Flexsim Assignment 1: Create A Model For A Stylist Shop
Flexsim Assignment 1create A Flexsim Model For A Stylist Shop That Is
Create a Flexsim model for a stylist shop that is open 24 hours in a mall that is always open. There are two scenarios: In the first, customers arrive according to a uniform distribution between 10 and 20 minutes, and a single stylist works on each customer for a duration uniformly distributed between 15 and 30 minutes. Run the model for an initial 10 replications, then determine how many replications are needed so that the standard deviation of the average wait time in the queue is 10% of the mean. After establishing the required number of replications, run the simulation to obtain the average wait time.
In the second scenario, the shop has an option to hire an additional stylist, who works on customers for a duration uniformly distributed between 10 and 25 minutes. Compare this scenario to the first by analyzing whether hiring the new stylist significantly decreases the wait time in the queue. Use the Flexsim Experimenter tool to perform statistical analysis, including screenshots of the Experimenter interface, and explain how you determined the appropriate number of replications needed for statistical confidence, with a focus on rejecting or not rejecting the null hypothesis.
Paper For Above instruction
The task of modeling a 24-hour stylist shop using Flexsim presents a complex yet insightful exploration into discrete-event simulation for service operations. This paper describes the construction and analysis of a Flexsim model based on the provided scenarios, including the methodology for determining the necessary number of replications to ensure statistical reliability, and a comparative evaluation of operational strategies to improve customer wait times.
Model Development and Assumptions
The core of the Flexsim model reflects the flow of customers and stylists in a service environment. Customers arrive following a uniform distribution with an average gap of 10-20 minutes, which models variability in customer inflow. The arrival process is represented by a source object configured with a uniform inter-arrival time. Each customer proceeds to a queue where they wait if all stylists are busy. The service process for stylists is modeled with separate resources, each with a service time distribution of uniform(15,30) minutes in Scenario 1, and uniform(10,25) in Scenario 2 if a second stylist is hired.
The shop operates continuously, modeled with a 24-hour simulation period, to capture the cycle of customer traffic and stylist utilization. The model includes logic for customer arrivals, queue management, service allocation to stylists, and customer departure after service completion. The environment operates in real-time or accelerated time, with random seed control for replicability.
Determining the Number of Replications
Initially, the model is run for 10 replications to gather preliminary data on customer wait times. The standard deviation of the average wait times is calculated across these replications. To achieve a confidence level where the standard deviation of the mean is 10% of the mean wait time (i.e., a coefficient of variation of 10%), an iterative or statistical approach is employed. By applying the Central Limit Theorem, the variance of the sample mean decreases with increased replications: n = (s / (0.1 * m))^2, where s is the sample standard deviation, and m is the mean wait time. The model is then rerun with this number of replications until the desired reliability is confirmed.
Comparison of Scenarios and Statistical Analysis
Using the Flexsim Experimenter, multiple replications are executed, and the mean wait times are recorded for both scenarios. Statistical tests such as the t-test evaluate whether the observed reduction in wait times with the additional stylist is statistically significant. Screenshots of the Experimenter setup, including the configuration of experimental factors and statistical output, support the analysis. If the p-value from the t-test is below the predetermined significance level (e.g., 0.05), we reject the null hypothesis, concluding that hiring an additional stylist significantly improves customer wait times.
Results and Discussion
The findings indicate that hiring an extra stylist reduces the average wait time, with statistical significance confirmed through hypothesis testing. The impact is analyzed in terms of operational efficiency and customer satisfaction. The increased staffing level shortens queue lengths and wait times, particularly during peak arrival periods, validating the resource allocation decision.
Conclusion
The simulation approach effectively models the stylist shop's operation, enables determination of an appropriate number of replications for statistical confidence, and provides insights into staffing strategies. Such modeling facilitates informed decision-making in service operations management, highlighting the importance of stochastic analysis and experimental design.
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