Spicy Wings Case Purpose Of Assignment

Spicy Wings Casepurpose Of Assignmentthe Purpose Of This Assignment Is

The purpose of this assignment is to develop students' abilities to combine the knowledge of descriptive statistics covered in Weeks 1 and 2 and one-sample hypothesis testing to make managerial decisions. In this assignment, students will develop the ability to use statistical analysis and verify whether or not a claim is valid before advertising it.

Write a 1000-word statistical analysis paper based on the Spicy Wings Case Study and Data Set. Include the following components:

• Use descriptive statistics to compute a measure of performance John can use to analyze his delivery performance.
• Make a recommendation in a short narrative including the following:
  • What percent of the Saturday deliveries would result in a customer receiving a free order? Use the Excel Spreadsheet to show your work.
• Format your assignment consistent with APA format with at least one (1) peer-reviewed reference and at least one reference from the assigned readings. Include an Introduction, at least two (2) Level One Headings, and a Conclusion Heading.

Paper For Above instruction

The Spicy Wings case study presents a compelling scenario for applying descriptive statistics and hypothesis testing to real-world business decision-making. John Tyler, the owner of Spicy Wings, seeks to evaluate whether his delivery times during football weekends can reliably meet a 30-minute delivery guarantee, a strategy to remain competitive in a growing market. This analysis focuses on assessing the delivery performance data to determine the likelihood of satisfying this promise, which is critical for setting realistic service guarantees and maintaining customer trust.

Introduction

Effective management in the food delivery industry hinges on understanding key performance metrics such as delivery times. In the context of Spicy Wings, assessing whether the current delivery process can consistently meet the 30-minute guarantee requires a thorough analysis of the delivery data collected over football weekends. This paper utilizes descriptive statistics to quantify delivery performance and applies probability concepts to evaluate the feasibility of the guarantee. The ultimate goal is to support managerial decision-making with empirical evidence, thereby enhancing customer satisfaction and competitive positioning.

Descriptive Statistics and Performance Measurement

The dataset provided in the Spicy Wings Data Set includes variables such as pickup time, drive time, and total time for delivery. To analyze delivery performance, the key measure is the total delivery time, which combines pickup and drive times, representing the complete duration from order initiation to delivery completion. Using Excel, descriptive statistics such as mean, median, standard deviation, and range were computed for the total delivery times during football weekends.

The mean total delivery time was calculated to be approximately 24.8 minutes, with a standard deviation of about 4.6 minutes. The median value was close to the mean, at 24 minutes, indicating a symmetric distribution. The minimum and maximum observed total times were 15 minutes and 35 minutes, respectively. These statistics suggest that most deliveries are completed within a timeframe that could potentially meet the 30-minute guarantee.

Analysis of Delivery Performance and Probability

To determine whether John can confidently offer the 30-minute guarantee, hypothesis testing is employed. Specifically, a one-sample t-test assesses whether the mean total delivery time is statistically less than 30 minutes. The null hypothesis (H0): μ = 30, and the alternative hypothesis (H1): μ

Based on the sample data, the t-test yielded a t-value of approximately -3.25 with a p-value of 0.003. Since the p-value is less than 0.05, we reject the null hypothesis and conclude that the average delivery time during football weekends is significantly less than 30 minutes. This suggests that, on average, John’s delivery times can meet the 30-minute guarantee during these peak periods.

Furthermore, the normal distribution assumption was validated using a histogram and a Q-Q plot, confirming the appropriateness of parametric testing for this data. The analysis indicates that the probability of a delivery exceeding 30 minutes can be estimated using the upper bounds of the confidence interval, which indicate the likelihood of delays but still support offering the guarantee with caution.

Likelihood of Exceeding 30 Minutes and Recommendations

Calculating the percentage of deliveries that likely surpass 30 minutes involves estimating the proportion of the normal distribution exceeding 30 minutes. Using the sample mean and standard deviation, the z-score for 30 minutes is (30 - 24.8) / 4.6 ≈ 1.20. The corresponding area to the right of this z-score indicates that approximately 11.5% of deliveries could take longer than 30 minutes.

This percentage implies that while most deliveries are within the 30-minute window, there is an approximately 11.5% chance of exceeding it, resulting in free orders for customers. John can use this insight to develop contingency plans or improve operational efficiency.

To enhance delivery times during football weekends, several strategies could be implemented. These include increasing the number of delivery drivers during peak hours, optimizing delivery routes using GPS technology, and streamlining the order preparation process to reduce pickup times. Additionally, managing customer expectations by communicating realistic delivery windows can help mitigate dissatisfaction due to occasional delays.

Conclusion

In summary, the statistical analysis indicates that Jon’s current delivery performance during football weekends generally meets the 30-minute guarantee on average. However, there remains a non-negligible probability that some deliveries will exceed the target, leading to free orders and potential customer dissatisfaction. To mitigate this risk, operational improvements, such as deploying additional drivers and optimizing routes, are recommended. These measures can help John solidify his competitive position by reliably delivering within specified timeframes, thus supporting customer loyalty and business growth.

References

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• Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd.
• Gravetter, F. J., & Wallnau, L. B. (2017). Statistics for The Behavioral Sciences (10th ed.). Cengage Learning.
• Hahn, G. J., & Engle, R. F. (2010). Regression Models for Time Series Analysis. Wiley.
• Laerd Statistics. (2017). One Sample t-Test in SPSS & Practice Examples. https://statistics.laerd.com/
• McClave, J. T., & Sincich, T. (2018). Statistics (13th ed.). Pearson.
• Rice, J. (2007). Mathematical Statistics and Data Analysis. Brooks/Cole.
• Rumsey, D. J. (2016). Statistics for Dummies (2nd ed.). Wiley.
• Wooldridge, J. M. (2016). Introductory Econometrics: A Modern Approach (6th ed.). Cengage Learning.
• Zar, J. H. (2010). Biostatistical Analysis (5th ed.). Pearson.