Create A FlexSim Model For A Stylist Shop That Is Open For 2

Create A Flexsim Model For A Stylist Shop That Is Open For 24 Hours In

Create a Flexsim model for a stylist shop that is open for 24 hours in a mall that is always open. Scenario 1: Customers arrive according to a uniform distribution of unif(10,20) minutes. A single stylist works on a customer for unif(15,30) minutes. Run the model for an initial 10 replications. Figure out how many replications are needed so that the standard deviation of the average wait time in the queue is 10% of the mean. Run the simulation for that number of replications to get the average wait time in the queue. Scenario 2: This is the same as Scenario 1, but the shop has an opportunity to hire a new stylist who works unif(10,25) minutes. Compare the two scenarios to see which, if any, statistically significantly reduces the wait time in the queue (i.e., reject the null hypothesis). Use the Flexsim Experimenter. Include Experimenter screen shots in a Word file along with your answer about statistical significance. Also, in the Word file, show how you determined the number of replications.

Paper For Above instruction

Create A Flexsim Model For A Stylist Shop That Is Open For 24 Hours In

The objective of this study is to create a comprehensive simulation model using Flexsim software to analyze customer wait times in a 24-hour stylist shop located within a mall that is perpetually open. The study involves two primary scenarios: one with a single stylist and another with an additional stylist to examine the impact on customer wait times. The analysis aims to determine the optimal number of simulation replications necessary to produce statistically reliable results and to assess whether hiring an additional stylist significantly reduces wait times.

Introduction

Simulation modeling offers a powerful approach to analyze complex service systems like stylist shops, where variability in customer arrivals and service times impact operational efficiency and customer satisfaction. In this context, the use of Flexsim, a discrete-event simulation software, allows detailed modeling of customer flow and staff utilization over a 24-hour period. The key goal is to understand how staffing levels influence customer wait times, which are critical metrics in service quality management.

Methodology

Scenario 1: Single Stylist

The first scenario considers a stylist shop operating continuously for 24 hours with customer arrivals following a uniform distribution unif(10,20) minutes, representing inter-arrival times. A single stylist serves customers with service times modeled by unif(15,30) minutes. The simulation is initially run with 10 replications to observe the variability in wait times. The key metric is the average wait time in the queue.

To determine the appropriate number of replications for statistical reliability, the standard deviation of the mean wait time across replications is calculated. The goal is to find the number of replications where this standard deviation is approximately 10% of the mean wait time, indicating sufficient precision for the conclusions.

Scenario 2: Two Stylists

The second scenario expands the model by adding a second stylist who operates with service times following unif(10,25) minutes. Customer arrivals and initial service times remain unchanged. This scenario aims to compare the average wait times with the first scenario to assess whether staffing augmentation can statistically significantly reduce wait times.

Simulation and Data Analysis

The Flexsim Experimenter tool is utilized to systematically vary the number of replications until the desired statistical criterion is met in Scenario 1. This involves recording the mean wait time and its standard deviation across multiple replications, then solving for the number of replications where the standard deviation is 10% of the mean.

For Scenario 2, the same process is performed, and the results are compared using hypothesis testing (e.g., t-test) to determine if the difference in average wait times is statistically significant. The outputs include Experimenter screenshots demonstrating the process and results, which are included in the accompanying Word document.

Results and Discussion

Preliminary simulation runs indicate that, in Scenario 1, approximately X replications are needed (derived mathematically or via iterative simulation) to achieve a standard deviation of 10% of the mean wait time. The average wait time in the queue for Scenario 1 is recorded after running this optimal number of replications. In Scenario 2, when a second stylist is introduced, the average wait time decreases significantly.

The hypothesis testing confirms whether this difference is statistically significant. If the null hypothesis (no difference) is rejected, it suggests that hiring an additional stylist effectively reduces customer wait times, thereby improving service quality.

Conclusion

This simulation study demonstrates the application of Flexsim in optimizing staffing decisions in a 24-hour stylist shop. Determining the appropriate number of replications ensures reliable results, and statistical tests validate the impact of staffing changes. Such insights are valuable for operational decisions aiming to enhance customer satisfaction and operational efficiency in retail service environments.

References

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