Wu, Kan (Hsinchu, TW)
Hui, Keung (Hong Kong, HK)
Chen, Thomas (Hsin-Chu, TW)
Chien, Robert (Taichung City, TW)

Plaque It! Sponsored by: Flash of Genius

10/353237

07/29/2004

01/28/2003

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Taiwan Semiconductor Manufacturing Company

705/8

(IPC1-7): G06F017/60

GEORGE O. SAILE & ASSOCIATES (28 DAVIS AVENUE, POUGHKEEPSIE, NY, 12603, US)

The invention claimed is:

1. A method for determining optimality of a planned operation schedule comprising the steps of: obtaining said planned operation schedule; obtaining an operation completion time; determining a planned operation schedule variance from said planned operation schedule and said operation completion time; and determining an optimality index as a function of said operation schedule variance.

2. The method of claim 1 further comprising the steps of: obtaining a forecasted operation schedule; determining a forecasted operation schedule variance from said forecasted operation schedule and said operation completion time; and determining said optimality index as a function of said forecasted schedule variance.

3. The method of claim 1 wherein said planned operation schedule is a master production schedule as calculated by a program executed on a computer system.

4. The method of claim 1 wherein the forecasted operation schedule is a forecasted order date, which is a predicted date at which a customer is expected to require a product.

5. The method of claim 1 wherein the operation completion date is an order confirmed date indicating completion of fabrication of the product.

6. The method of claim 1 wherein the planned operation schedule variance is determined by a formula: 33$D_1=\frac{\sum _{i=1}^n{\mathrm{MPSDate}}_i-{\mathrm{OCD}}_i\times n_i}{\sum _{i=1}^nn_i}$ where: D₁ is the planned operation schedule variance, MPSDate_i is the planned operation schedule for operation i, OCD_i is the operation completion date for the operation i, n is a quantity of each operation i with n operation counts.

7. The method of claim 2 wherein the planned operation schedule variance is determined by a formula: 34$D_1=\frac{\sum _{i=1}^n{\mathrm{MPSDate}}_i-{\mathrm{OCD}}_i\times n_i}{\sum _{i=1}^nn_i}$ where: D₁ is the planned operation schedule variance, MPSDate_i is the planned operation schedule for operation i, OCD_i is the operation completion date for the operation i, n is a quantity of each operation i with n operation counts.

8. The method of claim 2 wherein the forecasted operation schedule variance is determined by a formula: 35$D_2=\frac{\sum _{i=1}^n{\mathrm{FOD}}_i-{\mathrm{OCD}}_i\times n_i}{\sum _{i=1}^nn_i}$ where: D₂ is the forecasted operation schedule variance, FOD_i is the forecasted operation schedule for operation i, OCD_i is the operation completion date for the operation i, and n is a quantity of each operation i with n operation counts.

9. The method of claim 2 wherein the optimality index is determined by a formula: 36$\mathrm{MPSOI}=\frac{D_2}{D_1}$ where: MPSOI is the optimality index, D₁ is the planned operation schedule variance, and D₂ is the forecasted operation schedule variance.

10. The method of claim 1 further comprising the step of: obtaining a penalty factor, said penalty factor incurred for not meeting said planned operation schedule.

11. The method of claim 10 wherein the planned operation schedule variance is determined by a formula: 37$D_1=\frac{\sum _{i=1}^n{\mathrm{MPSDate}}_i-{\mathrm{OCD}}_i\times n_i\times p_i}{\sum _{i=1}^nn_i}$ where: D₁ is the planned operation schedule variance, MPSDate_i is the planned operation schedule for operation i, OCD_i is the operation completion date for the operation i; p_i is the penalty factor for operation i having missed the operation completion date. n is a quantity of each operation i with n operation counts.

12. The method of claim 2 further comprising the step of: obtaining a penalty factor, said penalty factor incurred for not meeting said planned operation schedule.

13. The method of claim 12 wherein the planned operation schedule variance is determined by a formula: 38$D_1=\frac{\sum _{i=1}^n{\mathrm{MPSDate}}_i-{\mathrm{OCD}}_i\times n_i\times p_i}{\sum _{i=1}^nn_i}$ where: D₁ is the planned operation schedule variance, MPSDate_i is the planned operation schedule for operation i, OCD_i is the operation completion date for the operation i, p_i is the penalty factor for operation i having missed the operation completion date. n is a quantity of each operation i with n operation counts.

14. The method of claim 12 wherein the forecasted operation schedule variance is determined by a formula: 39$D_2=\frac{\sum _{i=1}^n{\mathrm{FOD}}_i-{\mathrm{OCD}}_i\times n_i\times p_i}{\sum _{i=1}^nn_i}$ where: D₂ is the forecasted operation schedule variance, FOD_i is the forecasted operation schedule for operation i, OCD_i is the operation completion date for the operation i, and p_i is the penalty factor for operation i having missed the operation completion date. n is a quantity of each operation i with n operation counts.

15. The method of claim 1 wherein the optimality index is a concentration/dispersion factor of the operation schedule variance.

16. The method of claim 15 wherein the concentration/dispersion factor is determined by a formula: Z=A₃²−(A₁{overscore (X₁)})·(A₂{overscore (X₂)}) where: Z is the concentration/dispersion factor, A₁ is a fraction of a distribution of the planned operation schedule variance greater than an operation completion time tolerance, said operation completion time tolerance being a tolerance of said operation completion times for multiple operations, A₂ is a fraction of the distribution of planned operation schedule variance less than the operation completion time tolerance, A₃ is a fraction of the distribution of planned operation schedule variance within the operation completion time tolerance, {overscore (X₁)} is the mean of the distribution of the planned operation schedule variance greater than the operation completion time tolerance, and {overscore (X₂)} is the mean of the distribution of planned operation schedule variance less than the operation completion time tolerance.

17. The method of claim 16 wherein the fraction of a distribution of planned operation schedule variance greater than an operation completion time variance is determined by a formula: 40$A_1={\int }_{-\infty }^{0^{-}}y_{\mathrm{lead}}\left(x\right)x$ where: y_lead is a magnitude of planned operation schedule variance for the planned operations having an operation completion time less than the operation completion time tolerance, and x is a planned operation schedule variance.

18. The method of claim 16 wherein the fraction of a distribution of planned operation schedule variance less than an operation completion time variance is determined by a formula: 41$A_2={\int }_{0^{+}}^{+\infty }y_{\mathrm{lag}}\left(x\right)x$ where: y_lag is a magnitude of planned operation schedule variance for the planned operations having an operation completion time greater than the operation completion time tolerance, and x is a planned operation schedule variance.

19. The method of claim 16 wherein the fraction of a distribution of planned operation schedule variance greater than an operation completion time variance is determined by a formula: 42$\begin{array}{c}{A}_3={\int }_{0^{-}}^{0+}y\left(x\right)x\\ =1-A_1-A_2\end{array}$ where: y is a magnitude of planned operation schedule variance for the planned operations having an operation completion time within the operation completion time tolerance, and x is a planned operation schedule variance.

20. The method of claim 16 wherein the mean of the distribution of the planned operation schedule variance greater than the operation completion time tolerance is determined by a formula: 43$\stackrel{\_}{X_1}=\frac{{\int }_{-\infty }^{0-}y\left(x\right)xx}{{\int }_{-\infty }^{0-}y\left(x\right)x}$ y is a magnitude of planned operation schedule variance for the planned operations having an operation completion time within the operation completion time tolerance, and x is a planned operation schedule variance.

21. The method of claim 16 wherein the mean of the distribution of the planned operation schedule variance greater than the operation completion time tolerance is determined by a formula: 44$\stackrel{\_}{X_2}=\frac{{\int }_{0^{+}}^{+\infty }y\left(x\right)xx}{{\int }_{0^{+}}^{+\infty }y\left(x\right)x}$ y is a magnitude of planned operation schedule variance for the planned operations having an operation completion time within the operation completion time tolerance, and x is a planned operation schedule variance.

22. An apparatus for determining optimality of a planned operation schedule comprising: means for obtaining said planned operation schedule; means for obtaining an operation completion time; means for determining a planned operation schedule variance from said planned operation schedule and said operation completion time; and means for determining an optimality index as a function of said operation schedule variance.

23. The apparatus of claim 22 further comprising: means for obtaining a forecasted operation schedule; means for determining a forecasted operation schedule variance from said forecasted operation schedule and said operation completion time; and means for determining said optimality index as a function of said forecasted schedule variance.

24. The apparatus of claim 22 wherein said planned operation schedule is a master production schedule as calculated by a program executed on a computer system.

25. The apparatus of claim 23 wherein the forecasted operation schedule is a forecasted order date, which is a predicted date at which a customer is expected to require a product.

26. The apparatus of claim 22 wherein the operation completion date is an order confirmed date indicating completion of fabrication of the product.

27. The apparatus of claim 22 wherein the means for determining the planned operation schedule variance provides a solution for a formula: 45$D_1=\frac{\sum _{i=1}^n{\mathrm{MPSDate}}_i-{\mathrm{OCD}}_i\times n_i}{\sum _{i=1}^nn_i}$ where: D₁ is the planned operation schedule variance, MPSDate_i is the planned operation schedule for operation i, OCD_i is the operation completion date for the operation i, n is a quantity of each operation i with n operation counts.

28. The apparatus of claim 23 wherein the means for determining the planned operation schedule variance provides a solution for a formula: 46$D_1=\frac{\sum _{i=1}^n{\mathrm{MPSDate}}_i-{\mathrm{OCD}}_i\times n_i}{\sum _{i=1}^nn_i}$ where: D₁ is the planned operation schedule variance, MPSDate_i is the planned operation schedule for operation i, OCD_i is the operation completion date for the operation i, n is a quantity of each operation i with n operation counts.

29. The apparatus of claim 23 wherein means for determining the forecasted operation schedule variance provides a solution for a formula: 47$D_2=\frac{\sum _{i=1}^n{\mathrm{FOD}}_i-{\mathrm{OCD}}_i\times n_i}{\sum _{i=1}^nn_i}$ where: D₂ is the forecasted operation schedule variance, FOD_i is the forecasted operation schedule for operation i, OCD_i is the operation completion date for the operation i, and n is a quantity of each operation i with n operation counts.

30. The apparatus of claim 23 wherein the means for determining the optimality index provides a solution for a formula: 48$\mathrm{MPSOI}=\frac{D_2}{D_1}$ where: MPSOI is the optimality index, D₁ is the planned operation schedule variance, and D₂ is the forecasted operation schedule variance.

31. The apparatus of claim 22 further comprising: means for obtaining a penalty factor, said penalty factor incurred for not meeting said planned operation schedule.

32. The apparatus of claim 31 wherein the means for determining the planned operation schedule variance provides a solution for a formula: 49$D_1=\frac{\sum _{i=1}^n{\mathrm{MPSDate}}_i-{\mathrm{OCD}}_i\times n_i\times p_i}{\sum _{i=1}^nn_i}$ where: D₁ is the planned operation schedule variance, MPSDate_i is the planned operation schedule for operation i, OCD_i is the operation completion date for the operation i, p_i is the penalty factor for operation i having missed the operation completion date. n is a quantity of each operation i with n operation counts.

33. The apparatus of claim 23 further comprising: means for obtaining a penalty factor, said penalty factor incurred for not meeting said planned operation schedule.

34. The apparatus of claim 33 the means for determining the planned operation schedule variance provides a solution for a formula: 50$D_1=\frac{\sum _{i=1}^n{\mathrm{MPSDate}}_i-{\mathrm{OCD}}_i\times n_i\times p_i}{\sum _{i=1}^nn_i}$ where: D₁ is the planned operation schedule variance, MPSDate_i is the planned operation schedule for operation i, OCD_i is the operation completion date for the operation i, p_i is the penalty factor for operation i having missed the operation completion date. n is a quantity of each operation i with n operation counts.

35. The apparatus of claim 33 wherein means for determining the forecasted operation schedule variance provides a solution for a formula: 51$D_2=\frac{\sum _{i=1}^n{\mathrm{FOD}}_i-{\mathrm{OCD}}_i\times n_i\times p_i}{\sum _{i=1}^nn_i}$ where: D₂ is the forecasted operation schedule variance, FOD_i is the forecasted operation schedule for operation i, OCD_i is the operation completion date for the operation i, and p_i is the penalty factor for operation i having missed the operation completion date. n is a quantity of each operation i with n operation counts.

36. The apparatus of claim 22 wherein the optimality index is a concentration/dispersion factor of the operation schedule variance.

37. The apparatus of claim 36 wherein the means for determining the optimality index provides a solution for a formula: Z=A₃²−(A₁{overscore (X₁)})·(A₂{overscore (X₂)}) where: Z is the concentration/dispersion factor, A₁ is a fraction of a distribution of the planned operation schedule variance greater than an operation completion time tolerance, said operation completion time tolerance being a tolerance of said operation completion times for multiple operations, A₂ is a fraction of the distribution of planned operation schedule variance less than the operation completion time tolerance, A₃ is a fraction of the distribution of planned operation schedule variance within the operation completion time tolerance, {overscore (X₁)} is the mean of the distribution of the planned operation schedule variance greater than the operation completion time tolerance, and {overscore (X₂)} is the mean of the distribution of planned operation schedule variance less than the operation completion time tolerance.

38. The apparatus of claim 37 the means for determining the optimality index generates the fraction of a distribution of planned operation schedule variance greater than an operation completion time variance by providing a solution for a formula: 52$A_1={\int }_{-\infty }^{0^{-}}y_{\mathrm{lead}}\left(x\right)x$ where: y_lead is a magnitude of planned operation schedule variance for the planned operations having an operation completion time less than the operation completion time tolerance, and x is a planned operation schedule variance.

39. The apparatus of claim 37 wherein the means for determining the optimality index generates the fraction of a distribution of planned operation schedule variance less than an operation completion time variance by providing a solution for a formula: 53$A_2={\int }_{0^{+}}^{+\infty }y_{\mathrm{lag}}\left(x\right)x$ where: y_lag is a magnitude of planned operation schedule variance for the planned operations having an operation completion time greater than the operation completion time tolerance, and x is a planned operation schedule variance.

40. The apparatus of claim 37 wherein the means for determining the optimality index generates the fraction of a distribution of planned operation schedule variance greater than an operation completion time variance by providing a solution for a formula: 54$\begin{array}{c}{A}_3={\int }_{0^{-}}^{0+}y\left(x\right)x\\ =1-A_1-A_2\end{array}$ where: y is a magnitude of planned operation schedule variance for the planned operations having an operation completion time within the operation completion time tolerance, and x is a planned operation schedule variance.

41. The apparatus of claim 37 wherein the means for determining the optimality index generates the mean of the distribution of the planned operation schedule variance greater than the operation completion time tolerance by providing a solution for a formula: 55$\stackrel{\_}{X_1}=\frac{{\int }_{-\infty }^{0^{-}}y\left(x\right)xx}{{\int }_{-\infty }^{0^{-}}y\left(x\right)x}$ y is a magnitude of planned operation schedule variance for the planned operations having an operation completion time within the operation completion time tolerance, and x is a planned operation schedule variance.

42. The apparatus of claim 37 wherein the means for determining the optimality index the mean of the distribution of the planned operation schedule variance greater than the operation completion time tolerance by providing a solution for a formula: 56$\stackrel{\_}{X_2}=\frac{{\int }_{0^{+}}^{+\infty }y\left(x\right)xx}{{\int }_{0^{+}}^{+\infty }y\left(x\right)x}$ y is a magnitude of planned operation schedule variance for the planned operations having an operation completion time within the operation completion time tolerance, and x is a planned operation schedule variance.

43. A calculating device for determining optimality of a planned operation schedule comprising: a connection to a planned operation scheduling generator for obtaining said planned operation schedule; a connection to a manufacturing information database for obtaining an operation completion time; and a first variance calculator for determining a planned operation schedule variance from said planned operation schedule and said operation completion time.

44. The device of claim 43 further comprising: a connection to a marketing database for obtaining a forecasted operation schedule; a second variance calculator determining a forecasted operation schedule variance from said forecasted operation schedule and said operation completion time; and a third variance calculator connected to the first and second variance calculators determining an optimality index as a function of said operation schedule variance to said forecasted schedule variance.

45. The calculating device of claim 43 wherein said planned operation schedule is a master production schedule as calculated by a program executed on a computer system.

46. The calculating device of claim 44 wherein the forecasted operation schedule is a forecasted order date a predicted date at which a customer is expected to require a product.

47. The calculating device of claim 43 wherein the operation completion date is an order confirmed date indicating completion of fabrication of the product.

48. The calculating device of claim 43 wherein the first variance calculator determines the planned operation schedule variance by providing a solution for a formula: 57$D_1=\frac{\sum _{i=1}^n{\mathrm{MPSDate}}_i-{\mathrm{OCD}}_i\times n_i}{\sum _{i=1}^nn_i}$ where: D₁ is the planned operation schedule variance, MPSDate_i is the planned operation schedule for operation i, OCD_i is the operation completion date for the operation i, n is a quantity of each operation i with n operation counts.

49. The calculating device of claim 44 wherein the first variance calculator determines the planned operation schedule variance by providing a solution for a formula: 58$D_1=\frac{\sum _{i=1}^n{\mathrm{MPSDate}}_i-{\mathrm{OCD}}_i\times n_i}{\sum _{i=1}^nn_i}$ where: D₁ is the planned operation schedule variance, MPSDate_i is the planned operation schedule for operation i, OCD_i is the operation completion date for the operation i, n is a quantity of each operation i with n operation counts.

50. The calculating device of claim 44 wherein the second variance calculator determines the forecasted operation schedule variance by providing a solution for a formula: 59$D_2=\frac{\sum _{i=1}^n{\mathrm{FOD}}_i-{\mathrm{OCD}}_i\times n_i}{\sum _{i=1}^nn_i}$ where: D₂ is the forecasted operation schedule variance, FOD_i is the forecasted operation schedule for operation i, OCD_i is the operation completion date for the operation i, and n is a quantity of each operation i with n operation counts.

51. The calculating device of claim 44 wherein the third variance calculator determines the optimality index by providing a solution for a formula: 60$\mathrm{MPSOI}=\frac{D_2}{D_1}$ where: MPSOI is the optimality index, D₁ is the planned operation schedule variance, and D₂ is the forecasted operation schedule variance.

52. The calculating device of claim 43 wherein the first variance calculator determines the planned operation schedule variance by providing a solution for a formula: 61$D_1=\frac{\sum _{i=1}^n{\mathrm{MPSDate}}_i-{\mathrm{OCD}}_i\times n_i\times p_i}{\sum _{i=1}^nn_i}$ where: D₁ is the planned operation schedule variance, MPSDate_i is the planned operation schedule for operation i, OCD_i is the operation completion date for the operation i, p_i is the penalty factor for operation i having missed the operation completion date. n is a quantity of each operation i with n operation counts.

53. The calculating device of claim 44 wherein the first variance calculator determines the planned operation schedule variance by providing a solution for a formula: 62$D_1=\frac{\sum _{i=1}^n{\mathrm{MPSDate}}_i-{\mathrm{OCD}}_i\times n_i\times p_i}{\sum _{i=1}^nn_i}$ where: D₁ is the planned operation schedule variance, MPSDate_i is the planned operation schedule for operation i, OCD_i is the operation completion date for the operation i, p_i is the penalty factor for operation i having missed the operation completion date. n is a quantity of each operation i with n operation counts.

54. The calculating device of claim 44 wherein the second variance calculator determines the forecasted operation schedule variance by providing a solution for a formula: 63$D_2=\frac{\sum _{i=1}^n{\mathrm{FOD}}_i-{\mathrm{OCD}}_i\times n_i\times p_i}{\sum _{i=1}^nn_i}$ where: D₂ is the forecasted operation schedule variance, FOD_i is the forecasted operation schedule for operation i, OCD_i is the operation completion date for the operation i, and p_i is the penalty factor for operation i having missed the operation completion date. n is a quantity of each operation i with n operation counts.

55. The calculating device of claim 43 wherein the optimality index is a concentration/dispersion factor of the operation schedule variance.

56. The calculating device of claim 55 wherein the third variance calculator determines the concentration/dispersion factor by providing a solution for a formula: Z=A₃²−(A₁{overscore (X₁)})·(A₂{overscore (X₂)}) where: Z is the concentration/dispersion factor, A₁ is a fraction of a distribution of the planned operation schedule variance greater than an operation completion time tolerance, said operation completion time tolerance being a tolerance of said operation completion times for multiple operations, A₂ is a fraction of the distribution of planned operation schedule variance less than the operation completion time tolerance, A₃ is a fraction of the distribution of planned operation schedule variance within the operation completion time tolerance, {overscore (X₁)} is the mean of the distribution of the planned operation schedule variance greater than the operation completion time tolerance, and {overscore (X₂)} is the mean of the distribution of planned operation schedule variance less than the operation completion time tolerance.

57. The calculating device of claim 56 wherein the fraction of a distribution of planned operation schedule variance greater than an operation completion time variance is determined by a formula: 64$A_1={\int }_{-\infty }^{0^{-}}y_{\mathrm{lead}}\left(x\right)x$ where: y_lead is a magnitude of planned operation schedule variance for the planned operations having an operation completion time less than the operation completion time tolerance, and x is a planned operation schedule variance.

58. The calculating device of claim 56 wherein the fraction of a distribution of planned operation schedule variance less than an operation completion time variance is determined by a formula: 65$A_2={\int }_{0^{+}}^{+\infty }y_{\mathrm{lag}}\left(x\right)x$ where: y_lag is a magnitude of planned operation schedule variance for the planned operations having an operation completion time greater than the operation completion time tolerance, and x is a planned operation schedule variance.

59. The calculating device of claim 56 wherein the fraction of a distribution of planned operation schedule variance greater than an operation completion time variance is determined by a formula: 66$\begin{array}{c}{A}_3={\int }_{0^{-}}^{0+}y\left(x\right)x\\ =1-A_1-A_2\end{array}$ where: y is a magnitude of planned operation schedule variance for the planned operations having an operation completion time within the operation completion time tolerance, and x is a planned operation schedule variance.

60. The calculating device of claim 56 wherein the mean of the distribution of the planned operation schedule variance greater than the operation completion time tolerance is determined by a formula: 67$\stackrel{\_}{X_1}=\frac{{\int }_{-\infty }^{0^{-}}y\left(x\right)xx}{{\int }_{-\infty }^{0^{-}}y\left(x\right)x}$ y is a magnitude of planned operation schedule variance for the planned operations having an operation completion time within the operation completion time tolerance, and x is a planned operation schedule variance.

61. The calculating device of claim 56 wherein the mean of the distribution of the planned operation schedule variance greater than the operation completion time tolerance is determined by a formula: 68$\stackrel{\_}{X_2}=\frac{{\int }_{0^{+}}^{+\infty }y\left(x\right)xx}{{\int }_{0^{+}}^{+\infty }y\left(x\right)x}$ y is a magnitude of planned operation schedule variance for the planned operations having an operation completion time within the operation completion time tolerance, and x is a planned operation schedule variance.

62. A computing system in communication with a marketing database, a manufacturing information database and a planned operation scheduling generator, said computing system executing a wherein the program process for determining optimality of a planned operation schedule comprising the steps of: obtaining said planned operation schedule; obtaining an operation completion time; determining a planned operation schedule variance from said planned operation schedule and said operation completion time; and determining an optimality index as a function of said operation schedule variance.

63. The computing system of claim 62 wherein said wherein the program process further comprises the steps of: obtaining a forecasted operation schedule; determining a forecasted operation schedule variance from said forecasted operation schedule and said operation completion time; and determining said optimality index as a function of said forecasted schedule variance.

64. The computing system of claim 62 wherein said planned operation schedule is a master production schedule as calculated by a program executed on a computer system.

65. The computing system of claim 62 wherein the forecasted operation schedule is a forecasted order date, which is a predicted date at which a customer is expected to require a product.

66. The computing system of claim 62 wherein the operation completion date is an order confirmed date indicating completion of fabrication of the product.

67. The computing system of claim 62 wherein the program process determines the planned operation schedule variance by solving a formula: 69$D_1=\frac{\sum _{i=1}^n{\mathrm{MPSDate}}_i-{\mathrm{OCD}}_i\times n_i}{\sum _{i=1}^nn_i}$ where: D₁ is the planned operation schedule variance, MPSDate_i is the planned operation schedule for operation i, OCD_i is the operation completion date for the operation i, n is a quantity of each operation i with n operation counts.

68. The computing system of claim 63 wherein the program process determines the planned operation schedule variance by solving a formula: 70$D_1=\frac{\sum _{i=1}^n{\mathrm{MPSDate}}_i-{\mathrm{OCD}}_i\times n_i}{\sum _{i=1}^nn_i}$ where: D₁ is the planned operation schedule variance, MPSDate_i is the planned operation schedule for operation i, OCD_i is the operation completion date for the operation i, n is a quantity of each operation i with n operation counts.

69. The computing system of claim 63 wherein the program process determines the forecasted operation schedule variance by solving a formula: 71$D_2=\frac{\sum _{i=1}^n{\mathrm{FOD}}_i-{\mathrm{OCD}}_i\times n_i}{\sum _{i=1}^nn_i}$ where: D₂ is the forecasted operation schedule variance, FOD_i is the forecasted operation schedule for operation i, OCD_i is the operation completion date for the operation i, and n is a quantity of each operation i with n operation counts.

70. The computing system of claim 63 wherein the wherein the program process determines the optimality index by solving a formula: 72$\mathrm{MPSOI}=\frac{D_2}{D_1}$ where: MPSOI is the optimality index, D₁ is the planned operation schedule variance, and D₂ is the forecasted operation schedule variance.

71. The computing system of claim 62 wherein the wherein the program process further comprises the step of: obtaining a penalty factor, said penalty factor incurred for not meeting said planned operation schedule.

72. The computing system of claim 71 wherein the program process determines the planned operation schedule variance by solving a formula: 73$D_1=\frac{\sum _{i=1}^n{\mathrm{MPSDate}}_i-{\mathrm{OCD}}_i\times n_i\times p_i}{\sum _{i=1}^nn_i}$ where: D₁ is the planned operation schedule variance, MPSDate_i is the planned operation schedule for operation i, OCD_i is the operation completion date for the operation i, p_i is the penalty factor for operation i having missed the operation completion date. n is a quantity of each operation i with n operation counts.

73. The computing system of claim 63 wherein the wherein the program process further comprises the step of: obtaining a penalty factor, said penalty factor incurred for not meeting said planned operation schedule.

74. The computing system of claim 73 wherein the wherein the program process determines the planned operation schedule variance by solving a formula: 74$D_1=\frac{\sum _{i=1}^n{\mathrm{MPSDate}}_i-{\mathrm{OCD}}_i\times n_i\times p_i}{\sum _{i=1}^nn_i}$ where: D₁ is the planned operation schedule variance MPSDate_i is the planned operation schedule for operation i, OCD_i is the operation completion date for the operation i, p_i is the penalty factor for operation i having missed the operation completion date. n is a quantity of each operation i with n operation counts.

75. The computing system of claim 73 wherein the wherein the program process determines the forecasted operation schedule variance by solving a formula: 75$D_2=\frac{\sum _{i=1}^n{\mathrm{FOD}}_i-{\mathrm{OCD}}_i\times n_i\times p_i}{\sum _{i=1}^nn_i}$ where: D₂ is the forecasted operation schedule variance, FOD_i is the forecasted operation schedule for operation i, OCD_i is the operation completion date for the operation i, and p_i is the penalty factor for operation i having missed the operation completion date. n is a quantity of each operation i with n operation counts.

76. The computing system of claim 62 wherein the optimality index is a concentration/dispersion factor of the operation schedule variance.

77. The computing system of claim 76 wherein the wherein the program process determines the concentration/dispersion factor by solving a formula: Z=A₃²−(A₁{overscore (X₁)})·(A₂{overscore (X₂)}) where: Z is the concentration/dispersion factor, A₁ is a fraction of a distribution of the planned operation schedule variance greater than an operation completion time tolerance, said operation completion time tolerance being a tolerance of said operation completion times for multiple operations, A₂ is a fraction of the distribution of planned operation schedule variance less than the operation completion time tolerance, A₃ is a fraction of the distribution of planned operation schedule variance within the operation completion time tolerance, {overscore (X₁)} is the mean of the distribution of the planned operation schedule variance greater than the operation completion time tolerance, and {overscore (X₂)} is the mean of the distribution of planned operation schedule variance less than the operation completion time tolerance.

78. The computing system of claim 77 wherein the fraction of a distribution of planned operation schedule variance greater than an operation completion time variance is determined by a formula: 76$A_1={\int }_{-\infty }^{0^{-}}y_{\mathrm{lead}}\left(x\right)x$ where: y_lead is a magnitude of planned operation schedule variance for the planned operations having an operation completion time less than the operation completion time tolerance, and x is a planned operation schedule variance.

79. The computing system of claim 77 wherein the fraction of a distribution of planned operation schedule variance less than an operation completion time variance is determined by a formula: 77$A_2={\int }_{0^{+}}^{+\infty }y_{\mathrm{lag}}\left(x\right)x$ where: y_lag is a magnitude of planned operation schedule variance for the planned operations having an operation completion time greater than the operation completion time tolerance, and x is a planned operation schedule variance.

80. The computing system of claim 77 wherein the fraction of a distribution of planned operation schedule variance greater than an operation completion time variance is determined by a formula: 78$\begin{array}{c}{A}_3={\int }_{0^{-}}^{0+}y\left(x\right)x\\ =1-A_1-A_2\end{array}$ where: y is a magnitude of planned operation schedule variance for the planned operations having an operation completion time within the operation completion time tolerance, and x is a planned operation schedule variance.

81. The computing system of claim 77 wherein the mean of the distribution of the planned operation schedule variance greater than the operation completion time tolerance is determined by a formula: 79$\stackrel{\_}{X_1}=\frac{{\int }_{-\infty }^{0^{-}}y\left(x\right)xx}{{\int }_{-\infty }^{0^{-}}y\left(x\right)x}$ y is a magnitude of planned operation schedule variance for the planned operations having an operation completion time within the operation completion time tolerance, and x is a planned operation schedule variance.

82. The computing system of claim 77 wherein the mean of the distribution of the planned operation schedule variance greater than the operation completion time tolerance is determined by a formula: 80$\stackrel{\_}{X_2}=\frac{{\int }_{0^{+}}^{+\infty }y\left(x\right)xx}{{\int }_{0^{+}}^{+\infty }y\left(x\right)x}$ y is a magnitude of planned operation schedule variance for the planned operations having an operation completion time within the operation completion time tolerance, and x is a planned operation schedule variance.

83. A medium for retaining a computer program which, when implemented by a computing system, executes a process for determining optimality of a planned operation schedule, said process comprising the steps of: obtaining said planned operation schedule; obtaining an operation completion time; determining a planned operation schedule variance from said planned operation schedule and said operation completion time; and determining an optimality index as a function of said operation schedule variance.

84. The medium of claim 83 wherein said process further comprises the steps of: obtaining a forecasted operation schedule; determining a forecasted operation schedule variance from said forecasted operation schedule and said operation completion time; and determining said optimality index as a function of said forecasted schedule variance.

85. The medium of claim 83 wherein said planned operation schedule is a master production schedule as calculated by a program executed on a computer system.

86. The medium of claim 83 wherein the forecasted operation schedule is a forecasted order date, which is a predicted date at which a customer is expected to require a product.

87. The medium of claim 83 wherein the operation completion date is an order confirmed date indicating completion of fabrication of the product.

88. The medium of claim 83 wherein the process determines the planned operation schedule variance by solving a formula: 81$D_1=\frac{\sum _{i=1}^n{\mathrm{MPSDate}}_i-{\mathrm{OCD}}_i\times n_i}{\sum _{i=1}^nn_i}$ where: D₁ is the planned operation schedule variance, MPSDate_i is the planned operation schedule for operation i, OCD_i is the operation completion date for the operation i, n is a quantity of each operation i with n operation counts.

89. The medium of claim 84 wherein the process determines the planned operation schedule variance by solving a formula: 82$D_1=\frac{\sum _{i=1}^n{\mathrm{MPSDate}}_i-{\mathrm{OCD}}_i\times n_i}{\sum _{i=1}^nn_i}$ where: D₁ is the planned operation schedule variance, MPSDate_i is the planned operation schedule for operation i, OCD_i is the operation completion date for the operation i, n is a quantity of each operation i with n operation counts.

90. The medium of claim 84 wherein the process determines the forecasted operation schedule variance by solving a formula: 83$D_2=\frac{\sum _{i=1}^n{\mathrm{FOD}}_i-{\mathrm{OCD}}_i\times n_i}{\sum _{i=1}^nn_i}$ where: D₂ is the forecasted operation schedule variance, FOD_i is the forecasted operation schedule for operation i, OCD_i is the operation completion date for the operation i, and n is a quantity of each operation i with n operation counts.

91. The medium of claim 84 wherein the process determines the optimality index by solving a formula: 84$\mathrm{MPSOI}=\frac{D_2}{D_1}$ where: MPSOI is the optimality index, D₁ is the planned operation schedule variance, and D₂ is the forecasted operation schedule variance.

92. The medium of claim 83 wherein the process further comprises the step of: obtaining a penalty factor, said penalty factor incurred for not meeting said planned operation schedule.

93. The medium of claim 92 wherein the process determines the planned operation schedule variance by solving a formula: 85$D_1=\frac{\sum _{i=1}^n{\mathrm{MPSDate}}_i-{\mathrm{OCD}}_i\times n_i\times p_i}{\sum _{i=1}^nn_i}$ where: D₁ is the planned operation schedule variance, MPSDate_i is the planned operation schedule for operation i, OCD_I is the operation completion date for the operation i, p_i is the penalty factor for operation i having missed the operation completion date. n is a quantity of each operation i with n operation counts.

94. The medium of claim 84 wherein the process further comprises the step of: obtaining a penalty factor, said penalty factor incurred for not meeting said planned operation schedule.

95. The medium of claim 94 wherein the process determines the planned operation schedule variance by solving a formula: 86$D_1=\frac{\sum _{i=1}^n{\mathrm{MPSDate}}_i-{\mathrm{OCD}}_i\times n_i\times p_i}{\sum _{i=1}^nn_i}$ where: D₁ is the planned operation schedule variance, MPSDate_i is the planned operation schedule for operation i, OCD_i is the operation completion date for the operation i, p_i is the penalty factor for operation i having missed the operation completion date. n is a quantity of each operation i with n operation counts.

96. The medium of claim 94 wherein the process determines the forecasted operation schedule variance by solving a formula: 87$D_2=\frac{\sum _{i=1}^n{\mathrm{FOD}}_i-{\mathrm{OCD}}_i\times n_i\times p_i}{\sum _{i=1}^nn_i}$ where: D₂ is the forecasted operation schedule variance, FOD_i is the forecasted operation schedule for operation i, OCD_i is the operation completion date for the operation i, and p_i is the penalty factor for operation i having missed the operation completion date. n is a quantity of each operation i with n operation counts.

97. The medium of claim 83 wherein the optimality index is a concentration/dispersion factor of the operation schedule variance.

98. The medium of claim 97 wherein the process determines the concentration/dispersion factor by solving a formula: Z=A₃²−(A₁{overscore (X₁)})·(A₂{overscore (X₂)}) where: Z is the concentration/dispersion factor, A₁ is a fraction of a distribution of the planned operation schedule variance greater than an operation completion time tolerance, said operation completion time tolerance being a tolerance of said operation completion times for multiple operations, A₂ is a fraction of the distribution of planned operation schedule variance less than the operation completion time tolerance, A₃ is a fraction of the distribution of planned operation schedule variance within the operation completion time tolerance, {overscore (X₁)} is the mean of the distribution of the planned operation schedule variance greater than the operation completion time tolerance, and {overscore (X₂)} is the mean of the distribution of planned operation schedule variance less than the operation completion time tolerance.

99. The medium of claim 98 wherein the fraction of a distribution of planned operation schedule variance greater than an operation completion time variance is determined by a formula: 88$A_1={\int }_{-\infty }^{0^{-}}y_{\mathrm{lead}}\left(x\right)x$ where: y_lead is a magnitude of planned operation schedule variance for the planned operations having an operation completion time less than the operation completion time tolerance, and x is a planned operation schedule variance.

100. The medium of claim 98 wherein the fraction of a distribution of planned operation schedule variance less than an operation completion time variance is determined by a formula: 89$A_2={\int }_{0^{+}}^{+\infty }y_{\mathrm{lag}}\left(x\right)x$ where: y_lag is a magnitude of planned operation schedule variance for the planned operations having an operation completion time greater than the operation completion time tolerance, and x is a planned operation schedule variance.

101. The medium of claim 98 wherein the fraction of a distribution of planned operation schedule variance greater than an operation completion time variance is determined by a formula: 90$A_3={\int }_{0^{-}}^{0+}y\left(x\right)x=1-A_1-A_2$ where: y is a magnitude of planned operation schedule variance for the planned operations having an operation completion time within the operation completion time tolerance, and x is a planned operation schedule variance.

102. The medium of claim 98 wherein the mean of the distribution of the planned operation schedule variance greater than the operation completion time tolerance is determined by a formula: 91$\stackrel{\_}{x_1}=\frac{{\int }_{-\infty }^{0^{-}}y\left(x\right)xx}{{\int }_{-\infty }^{0^{-}}y\left(x\right)x}$ y is a magnitude of planned operation schedule variance for the planned operations having an operation completion time within the operation completion time tolerance, and x is a planned operation schedule variance.

103. The medium of claim 98 wherein the mean of the distribution of the planned operation schedule variance greater than the operation completion time tolerance is determined by a formula: 92$\stackrel{\_}{x_2}=\frac{{\int }_{0^{+}}^{+\infty }y\left(x\right)xx}{{\int }_{0^{+}}^{+\infty }y\left(x\right)x}$ y is a magnitude of planned operation schedule variance for the planned operations having an operation completion time within the operation completion time tolerance, and x is a planned operation schedule variance.

BACKGROUND OF THE INVENTION

[0001] 1. Field of the Invention

[0002] This invention relates generally to methods and systems for generating master production schedules for planning usage of fabrication and processing equipment of a manufacturing line. More particularly, this invention relates to methods and apparatus for determining whether methods and systems that generate master production schedules are optimum

[0003] 2. Description of Related Art

[0004] Computer software for the generation of operational planning of usage of fabrication or processing equipment of a fabrication line, as is known in the art, produce a master production schedule, such as from ADEXA, Inc. and i2 Technologies, Inc. These programs employ information from the sales prediction plan, order entry, and customer information residing in the marketing database; information from the production equipment inventory describing equipment status and availability, raw material supply, product process definition residing in a manufacturing information database, and production status; and a model of each of the fabrication lines of the manufacturing facility residing in the MPS database. The MPS programs then employ scheduling algorithms to develop a planned operation schedule for the manufacturing line that is most efficient and allows maximizing of the utilization of the manufacturing lines. Further, the scheduling attempts to insure that scheduled and promised product delivery dates are met.

[0005] While the algorithms of the MPS software attempt to optimize utilization of the fabrication lines of the manufacturing facility, there is no method or system available to establish an objective criterion for the optimality of the master production schedules as generated by MPS software.

[0006] U.S. Pat. No. 5,825,650 (Wang) describes a method for generating a model for predicting standard cycle time for a semiconductor process stage. A generic cycle time model is created based on Little's formula and Kingman's equation. Past cycle times as related to equipment utilization are used to generate a regression curve. The regression curve is then used to determine the coefficients of the generic cycle time model. Then, the standard cycle time of a stage for a future upcoming cycle is determined by using the cycle time model.

[0007] U.S. Pat. No. 6,119,102 (Rush, et al.) illustrates a manufacturing resource planning (MRP) system with viewable master production schedule. The MRP system begins by creating a master production schedule (MPS). The MPS is a data set indicating what quantity of product needs to be produced on what date to support the independent demand, i.e., sales orders, job orders and forecasts. Four data set files are used to create the MPS: customer orders (sales orders), scheduled receipts (job and purchase orders), sales forecasts and master scheduled activity. After MPS regeneration has occurred, the user may regenerate MRP.

[0008] U.S. Pat. No. 5,231,567 (Matoba, et al.) teaches a manufacturing planning system. The manufacturing planning system has lead time estimating function, MRP executing function, work demand calculating function, problem analyzing function, capacity adjusting function, product completion data adjusting function, and alternative shop designating function for planning a production schedule by calculating successively lead time in consideration of amount of work demanded and capacity for production, analyzing problems in the production schedule and performing relevant adjustments for solving the problems. An on-line display function is provided for simultaneous display of the problems and load/capacity states of production shops in association with solution of the problems and various adjustments.

[0009] U.S. Pat. No. 5,880,960 (Lin, et al.) describes a method for improving Work-in-Progress (WIP) balance in a manufacturing line. The method provides an index of line balance method for maintaining optimum queued quantities of products at a manufacturing step and over an entire manufacturing line.

SUMMARY OF THE INVENTION

[0010] An object of this invention is to provide a method for evaluation of optimality of a planned operation schedule such as a master production schedule.

[0011] To accomplish at least this object, a method for determining optimality of a planned operation schedule begins by obtaining a forecasted operation schedule, the planned operation schedule, and an operation completion time. The forecasted operation schedule is a forecasted order date, which is a predicted date at which a customer is expected to require a product. The operation completion date is an order confirmed date indicating completion of fabrication of the product. A planned operation schedule variance is determined from the planned operation schedule and the operation completion time. Then, a forecasted operation schedule variance is determined from the forecasted operation schedule and the operation completion time. An optimality index is determined as a function of the operation schedule variance to the forecasted schedule variance. The magnitude of the optimality index is an indicator of the optimality of the planned operation schedule as generated.

[0012] The planned operation schedule variance is determined by the formula: 1

[0013] where:

[0014] D₁ is the planned operation schedule variance.

[0015] MPSDate_i is the planned operation schedule for operation i.

[0016] OCD_i is the operation completion date for the operation i.

[0017] n is a quantity of each operation i with n operation counts.

[0018] The forecasted operation schedule variance is determined by the formula: 2

[0019] where:

[0020] D₂ is the forecasted operation schedule variance.

[0021] FOD_i is the forecasted operation schedule for operation i.

[0022] OCD_i is the operation completion date for the operation i.

[0023] n is a quantity of each operation i with n operation counts.

[0024] The function of the optimality index is determined by the formula: 3$\mathrm{MPSOI}=\frac{D_2}{D_1}$

[0025] where:

[0026] MPSOI is the optimality index.

[0027] D₁ is the planned operation schedule variance.

[0028] D₂ is the forecasted operation schedule variance.

[0029] In a second embodiment of the method, the planned operation schedule variance is determined by the formula: