1. IOE 481 Team 7: John Li Jamie Nolan Naina Singh December 13, 2016
UMHS Supply Chain Services Department
Receiving Operations Simulation
Final Presentation
IOE 481 Team 7:
John Li
Jamie Nolan
Naina Singh
December 13, 2016

2. Client and Coordinators
Roy Yoo, Project Manager
Coordinators
Andrew Sweeney, Industrial Engineer
Arnold Yin, Industrial Engineer
Mary Garves, Industrial Engineer

3. Introduction Current state: two-way matching
Item is ordered
Item is shipped to dock
Invoice received
Vendor payment
Accounts Payable
Department
Delivery Service
Item is delivered to department
Future state: three-way matching
Item and quantity verified
Item receipt is recorded into PeopleSoft
Item is delivered to department
Central Receiving Team
Item is ordered
Item is shipped to dock
Invoice received
Vendor payment
Accounts Payable
Department
Delivery Service
Not needed for Wednesday - talk more about scope of our project

4. Goals and Objectives
Simulate the central receiving process on ProModel
Collect data on the process
Conduct time studies
Help the UMHS Supply Chain Department
See the effects of different variables without hindering current operations
Transition to three-way matching to
avoid losing packages
reduce costs

5. Methods Time Studies Measurements Surveys Literature Search
Travel routes
Verify item
Record item
Measurements
Hospital blueprints
Route distances
Flatbed size
Surveys
Truck arrival times
Packages per room
Delivery times
Literature Search
Elevator wait times

6. Time Studies Speed = 3.5 ft/sec Total Time = U(3,2) min
Time Taken to Travel 256 ft (Collected on 12/5/16 at 2:45 PM)
Speed =
3.5 ft/sec
Trial 1 2
Average
Time
1:16:99
1:12:56
1:14:78
O&M Time Studies (Collected on 11/7/16 at 9:30 AM, N=20)
Process
Average Time
Min Time
Max Time
Find Packing Slip
00:13.74
00:05.47
00:31.54
Verify
00:35.76
00:06.43
03:12.49
Enter
00:40.90
00:21.60
02:16.52
Print
00:18.11 -
Staple
00:10.23
Total
01:58.73
01:01.84
06:28.29
Total Time =
U(3,2) min

7. Measurements Speed = 3.5 ft/sec Building Dock to Room 1 (ft)
Room 1 to Room 2 (ft)
Room 2 to Dock (ft) UH
306
506
432
CVC
553
173
628
C&W
1665
313
1903
Speed = 3.5 ft/sec
Building
Dock to Room 1 (min)
Room 1 to Room 2 (min)
Room 2 to Dock (min) UH 87
145
123
CVC
158 50
179
C&W
476 89
544

8. Surveys 3 departments out of 11 provided complete data
UH OR had most complete data  Used as basis

9. Calculated from survey percentages
Surveys
Truck Arrivals (Data collected 10/13-10/14 and 10/17-10/18)
Type of Service
O&M
FedEx Overnight
FedEx Ground
UPS Ground
FedEx Express
Arrival Time
Night before
7:16 AM
8:22 AM
8:35 AM
9:01 AM
Number of packages 23 17 52 56
122
Calculated from survey percentages
Distribution of Packages by Room (Data collected 11/11-11/18 and 11/21-11/23)
Room Number
% of Total
UH Room 1
B1F244D
13%
UH Room 2
1D204
35%
CVC Room 1
2A581
CVC Room 2
4747
17%
CW Room 1
6-610B
18%
CW Room 2
11-531 5%

10. Literature Search Worcester Polytechnic University
Creating a distribution based on total waiting times, number of passengers, highest floor
Columbia University
Arrival times are random, thus elevator times are random
Our method
Based wait times off of time of day (shift switches and lunch times are busiest)
Random arrivals of emergency patients that will take priority (1 in 20 probability)

11. Key Assumptions UH OR is the model for the system It’s a perfect world
Each worker walks at the same speed
Flatbeds leave if full (30 items) or if an hour has elapsed
Assuming perfect world
UH OR is model for system

12. Simulation Runs from 5 AM – 1 PM Simulates 7 AM - 1 PM shift
Entities include item and flatbed
Three routes that represent the three buildings (UH, CW, CVC)
Locations include dock, inspection area, two destinations per route
Item gets assigned itemtype on arrival which determines destination
Capacity of 30 Items or 1 hour passes

13. Simulation Results
2 workers
3 workers
4 workers

14. Key Takeaways Focus areas We recommend
Maximize worker utilization
Minimize time in system for items
We recommend
3 workers
6 flatbeds
Drive to CW instead of walking
Workers will also do other tasks such as
Clearing the work area
Sorting other items in the area

15. Impact Established a basis for collecting data in the future
Easily editable model that can be continuously updated with data
Way to test changes to the process without impacting operations
Manual to change it

16. Next Steps
Continue to update simulation as more accurate data is collected
Prepare for three-way matching pilot in early 2017
Expand simulation to be able to test data for other departments

17. Acknowledgements
We’d like to thank the following people for their support throughout our time on this project:
Roy Yoo
Andrew Sweeney
Mary Garves
Arnold Yin
Professor Van Oyen
Jai Sura
Mary Lind
Mary Duck
Professor Guzman
IOE 481 Class
UMHS System
Guzman

18. Thank You!
Any Questions?

19. Works Cited
[1] R. Yoo. “Three Way Match and Receiving.” University Hospital: Ann Arbor, Michigan. Presentation. 16 Sept
[2] J. Dong, Q. Zafar, “Elevator Scheduling.” Columbia University: New York, New York. PDF. 20 Oct <
[3] A. Hsu, R. LaBarre, S. Stricevic, “Need a Lift? An Elevator Queueing Problem,” Worcester Polytechnic Institute: Worcester, Massachusetts. PDF. 18 Oct <

20. Simulation Process