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Project Proposal
Prepared for: Music and Theater Department, SUNY Plattsburgh
Prepared by: Taylor Manor, Project/Service Management Intern
April 15, 2015
!


!
EXECUTIVE SUMMARY
!
Objective
The primary objective of this project is to develop strategies that will ensure a successful transition to a fully online
ticketing system. This will be accomplished by analyzing the current ticket purchasing process and providing
recommendations based upon this analysis. Enhancing ticketing functionality will:
• shorten and simplify the process of purchasing tickets for music and theater;
• reduce the workload of the will call window;
• increase customer satisfaction;
• ensure authenticity;
• and simplify event operations
Environmental Scan
• Online tickets have been available for theater and music events for purchase to general admission and students,
but buyers have not had the ability to print or electronically download their tickets
• A large majority of customers are aware of online tickets and utilize it.
Key Result Areas
• An increase in the number of customers who print or electronically download their ticket
• Decrease in the will call and box office lines
• Increased level of customer satisfaction as measured by attendee satisfaction surveys
• Decrease of user time on site and in lines
Next Actions
• Determine the best options for theater for scanner acquisition
• Utilize the marketing functions of University Tickets
• Make purchasing and printing tickets as user friendly as possible
• Define a process for events that ensures all staff are prepared for this transition
!
!
SITUATION ANALYSIS
!
Waiting Line Model Box Office (as opposed to the will call line)
Assumptions:
1. Poisson Probability Distribution (reasonable approximation of arrival distribution)
2. Exponential Probability Distribution (reasonable approximation of service time)
3. Arrival rate is based upon the number of customers served not upon the number of tickets sold
!
!
!
!
!
!
!
!
After observing the line and service at the box office, I determined a mean time per service of 42.5 seconds.
Relative to that mean (42.5 seconds) you could serve 85 customers per hour. 

Inputs
Unit of time 1 hour
Arrival Rate (lambda) 48 customers per hour
Service Rate (mu) 85 customers per hour
Number of servers 1
Outputs
Mean time per service 42.5 seconds
!
SITUATION ANALYSIS
!
Waiting Line Model Box Office
!
!
!
!
The box office line on average has .73 customers
waiting in line and 1.3 customers in the system which
includes waiting in line and being served. This means
that at any given time there is a 43.53 percent chance
that there are no customers waiting in line or being
served at the window. The average time of waiting in the
box office line is .916 minutes and the average time a
customer spends in the system is 1.62 minutes (waiting
in line and being served). There is a 56.47 percent
chance that the server at the box office is busy. Seeing
as your server is only busy a little over half the time they
are there is a strong indication that majority of your
customers buy online as opposed to coming and
purchasing their ticket at the box office. 

Summary Measures
Average number of customers waiting in line 0.73259141
Average number of customers in the system 1.2972973
Average time waiting in line .91573927 minutes
Average time in the system 1.62162162 minutes
Probability of no customers in the system 43.53 percent
Probability that the server is busy 56.47 percent
DISTRIBUTION OF NUMBER
OF CUSTOMERS IN THE
SYSTEM
Probability
0%
7.5%
15%
22.5%
30%
(n) customers in the system
1 2 3 4 5 6 7
!
SITUATION ANALYSIS
!
Waiting Line Model Will Call Line
Assumptions:
1. Poisson Probability Distribution (reasonable approximation of arrival distribution)
2. Exponential Probability Distribution (reasonable approximation of service time)
3. Arrival rate is based upon the number of customers served not upon the number of tickets sold
!
!
!
!
!
After observing the line and service at the will call window, I determined a mean time per service of 26.33 seconds.
Relative to that mean (26.33 seconds) you could serve 136 customers per hour.

Inputs
Unit of time 1 hour
Arrival Rate (lambda) 118 customers per hour
Service Rate (mu) 136 customers per hour
Number of servers 1
Outputs
Mean time per service 26.33 seconds
!
SITUATION ANALYSIS
!
Waiting Line Model Will Call Line
!
!
!
The will call line on average has 5.41 customers
waiting in line and 6.27 customers in the system
(waiting in line and time being served). This means that
at any given time there is a 13.74 percent chance that
there are no customers waiting in line or being served
at the window. The average time of waiting in the will
call line is 2.75 minutes and the average time a
customer spends in the system (waiting and being
served) is 3.19 minutes. There is an 86.26 percent
chance that the server in the will call window is busy.
Though the process time is much shorter than the box
office, the waiting line is much longer. Scanners would
help to shorten the waiting time in line dramatically for
customers. 

Summary Measures
Average number of customers waiting in line 5.41402265
Average number of customers in the system 6.27659574
Average time waiting in line 2.75289287 minutes
Average time in the system 3.191489362 minutes
Probability of no customers in the system 13.74 percent
Probability that the server is busy 86.26 percent
DISTRIBUTION OF NUMBER OF
CUSTOMERS IN THE SYSTEM
Probability
0%
3%
6%
9%
12%
(n) customers in the system
1 2 3 4 5 6 7 8 9 10
!
SITUATION ANALYSIS
!
!
!
!


PERCENTAGE OF THOSE
WHO HAVE PURCHASED
TICKETS ONLINE VIA
UNIVERSITY TICKETS FOR
EVENTS AT SUNY
PLATTSBURGH BEFORE
No
59%
Yes
41%
PERCENTAGE OF THOSE
WHO ANSWERED NO
THAT WOULD CONSIDER
BUYING ONLINE TICKETS
IN THE FUTURE
0%
17.5%
35%
52.5%
70%
Yes No
PREFERENCE OF RECIEVING A TICKET
NumberofPeople
0
2
4
6
8
Not Available
No Preference
Smart Phone
Print Myself
Box Office
AGE RANGE OF THOSE SURVEYED
18-24
25-39
40-49
50-59
60+
0 3.5 7 10.5 14
Results were collected from a survey
conducted at the Hartman Theater
!
SITUATION ANALYSIS
!
!
Shows how the current processes of online tickets at Hartman Theater are operating through a process flow
diagram
!
!
!
SITUATION ANALYSIS
!
Shows how the current processes of online tickets for Hartman Theater are operating through a service blueprint
diagram

!
SITUATION ANALYSIS
!
Shows how the future processes of online tickets for Hartman Theater will be operating through a process flow
diagram
!
!
!
!
!
!
!
!
SITUATION ANALYSIS
!
Shows how the future processes of online tickets for Hartman Theater will be operating through a service blueprint
diagram
!
!
!
!
!
!
!
!
!
!
!
!
!
!
DECREASE OF USER TIME ON SITE
!
!
!
!
!
!
!
!
!
!
Scanners will substantially reduce the amount of time your customers spend waiting to get into an event
TARGET NUMBER OF PRINTED OR
ELECTRONICALLY DOWNLOADED TICKETS
SCANNED NEXT SCHOOL YEAR
0
75
150
225
300
October December Feburary April
Printed Tickets Electonic Tickets
PROJECT PERFORMANCE
!
Key Performance Indicators
!
RISKS AND ISSUES
!
1. Risks associated with scanner malfunction
2. Limited battery life
3. Possibility of network failure affecting scanners and University Tickets
4. Accidental scanner damage
5. Overlap of scanner reservations with other groups on campus
!
CONTINGENCY/MITIGATION PLAN
!
1. Have a clear and defined back up plan to go to a paper system at the box office if necessary
2. Plan accordingly by having scanners fully charged before events and plan on having backups for long events
like festivals
3. Have a list of technical contacts and phone numbers available to event staff so they know who to contact in
the event of a network failure
4. Purchase lanyards for your scanners to minimize accidents
5. Reserve additional scanners at the Center for Student Involvement well before the event’s date to take priority
over other groups on campus

Probability
Risk
Reservation Overlap
Scanner Damage
Network Failure
Battery Life
Scanner Malfunction
!
• Another option theater has is to potentially buy two scanners and reserve the other two through the
student involvement center ahead of time. In the long run, buying two scanners as opposed to leasing
would save 2800 by year five.
• Buying two scanners also requires room to work with in the budget. It could be suggested that:
• The Hartman theater could raise ticket prices to help with the costs and take into account labor
and other costs associated with the box office and will call window.
• Have a discussion with Barry and Adrienne about contributing money to scanners and creating a
scanner schedule much like an employee schedule.
!
Year 1 Year 2 Year 3 Year 4 Year 5
Lease (4) 3000 3000 3000 3000 3000
Buy (4) 4600 1200 1200 1200 1200
Choosing to buy
vs lease
-1600 200 2000 3800 5600
Year 1 Year 2 Year 3 Year 4 Year 5
Lease (2) 1500 1500 1500 1500 1500
Buy (2) 2300 600 600 600 600
Choosing to buy
vs lease
-800 100 1000 1900 2800
RECOMMENDATIONS
!
1. Scanners are necessary for the online ticketing service to be completed. There are a few different options for Hartman
theater to get scanners for all four doors on events.
• One scanner cost 850 dollars to buy and 300 dollars annually for updates
• One scanner cost 750 dollars to rent for one year including updates
• If it was feasible for theater to purchase four scanners, that would be the best recommendation in the long run.
There doesn't appear to room in the budget for the purchase of four scanners.
!
RECOMMENDATIONS
!
2. Utilize the marketing functions of University Tickets by:
• Analyzing the demographics that are provided through making an account on University Tickets
• Reach out to those who have made accounts through email and update them about upcoming events
• Keep track of customer history to show you your most loyal customers and how well you are doing at
getting people to come back for the next show
!
3. Make purchasing and printing tickets as user friendly as possible by:
• Prominent or tactical placement of tickets link on key Plattsburgh web pages
• Having a University Tickets portal link via an icon in the My Plattsburgh App Panel (next to Moodle or Gmail
icon) for students to conveniently purchase their tickets.
• Use social media to share the link to online tickets
!
4. Define a process for events that ensures all staff are prepared for this transition by:
• Ensuring all employees and staff at the Hartman Theater are properly trained and informed on new
procedures for events. Example (scanner use, potential for malfunction, line direction, and knowledge of
University Tickets)
• Have a back up plan in the event of network or scanner failure
• Better directional signage in general and more prominent signs for will call window (if still used)
!
!
!
!
!
!
!
!

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Theater project Proposal-2

  • 1. ! Project Proposal Prepared for: Music and Theater Department, SUNY Plattsburgh Prepared by: Taylor Manor, Project/Service Management Intern April 15, 2015 ! 

  • 2. ! EXECUTIVE SUMMARY ! Objective The primary objective of this project is to develop strategies that will ensure a successful transition to a fully online ticketing system. This will be accomplished by analyzing the current ticket purchasing process and providing recommendations based upon this analysis. Enhancing ticketing functionality will: • shorten and simplify the process of purchasing tickets for music and theater; • reduce the workload of the will call window; • increase customer satisfaction; • ensure authenticity; • and simplify event operations Environmental Scan • Online tickets have been available for theater and music events for purchase to general admission and students, but buyers have not had the ability to print or electronically download their tickets • A large majority of customers are aware of online tickets and utilize it. Key Result Areas • An increase in the number of customers who print or electronically download their ticket • Decrease in the will call and box office lines • Increased level of customer satisfaction as measured by attendee satisfaction surveys • Decrease of user time on site and in lines Next Actions • Determine the best options for theater for scanner acquisition • Utilize the marketing functions of University Tickets • Make purchasing and printing tickets as user friendly as possible • Define a process for events that ensures all staff are prepared for this transition !
  • 3. ! SITUATION ANALYSIS ! Waiting Line Model Box Office (as opposed to the will call line) Assumptions: 1. Poisson Probability Distribution (reasonable approximation of arrival distribution) 2. Exponential Probability Distribution (reasonable approximation of service time) 3. Arrival rate is based upon the number of customers served not upon the number of tickets sold ! ! ! ! ! ! ! ! After observing the line and service at the box office, I determined a mean time per service of 42.5 seconds. Relative to that mean (42.5 seconds) you could serve 85 customers per hour. 
 Inputs Unit of time 1 hour Arrival Rate (lambda) 48 customers per hour Service Rate (mu) 85 customers per hour Number of servers 1 Outputs Mean time per service 42.5 seconds
  • 4. ! SITUATION ANALYSIS ! Waiting Line Model Box Office ! ! ! ! The box office line on average has .73 customers waiting in line and 1.3 customers in the system which includes waiting in line and being served. This means that at any given time there is a 43.53 percent chance that there are no customers waiting in line or being served at the window. The average time of waiting in the box office line is .916 minutes and the average time a customer spends in the system is 1.62 minutes (waiting in line and being served). There is a 56.47 percent chance that the server at the box office is busy. Seeing as your server is only busy a little over half the time they are there is a strong indication that majority of your customers buy online as opposed to coming and purchasing their ticket at the box office. 
 Summary Measures Average number of customers waiting in line 0.73259141 Average number of customers in the system 1.2972973 Average time waiting in line .91573927 minutes Average time in the system 1.62162162 minutes Probability of no customers in the system 43.53 percent Probability that the server is busy 56.47 percent DISTRIBUTION OF NUMBER OF CUSTOMERS IN THE SYSTEM Probability 0% 7.5% 15% 22.5% 30% (n) customers in the system 1 2 3 4 5 6 7
  • 5. ! SITUATION ANALYSIS ! Waiting Line Model Will Call Line Assumptions: 1. Poisson Probability Distribution (reasonable approximation of arrival distribution) 2. Exponential Probability Distribution (reasonable approximation of service time) 3. Arrival rate is based upon the number of customers served not upon the number of tickets sold ! ! ! ! ! After observing the line and service at the will call window, I determined a mean time per service of 26.33 seconds. Relative to that mean (26.33 seconds) you could serve 136 customers per hour.
 Inputs Unit of time 1 hour Arrival Rate (lambda) 118 customers per hour Service Rate (mu) 136 customers per hour Number of servers 1 Outputs Mean time per service 26.33 seconds
  • 6. ! SITUATION ANALYSIS ! Waiting Line Model Will Call Line ! ! ! The will call line on average has 5.41 customers waiting in line and 6.27 customers in the system (waiting in line and time being served). This means that at any given time there is a 13.74 percent chance that there are no customers waiting in line or being served at the window. The average time of waiting in the will call line is 2.75 minutes and the average time a customer spends in the system (waiting and being served) is 3.19 minutes. There is an 86.26 percent chance that the server in the will call window is busy. Though the process time is much shorter than the box office, the waiting line is much longer. Scanners would help to shorten the waiting time in line dramatically for customers. 
 Summary Measures Average number of customers waiting in line 5.41402265 Average number of customers in the system 6.27659574 Average time waiting in line 2.75289287 minutes Average time in the system 3.191489362 minutes Probability of no customers in the system 13.74 percent Probability that the server is busy 86.26 percent DISTRIBUTION OF NUMBER OF CUSTOMERS IN THE SYSTEM Probability 0% 3% 6% 9% 12% (n) customers in the system 1 2 3 4 5 6 7 8 9 10
  • 7. ! SITUATION ANALYSIS ! ! ! ! 
 PERCENTAGE OF THOSE WHO HAVE PURCHASED TICKETS ONLINE VIA UNIVERSITY TICKETS FOR EVENTS AT SUNY PLATTSBURGH BEFORE No 59% Yes 41% PERCENTAGE OF THOSE WHO ANSWERED NO THAT WOULD CONSIDER BUYING ONLINE TICKETS IN THE FUTURE 0% 17.5% 35% 52.5% 70% Yes No PREFERENCE OF RECIEVING A TICKET NumberofPeople 0 2 4 6 8 Not Available No Preference Smart Phone Print Myself Box Office AGE RANGE OF THOSE SURVEYED 18-24 25-39 40-49 50-59 60+ 0 3.5 7 10.5 14 Results were collected from a survey conducted at the Hartman Theater
  • 8. ! SITUATION ANALYSIS ! ! Shows how the current processes of online tickets at Hartman Theater are operating through a process flow diagram ! !
  • 9. ! SITUATION ANALYSIS ! Shows how the current processes of online tickets for Hartman Theater are operating through a service blueprint diagram

  • 10. ! SITUATION ANALYSIS ! Shows how the future processes of online tickets for Hartman Theater will be operating through a process flow diagram ! ! ! ! ! ! !
  • 11. ! SITUATION ANALYSIS ! Shows how the future processes of online tickets for Hartman Theater will be operating through a service blueprint diagram ! ! ! !
  • 12. ! ! ! ! ! ! ! ! ! ! DECREASE OF USER TIME ON SITE ! ! ! ! ! ! ! ! ! ! Scanners will substantially reduce the amount of time your customers spend waiting to get into an event TARGET NUMBER OF PRINTED OR ELECTRONICALLY DOWNLOADED TICKETS SCANNED NEXT SCHOOL YEAR 0 75 150 225 300 October December Feburary April Printed Tickets Electonic Tickets PROJECT PERFORMANCE ! Key Performance Indicators
  • 13. ! RISKS AND ISSUES ! 1. Risks associated with scanner malfunction 2. Limited battery life 3. Possibility of network failure affecting scanners and University Tickets 4. Accidental scanner damage 5. Overlap of scanner reservations with other groups on campus ! CONTINGENCY/MITIGATION PLAN ! 1. Have a clear and defined back up plan to go to a paper system at the box office if necessary 2. Plan accordingly by having scanners fully charged before events and plan on having backups for long events like festivals 3. Have a list of technical contacts and phone numbers available to event staff so they know who to contact in the event of a network failure 4. Purchase lanyards for your scanners to minimize accidents 5. Reserve additional scanners at the Center for Student Involvement well before the event’s date to take priority over other groups on campus
 Probability Risk Reservation Overlap Scanner Damage Network Failure Battery Life Scanner Malfunction
  • 14. ! • Another option theater has is to potentially buy two scanners and reserve the other two through the student involvement center ahead of time. In the long run, buying two scanners as opposed to leasing would save 2800 by year five. • Buying two scanners also requires room to work with in the budget. It could be suggested that: • The Hartman theater could raise ticket prices to help with the costs and take into account labor and other costs associated with the box office and will call window. • Have a discussion with Barry and Adrienne about contributing money to scanners and creating a scanner schedule much like an employee schedule. ! Year 1 Year 2 Year 3 Year 4 Year 5 Lease (4) 3000 3000 3000 3000 3000 Buy (4) 4600 1200 1200 1200 1200 Choosing to buy vs lease -1600 200 2000 3800 5600 Year 1 Year 2 Year 3 Year 4 Year 5 Lease (2) 1500 1500 1500 1500 1500 Buy (2) 2300 600 600 600 600 Choosing to buy vs lease -800 100 1000 1900 2800 RECOMMENDATIONS ! 1. Scanners are necessary for the online ticketing service to be completed. There are a few different options for Hartman theater to get scanners for all four doors on events. • One scanner cost 850 dollars to buy and 300 dollars annually for updates • One scanner cost 750 dollars to rent for one year including updates • If it was feasible for theater to purchase four scanners, that would be the best recommendation in the long run. There doesn't appear to room in the budget for the purchase of four scanners.
  • 15. ! RECOMMENDATIONS ! 2. Utilize the marketing functions of University Tickets by: • Analyzing the demographics that are provided through making an account on University Tickets • Reach out to those who have made accounts through email and update them about upcoming events • Keep track of customer history to show you your most loyal customers and how well you are doing at getting people to come back for the next show ! 3. Make purchasing and printing tickets as user friendly as possible by: • Prominent or tactical placement of tickets link on key Plattsburgh web pages • Having a University Tickets portal link via an icon in the My Plattsburgh App Panel (next to Moodle or Gmail icon) for students to conveniently purchase their tickets. • Use social media to share the link to online tickets ! 4. Define a process for events that ensures all staff are prepared for this transition by: • Ensuring all employees and staff at the Hartman Theater are properly trained and informed on new procedures for events. Example (scanner use, potential for malfunction, line direction, and knowledge of University Tickets) • Have a back up plan in the event of network or scanner failure • Better directional signage in general and more prominent signs for will call window (if still used) ! ! ! ! ! ! !
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