Training Project completed in June of 2001; completion of this project qualified me for certification as a Black Belt.

1. Training Project Sheraton North Charleston Jon Haid, Black Belt

5. Define - SIPOC

6. Stakeholder Analysis

7. Define – Customer Requirements

8. Constraints & Assumptions

9. Define – Process Mapping

10. Current Laundry Process

11. Metro Delivery Process

12. Measure – Key Measures

13. Measure – Performance Measures

14. Measure - Rolled Throughput Yield

15. Population Sampling Worksheet

16. Measurement Planning Worksheet

17. Measurement Assessment Worksheet

18. Measurement Selection Matrix

19. Data Collection Sheet This sheet was used to record the amount of clean linen delivered to each floor on a daily basis. Jon Haid: 2 - 8 refers to Floor Number

20. Travel Time Tracking Sheet This sheet was used by the Room Attendants to record each time they had to leave a guestroom or come downstairs to obtain linen Jon Haid: Each linen type represents a potential defect.

21. Linen Inventory Levels This sheet represents the amount of linen required to be fully up to par in each room. It’s broken down by floor and room types.

22. Actual Linen Inventory This sheet is a record of the physical inventory taken. Jon Haid: This inventory indicates a shortage of Full Size sheets, which if not available on each floor, would be a defect.

23. Shift in Project Scope

24. RACI Chart

25. Minitab - Descriptive Statistics This chart shows the amount of hours worked in the Laundry. The average is 16 hours per day, but there were days with either 1 or as many as 3 attendants.

26. Data Collection Sheet Jon Haid: This sheet was used to record Laundry Attendant hours worked, as well as Rooms Occupied, Departures, & Arrivals.

27. Analyze – VA / NVA Analysis

28. Analyze – Data Analysis Tools This Pareto Chart shows the most frequent item missing in the Guestroom is Wash Cloths, followed by Double Sheets & Pillow Cases.

29. Analyze – Data Analysis Tools

30. Analyze – Data Analysis Tools Mean

31. Analyze – Data Analysis Tools Travel Times

32. Analyze – Root Cause Analysis Jon Haid: Project focus is on metro delivery process.

33. Analyze – Root Cause Analysis

34. Analyze – Root Cause Analysis

35. Analyze – Correlation and Regression This Scatter Plot shows there is No Correlation between minutes of travel time and occupied rooms.

36. Analyze – Correlation Coefficient Jon Haid: Since r, the Pearson correlation, is .303 there is no meaningful correlation between the minutes of travel time and occupied rooms.

37. Analyze – Correlation and Regression Jon Haid: The “prediction equation” is produced by Minitab.

38. Analyze – Correlation and Regression This Scatter Plot shows consistently lower travel times when linen was delivered before the shift. The minutes shown as negative values represent delivery after the shift started which showed a substantial increase in travel times.

39. Analyze – Positive Correlation Jon Haid: When comparing Travel Time during the Housekeeper’s shift to the number of minutes prior to the shift the metro was delivered, we get a meaningful correlation, because the Pearson correlation is below -.65.

40. Regression Analysis

43. “As Is” Mapping Travel time is shown as rework, or loops in this process.

44. “Should Be” Mapping Jon Haid: Our solution will eliminate or reduce the travel time, or rework.

46. Improve – Criteria Matrix Jon Haid: This was the chosen solution for the pilot plan

47. Improve - Implementation Plan

48. Improve – Pilot Plan Jon Haid: Pilot plan was to deliver metros to floors before Housekeepers came in each day.

49. Improve - FMEA Jon Haid: Loading of metros has an RPN (risk-priority number) of 360, which means this will be a high priority to focus on.

50. Improve - Implementation Summary

51. Control – Control Charts Data point 1 appears outside of the control limits, on this particular day, there was no laundry attendant available to deliver the metros, delivery was completed later in the morning by the Supervisor. This is known as "Special Cause" variation. Because of this "Special Cause", this chart is "Out of Control".

52. Control – Control Charts The top chart of Individual Travel Times shows the trend over time. The bottom chart or Moving Ranges shows the difference in consecutive values over time. Data point 1 is causing both charts to be "Out of Control".

53. Control – Control Charts This P-Chart shows the proportion defective; in this case the number of rooms requiring travel time as a proportion of the total rooms occupied for that day.

54. Control – Documentation Plan

55. Process Management Chart

56. Process Management Chart

57. Control - Voice of the Customer Comments from Housekeepers & Laundry Associates after implementation.

58. Control - RACI Chart

59. Dashboards Housekeepers will continue to document any travel time on this sheet & will turn these sheets in to the Supervisor each day. Times will be posted on Control Charts and posted in Department and reviewed at morning line-up meetings .

60. Dashboards Jon Haid: Projected savings are replaced with Actuals each month.

61. Control - Dashboards

62. Dashboard - November 2001 Jon Haid: This Control Chart shows that the process has been in control for over 50 days.

63. Dashboard - Sigma Calculation Jon Haid: Sigma Value went from 2.8 to 3.6.

64. Control - Lessons Learned

65. Quick Hit Generated At the start of this project, we made a change in how we handled all of our Food & Beverage linen. By sending all Food and Beverage items to an outside vendor, we were able to eliminate one FTE in the laundry. We estimate potential savings over the first 18 months to be approximately $27,000, in payroll and benefits expense.

66. Quick Hit - Potential Savings