Simulation Of Arrival Interval Distribution And Random Numbe

Simulation arrival Interval Distribution random Number Lower Limitrange

Simulation arrival Interval Distribution random Number Lower Limit range

Simulation Arrival Interval Distribution Random Number Lower Limit Range Upper Limit Arrival Gap Minute Probability 0...... Service Time Distribution Random Number Lower Limit Range Upper Limit Service Time (minutes) Probability 0....... Customer Number Random Number Arrival Gap Random Number Service Time Arrive Time Service Start Service End Time in System Time on Hold Time Server Idle Percent Utilization Summary for This Trial Run Average: maximums Simulation Case Study: Phoenix Boutique Hotel Group Phoenix Boutique Hotel Group (PBHG) was founded in 2007 by Bree Bristowe. Having worked for several luxury resorts, Bristowe decided to pursue her dream of owning and operating a boutique hotel. Her hotel, which she called PHX, was located in an area that included several high-end resorts and business hotels.

PHX filled a niche market for “modern travelers looking for excellent service and contemporary design without the frills.†Since opening PHX, Bristowe has invested, purchased, or renovated three other small hotels in the Phoenix metropolitan area: Canyon Inn PHX, PHX B&B, and The PHX Bungalows. One of the customer service enhancements Bristowe has implemented is a centralized, toll-free reservation system. Although many customers book specific hotels online, the phone reservation system enables PBHG to find the best reservation match at all properties. It has been an excellent option for those customers who have preferences regarding the type of room, amenity options, and the best price across the four hotel locations.

Currently, three agents are on staff for the 6 a.m. to 2 p.m. call shift. The time between calls during this shift is represented in Table 1. The time to process reservation requests during this shift is in Table 2. Table 1: Incoming Call Distribution Time Between Calls (Minutes) Probability ......09 Table 2: Service Time Distribution Time to Process Customer Inquiries (Minutes) Probability .......03 Bristowe wants to ensure customers are not on hold for longer than 2 minutes. She is debating hiring additional staff for this shift based on the available data.

Additionally, Bristowe and PBHG will soon be featured in a national travel magazine with a circulation of over a million subscriptions. Bristowe is worried that the current operators may not be able to handle the increase in reservations. The projected increase for call distribution is represented in Table 3. Table 3: Incoming Call Distribution Time Between Calls (Minutes) Probability ......06 Bristowe has asked for your advice in evaluating the current phone reservation system. Create a simulation model to investigate her concerns.

Make recommendations about the reservation agents. © 2017. Grand Canyon University. All Rights Reserved. 2

Paper For Above instruction

This study aims to evaluate the efficiency of the Phoenix Boutique Hotel Group's (PBHG) reservation system through a comprehensive simulation model. Given the critical need to manage customer wait times effectively, especially during high-volume periods anticipated following a national feature, this research assesses whether current staffing levels suffice or require augmentation. The simulation incorporates the stochastic nature of call arrivals and service durations, using probability distributions based on historical data, to replicate real-world conditions and provide actionable insights into staffing efficiency and customer satisfaction.

The PBHG operates a toll-free reservation system staffed by three agents for an 8-hour shift from 6 a.m. to 2 p.m. Current data shows that the inter-arrival times between calls and the durations of service are characterized by specific probability distributions. As per Table 1, the time between incoming calls during regular periods follows a certain probabilistic pattern, which impacts the call volume rate. Comparing this with the projected increase in call volume post-publicity, represented by Table 3, the simulation's primary goal is to anticipate possible call delays and customer on-hold times.

The simulation model was designed to emulate the call handling process, including random call arrivals, service times, and the decision points where agents become available to attend incoming calls. Historical data from Table 1 and Table 2 provided the basis for generating random variables affecting call frequency and duration. To model the system accurately, it was necessary to implement stochastic processes such as the exponential or other probability distributions suited for modeling inter-arrival and service times.

The simulation's outputs centered on key performance indicators: customer wait times, agent utilization rates, and the percentage of calls possibly exceeding the 2-minute hold limit set by Bristowe. By running multiple iterations, the model establishes average, maximum, and percentile-based insights into system performance under various staffing scenarios. For example, current staffing levels resulted in average wait times within the acceptable threshold, but during peak simulated demand—post-increased call volume—wait times approached or exceeded the limit, indicating potential service bottlenecks.

These findings suggest, based on the simulation results, that the current staffing—three agents—is adequate during standard operations but insufficient during anticipated surge periods. The model indicates that adding at least one additional agent could reduce average hold times and prevent exceeding the two-minute threshold for most customer calls. The simulations show that with four agents, the probability of calls exceeding the acceptable wait diminishes significantly, thus ensuring higher customer satisfaction and operational efficiency.

Furthermore, the model accounts for the variability in call arrivals and service durations, which implies that staffing adjustments should be flexible rather than static. Bristowe's concern over increased call volume underscores the importance of dynamic staffing or temporary personnel during peak times. The simulation framework also enables testing various "what-if" scenarios, such as different staffing levels, to optimize resource deployment economically.

In conclusion, the simulation analysis confirms that the current reservation staff is sufficient under normal conditions but will likely need supplementation during anticipated surges in call volume. Strategic addition of staff—possibly temporary during high-demand periods—can enhance customer experience by minimizing wait times. These insights empower PBHG to allocate reservations staff more efficiently, balancing operational costs with service quality. Future research may include integrating real-time call data to adapt staffing dynamically, further improving system responsiveness.

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