1. 1 Data Collection and Predictive Modeling in Industrialized Housing A Presentation at IFORS 2005 Honolulu By Dr. Mike Mullens, PE Scott Broadway July 15, 2005

2. 2 Agenda Background Technology overview Beta test results

3. 3 Mission: Create production innovations for U.S. homebuilders to produce high quality, affordable, energy-efficient homes.

4. 4 Modular Homes

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10. 10 Modular Homes

11. 11 Manufacturing Challenge: High & Variable Labor Content

12. 12 Manufacturing Challenge: Many Highly Interrelated Activities

13. 13 Manufacturing Challenge: Small, Trade-oriented Teams

14. 14 Manufacturing Challenge: Messy Processes

15. 15 Manufacturing Challenge: Tight Production Flow Lines

16. 16 Manufacturing Challenge: Near-Synchronous Line Movement

17. 17 Manufacturing Challenge: Large Components

18. 18 Manufacturing Challenge: Location Constraints for Activities

19. 19 Manufacturing Challenge: Floating Bottlenecks Custom Homebuilding Variable Production Processes Synchronous Production Lines Activity Location Constraints

20. 20 Floating Bottlenecks: Upstream Queues

21. 21 Floating Bottlenecks: Downstream Line Starvation

22. 22 Floating Bottleneck: Off-quality & Rework

23. 23 Floating Bottlenecks: Other Impacts Hurry  exhaustion, frustration Overtime  higher costs, turnover Unfinished work in yard Lost production capacity

24. 24 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?

25. 25 STACS Architecture 1 Barcode Scanners Employee Activity Module 12345 98361 3875* At each Work Location Wireless link 2 Parser Units Organize/verify Scans Buffer Data Send to Database On the Factory Floor Wireless Network 3 STACS Database Log data Intelligent data error ID/repair Database Server 4 Info. System Live production status Historical reporting Labor modeling/prediction Production scheduling Decision Support Corporate Intranet

26. 26 Module Scan

27. 27 Real Time Monitoring

28. 28 Dashboard

29. 29 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

30. 30 Regression1.15 hours 0 2 4 6 8 10 12 14 16 7553D 7553E 7553A 7576C 7574B7570B 7570A7625A7626A 7627B 7628E 7628A7623A Production Schedule Total Labor Hours Average=8.6 labor hours 4-6 finishers Actual Finish Time Predicted PredictionMean Error Average1.77 hours Alpha Test in Drywall Labor Modeling: Finishing

31. 31 Predictive Modeling Two activities chosen for analysis Roofing Rough electric

32. 32 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

33. 33 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.

34. 34 Filtering the Data For each module # employees who scanned # scans Total labor hours Resulting data set Reduced from 255 to 67 modules

35. 35 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)

36. 36 Regression Results R 2 range:.05 -.20 Few independent variables significant – less important variables Mean absolute error using model greater than error using average labor content

37. 37 Regression4.2 hours Actual Roofing Time Predicted PredictionMean Error Average4.1 hours Beta Test Labor Modeling: Roofing

38. 38 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)

39. 39 Future Research Labor estimating Linear regression Neural nets Automate scanning - RF tag technology Operational decision support Production scheduling