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The ABC Hotel is a small hotel operating 24/7 with the goal of developing a comprehensive model of its daily operations to enhance service quality and efficiency. The hotel accommodates various types of customers, including daily visitors and residents, each with distinct behaviors and requirements. This report outlines the problem, including objectives, assumptions, key performance indicators, and methods for analyzing and simulating the hotel's operations. It details the entities involved, their attributes, activities, state variables, and relevant events. Additionally, a flowchart provides a visual representation of the operational process. Data collection methods suitable for capturing relevant information are discussed, alongside the development of a simulation model to emulate 40 customer arrivals. Several experiments aim to optimize performance metrics such as total simulation time, queue lengths, resource utilization, and customer wait times. Comparative analysis using Excel diagrams evaluates the "As-Is" scenario against proposed improvements. Finally, strategic recommendations for further enhancements are provided, supported by visual animations of the simulation model.

Paper For Above instruction

Introduction and Problem Overview

The hospitality industry continuously seeks ways to optimize operational efficiency while maintaining high service standards. For the ABC Hotel, a small, 24/7 establishment, developing a precise operational model is crucial for understanding customer flows, resource utilization, and bottlenecks. The primary aim is to create a simulation that accurately reflects daily activities, enabling managers to test different scenarios and identify strategies for improvements.

The objectives include analyzing customer arrival patterns, managing room allocations efficiently, reducing waiting and processing times, and maximizing resource utilization. Key performance indicators (KPIs) encompass average customer wait times, room occupancy rates, queue lengths at check-in and service points, and overall customer satisfaction levels. Assumptions made in the model include the randomness of arrivals, predefined service time distributions, and priorities given to booked or preferred rooms.

Entities, Attributes, Activities, State Variables, and Events

Flowchart and Explanations

The flowchart visualizes the hotel's operational process from customer arrival to departure. Customers arrive at varying intervals, modeled as per the input analysis. Upon arrival, they are categorized as either daily visitors or residents. Daily visitors choose facilities—gym or restaurant—based on probabilistic preferences, with service times modeled as triangular distributions. Some gym visitors may visit the restaurant afterward, adding complexity to the flow.

Resident customers with prior bookings are prioritized in room allocation, with preferences for room types proportionate to surveyed data. Non-booked residents face delays or opt to leave if no rooms are available. Actual check-in involves reception processing, modeled with variable times, after which the customer's length of stay begins. Upon completion, customers check out, freeing rooms for others. The flowchart captures all these steps, with decision points, process durations, and resource allocations clearly depicted.

Data Collection and Input Analysis

Accurate modeling relies on empirical data collection. Data collection methods suitable for this scenario include time-motion studies to record service times and arrival patterns, customer surveys to determine preferences, and automated logging systems for arrival and service durations. Input analysis involves fitting distributions to collected data—triangular distributions are used for processing times and lengths of stay due to their simplicity and the nature of limited data. The customer inter-arrival times will be analyzed using statistical software to identify the best fit distribution, such as exponential or Poisson models, depending on the observed data.

Simulation Model Development

The simulation model replicates the hotel's operations with a focus on the "As-Is" scenario involving 40 customer arrivals. Using a discrete-event simulation approach, the model incorporates customer arrivals, service processes (reception, facilities), room allocations, and customer departures. Simulation runs will be executed five times to account for variability, capturing outputs such as average wait times, room utilization rates, and queue lengths. Sensitivity to initial conditions will be considered, ensuring robust results and reliable insights into operational performance.

Scenario Experiments and Optimization

Two primary scenarios are designed to improve operations. The first aims to reduce overall simulation time and queue lengths by increasing staffing levels or streamlining check-in procedures. The second scenario seeks to boost resource utilization—such as adding more rooms or facilities—and reduce average customer wait times. Each scenario's impact will be measured against the baseline "As-Is" model by comparing metrics like customer wait times, occupancy rates, and queue lengths, using Excel diagrams such as line charts, histograms, and stacked bar charts.

Comparison and Analysis

Excel-based visualizations will illustrate differences across scenarios. For instance, line graphs can show trends in average wait times, while bar charts compare utilization rates. This comparative analysis helps identify the most effective interventions, supporting data-driven decision-making for operational enhancements.

Recommendations for Further Improvement

• Implement an advanced booking system integrated with real-time room management to reduce check-in delays.
• Introduce self-service kiosks to streamline check-in/out and reduce receptionist workload.
• Expand room capacity or add additional amenities to balance customer flow and prevent bottlenecks.
• Adjust staffing schedules based on peak arrival periods to minimize customer wait times.
• Incorporate dynamic pricing strategies to manage demand during peak times.
• Utilize mobile apps for service requests, reducing physical queues and increasing convenience.
• Enhance staff training to improve service efficiency and customer satisfaction.
• Regularly collect data to refine and update the simulation model for ongoing improvements.
• Explore environmentally sustainable practices to improve operational efficiency and reputation.
• Develop a comprehensive customer feedback system to identify pain points and tailor services accordingly.

Simulation Animation and Visualization

The developed "As-Is" model incorporates basic animation features to visualize customer movements, queue formations, and room occupancy dynamically. This animation helps managers and stakeholders better understand operational dynamics and identify bottlenecks visually. The animation is interactive, allowing users to pause, replay, or modify simulation parameters in real time, providing valuable insights into process flows and areas needing attention.

References

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