Spicy Wings is an installment owned by John Tyler. In recent times, his business has come across difficult times with increasing competition from fast-food restaurants within the small town of Hood. The growth of the town has led to significant increases in fast-food producing joints. Moreover, these joints offer thirty minute guarantees, where the customer gets free food once the 30-minute mark is passed. With this, John has lost a part of his market share to faster and more efficient services in the town. Nonetheless, John sees the introduction of this policy to his restaurant as his saving grace that will keep his business afloat. This paper samples and analyzes data from John’s study to determine important factors regarding the possibility of introducing this 30-minute guarantee to his business.
Over the last few football weekends, John has collected data on the times taken for the full delivery of orders to clients. Considering the data provided by John, descriptive statistics was done on the data to determine the most initial conclusions that could be obtained from said data. The table below shows the descriptive statistics:
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Descriptive Statistics for John's Delivery Time Data | |
| Mean | 22.5226 |
| Standard Error | 0.344652992 |
| Median | 22.895 |
| Mode | 15.95 |
| Standard Deviation | 4.87412935 |
| Sample Variance | 23.75713692 |
| Kurtosis | 0.025309691 |
| Skewness | -0.522572959 |
| Range | 23.15 |
| Minimum | 8.75 |
| Maximum | 31.9 |
| Sum | 4504.52 |
| Count | 200 |
| Confidence Level (95.0%) | 0.679640722 |
From above, it is noted that the mean time for delivery is 22.5 minutes while the median time is equally close by at 22.8 minutes. With this data, it is quickly determined that most of the orders were squarely within the thirty minute deadline required for the new policy of delivery. Furthermore, this data was subjected to confidence level testing, showing that almost 68% of the orders would fall within range. Furthermore, it was noted that among the 200 samples collected, only seven fell above the 30-minute deadline even on busy weekends, representing only 3.5% of the sample data provided. This is shown below in a sorted list of values:
Pick-up Time | Drive Time | Total Time |
4.94 | 26.96 | 31.9 |
3.39 | 28.15 | 31.54 |
5.34 | 24.78 | 30.12 |
4.57 | 26.12 | 30.69 |
6.13 | 25.22 | 31.35 |
4.69 | 26.74 | 31.43 |
4.64 | 26.25 | 30.89 |
From the above data, it is possible to make an initial assumption that up to 68% of all orders will be delivered within the median time provided above, which is 22.8 minutes. Moreover, only 7 out of the 200 orders made in the busy season had their time exceeding 30 minutes. Extrapolating the statistics of order which remained undelivered within the 30-minute mark, the business is only likely to deliver fewer than 4% of all its orders after half an hour. This means that 96% of all orders remain delivered safely before the thirty-minute deadline. As a result, it becomes safe to say that, using this approach, the business will still enjoy a lot of efficiently delivered orders at the half-hour mark.
However, considering it from a statistical perspective, the confidence level of the statistic is still quite low. A much higher value is required to achieve higher efficiency. An assorted count shows that 102 deliveries went beyond the median time, thereby creating the need to ensure that delivery time is reduced. This will ultimately mean that there is less likelihood of having free food being distributed.
In this case then, John could implement the thirty-minute policy but invest in having additional drivers for football weekends. This will ensure that there are fewer backlogs of orders, which have been significantly increasing the pick-up time. Once the pick-up time is reduced, the general total time is also significantly reduced, putting the orders within the thirty minute deadline. In this way, John can effectively put the ‘30-minute or it’s free policy’ and still offer the financial benefit to his business. Nevertheless, at the current rate, John can still offer the policy and still enjoy up to 96% of his orders being delivered within the deadline, if all factors remain constant. With additional drivers for busy weekends, however, it is possible to achieve 100% efficiency, albeit it will incur some expense.
References
Sullivan, M., & Verhoosel, J. C. M. (2013). Statistics: Informed decisions using data . Pearson.
Siegel, A. (2016). Practical business statistics . Academic Press.
Anderson, D. R., Sweeney, D. J., Williams, T. A., Camm, J. D., & Cochran, J. J. (2014). Statistics for business & economics, revised . Cengage Learning.
Stine, R., & Foster, D. (2014). Statistics for Business: Decision Making and . Addison-Wesley SOFTWARE-JMP.
