_in_date.weekday() == 6:  # Sunday
            day_multiplier = Decimal('0.95')
        else:
            day_multiplier = Decimal('1.00')

        # Calculate final rate
        dynamic_rate = base_rate * demand_multiplier * window_multiplier * day_multiplier

        # Round to nearest dollar
        dynamic_rate = dynamic_rate.quantize(Decimal('1'))

        return dynamic_rate

    def forecast_demand(self, start_date: date, days: int) -> dict:
        """Forecast demand for upcoming period"""
        forecast = {}

        for i in range(days):
            forecast_date = start_date + timedelta(days=i)

            # Simplified demand forecast
            # In production, would use ML models
            base_demand = 70.0  # 70% base occupancy

            # Day of week factor
            if forecast_date.weekday() in [4, 5]:  # Weekend
                day_factor = 15
            elif forecast_date.weekday() == 6:
                day_factor = -10
            else:
                day_factor = 0

            # Seasonality factor (simplified)
            month = forecast_date.month
            if month in [6, 7, 8]:  # Summer
                season_factor = 10
            elif month in [12, 1]:  # Holiday season
                season_factor = 15
            else:
                season_factor = 0

            forecasted_occupancy = base_demand + day_factor + season_factor
            forecasted_occupancy = min(100, max(0, forecasted_occupancy))

            forecast[forecast_date.isoformat()] = {
                'date': forecast_date.isoformat(),
                'forecasted_occupancy': forecasted_occupancy,
                'confidence': 'high' if i < 14 else 'medium' if i < 30 else 'low'
            }

        return forecast

    def optimize_inventory(self, total_rooms: int, date_range: tuple) -> dict:
        """Optimize room inventory allocation"""
        # Allocate rooms across different channels
        # Direct bookings, OTAs, corporate contracts, etc.

        allocation = {
            'direct': int(total_rooms * 0.40),  # 40% direct
            'ota': int(total_rooms * 0.35),     # 35% OTAs
            'corporate': int(total_rooms * 0.15),  # 15% corporate
            'walk_in': int(total_rooms * 0.10)  # 10% walk-ins
        }

        return {
            'total_rooms': total_rooms,
            'allocation': allocation,
            'date_range': {
                'start': date_range[0].isoformat(),
                'end': date_range[1].isoformat()
            }
        }

    def calculate_revpar(self, revenue: Decimal, available_rooms: int) -> Decimal:
        """Calculate Revenue Per Available Room"""
        if available_rooms == 0:
            return Decimal('0')

        revpar = revenue / available_rooms
        return revpar.quantize(Decimal('0.01'))

    def calculate_adr(self, revenue: Decimal, rooms_sold: int) -> Decimal:
        """Calculate Average Daily Rate"""
        if rooms_sold == 0:
            return Decimal('0')

        adr = revenue / rooms_sold
        return adr.quantize(Decimal('0.01'))