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

5

Modular Homes

6

Modular Homes

7

Modular Homes

8

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