Wings of fire case study

Wings of fire case study. Determine any necessary assumptions along with justification for making the assumptions.Must be 1 page APA format includes references and citations.
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Hypothesis Test Chapter 10 Team Project: Wings of Fire
General Instructions:
1.Identify the central issue(s) of the case.
2.Present an overview of the statistical methods that will be used to help deal with the issue(s).
3.Outline the facts of the case.
4.Determine any necessary assumptions along with a justification for making the assumptions.
5.Present an analysis of the facts of the case, including use of statistical tables and charts.
6.Present conclusions tied back to the central issues of the case.
All this should be presented in a professional format, including typed narrative and neatly
presented tables and figures. Statistical software (Excel, etc.) should be used to make the
calculations and the statistical output shown.
Wings of Fire Case Study:
Following his graduation from college, Tony Smith wanted to continue to live and work in
Oxford. However the community was small and there were not a lot of readily available
opportunities for a new college graduate. Fortunately tony had some experience working in the
food service industry gained in the summers and throughout high school at his uncles’
restaurant in Buffalo. When Tony decided to leverage his experience into a small delivery and
take out restaurant located close to the university he thought he had hit on a great idea. Tony
would offer a limited fare consisting of the buffalo wings his uncle had perfected at his
restaurant. Tony called his restaurant Wings of Fire. Although success came slowly, the
uniqueness of Tony’s offering coupled with the growth of the university community made Wings
of Fire a success.
Tony’s business was pretty simple. He purchased wings locally, and the wings were then
seasoned and prepared in his restaurant. Once an order was received Tony cooked the wings,
which were then delivered or picked up by the customer. His establishment was small and there
was no place for customers to dine in the restaurant. However his wings proved so popular that
over time Tony hired several employees including three delivery drivers. Business was steady
and predictable during the week, with the biggest days being the home football Saturdays.
A little over a year ago the city really began to grow and expand. Tony noticed that his business
was beginning to suffer when other fast food delivery restaurants opened around campus.
Some of these restaurants were offering guarantees such as “30 minutes or its free.” Tony’s
Wings of Fire now had to compete with fish tacos, specialty pizzas and gourmet burgers. Most
of these new restaurants however were dine in establishments that provided carry out and
delivery as a customer convenience. However Tony was certain that he would need to offer a
delivery guarantee to remain competitive with the newer establishments.
Tony was certain that a delivery guarantee of “30 minutes or its free” could easily be
accomplished every day except on football Saturdays. Tony thought that if he could offer a 30
minute guarantee on his busiest day he would be able to hold onto and perhaps even recover
market share from the competition. However before he was willing to commit to such a
guarantee, he wanted to ensure that it was possible to meet the 30 minute promise
He knew it would be no problem for customers to pick up orders within 30 minutes of phoning
them in. However he was less confident about delivering orders to customers in 30 minutes or
less. Not only would the wings need to be cooked and packaged but the delivery time might be
affected by the availability of drivers. Tony decided that he needed to analyze the opportunities
further.
As a part of his analysis Tony decided to take a random sample of deliveries over five different
football weekends. Cooking time and packaging time were not considered in his analysis
because wings were not cooked for individual orders. Rather, large number of wings were
cooked at a single time and then packaged in boxes of 12. Tony therefore decided to focus his
analysis on the time required to deliver cooked and packaged wings. He collected information
on the amount of time an order had to wait for a driver (the pickup time) as well as the amount
of time required to transport the wings to the customer (the driver time). The sampled
information is in the file Wings of Fire (ATTACHED). Tony is not willing to offer the guarantee on
football Saturdays unless he can be reasonably sure that the total time to deliver a customers’
order is less than 30 minutes on average. Tony would like to know how likely it is that the total
time to make a delivery would take more than 30 minutes. Based on the sample data should
Tony offer the guarantee? What percent of the Saturday deliveries would result in a customer
receiving a free order? What recommendations might help him improve his Saturday delivery
times?
The tasks for this case study are:
1) use the sample information to compute a measure of performance that Tony can use to
analyze his delivery performance.
2) State a hypothesis test that would help Tony decide to offer the delivery guarantee or not.
3) Calculate sample statistics and formally test the hypothesis stated in (2)
4) Estimate the probability of an order taking longer than 30 minutes
5) Summarize your findings and make a recommendation in a short report.
Helpful Hints:
• Pick-up time and Drive time must be combined so you will analyze “Total Time” within the file
called WingsofFire Data.
• For task 1, use Excel’s Data Analysis Tool > Descriptive Statistics to calculate performance
measures
• For task 2, when you conduct your hypothesis test, your alternative hypothesis is that the total
time for a customer to receive an order is less than 30 minutes: Ha: U < 30 minutes. • For task 3, you need to decide the appropriate hypothesis test to compare the sample mean total delivery time in your data with the 30 minute standard. Note that we do not know the population standard deviation, only the sample standard deviation. For background, a good example to follow is the McFarland Insurance Company example beginning on page 331-333 of your text. • For task 4, estimating the probability of an order taking longer than 30 minutes, you can take either of two approaches: o Use the sample data to arrive at a relative frequency (see p. 30 in your text for a definition), and then calculate a 95% confidence interval around that relative frequency (proportion) using formula 9.4 on page 298……OR….. o Calculate the probability of an order taking more than 30 minutes from the distribution, using your sample mean delivery time and standard deviation you calculated in task 1 from the data, and 30 minutes as your “u” value, using the formula 7.5 on p. 214. The example on pages 218219 show you how to calculate your z-score and then determine the probability of getting that zscore using the standard normal table. If you don’t want to use the table to determine your probability, then you can use Excel’s function NORM.DIST (see p.219), where you can plug in your x value, and the mean and standard deviation of delivery time from your data, make sure the “Cumulative” box says “TRUE”, and obtain the probability that way. Note that putting “TRUE” in the cumulative box gives you the probability of a value up to and including the X
value (in this case 30) that you put in. If you want the probability of the value beyond X, then
you need to subtract the probability that Excel’s NORM.DIST function gives you from 1.0.

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