Project #3: Production Cost Savings
at Washington Post
SYST 798/OR 680
Final Presentation
6 May 2011
Team Power Wash Post
Thomas Kuklinski
Timothy Smith
Ling Wu
Vladimir Zivkovic
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Overview
•
•
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Clients and Introduction
Background, Objective, and Scope
Technical Approach
Model Architecture
Results and Analysis
Recommendations
Future Work
Acknowledgements
Questions
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Clients and Introduction
• Kent Renk, Materials Handling Foreman,
renkk@washpost.com
• Kim Hammett, Assistant Superintendent for
Materials Handling, hammettk@washpost.com
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Background
• In the Fall of 2010, GMU students did initial static
analysis of the Washington Post Shipping and Receiving
Department in Springfield, VA
• They conducted a process evaluation, an analysis of labor,
and an analysis of routes which resulted in three groups of
recommendations:
• Labor reductions (implemented)
• Route simplifications
• Improved data tracking
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Objective and Scope
• Objective
• Provide a baseline simulation model that helps the
Washington Post maximize the efficiency of the materials
handling processes
• Scope
• Build upon the static analysis of the previous group by
modeling the helper component of the Shipping and
Receiving Department
• Deliver a flexible simulation model that can be used by the
Post to make workforce planning decisions
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Technical Approach
• Conduct a System Process Analysis
•
•
Build upon last group’s work done on process evaluation
Develop a complete and detailed qualitative understanding of how the Shipping and
Receiving Department operates
• Collect and Analyze Data
•
•
Collect data from both the materials tracking system (i.e. MTC) and subject matter
experts
Obtain descriptive statistics on each component of the system
• Select and Construct Model
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•
Evaluate model alternatives based on quantitative/qualitative data and client needs
Construct and test model (e.g. turn system processes into Arena modules)
• Perform Output Analysis
•
•
Evaluate output in terms of helper utilization, moves, and total production
Iterate
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Model Architecture
• Process Analysis Overview
• General Arena Module
• Helpers zones and arrivals
• General Process Modules
• Daily Insert/Sunday Packaging raw ads from rack to machine
processing
• Completed Daily Insert pallets from SLS 1-6 to CSLD/NDSL
• Completed Sunday Packaging from Collators 1-4 to rack/NDSL
• Completed Sunday Packaging from rack to CSLD
• Schedule Module
• Output Analysis Module
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Process Analysis (actual)
Trucks
1. Daily Insert Ads & Sunday Packaging
Daily Insert
Materials
Handling
Racks
2c. Jackets
Run of Press
Sunday Packaging
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Process Analysis (scope)
Daily Insert
Materials
Handling
Output: Head Sheets
Racks
Sunday Packaging
Run of Press
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General Module
Monday, Shift 1
Raw Materials
EXP(5)
EXP(5)
TRIA(6,8,9)
C1HS
C1PZ
Rack
Dock
S1HS
C1PZ
Production Targets
TRIA(2,5,6)
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General Process Modules
• Utilizes “Transporter” modules and non-stationary Poisson Process schedule
• Internal business logic dictated by historical statistics (e.g. CSLD vs. NDSL)
and internal business logic (e.g. Z-Loader selection)
SI Palle t Ar r iv al
S16PZ Count S1
S16PZ W r it eS1
S1PZ
0
0
( ( N Q ( Z 1 C o n v e y D o w n . Q u e u e ) < = ( N Q ( Z 2 C o n v e y D o w n . Q u e u e ) + N Q ( Z 2 C o n v e y U p . Q u e u e ) ) ) & & ( N Q ( Z 1 C o n v e y D o w n . Q u e u e ) < = ( N Q ( Z 3 C o n v e y D o w n . Q u e u e ) + N Q ( Z 3 C o n v e y U p . QT r uueeu e ) ) ) )
( ( ( NQ ( Z 2 Co n v e y Do wn . Q u e u e ) + NQ ( Z 2 Co n v e y Up . Q u e u e ) ) < = NQ ( Z 1 Co n v e y Do wn . Q u e u e ) ) && ( ( NQ ( Z 2 Co n v e y Do wn . Q u e u e ) + NQ ( Z 2 Co n v e y Up . Q u e u e ) ) < = ( NQ ( Z 3 Co n v e y Do wn . Q u e u e ) + NQ ( Z 3 Co n v e y Up . Q u e u e ) ) ) )
S14PZ Z1
Request
S2PZ
S2 Palle t Ar r iv al
S16PZ Count S2
S14PZ Z1
Tr anspor t
Z1 O ut
S14 Recor d2
S14 W r it e2
Z1 Convey Down
0
0
S16PZ W r it eS2
DI vs. RO P
0
S1 Z123
S14PZ Z2
Request
S14PZ Z2
Tr anspor t
Z2 O ut
S16PZ Count S3
F a ls e
NDSL Request
NDSL Tr anspor t
CSLD Request
CSLD Tr anspor t
0 Tr ue
Z2 Convey Down
S14 Recor d3
S14 W r it e3
0
F a ls e
0
E ls e
S3 Palle t Ar r iv al
Dock Check
S3PZ
S16PZ W r it eS3
S14PZ Z3
Request
0
S14PZ Z3
Tr anspor t
Z3 Convey Down
Z3 O ut
S14 Recor d4
RO P Dock Check
S14 W r it e4
0 Tr ue
0
0
S4 Palle t Ar r iv al
S16PZ Count S4
F a ls e
S4PZ
S16PZ W r it eS4
0
RO P
RO PI n Count
RO PI n W r it e
RO P Holder
0
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Schedule Module
• Utilizes “Halt” and “Activate” modules
S56 M on2 Ac t 3
Set up M on2 Ac t 1
S c h e d u le r
U n lo a d e r L o a d e r
C r e a Mt e o n 1 T r a c k
M o n 1 A c t 1 M o n 1 W r ti e
M on2 Tr ack
M on1
S56 M on2 Ac t 2
0
C3 F T M o n 3 Ac t 1
M o n 2 W r it e
U n lo a d e r L o a d e r
De e p Re a c h
Ac t 2
M on2 Ac t 1
Set up M on2 Ac t 0
M on2
De e p Re a c h
U n lo a d e r L o a d eMr o n 3 W r it e
M on3 Ac t 0
M on3 Ac t 1
S 5 6 M o n 3 A Cc Dt R0 M o n 3 C A1 cF tT 1 M o n 3 A c t 0
M on3 Tr ack
M on3
C3 F T M o n 3 Ac t 0
S56 M on2 Ac t 1
S14 Tues 1 Ac t 1
S56 Tues 1 Ac t 1
T u e s 1 W r it e
Tues1 Tr ack
Z lo a d e r T u e s 3
Ac t 2
S14 Tues 3 Ac t 2
S56 Tues 3 Ac t 2
U n lo a d e r L o a d e r
Tues 3 Ac t 2
C3 F T T u e s 3 Ac t 1
De e p Re a c h
S e t u p T u e s 3 T u e s 3 W r it e
T u e s 3 A c t 1Z lo a d e r T u e s 3
Ac t 0
Ac t 1
T u e s 2 W r it e
D e e p R e a c h Z lo a d e r T u e s 1
Tues 1 Ac t 1
Ac t 1
S14 Tues 1 Ac t 2
C 1 F T T u e s 1 A cCt 3 1F T T u e s 1 A c t 1
U n lo a d e r L o a d e r
S56 Tues 1 Ac t 2
Tues 1 Ac t 2
Tues1
C1 F T T u e s 3 Ac t 0
T u e s 2 T u e s 3 T r a c kS 1 4 T u e s 3 A c t 1
Z lo a d e r AUcnt lo3a d e r L o a Sd ee tr u p T u e s 2
S 1 4 T u e s 2 AS c5 t6 3 T u e s 3 A c t T1 u e s 2
Tues 2 Ac t 3 Ac t 1
Tues2 Tr ack
Tues3
Z lo a d e r T u e s 1
De e p Re a c h
Ac t 2
C3 F T T u e s 1 Ac t 2
Tues 1 Ac t 2
S14 Tues 1 Ac t 3
S56 Tues 3 Ac t 0
S56 Tues 1 Ac t 3
C3 F T T u e s 3 Ac t 0
S14 Tues 3 Ac t 0
Z lo a d e r T u eU sn 3lo a d e r L o a d e r
Ac t 0
Tues 3 Ac t 1
S14 Tues 1 Ac t 4
S14 W ed1 Ac t 1
S56 W ed1 Ac t 1
U n lo a d e r L o a d e r
W ed2 Ac t 3
S56 W ed3 Ac t 2
W e d 1 W r it e
W ed1 Tr ack
S14 W ed1 Ac t 2
C 1 F T W e d 1 A c t 1C 3 F T W e d 1 A c t 1
S56 W ed1 Ac t 2
De e p Re a c h
W ed1 Ac t 2
S14 W ed1 Ac t 3
C3 F T W e d 1 Ac t 2
W e d 2 W r it e
S14 W ed3 Ac t 2
Z lo a d e r W e d 1
Ac t 1
Z lo a d e r W e d 3
Ac t 1
C3 F T W e d 3 Ac t 1
U n lo a d e r L o a d e r
W e d 1 A c t 2S e t u p W e d 1
Ac t 1
Z lo a d e r W e d 1
Ac t 2
W e d 1 W e d 2 T r a cSk1 4 W e d 2 ASc5 t 6 3 W e d 3 A c t 1
Set up W ed2
Ac t 0
W ed2
De e p Re a c h
W ed3 Ac t 1
W e d 3 T r a cSk1 4 W e d 3C 1 AF cT t 1W e d 3 A c t 0
S56 W ed3 Ac t 0
S14 Thur s 1 Ac t 1
S56 Thur s 1 Ac t 1
T h u r s 2 W r it e
S14 Thur s 1 Ac t 2
S56 Thur s 1 Ac t 2
C1 F T T h u r s 1
Ac t 1
C3 F T T h u r s 1
Ac t 1
Z lo a d e r T h u r s 1
Ac t 1
De e p Re a c h
Thur s 1 Ac t 2
Thur s1 Tr ack
S14 Thur s 1 Ac t 3
C3 F T T h u r s 1
Ac t 2
S14 Thur s 3 Ac t 2
S56 Thur s 3 Ac t 2
T h u r s 1 W r it e
C3 F T T h u r s 3
Ac t 1
U n lo a d e r L o a d e r
Thur s 1 Ac t 2
Thur s1
Thur s2 Tr ack
S56 Thur s 3 Ac t 1
U n lo a d e r L o a d e r
Thur s 2 Ac t 3
Thur s2 Thur s 3 Tr ac k
S14 Thur s 3 Ac t 1
Z lo a d e r T h u r s 1
Ac t 2
S56 Thur s 3 Ac t 0
Z lo a d e r T h u r s 3
Ac t 1
De e p Re a c h
Thur s 3 Ac t 1
C1 F T T h u r s 3
Ac t 0
C3 F T T h u r s 3
Ac t 0
S56 Thur s 1 Ac t 3
W ed3
U n lo a d e r L o a d e r
W ed3 Ac t 1
S14 W ed3 Ac t 0
S14 W ed1 Ac t 4
W e d 3 W r it e
Z lo a d e r W e d 3
Ac t 0
C3 F T W e d 3 Ac t 0
S56 W ed1 Ac t 3
U n lo a d e r L o a d e r
W ed3 Ac t 2
U n lo a d e r L o a d e r
Thur s 3 Ac t 2
T h u r s 3 W r it e
Thur s3
Z lo a d e r T h u r s 3
Ac t 0
U n lo a d e r L o a d e r
Thur s 3 Ac t 1
S14 Thur s 3 Ac t 0
S14 Thur s 1 Ac t 4
S14 Thur s 3 Ac t 3
C 3 F T F r i2 A c t 1
C 3 F T F r i1 A c t 1
F r i1 T r a c k
C 1 F T F r i1 A c t 1
C 3 F T F r i1 A c t 2
C 4 F T F r i1 A c t 1
D e e p R e a c h F Ur ni1 lo a d e r L o a d e r F r i1 W r it e
Ac t 0
F r i1 A c t 0
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C 1 F T F r i2 A c t 0
F r i1
C D R F r i2 A c Ct 40 F T F r i2 A c t 0
F r i2 T r a c k
C 3 F T F r i2 A c t 0
F r i2 W r it e
F r i2
F r i3 T r a c k
F r i3 1 W r it e
F r i3
12
S c h e d u le r
T e r m ni a t e
0
Output Analysis Modules
• Utilizes “Record” and “ReadWrite” modules
• Records current markup
• Records utilization time by helper by shift/day
• Records helper moves by helper by shift/day
• Records total pallets moved through model by week
Calculate Helper
Stats
CDR Stats
C1FT Stats
C2FT Stats
Zloader Stats
DeepReach
Stats
LoaderUnloader
C3FT Writ e C4FT Writ e
S14 Writ e
S56 Writ e
Zloader Writ e
Wri
t
e
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DeepReach
Writ e
C3FT Stats
C4FTStats
S14 Stats
S56 Stats
LoaderUnloader
Stats
Setup Stats
0
CDR Writ e
C1FT Writ e
C2FT Writ e
Setup Writ e
Terminate Helper
Stats
13
0
Model Architecture (Assumptions)
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•
•
•
•
Raw materials start at rack (95% go to rack first)
Exponential/Poisson arrival processes
Triangular route time and Z-Loader distributions
Machine and rack processes as terminate and arrive modules
Internal business logic
• NDSL vs. CSLD; NDSL vs. Rack
• Least busy Z-Loaders machines are used
• Unlimited trucks available for delivery of completed materials
• 1 week reflects all weeks
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Model Architecture (Limitations)
• Lack of route time data
• Plenty of arrival data
• Changing work center business logic makes it
hard to keep up
• Lack of fully developed business logic for the internal
model process (e.g. 50% NDSL vs. CSLD)
• Recipes for advertisements and rack waiting times make it
difficult to model a seamless transition of materials
through the system
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Results and Analysis
• Input Analysis
• Labor Analysis
• Route Analysis
• Arrival Process Analysis
• Output Analysis
• Base vs. Suggested Schedule
• Helper utilization and moves
• Total production
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Labor Input Analysis
• Obtained labor markup data for this period
• Broken down by work center, day, shift, and role
• Assigned helpers within the work centers to specific
routes they were responsible for
• This required splitting helpers assigned to specific
routes into subsets (e.g. Collator – Deep Reach)
• Trimmed off helpers in the markup that would be
working on tasks not reflected in the model (i.e.
Loaders/Unloaders working at the receiving dock)
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Route Input Analysis
• Mapped all of the routes to specific areas within our system
• Routes obtained from MTC Pallet Move Summary Report
for February 7-12, 2011
• Obtained route time and distance for all relevant routes from
client
• Client provided estimated max, min, and mean routes times
for a triangular distribution
• Client recommended this approach because of “bad” MTC
data
• Used route times, distances, and helper assignments to determine
velocities of each helper for model input
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Arrival Processes Input Analysis
• Obtained arrival timestamp summaries for components of the
system from MTC
• Rack Sunday Packaging and Daily Insert raw ads
• SLS 1-6 & Collators 1-4 finished pallets
• Empty Run of Press jackets
• Rack finished Sunday Packaging
• Used Arena’s Input Analyzer to determine distributions on the
arrival processes for the components based on timestamps
• Distributions broken down by shift/day and machine
• Exponential inter-arrival times
• Poisson arrival process
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Output Analysis
• Output analysis was conducted as a demonstration of
how the model could be used to inform decisions
• Primary deliverable was usable decision tool going forward
• Technical session and model instructions ensured client
understanding of the model functionality
• Compared base schedule and suggested schedule
• Utilization: Percentage of workers busy at any given time
• Helper Moves: Moves per helper-hour
• Total Production: Total weekly moves
• Sensitivity analysis on arrival processes and helper
velocities confirmed results
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Base Schedule
• 165 total shifts
over a 5-day
week
• 6,500 total
helper moves
• 20% utilization
• 6 moves per
helper-hour
Base Schedule: % utilization per helper zone in one
WW
100%
80%
60%
50%
41%
40%
20%
20%
12%
2%
17%
1%
25%
12%
13%
0%
Base Schedule: Average # of moves per helper per
hour
15.4
9.9
7.9
1.2
5.5
3.4
0.0
0.3
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4.7
2.6
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Suggested Schedule
• 75 total shifts over
a 5-day week
• 90 less than base
• 6,500 total helper
moves
Suggested Schedule: % utilization per helper zone
in one WW
100%
80%
69%
58%
53%
49%
60%
36%
40%
21%
4%
20%
41%
17%
8%
0%
• Same as base
• 40% utilization
• Double base
Suggested Schedule: Average # of moves per
helper per hour
• 12 moves per
helper-hour
• Double base
26.8
14.6
16.1
12.2
10.5
2.3
5.8
4.0
6.5
6.9
0.0
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Future Work
• Build an Excel-based automation of the input data
• Develop animation capabilities to help visualize the
processes internal to the model
• Enhance the output analysis capabilities to capture more
specific details about the statistics of the model
• Continue to flesh out big assumptions within the model (e.g.
machine processing recipes)
• Build a simulation-based optimization engine around the
model
• Production target goals
• Schedule variables
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Acknowledgements
• Washington Post
 Mr. Kent Renk
 Mr. Bill Thompson
• GMU Team I’m PRO WasP
 Justine Blaho
 Catalina Gomolka
 Ryan Graziano
 Laura Rodriguez Lopez
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Questions?
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