1. Geoffrey Cheung IME 301 - Winter 2010
30 Minutes
30 Minutes.
What is it? 30 minutes is the time it takes for a pizza to be delivered to you from the time you order.
Hi, my name is Geoffrey Cheung and my statistics project is pizza delivery time.
How long does it take to get a pizza delivered?
Geoffrey Cheung
IME Winter 2010

2. Pizza Delivery Time Problem: Determine the time it takes
for pizza to be delivered.
Hypothesis: Delivery time is the same all day, every day.
Importance: Delivery time is an in important factor in the business for customer satisfaction.
Data Collection Process: 8 weeks of hourly data
Through this project, I wanted to determine a couple of things:
How long does it take for a pizza to be delivered (on average)? Is it less than 30 minutes?
Is the delivery time the same every day?
Is the delivery time the same all hours of the day?
Knowing this is important to a pizza business because if the delivery time is too high, customers are lost. If I can determine where our delivery times are longer, we can work towards reducing that delay and hopefully reach more customers. Consistent delivery time regardless of day of week or time of day is important because it in turn allows us to create better models for predicting the number of drivers needed throughout the day.
The data being used is average delivery time for each hour over the course of 8 weeks, starting from December 2009 and ending at the end of January of this year. Data was collected by the point of sale system in the store and a report was generated which provided the delivery time averages for each hour.

3. Overall Delivery Time
To start things off, here is a histogram of the overall delivery time. What I was looking for and what is important here is that the mean and median delivery time is below 30 minutes. Ideally, the entire distribution should be under 30 minutes. From this graph we can see that this is indeed the case. We can also see that the average time it takes for pizza to be delivered is about 22 to 23 minutes (exact minutes). We can also see that the distribution of this graph is more towards the left. This means that we have a high amount of short delivery times.

4. Weekday vs. Weekend Delivery
Moving on, I wanted to see whether there was a difference in delivery time when ordering on the weekdays versus the weekends. I separated the data by weekday and weekend and made a boxplot. Weekday is on the left (yellow) and weekend is on the right (blue). From this we can see that on average, delivery time on the weekdays is slightly faster than on the weekends. Let’s take a closer look at this.

5. Delivery Time by Day
Focusing in, what we have here is a box plot of the delivery time (in minutes) by day of the week. The colors represent the same thing as in the previous slide. Yellow is weekday, blue is weekend. From this graph we can see the trend of increasing delivery time as the week progresses (week starting on Monday, ending on Sunday). What is important to take note of is that looking just at the weekday boxplots, Friday’s distribution of delivery times is much higher than the rest of the weekdays (Mon. – Thurs.). Now, looking at the weekend boxplots, we see that Sunday’s distribution is more spread than Saturday’s.
I mentioned earlier that consistency in delivery time is important. This graph also does a good job of showing that while on some days we were consistent, on others, our delivery time varied much more. Friday is the day with the most consistent delivery time (least amount of spread) while it appears that Sunday and Monday are the least consistent (greatest amount of spread). These are probably the days that we want to work on improving delivery times of.
But we can’t just look at this data by day of the week. In this type of business we have two rushes each day: the lunch rush and the dinner rush. Everything else before, after, and in between the two order rushes can essentially be disregarded because of the very low volume of orders. Let’s take a look at how lunch delivery time compares to dinner delivery times.

6. Lunch Rush vs. Dinner Rush
Here is a box plot of lunch and dinner delivery times. Lunch is on the left (blue) and dinner is on the right (green). From this we can see that our dinner delivery time is on average longer than our lunch delivery time. We also have a much wider spread on our dinner delivery time. Once again, let’s take a closer look by comparing lunch and dinner time by day.

7. Lunch Rush vs. Dinner Rush (By Day)
Here is a box plot of lunch and dinner delivery times by day. Dinner is on the left (green) and lunch is on the right (blue). We can see how the green box plots gradually move up (and thus our delivery time increases) as the week progresses. However, our lunch delivery times (blue box plots) are fairly consistent, 3 out of the 7 days have about the same delivery times, one day has less, and two have more.
There are several probable reasons why the lunch delivery time distribution is the way that it is above. Mondays to Wednesday are days office workers don’t order lunch. Maybe they bring their own lunches in. Thursday is near the end of the week so we get a significant amount of office orders. Friday is a party day. You have office parties, school parties, etc. Thus we see the increase in lunch deliveries on Fridays. Saturday and Sunday people aren’t at work so you don’t have the office people ordering lunches. For the dinner delivery time distributions a similar reasoning follows.
This graph also shows that our delivery time are not as consistent as they could be. Good examples of consistent delivery times are lunch deliveries Monday, Tuesday, Wednesday, Saturday and dinner delivery times on Thursday and Saturday.

8. Conclusion Hypothesis: Delivery time is the same all day.
Actual: Shorter during lunch on some days.
Hypothesis: Delivery time is the same every day.
Actual: Shorter Monday to Thursday.
Questions?
To recap, my hypothesis was that delivery time is the same all day and that delivery time is the same every day. In actuality, our delivery time is shorter during lunch on some of the days and longer on others. Our delivery time was not the same every day, it was shorter Monday to Thursdays.
This project has shown various areas that we can improve upon for our delivery. For days with shorter delivery time we can reduce staffing as there may be excess labor. For days with longer delivery times, we can increase staffing as there is most likely a labor shortage. In order to increase consistency in delivery times we can work on improving drivers’ knowledge of back roads/shortcuts to bypass congested roads and we can also send multiple deliveries with a single driver if the delivery locations are close by (within several blocks) as opposed to sending multiple drivers out.
Please note that in order to receive and use the data for this project, I agreed not to reveal the place of business.
Are there any questions?