Introduction to Quality Assurance 255323 255323

Six-Sigma.....

6-sigma (Six-Sigma) ?g ( g ) (

) )

()

() () ; ; n

(normal distribution)

6-sigma() 3-sigma()

6-sigma (Six-Sigma) ? ()g ( g ) ( )

(defect-free)

6-Sigmag$

6-sigma - , , ,

()- , ,

6-Sigmag .. 1987 Motorola 6 5 1988 Motorola Kodak Raytheon ABB CDI .. 1988 Motorola, Kodak, Raytheon, ABB, CDI

6 .. 1989/1990 IBM, DEC 6

.. 1993 AlliedSignal

1995 General Electric .. 1995 General Electric

99% ? %

99% ( 3 sigma) 99 99966% 6-sigma ( 6-sigma) 300 99% ( 3 sigma) 5,000

99.99966% 6-sigma ( 6-sigma) 1.7

200,000 7

68 1

2

34

1 5

Mikel J. Henry, 1997, the vision of six-sigma: a road map for breakthrough, 5 ed., vol;1

3-sigma vs 6-sigma3 g g 3-sigma = 6,210 1,000,000 6 i 3 4 1 000 000 6-sigma = 3.4 1,000,000 20

3 sigma 6 210/1 000 000 0 00621 3-sigma = 6,210/1,000,000 = 0.00621; = 1-0.00621 = 0.99379 0.99379 20 0.88286 88.286% % 6-sigma 0.9999932 99.99932%

3-sigma vs 6-sigma3 g g shift ( ) 1.5-sigma

3 sigma = 66 811 1 000 000 3-sigma = 66,811 1,000,000 93.33189% LSL USL

3-sigma ( hift ) 1 5 i

6-sigma ( hift ) 1 5 i

6-sigma = 3.4 1,000,000 99 9999998% LSL

(shift mean) 1.5 sigma (shift mean) 1.5 sigma

99.9999998% LSL USL

DMAIC Model

DPMO = defects per million opportunities 1,000,000

DMAIC Model 6-sigma 5 D D fi D = Define

- -

- ( 1 )

Affi it di - Affinity diagram, , , CTQ tree (Critical-to-Quality tree: (voice of customer) ), customer) ),

DMAIC Model ()( ) M = Measure

- ( )-

DMAIC Model ()( ) A = Analyze

(process mapping) flowchart (process mapping) flowchart process mapping input process variables

(x x x ) (x1, x2, , xn)

(M=Measure) (M Measure) 3 , ,

DMAIC Model ()( ) A = Analyze

(brainstorming) (brainstorming), , , , (tree diagram)

I = Improve I Improve

- C = Control