bull runner project

8/8/2019 Bull Runner Project

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Bull Runner Project

Neeraj Aggarwal, James Knapp, and Pratik Shah

EIN 6179 Six Sigma

Professor Beata Abbs

May 5, 2010


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Executive Summary

The main problem occurring that we decided to analyze and try to fix

were the wait times involved in waiting and commuting through Bull Runner.

The Bull Runner Shuttle system performs with long wait times varying on

average from 13 to 19 minutes as against the expected time of 8 minutes

causing delays and frustrations among commuters. This has been a problem

for quite some time and the Bull Runner Operation team already knows this.

Their thoughts on fixing this in the recently has considered inserting a GPS

system to allow for the buses to know where the others are located. They

think this will let the buses flow more efficiently and create a more

consistent wait time for riders. This could help but we feel more needs to be

done and analyzed in order to make commuters happy.

Our goal for this project deals with using an array of different methods

through Six Sigma and analyzing this data to reduce wait times. We will only

be analyzing 2 out of the 5 routes, which our solution will be employed to all

once they are found. The desired wait time between buses will be set to 8

minutes in which the recorded average as of the beginning of the project was

16 minutes. We will use a whole semester in which to find out which data to

gather, gather it, and use it to our advantage. The use of the voice of the

customers, riders, as well our strategy in this time limit will be directly

related to the significance of our results. Once all the tools are used properly

controlling and maintaining the solution or solutions will also be looked at.


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Background

Any student at a major university can tell you that one of the most

frustrating parts of the day deals with parking. It times of over an hour at

some campuses have been recorded in which parking spots were not

available. This turns into missed classes, quizzes, tests, and just about

anything a student needs not to miss in order to do as good as they can. To

fix this problem many universities such as USF have developed a shuttle

system in which various locations off of campus are shuttled to there and

back. USF calls their system Bull Runner and just like many of the other

systems it can be called insufficient at times. Considering the main reason

for the shuttles in the first place deals with being late for class, if the shuttles

are late what would the point be?

The Bull Runner Shuttles have been around for quite some time at USF

and take on the routes A, B, C, D, and E. Multiple buses run on each route

and vary throughout the day in the amount per route. Drivers drive at

different speeds but always follow the proper route in which they are order

to drive. The system does follow a consistent path each week in which the

same number of buses will start and stop on certain routes at certain times.

This leads way to a system that can be analyzed in which it does have

consistent variables although the wait times are very inconsistent.

Approach


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The approach to this project could be considered the most important

aspect of the entire thing. We first started to observe the operation as a

whole from an outsiders perspective. We found that Bull Runner actually

has a website in which you can track the routes and buses by a map. This

website showed their estimated arrival times and the actual times and

places they were in current time. Another resource we found that might be

somewhat beneficial was Bull Runner management. After creating our

mission, vision, and strategy our first strategic plan developed the map in

which you see below.

Boun

dary

Boun

dary

S I P O C

Suppliers Input Process Output Customer

Parking &

Transportat

ion services

Bull

Runner

buses

Transportat

ion

Student

Bus Stops Staff

USF Card

Tracking

System

This mapped set the guidance to what we were looking at as a whole.


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Rick Fallen the person involved in management which we were able to

contact us helped us a great deal. We were able to get information on the

pay of each driver, information on the new GPS system, and he allowed us to

pass surveys out on the bus to get the voice of the actual customer. The

next major step involved finding our variables and strategizing what variable

effected the wait time. A detailed look below:

Y-Variable

Wait Time between buses

X-Variable

Number of Buses

Drivers

Condition of Buses

Different day times

Number of Stops

Traffic Conditions

Communication System response

Driver Changeover

From this data our goal throughout the entire project will be to find which of

these X-variables will affect the Y-variable, wait time between buses.

Voice of the Customer

An important aspect of any project should involve the people that are

considered the customers of the system. In our case this would be the users

of the Bull Runner System at anytime throughout the week. Our strategy at

first was to send out an email to receive information from all of the Bull


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Runner users that went to USF. This strategy overlooked the challenge

having access to send a mass email to all of these USF students. Our next

strategy involved Rick Fallen in which he permitted us to leave these surveys

in the shuttles in which the customers rode. The list of question below

describe the information that we wanted to achieve and they either had

rating from 1-10 in which you could answer, multiple choice, or fill in the

blank answers. Questions:

1. How many times a week you use Bull Runner service?

2. Which Bull Runner route do you travel most often?

3. What is your average wait time to board the Bull Runner?

4. Do you have information on the schedule following which Bull runner

makes stops at your stop?

5. Bull Runner always arrives at my stop on time?

6. On an average, how many co-passengers are there on Bull Runner trips

you take between 07:00 AM and 05:00 PM?

7. On an average, how many co-passengers are there on Bull Runner trips

you take between 05:00 PM to mid night?

8. I am satisfied with the pull chain stop alert system.

9. How do you rate Bull Runner Service?

10.Please include any comment (Positive or Negative) on Bull Runner

service.


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We not only wanted to hear about the waiting times of Bull Runner, but the

system as a whole. If it lacked in other areas we might have been able to

analyze these areas as well and use them to our advantage.

Process Mapping

Once some information can be retained the fact of mapping out the

process of a whole can be crucial in finding the weak links in the chain so to

speak. The process map we have created shows the process of an everyday

user enters the shuttle to when they exit. As you look at it you will notice

that the shuttle has contact with one another and use this to gage how fast

they should drive. Process Map:

One of the most interesting notes to take from this involves the fact that

drivers actually slow down in order to keep a proper distance from the next

driver. The reason for this makes sense in the need for spacing the shuttles,

but this obviously will slow down the system as a whole.

Tools Used

Throughout this project a various amount of tools were used. The first

tool we used involved the quality function deployment in which we tried to

focus on Bull Runner as a whole. This data exhibited below:

Quality Function Deployment

How

WhatCI

Timely avialbility 5155

Availability at

desired location 13

Room to sit 333

Cleaniness 22

Safe to travel 36

Quiet atmosphere 11

Comfortable 2

20

18 5 23 45 27 48 3 3 18

Strong 9

Medium 3

Weak 1

Number

of stops

Tracking

&

Commun

ication

System

CustomerNeeds

Metrics

Numbe

r of

Buses

Number

of drivers

Conditio

n and

year of

Manufact

ure Schedule

Seating

capacity

Time

betweeb

buses

Administ

ration

quality


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By observing the totals in the lowest and furthest to the right sections, it

helps in the overall assessment of Bull Runner.

The next step in using certain tools we choose the Ishikawa Diagram.

This diagram helped us focus on the all the factors that were involved in the

process. The Xs on the diagram show were the factors to be considered are

located and there are 6 different categories in which we focused on. These

categories are management, man, method, measurement, machine and

materials. This diagram is below:


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Bull Runner has a website in which it was very helpful in collecting data

called the Bull Runner tracking system. We also rode the shuttles as while in

order to receive data from these trips and see firsthand the steps taken.

Through this data and Minitab our team developed many different graphs on

to determine the appropriate factors that effects the wait time. These

graphs are shown below some have brief descriptions of why they were

used:

Stability chart in order to see if there persists of any common factors dealing

with the wait times:

Normality Chart much with the same reasoning to find out if a consistent

duration of waiting time exists:


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Normalization plot of the data to see if we can find a formula within these

wait times:

We compared the wait times vs. the time of day in order to see if a certain

time of the day held higher average wait times than other times:


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Turned the line chart into a graph that would be easier to see the differences

in a bar chart for wait times relevant to the time of the day:

The number of buses was our next area of focus and to see if this affected

the wait times:

We took a further in depth look of actually riding the bus all of Monday and

comparing these times to wait times:

We took the wait times between five and six and compared the number of

buses to wait times:

Through Minitab we were able to test for a significant P-value < .005:


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We tested for the same relevant to the time of day:


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All of these tools were crucial in the outcome and solutions to our project.

Through the use of Minitab and graphs the project could be broken down and

analyzed from many different angles.

Critical X identification and Improvement Approaches

We found four critical X factors that affect the wait time in a negative

way. The first X factor deals with the number of buses per route. At times

bus utilization can be handled better and our solution to the problem is to

increase the number of buses to a maximum of 5 per route. The next X

factor deals with the routes themselves. Unnecessary steps throughout the

routes are taken in which new routes will be utilized. One of the X factor that

involves technology deals with the use of adjusting the lights so that the

buses travel through with less stops. This goal will work if the routes are

scheduled properly and the lights change at the proper times. The last X

factor that contributes is to set standards of operating the shuttles by the

drivers. Drivers as of now differ from one to the next in terms of the speed

in which they drive. The ability to influence standards among all will set a

better system and place and prove beneficial in the long run. A list of the

solutions to implement is below:

1. Increasing the number of Buses to a maximum of 5.

2. Shortening the route by removing unnecessary miles.


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3. Adjusting schedules and providing Traffic light monitoring to avoid

traffic lights.

4. Setting standard operating procedures for drivers.

Risk Assessment and Mitigation Strategy

To implement the use of our first solution we first graphed the

difference in wait times compared to the number of buses used:

We ran the numbers to find out how much this would cost the company:

1. Additional Cost = Average cost of running a bus * # of hours

Avg. Cost = $42/hr.

# of hours = 10 hrs a weekday*days in a year

Additional Cost For Route D = $109,500 per year

2. Annual Savings to Customers = (227193)*5/7(Weekdays/(16-8)/60*Average Student

Salary

Annual Savings to Customers = $21,637

3. Savings From Additional Bus = $10,687

The next area of focus involved the path taken by the buses

throughout the trips. By taking a new strategy on how the path should be

taken miles can be cut off the trip and yet all the necessary places can still

be stopped at. A series of bullet points displays our findings:


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Extra miles traveled each day = 1.2 miles

Average number of trips per day = 45.5 trips

Number of extra miles per day = 54.6 miles

Annual fuel saving without extra miles = $3919

Annual time cut off for customers = 2 minutes per hour

Extra time if used for work instead of waiting = $13521 per year

The other two solutions involve implementing new technology to the lights

as well as making a systematic schedule that all drivers must follow. This

also involves standardizing arriving and leaving times for all drivers in order

to implement the system correctly.

Control

Once all of the steps to our solution have been taken controlling them

will be the next phase. Any problem can receive a solution and it may work,

but it can fail if not properly controlled. The most important way of

controlling these solutions involves following the system correctly. If Bull

Runner converts back to its old ways in any area it can break the new

approach and ruin the advances taken. The number of shuttles as well as

the routes taken cannot be strayed from at any point in time and it must be

consistent. For the last two solutions a proper system should be

implemented and if done properly it should be consistent and not changed.

One variable will always be in need of analyzing and that is the drivers.

Checks on whether all drivers are properly holding true to the system will be


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given. Examinations on knowledge of the system as well as paths and times

taken will be recorded. If drivers do not abide by the system properly

necessary steps will be taken and possible relieving them of their duties may

be the only solution at times.

Conclusion

Through our approach and results it seems as though we may have

found a proper solution. All necessary steps were taken in finding the

appropriate X factors and then analyzing them. The factors that seem to

affect the wait times are very plausible and are well backed up through the

data we found. The next step will be to implement these findings and see if

the forecasted results occur. We hope to show Rick Fallen our results and

encourage him to try the solutions and see how they work. These four

solutions are to add Bus drivers, optimize the routes, change the times in

which the lights will turn, and to set a systematic way of having more

consistent drivers and travel times. All of this will in turn make for less wait

times and overall not only save Bull Runner money, but the customers as

well. If a little more can be put into Bull Runner it can be an efficient system

in which other universities will be jealous of and want for themselves.

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