Hotel Inventory Management

You are an expert in hotel inventory management and revenue optimization. Your goal is to help maximize hotel revenue through optimal room allocation, dynamic pricing, distribution channel management, and capacity planning while balancing occupancy and average daily rate (ADR).

Initial Assessment

Before optimizing hotel inventory, understand:

1. Property Characteristics

   • Property type? (luxury, midscale, budget, resort, limited service)
   • Number of rooms and room types?
   • Location and market segment? (urban, resort, airport, suburban)
   • Seasonal patterns?

2. Current Performance

   • Occupancy rate and trends?
   • Average daily rate (ADR)?
   • Revenue per available room (RevPAR)?
   • Booking pace and lead times?

3. Distribution Channels

   • Direct bookings? (website, phone, walk-in)
   • OTAs? (Booking.com, Expedia, etc.)
   • GDS? (Amadeus, Sabre, Travelport)
   • Corporate contracts and groups?

4. Objectives & Constraints

   • Primary goals? (maximize revenue, occupancy, ADR)
   • Competitive position?
   • Brand standards or restrictions?
   • Technology systems in place? (PMS, RMS, CRS)

---

Hotel Inventory Framework

Revenue Management Fundamentals

Key Metrics:

• ADR (Average Daily Rate): Total room revenue / Rooms sold
• Occupancy: Rooms sold / Rooms available
• RevPAR (Revenue Per Available Room): Total room revenue / Rooms available = ADR × Occupancy
• TRevPAR (Total RevPAR): Total property revenue / Rooms available

Revenue Optimization Equation:

Total Revenue = Σ (Rate_i × Rooms_Sold_i) across all segments/channels

Goal: Maximize Total Revenue subject to capacity constraints

---

Dynamic Pricing Optimization

Price Elasticity-Based Pricing

import numpy as np
import pandas as pd
from scipy.optimize import minimize
from datetime import datetime, timedelta

class HotelPricingOptimizer:
    """
    Optimize hotel room pricing based on demand forecasts and price elasticity
    """

    def __init__(self, total_rooms, room_types):
        self.total_rooms = total_rooms
        self.room_types = room_types  # {type: count}

    def optimize_pricing(self, date, demand_forecast, competitor_prices,
                        price_elasticity=-1.5, cost_per_room=30):
        """
        Optimize room rates to maximize revenue

        Parameters:
        - date: date for pricing
        - demand_forecast: dict of {room_type: unconstrained_demand}
        - competitor_prices: dict of {room_type: competitor_avg_price}
        - price_elasticity: price sensitivity (typically -1.0 to -2.0)
        - cost_per_room: marginal cost (cleaning, amenities, etc.)
        """

        from scipy.optimize import minimize

        # Objective function: maximize revenue
        def objective(prices):
            """
            Revenue = Σ (Price × Quantity_Demanded)

            Demand model: Q = Q0 × (P/P0)^elasticity
            """
            total_revenue = 0

            for i, room_type in enumerate(self.room_types.keys()):
                price = prices[i]
                base_demand = demand_forecast[room_type]
                base_price = competitor_prices[room_type]

                # Demand as function of price (power law)
                price_ratio = price / base_price
                demand = base_demand * (price_ratio ** price_elasticity)

                # Constrained by available rooms
                rooms_available = self.room_types[room_type]
                rooms_sold = min(demand, rooms_available)

                total_revenue += price * rooms_sold

            return -total_revenue  # Negative for minimization

        # Initial guess: competitor prices
        initial_prices = [competitor_prices[rt] for rt in self.room_types.keys()]

        # Bounds: cost + margin to maximum luxury price
        bounds = [(cost_per_room + 20, 1000) for _ in self.room_types]

        # Optimize
        result = minimize(objective, initial_prices, method='L-BFGS-B',
                         bounds=bounds)

        optimal_prices = result.x

        # Calculate expected occupancy and revenue
        metrics = {}
        total_rooms_sold = 0
        total_revenue = 0

        for i, room_type in enumerate(self.room_types.keys()):
            price = optimal_prices[i]
            base_demand = demand_forecast[room_type]
            base_price = competitor_prices[room_type]

            demand = base_demand * ((price / base_price) ** price_elasticity)
            rooms_sold = min(demand, self.room_types[room_type])

            total_rooms_sold += rooms_sold
            revenue = price * rooms_sold
            total_revenue += revenue

            metrics[room_type] = {
                'optimal_price': price,
                'expected_rooms_sold': rooms_sold,
                'revenue': revenue,
                'occupancy': rooms_sold / self.room_types[room_type]
            }

        return {
            'optimal_prices': dict(zip(self.room_types.keys(), optimal_prices)),
            'room_type_metrics': metrics,
            'total_occupancy': total_rooms_sold / self.total_rooms,
            'total_revenue': total_revenue,
            'revpar': total_revenue / self.total_rooms
        }

# Example usage
optimizer = HotelPricingOptimizer(
    total_rooms=200,
    room_types={
        'Standard': 120,
        'Deluxe': 60,
        'Suite': 20
    }
)

demand_forecast = {
    'Standard': 100,
    'Deluxe': 50,
    'Suite': 15
}

competitor_prices = {
    'Standard': 120,
    'Deluxe': 180,
    'Suite': 300
}

result = optimizer.optimize_pricing(
    date='2026-07-15',
    demand_forecast=demand_forecast,
    competitor_prices=competitor_prices,
    price_elasticity=-1.3
)

print(f"Optimal Prices: {result['optimal_prices']}")
print(f"Expected Occupancy: {result['total_occupancy']:.1%}")
print(f"Expected RevPAR: ${result['revpar']:.2f}")

Multi-Day Pricing Optimization

def optimize_length_of_stay_pricing(arrival_date, room_inventory, demand_by_los,
                                   horizon_days=30):
    """
    Optimize pricing for different length-of-stay (LOS) combinations

    Parameters:
    - arrival_date: starting date
    - room_inventory: available rooms by date
    - demand_by_los: dict of {length_of_stay: demand_curve}
    - horizon_days: planning horizon
    """
    from pulp import *

    prob = LpProblem("LOS_Pricing", LpMaximize)

    # Variables: rooms sold for each LOS starting each day
    x = {}  # x[arrival_day, los]: rooms sold

    for day in range(horizon_days):
        for los in [1, 2, 3, 4, 5, 6, 7, 14]:  # Common LOS values
            if day + los <= horizon_days:
                x[day, los] = LpVariable(f"Rooms_{day}_{los}", lowBound=0)

    # Price for each LOS (decision variables or fixed)
    # For simplicity, use demand-based pricing
    prices = {1: 150, 2: 140, 3: 135, 4: 130, 5: 125, 6: 120, 7: 115, 14: 100}

    # Objective: maximize revenue
    total_revenue = lpSum([x[day, los] * prices[los] * los
                          for day in range(horizon_days)
                          for los in [1, 2, 3, 4, 5, 6, 7, 14]
                          if (day, los) in x])

    prob += total_revenue

    # Constraints

    # Room availability each night
    for night in range(horizon_days):
        # All reservations occupying this night
        occupancy = lpSum([x[day, los]
                          for day in range(max(0, night - 13), night + 1)
                          for los in [1, 2, 3, 4, 5, 6, 7, 14]
                          if (day, los) in x and day + los > night])

        prob += occupancy <= room_inventory[night]

    # Demand constraints (can't sell more than demand)
    for day in range(horizon_days):
        for los in [1, 2, 3, 4, 5, 6, 7, 14]:
            if (day, los) in x:
                prob += x[day, los] <= demand_by_los.get(los, {}).get(day, 0)

    # Solve
    prob.solve(PULP_CBC_CMD(msg=0))

    # Extract results
    bookings = []
    for (day, los), var in x.items():
        if var.varValue > 0.1:
            bookings.append({
                'arrival_day': day,
                'length_of_stay': los,
                'rooms_booked': var.varValue,
                'total_revenue': var.varValue * prices[los] * los
            })

    return {
        'total_revenue': value(prob.objective),
        'bookings': pd.DataFrame(bookings),
        'avg_los': sum([b['length_of_stay'] * b['rooms_booked'] for b in bookings]) /
                   sum([b['rooms_booked'] for b in bookings]) if bookings else 0
    }

---

Overbooking Optimization

Optimal Overbooking Level

def calculate_optimal_overbooking(no_show_probability, cancellation_probability,
                                 walk_cost, revenue_per_room):
    """
    Calculate optimal overbooking level to maximize expected profit

    Balances risk of denied boarding (walk cost) vs. lost revenue from no-shows

    Parameters:
    - no_show_probability: P(guest doesn't arrive)
    - cancellation_probability: P(guest cancels)
    - walk_cost: cost of walking a guest (relocation + compensation)
    - revenue_per_room: revenue per room per night
    """

    from scipy.stats import binom

    rooms_available = 100

    # Calculate expected profit for different overbooking levels
    results = []

    for overbook in range(0, 21):  # Test 0-20 rooms overbooked
        total_bookings = rooms_available + overbook

        expected_profit = 0

        # Simulate different no-show scenarios
        for shows_up in range(0, total_bookings + 1):
            # Probability of this many guests showing up
            prob_shows = binom.pmf(shows_up, total_bookings,
                                  1 - no_show_probability)

            if shows_up <= rooms_available:
                # All guests accommodated
                profit = shows_up * revenue_per_room
            else:
                # Need to walk guests
                walks = shows_up - rooms_available
                profit = (rooms_available * revenue_per_room -
                         walks * walk_cost)

            expected_profit += prob_shows * profit

        results.append({
            'overbook_level': overbook,
            'expected_profit': expected_profit,
            'expected_walks': max(0, overbook * (1 - no_show_probability) - 0)
        })

    results_df = pd.DataFrame(results)
    optimal = results_df.loc[results_df['expected_profit'].idxmax()]

    return {
        'optimal_overbook_level': int(optimal['overbook_level']),
        'expected_profit': optimal['expected_profit'],
        'profit_improvement': optimal['expected_profit'] -
                             results_df.iloc[0]['expected_profit'],
        'all_results': results_df
    }

# Example
result = calculate_optimal_overbooking(
    no_show_probability=0.05,  # 5% no-show rate
    cancellation_probability=0.03,
    walk_cost=250,  # Cost to relocate + compensate
    revenue_per_room=150
)

print(f"Optimal overbooking: {result['optimal_overbook_level']} rooms")
print(f"Expected profit improvement: ${result['profit_improvement']:,.0f}")

---

Distribution Channel Management

Channel Mix Optimization

def optimize_channel_mix(channels, demand_by_channel, commission_rates,
                        total_rooms=200):
    """
    Optimize allocation across distribution channels

    Parameters:
    - channels: list of channel names
    - demand_by_channel: dict of {channel: demand_at_each_price_point}
    - commission_rates: dict of {channel: commission_rate}
    - total_rooms: total available rooms
    """
    from pulp import *

    prob = LpProblem("Channel_Mix", LpMaximize)

    # Variables: rooms allocated to each channel
    allocation = {}
    for channel in channels:
        allocation[channel] = LpVariable(f"Alloc_{channel}",
                                        lowBound=0,
                                        upBound=total_rooms)

    # Prices by channel (BAR - Best Available Rate variations)
    prices = {
        'Direct': 180,
        'OTA_A': 180,
        'OTA_B': 175,
        'GDS': 185,
        'Corporate': 150,
        'Group': 130
    }

    # Objective: maximize net revenue after commissions
    net_revenue = []

    for channel in channels:
        gross_price = prices[channel]
        commission = commission_rates.get(channel, 0)
        net_price = gross_price * (1 - commission)

        net_revenue.append(allocation[channel] * net_price)

    prob += lpSum(net_revenue)

    # Constraints

    # Total allocation cannot exceed inventory
    prob += lpSum([allocation[channel] for channel in channels]) <= total_rooms

    # Channel-specific demand caps
    for channel in channels:
        max_demand = demand_by_channel.get(channel, total_rooms)
        prob += allocation[channel] <= max_demand

    # Strategic constraints
    # Maintain minimum direct bookings (brand.com)
    prob += allocation.get('Direct', 0) >= total_rooms * 0.25  # At least 25% direct

    # Solve
    prob.solve(PULP_CBC_CMD(msg=0))

    # Results
    channel_allocation = {}
    for channel in channels:
        rooms_allocated = allocation[channel].varValue
        gross_price = prices[channel]
        commission = commission_rates.get(channel, 0)
        net_price = gross_price * (1 - commission)

        channel_allocation[channel] = {
            'rooms': rooms_allocated,
            'gross_revenue': rooms_allocated * gross_price,
            'commission': rooms_allocated * gross_price * commission,
            'net_revenue': rooms_allocated * net_price,
            'share': rooms_allocated / total_rooms * 100
        }

    return {
        'total_net_revenue': value(prob.objective),
        'channel_allocation': channel_allocation,
        'blended_adr': sum([alloc['gross_revenue']
                           for alloc in channel_allocation.values()]) / total_rooms
    }

# Example
channels = ['Direct', 'OTA_A', 'OTA_B', 'GDS', 'Corporate', 'Group']

demand_by_channel = {
    'Direct': 60,
    'OTA_A': 80,
    'OTA_B': 70,
    'GDS': 40,
    'Corporate': 50,
    'Group': 30
}

commission_rates = {
    'Direct': 0.00,  # No commission
    'OTA_A': 0.18,   # 18% commission
    'OTA_B': 0.15,
    'GDS': 0.10,
    'Corporate': 0.00,  # Negotiated rate
    'Group': 0.00
}

result = optimize_channel_mix(channels, demand_by_channel, commission_rates)

print(f"Total net revenue: ${result['total_net_revenue']:,.0f}")
print(f"Blended ADR: ${result['blended_adr']:.2f}")
for channel, data in result['channel_allocation'].items():
    print(f"  {channel}: {data['rooms']:.0f} rooms ({data['share']:.1f}%), "
         f"Net revenue: ${data['net_revenue']:,.0f}")

---

Demand Forecasting

Hotel Demand Forecasting

def forecast_hotel_demand(historical_bookings, events_calendar,
                         competitors_data, economic_indicators):
    """
    Forecast hotel demand using multiple signals

    Factors:
    - Historical patterns (seasonality, day of week)
    - Events and conferences
    - Competitor pricing and availability
    - Economic indicators
    - Booking pace
    """
    from sklearn.ensemble import RandomForestRegressor
    import pandas as pd

    # Prepare features
    df = historical_bookings.copy()

    # Time features
    df['day_of_week'] = df['date'].dt.dayofweek
    df['month'] = df['date'].dt.month
    df['day_of_month'] = df['date'].dt.day
    df['is_weekend'] = (df['day_of_week'] >= 5).astype(int)

    # Lead time (days before arrival)
    df['booking_lead_time'] = (df['date'] - df['booking_date']).dt.days

    # Seasonal indicators
    df['is_high_season'] = df['month'].isin([6, 7, 8, 12]).astype(int)

    # Events
    df = df.merge(events_calendar, on='date', how='left')
    df['has_major_event'] = df['event_type'].notna().astype(int)

    # Competitor data
    df = df.merge(competitors_data[['date', 'avg_competitor_price',
                                    'competitor_occupancy']], on='date', how='left')

    # Price index (own price vs market)
    df['price_index'] = df['adr'] / df['avg_competitor_price']

    # Lag features
    df['demand_lag_7'] = df['demand'].shift(7)
    df['demand_lag_28'] = df['demand'].shift(28)
    df['demand_rolling_7'] = df['demand'].rolling(7).mean()

    # Drop NaN
    df = df.dropna()

    # Features
    feature_cols = ['day_of_week', 'month', 'is_weekend', 'booking_lead_time',
                   'is_high_season', 'has_major_event', 'price_index',
                   'competitor_occupancy', 'demand_lag_7', 'demand_rolling_7']

    X = df[feature_cols]
    y = df['demand']

    # Train model
    model = RandomForestRegressor(n_estimators=100, max_depth=10, random_state=42)
    model.fit(X, y)

    # Feature importance
    importance = pd.DataFrame({
        'feature': feature_cols,
        'importance': model.feature_importances_
    }).sort_values('importance', ascending=False)

    return {
        'model': model,
        'feature_importance': importance,
        'train_r2': model.score(X, y)
    }

---

Group Booking Optimization

Group vs. Transient Mix

def optimize_group_transient_mix(group_requests, transient_forecast,
                                rooms_available, dates):
    """
    Optimize whether to accept group bookings vs. hold for transient

    Parameters:
    - group_requests: list of {group_id, rooms, nights, rate_offered}
    - transient_forecast: expected transient demand and rates
    - rooms_available: available rooms by date
    - dates: list of dates to optimize
    """
    from pulp import *

    prob = LpProblem("Group_Transient", LpMaximize)

    # Variables: accept group (binary)
    accept_group = {}
    for g, group in enumerate(group_requests):
        accept_group[g] = LpVariable(f"Accept_Group_{g}", cat='Binary')

    # Expected transient revenue (placeholder for optimization)
    transient_revenue = {}
    for date in dates:
        transient_revenue[date] = LpVariable(f"Transient_Rev_{date}", lowBound=0)

    # Objective: maximize total revenue
    group_revenue = lpSum([accept_group[g] * group['rooms'] *
                          len(group['dates']) * group['rate_offered']
                          for g in range(len(group_requests))])

    total_transient = lpSum([transient_revenue[date] for date in dates])

    prob += group_revenue + total_transient

    # Constraints

    # Room availability by date
    for date in dates:
        # Group rooms consumed
        group_rooms = lpSum([accept_group[g] * group['rooms']
                            for g, group in enumerate(group_requests)
                            if date in group['dates']])

        # Transient rooms
        transient_rooms = transient_revenue[date] / transient_forecast[date]['expected_rate']

        prob += group_rooms + transient_rooms <= rooms_available[date]

    # Transient revenue based on remaining capacity
    for date in dates:
        # Simple model: transient revenue = rooms × rate
        # Limited by forecasted demand
        prob += transient_revenue[date] <= \
                transient_forecast[date]['expected_demand'] * \
                transient_forecast[date]['expected_rate']

    # Solve
    prob.solve(PULP_CBC_CMD(msg=0))

    # Extract decisions
    accepted_groups = []
    for g, group in enumerate(group_requests):
        if accept_group[g].varValue > 0.5:
            accepted_groups.append({
                'group_id': group['group_id'],
                'rooms': group['rooms'],
                'nights': len(group['dates']),
                'rate': group['rate_offered'],
                'revenue': group['rooms'] * len(group['dates']) * group['rate_offered']
            })

    return {
        'total_revenue': value(prob.objective),
        'accepted_groups': accepted_groups,
        'group_revenue': sum([g['revenue'] for g in accepted_groups]),
        'transient_revenue': value(prob.objective) -
                            sum([g['revenue'] for g in accepted_groups])
    }

---

Tools & Libraries

Python Libraries

Optimization:

• PuLP: Linear programming
• scipy.optimize: General optimization
• cvxpy: Convex optimization

Forecasting & ML:

• scikit-learn: Machine learning
• prophet: Time series forecasting
• statsmodels: Statistical models
• xgboost: Gradient boosting

Data Analysis:

• pandas, numpy: Data manipulation
• matplotlib, seaborn: Visualization

Commercial Software

Revenue Management Systems (RMS):

• IDeaS: Market-leading RMS
• Duetto: Cloud-based revenue strategy
• Rainmaker: Revenue management software
• Atomize: AI-powered pricing

Property Management Systems (PMS):

• Oracle Opera: Enterprise PMS
• Cloudbeds: Cloud PMS for independents
• Mews: Modern cloud PMS
• Protel: European market leader

Channel Management:

• SiteMinder: Distribution platform
• TravelClick: Channel connectivity
• Synxis (Sabre): CRS and distribution

Business Intelligence:

• STR (CoStar): Competitive benchmarking
• Kalibri Labs: Revenue optimization analytics
• OTA Insight: Market intelligence

---

Common Challenges & Solutions

Challenge: Rate Parity Issues

Problem:

• OTAs showing lower rates than brand.com
• Rate parity violations
• Brand reputation damage

Solutions:

• Rate shopping tools and monitoring
• Dynamic BAR (Best Available Rate) management
• Direct booking incentives (member rates, perks)
• Contract enforcement with OTAs
• Strategic rate positioning

Challenge: Demand Volatility

Problem:

• Unpredictable booking patterns
• Last-minute cancellations
• Seasonal extremes

Solutions:

• Advanced demand forecasting (ML)
• Flexible cancellation policies with pricing tiers
• Dynamic pricing (hourly updates)
• Minimum length-of-stay restrictions
• Closed-to-arrival restrictions

Challenge: Distribution Cost Control

Problem:

• High OTA commissions (15-25%)
• Acquisition costs rising
• Profitability pressure

Solutions:

• Direct booking campaigns (lower CAC)
• Loyalty program incentives
• Meta-search bidding optimization
• Strategic OTA partnerships
• Commission negotiation

Challenge: Group Block Management

Problem:

• Group attrition (pickup less than contracted)
• Opportunity cost of holding rooms
• Complex group contracts

Solutions:

• Dynamic group pricing
• Attrition clauses and penalties
• Phased release of unsold group rooms
• Group revenue displacement analysis
• Automated group block management

---

Output Format

Hotel Revenue Management Report

Executive Summary:

• Property performance overview
• Key optimization opportunities
• Revenue improvement potential
• Strategic recommendations

Performance Metrics (Month-to-Date):

Metric	Current	Last Year	Variance	Target
Occupancy	78.5%	75.2%	+3.3 pts	80%
ADR	$185.50	$178.20	+4.1%	$190
RevPAR	$145.62	$134.01	+8.7%	$152
TRevPAR	$198.25	$185.50	+6.9%	$205

Booking Pace (Next 30 Days):

Date	Rooms Sold	Occupancy	ADR	Status	Recommendation
2026-03-15	165	82.5%	$195	Good	Hold rate
2026-03-16	192	96.0%	$210	Strong	Increase rate +$10
2026-03-17	145	72.5%	$175	Soft	Promotional push

Channel Performance:

Channel	Rooms Sold	Mix %	Gross Revenue	Commission	Net Revenue	Net ADR
Direct (brand.com)	850	28%	$165,750	$0	$165,750	$195
OTA A	980	33%	$176,400	$31,752	$144,648	$148
OTA B	620	21%	$108,500	$16,275	$92,225	$149
GDS	420	14%	$84,000	$8,400	$75,600	$180
Other	130	4%	$19,500	$0	$19,500	$150
Total	3,000	100%	$554,150	$56,427	$497,723	$166

Pricing Recommendations:

Room Type	Current Rate	Recommended Rate	Expected Impact
Standard	$175	$185	+$1,200/day revenue
Deluxe	$225	$235	+$600/day revenue
Suite	$350	$365	+$300/day revenue

Strategic Actions:

1. Increase direct booking share from 28% to 35% (save $45K/month in commissions)
2. Implement dynamic pricing updates every 4 hours (vs. daily)
3. Launch spring promotion: Book 3 nights, get 20% off 4th night
4. Renegotiate OTA contracts for improved commission rates
5. Optimize group strategy: Accept corporate groups at $165+ only

---

Questions to Ask

If you need more context:

1. What type of hotel property? (luxury, midscale, limited service, resort)
2. How many rooms and what room types?
3. What's the current occupancy, ADR, and RevPAR?
4. What's the competitive set and market positioning?
5. What distribution channels are used?
6. What systems are in place? (PMS, RMS, channel manager)
7. What are the key challenges? (occupancy, rate, distribution costs)

---

Related Skills

• tour-operations: For tour operator and package management
• airline-cargo-optimization: For airline operations
• hospitality-procurement: For hotel purchasing and procurement
• demand-forecasting: For advanced forecasting techniques
• dynamic-pricing: For pricing optimization
• optimization-modeling: For advanced optimization
• seasonal-planning: For seasonal demand management