1 data collection and predictive modeling in industrialized housing a presentation at ifors 2005...
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Data Collection and Predictive Modeling in Industrialized
Housing
A Presentation at
IFORS 2005Honolulu
By
Dr. Mike Mullens, PEScott Broadway
July 15, 2005
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Agenda
Background Technology overview Beta test results
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Mission: Create production innovations for U.S. homebuilders to produce high quality, affordable, energy-efficient homes.
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Modular Homes
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Modular Homes
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Modular Homes
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Modular Homes
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Modular Homes
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Modular Homes
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Modular Homes
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Manufacturing Challenge:High & Variable Labor Content
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Manufacturing Challenge:Many Highly Interrelated Activities
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Manufacturing Challenge:Small, Trade-oriented Teams
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Manufacturing Challenge:Messy Processes
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Manufacturing Challenge:Tight Production Flow Lines
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Manufacturing Challenge:Near-Synchronous Line Movement
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Manufacturing Challenge:Large Components
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Manufacturing Challenge:Location Constraints for Activities
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Manufacturing Challenge: Floating Bottlenecks
Custom Homebuilding
Variable Production ProcessesSynchronous
Production Lines
Activity Location
Constraints
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Floating Bottlenecks:Upstream Queues
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Floating Bottlenecks: Downstream Line Starvation
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Floating Bottleneck: Off-quality & Rework
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Floating Bottlenecks:Other Impacts
Hurry exhaustion, frustration
Overtime higher costs, turnover
Unfinished work in yard Lost production capacity
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Research Question How much labor is really required to
build a house to customer specs? Can we use these estimates to better manage the enterprise?
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STACS Architecture
11Barcode Scanners •Employee•Activity•Module
1234598361
3875*
At each Work Location
Wireless link 22Parser Units•Organize/verify Scans•Buffer Data•Send to Database
On the Factory Floor
WirelessNetwork
33STACS Database•Log data•Intelligent data error ID/repair
Database Server
44Info. System•Live production status•Historical reporting•Labor modeling/prediction•Production scheduling•Decision Support
Corporate Intranet
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Module Scan
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Real Time Monitoring
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Dashboard
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Milestones Alpha test – Summer 03 4 weeks
~25 employees in drywall activities Beta test – Spring/Summer 04
80-90 employees (entire plant touch labor) Web-based monitoring on the plant floor 255 modules
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ClosetsaDrywallAreLaborHours 0.34+0.0026+2.74
Regression 1.15 hours
0
2
4
6
8
10
12
14
16
7553
D
7553
E
7553
A
7576
C
7574
B
7570
B
7570
A
7625
A
7626
A
7627
B
7628
E
7628
A
7623
A
Production Schedule
Tota
l Lab
or
Hou
rs
Average=8.6 labor hours4-6 finishers
Actual Finish TimePredicted
Prediction Mean ErrorAverage 1.77 hours
Alpha Test in DrywallLabor Modeling: Finishing
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Predictive Modeling Two activities chosen for analysis
Roofing Rough electric
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Roofing Tasks Cut and lay-in insulation Position and nail OSB pieces over insulation @ eave and nail 1x3 strip
over top (to prevent insulation from blocking airflow at eave). Position and nail OSB sheathing (note spacers between OSB sheets) Locate and nail hinge strips for eave flip Position and nail eave flip panels Locate and nail hinge strips for ridge flip Position and nail ridge flip panels Install ice guard at eave Install 2 layers of felt at eave Roll out felt and staple Stack shingles on roof and separate before positioning Position shingles and nail, row by row, starting at bottom and working
up. When omitting row of shingles for flips, snap chalk line for positioning
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Roofing Data Set 255 initial data points – one for each
module produced Dependent variable – total labor hours Independent variables – key drivers
Roof dimensions – length, width, pitch Flip panels – ridge, eave Other features – attic decking,
dormers, etc.
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Filtering the Data For each module
# employees who scanned # scans Total labor hours
Resulting data set Reduced from 255 to 67 modules
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Linear Regression Strategy
Linear model Dependent (Y) variable transformation –
square, square root, inverse, e, ln Dependent (X) variable transformation –
square, square root, inverse, e, ln X, first degree cross terms
Analysis Conventional linear regression (Excel) Stepwise regression (Minitab)
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Regression Results R2 range: .05 - .20 Few independent variables significant
– less important variables Mean absolute error using model
greater than error using average labor content
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Regression 4.2 hoursActual Roofing TimePredicted
Prediction Mean ErrorAverage 4.1 hours
Beta TestLabor Modeling: Roofing
05
101520
2530
3540
1045
8A
1041
7
1024
3
1041
9C
1043
5A
1039
9D
1040
7B10
407A
1040
1D
1045
5F
1043
8B
1046
2D
1040
4D
1044
0CModule
Lab
or
Ho
urs
Actual
Estimate
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Conclusions Workers not conscientious in reporting
work Little encouragement or incentive from
management to report work reliably Many other extraneous factors
influence work – delays (bottlenecks, materials)
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Future Research
Labor estimating Linear regression Neural nets
Automate scanning - RF tag technology
Operational decision support Production scheduling