Eindhoven University of Technology MASTER the design and ...

Eindhoven University of Technology

MASTER

NXE volume organisationthe design and evaluation of an organisation structure for the volume production of ASMLsnew NXE machine type

Schepens, A.J.A.

Award date:2009

Link to publication

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NXE volume organisation

The design and evaluation of an organisation structure for the volume

production of ASMLs new NXE machine type

Appendices


2

Appendix I: Historical learning curve The following charts indicate a number of things. The red line is the cycle time norm as it was set over time, the black line is

a 84% learning curve and the bars indicate the realized cycle times for the test phase (SQ) of machines that were produced.


3


4

As can be seen on these graphs, the cycle time variates dramatically over time, so it is hard to obtain a precise learning

curve. Moreover the graphs only relate a part of the production proces. (SQ)

The following charts shows a study of the cycle time for all Twinscan types and PAS types, which shows that for a particular

generation of machine (of which ASML only has had 2 – the PAS and the Twinscan – and is now only starting on the third

NXE) the learning curve can also be loosely applied. So learning from one type of machine to another does take place.


5

Appendix II: Historical B-times

Weibull

Historical B-times are approximately weibull distributed. A large sample of B-times over a one year period was obtained and

tested with goodness-of-fit tests against a number of distributions. The weibull distribution was the distribution that most

closely resembled the historical B-times as can be seen in the two figures below.

The weibull has the unique property to resemble an exponential distribution when k is smaller then 1, which is the case here,

which explains why the characteristic weibull figure is absent in the figures above. As can be seen in the second figure –

where the historical times are plotted in a histogram against a fitted weibull distribution – the two resemble each other pretty

closely. The actual goodness of fit test however does reject the notion of a fit. This goodness of fit test can be seen in f igure

below.

Density Trace for B-time

B-time (hrs)

de

nsity

0 20 40 60 80 100

0

0,01

0,02

0,03

0,04

Histogram for B-time

B-time (seconds)

fre

qu

en

cy

-2 8 18 28 38(X 10000)

0

1

2

3

4(X 1000)


6

The reason for this rejection can be found in the first couple of rows of the chi square test however. B-times are not

automatically kept by SAP, but are registered by assemblers and testers individuals. Since they do not exactly track the time

it took them to solve a certain problem, this might influence the mentioned times. Especially the half an hour and whole

hours numbers are filled in a lot. This gives a skewed image of the trouble times even though it seems highly unlikely that 45

minute or 15 minute intervals do not occur. Consequently, the goodness of fit test results were ignored and the weibull

distribution was used to model B-times.

Ratio’s

The ASML standard ratios of 1:1 and 1:3 of A-time to B-time for test and assembly have also been verified with real data.

This can be seen in the table below.


7

Main Group Description A-time

(hr) A-time (day)

CT-SAP

Cumm. A-time (w)

History (Factor)

Corrected!

FASY Pick and Prepare 0,0 0,0 0,0 0,0 1

FASY 1 Assembly level 1 7,5 0,5 0,1 2,2 2,3








FASY 9 Assembly level 9 7,5 0,5 0,7 - 2,3

FASY FASY 67,5 4,9 0,7

FASY Init@FASY 9,9 0,7 3,9 0,8 8,0 2,3

FASY Transsf_FASY 4,4 0,3 1,0 0,9 1,8 1,4

FASY Total FASY 14,3 1,0 0,9 Yes

MAQ Configuration 67,2 4,9 1,0 1,6 1,2 1,4

MAQ WS/WH test0 0,0 0,0 3,1 1,6 2,9 4,4

MAQ WH test0 0,1 0,0 3,1 1,6 2,9 4,4

MAQ IH test0 0,6 0,0 3,1 1,6 2,9 4,4

MAQ WS test0 1,8 0,1 3,1 1,6 2,9 4,4

MAQ RS/RH test0 5,7 0,4 1,1 1,6 2,6 1,6

MAQ WS/WH Queue 0,0 0,0 3,5 1,6 6,8 4,9

MAQ WS BFC Queue 4,4 0,3 3,5 1,7 6,8 4,9

MAQ WS SPM Queue 15,3 1,1 3,5 1,8 6,8 4,9

MAQ WS Full MS Queue 13,1 1,0 3,5 2,0 6,8 4,9

WH to WS Queue 4,4 0,3 3,5 2,0 6,8 4,9

MAQ WS Dry Performance Queue 6,5 0,5 3,5 2,1 6,8 4,9

MAQ AA Coarse Queue 4,8 0,3 2,9 2,1 2,9 4,0

MAQ Level Sensor Queue 4,4 0,3 1,7 2,2 5,6 2,4

MAQ Koop/Upgrade 0,0 0,0 0,0 2,2 0,0 0,0

MAQ Lens Calibration test0 1,0 0,1 3,2 2,2 4,7 4,5

MAQ Illumination test0 5,2 0,4 1,7 2,3 3,1 2,4

MAQ RS/RH queue (to zero position) 13,7 1,0 0,7 2,4 1,7 1,0

MAQ Lens Calibration Queue 3,6 0,3 1,2 2,4 1,7 1,7

MAQ WS IH Wet Immersion Test/Queue 0,0 0,0 3,2 2,4 3,6 4,5

MAQ Immersion Wet Queue 8,4 0,6 3,2 2,5 3,6 4,5

Immersion Perf. Queue1 5,0 0,4 3,2 2,6 3,6 4,5

MAQ WS Wet Performance Queue 3,4 0,2 3,2 2,6 3,6 4,5

MAQ IS coarse / Lens Center Queue 5,2 0,4 4,8 2,7 7,5 6,7

MAQ Ilumination Coarse Queue1 1,5 0,1 1,0 2,7 1,8 1,4

MAQ RS/RH Queue to Lenscentre 2,9 0,2 1,2 2,7 2,8 1,7

MAQ Illumination coarse queue 2 3,6 0,3 0,8 2,7 2,3 1,1

MAQ Ilias Coarse Queue 1,8 0,1 1,1 2,8 3,6 1,5

MAQ Illumination Fine Queue 1 24,7 1,8 1,5 3,0 1,5 2,1

MAQ Illumination Fine manual 1,6 0,1 2,3 3,0 4,3 3,2

MAQ Illumination Fine Queue 2 13,1 1,0 3,8 3,2 4,9 5,3

MAQ Upgrade Slot 0,0 0,0 1,0 3,2 0,0 1,4

MAQ Transfer MAQ to next Department 4,4 0,3 1,0 3,2 0,1 1,4

MAQ 227,1 16,6 3,2 Yes

SQ Dynamic Performance 5,9 0,4 6,0 3,3 8,7 8,5

SQ C&T system performance test 0,0 0,0 2,0 3,3 2,7 2,8

SQ F&T Performance 2,8 0,2 2,0 3,3 2,7 2,8

SQ Immersion Perf. Queue2 5,7 0,4 2,0 3,4 2,7 2,8

SQ AA/IS/ILIAS fine queue 6,2 0,4 3,9 3,4 4,5 5,5

SQ Metrology Fine Setup 0,0 0,0 1,7 3,4 2,3 2,4

SQ Metrology SPM 4,6 0,3 1,7 3,5 2,3 2,4

SQ Metrology Grid 21,6 1,6 1,7 3,7 2,3 2,4

SQ Metrology Lens 7,9 0,6 1,7 3,8 2,3 2,4

SQ Metrology Fine 7,5 0,5 1,7 3,9 2,3 2,4

SQ Prepare System Qualification 1,6 0,1 4,8 3,9 4,4 6,7

SQ Stability day 1 3,8 0,3 4,0 3,9 3,4 5,6

SQ Stability day 2 3,4 0,2 1,0 4,0 0,7 1,4

SQ Stability day 3 3,8 0,3 1,4 4,0 2,2 2,0

SQ ATP +CSR 26,8 2,0 3 4,3 12,4 4,2

SQ finish FAT: QA process 0,0 0,0 0 4,3 1,0 1,0

SQ 101,4 7,4 4,3 Yes

Pack and Ship Pack and Ship 28,8 2,1

3 4,6

4,2

Pack and Ship 28,8 2,1 4,6

Yes

The rightmost column depicts a corrected factor that indicates a factor with which the A-time must be multiplicated to obtain

the cycle time. As can be seen the average of fasy is close to two (indicating a ratio of 1:1) and the test factor averages

around 4 (translating into a 1:3 ratio).


8

Appendix III: Forecast of NXE demand figures

The following chart indicates the demand pattern as currently (june 2009) forecasted by ASML.


9

Appendix IV: Training times of employees gebaseerd op werkinhoud building blocks + training kosten per functie

The training times of employees is based on their workcontent. In the first table below is the average workcontent per production

process shown. The training times are loosely related on these, with a factor for the specific production phases that is based on the

difficulty and particular type of work these phases entail.

production phase average Workcontent (hr) Training time

(yrs)

Fasy BOT 328,00 0,40

Fasy BOT MAQ 137,50 0,60

Fasy MID 152,00 0,20

Fasy MID MAQ 42,00 0,40

Fasy TOP 64,00 0,10

Fasy TOP MAQ 72,00 0,20

REIR 72,00 0,30

MF 240,00 0,50

WS 178,00 0,50

WS Test rig 110,00 0,50

MID 528,00 1,00

MID test rig 72,00 0,30

SQ 118,00 1,00

SIE 361,50 1,50 The total training times per function then are based on the table above. The training times per function are repeated below.

GENORMALISEERD

Org 1 Training

times Org 2 Training


times

WS build 0,5 WS build 0,5 WS build + fasy bot 0,9

WS test rig 0,5 WS test rig + bot MAQ 0,6 WS test rig + bot MAQ 0,6

REIR build 0,3 REIR build 0,3 REIR build + fasy bot 0,7

MF build 0,5 MF build 0,5 MF build + fasy bot 0,9

MID build 1,0 MID build 1,0 MID build + fasy mid 1,2

MID test rig 0,3 MID test rig + mid MAQ 0,4 MID test rig + mid MAQ 0,4

Fasy bot+mid&top 0,7 Fasy bot+mid&top 0,7 Top fasy + top MAQ+SQ 1,3

bot + mid + top MAQ + SQ 2,2 top MAQ + SQ 1,2 SIE 1,5

SIE 1,5 SIE 1,5

SOM 7,50 6,70 7,50

MAX 2,20 1,50 1,50

average 0,8 0,7 0,9 These training times have been related to the average yearly pay figure for employees, which is 60 000 euro. The costs that are

incurred for training each new employee are shown below.

Training costs

Org 1 Training



times

WS build 0,5 € 30.000,00 WS build 0,5 € 30.000,00 WS build + fasy bot 0,9 € 54.000,00

WS test rig 0,5 € 30.000,00 WS test rig + bot MAQ 0,6 € 36.000,00 WS test rig + bot MAQ 0,6 € 36.000,00

REIR build 0,3 € 18.000,00 REIR build 0,3 € 18.000,00 REIR build + fasy bot 0,7 € 42.000,00

MF build 0,5 € 30.000,00 MF build 0,5 € 30.000,00 MF build + fasy bot 0,9 € 54.000,00

MID build 1,0 € 60.000,00 MID build 1,0 € 60.000,00 MID build + fasy mid 1,2 € 72.000,00

MID test rig 0,3 € 18.000,00 MID test rig + mid MAQ 0,4 € 24.000,00 MID test rig + mid MAQ 0,4 € 24.000,00

Fasy bot+mid&top 0,7 € 42.000,00 Fasy bot+mid&top 0,7 € 42.000,00 Top fasy + top MAQ + SQ 1,3 € 78.000,00

bot + mid + top MAQ + SQ 2,2 € 132.000,00 top MAQ + SQ 1,2 € 72.000,00

SIE 1,5 € 90.000,00 SIE 1,5 € 90.000,00 SIE 1,5 € 90.000,00


10

Appendix V: Simulation assumptions

Process

1. The assembly and test productions operations have been aggregated to large process blocks in the simulation. 2. Optimal production sequence for NXE3100 is not in accordance with desired bottom, mid, top build sequence

because of technical (engineering) reasons. For the NXE3300 it is assumed that the desired bottom, mid, top build sequence is realized.

3. There will be a test rig for the waferstage on which approximately 50% of the tests of the bottom MAQ can be performed. As a consequence 50% of bottom MAQ tests will perform without errors.

4. There will be a test rig for the MID module on which approximately 75% of the tests of the mid MAQ can be performed. As a consequence 75% of mid MAQ tests will perform without errors.

5. The Source will be installed by Cymer personnel, which is not accounted for in the model. Source costs are assumed to have the personnel costs in them.

6. Test phases will take on average 4 times the A-time and assembly phases will take on average 2 times the A-time. 7. Test variation is modelled with the formulea: WEIB(2.1705,0.671684) * A-time + A-time. The first part (before the +

sign) generates the B-time for a specific produciton step. WEIB(2.1705,0.671684) generates a normalised weibull distribution with a mean of 3 and coefficient of variance of 1.83 (which is equal to the coefficient of variance of historical B-times)

8. Assembly variation is modelled with the formulea: WEIB(0.723499,0.671684) * A-time + A-time. The first part (before the + sign) generates the B-time for a specific produciton step. WEIB(1.447,0.671684) generates a normalised weibull distribution with a mean of 1 en coeficient of variance of 1.83 (which is equal to the coefficient of variance of historical B-times)

9. The TOP module is treated as being outsourced. It also always arrives in time. Costs will be incurred as soon as the top fasy phase starts.

Time

10. Time is modeled as being continous. Thereby losing the work schedule flexibility ASML in reality has. (resource utilization will therefore be worse as in reality)

11. The working schedule will entail 2 shifts on monday through friday (so 16 hrs a day) and 8 hrs on saturday and sunday. The number of (working) hours per year then equals 52 weeks * 96 hrs per week= 4992 hrs

Demand

12. Production starts of machines are spread out over the year. (for example 5 machines per year means a NXE machine start date every 4992/5 = 998.4 hours)

13. There are three different demand scenario’s: High, mid and low demand. These are official NXE demand forecast figures over the years 2011 to 2016, which can be seen in the table to the right.

Resources

14. POB, ILL en MID can be made by the same resources (function). 15. MF can be made by the same resources (function) 16. Work center facility capacities (cabins, tools, etc) are not included. It is assumed that there is

always enough capacity. 17. The number of resources that the MID module production steps require have been simplified. The number of

production step would have been to big otherwise. These changes do not impact cycle time and resource requirements.

18. Vacuum work center is not included in simulation. It is assumed that there is always enough capacity. 19. Training times of new employees remain the same over the duration of the simulation. (even though average CT

decreases and the likelihood of better work instructions is high) Training times are similar as in volume production of current machines.

20. Hiring is done with perfect knowledge in the simulation. This means that the CRP process is perfect in the simulation. Although this is not a realistic assumption it doesn’t matter for the choice between organizations, because the CRP process wont change with organizations. It will cause total costs to probably be lower then in reality.

21. No hiring costs are incurred for TLs, GLs, VIS and production planners. These are usually people from within the organization who are ready to do their jobs immediately because of their previous experience and so require no training.

22. No firing is done in the simulation. Since all demand scenarios increase year over year, this should not be a problem. Introducing firing can possibly increase hiring and firing costs by firing personnel that is needed one or 2 months later. Because of the high hiring costs (high training times), it is realistic to use this assumption. (i.e. almost no firing will be done anyway in all scenario’s, i.e. they are all upturn scenario’s)

23. Coordination need is captured in ratios of managers and support personnel to actual workers (assemblers and testers). This obviously differs between organisations. The ratios that are used in the simulations can be found below and are based on current Twinscan production ratios.

Organisation 1

Organisation 2

Organisation 3

Operator / TL 12 13 11

TL / GL 6 5 7

Testers / VIS 1,89 2 2

TL / pp 2 1.8 1.6

LOW MID HIGH

2011 10 10 10

2012 15 15 10

2013 25 15 10

2014 45 25 15

2015 55 45 20

2016 60 55 20


11

Learning curve 24. CT variability will decrease with more experience (i.e. with more machines built). 25. The base learning curve of the NXE3300 is modeled as following [8]:

Where, Un = the cycle time of the n-th machine V = the cycle time of the first machine N = the machine number S-1 = an exponent (0 < S < 1) Where different S’s are chosen for the different organisations such that organisation 1, 2 and 3 have a respective learning curve of 84%, 83,2% and 82,4%. (which is the percentage of cycle time with every doubling of the number of machines)

Costs

26. Personnel costs are 60 000 € per person per year. Since 96 hours a week are assumed. One “man” in the simulation equals 96 / 40 = 2.4 man in reality. This means that 1 simulation hour costs: (60 000 ∙ 2.4) / 4992 = 28,85 € per man.

27. Material costs are assumed to be similar as for the NXE3100. Only large material costs have been included. (above approximately 20 000 euros)

28. Material costs are incurred at the beginning of WC production. 29. Internal rate of return of ASML is 11% per year. This will be used for calculating the investment costs in WIP (holding

cost of goods). Since simulation time runs in hours, (see assumption 11) holding cost per hour is 11% / 4992 = 0.0022 % per hour. Compounding of interest has not been accounted for because of simulation software constraints. This is not seen as a serious shortcoming due to the short time intervals that apply.


12

Appendix VI: Simulation data

The amount of data that was obtained from the simulation was quite large and resulted in almost 450 mb worth of excel files.

This data was essential agregated in to tables that summarize the result. These tables will be given below. As there were 3

demand scenarios, the data will be presented per demand scenario.

High demand scenario

Cycle time

Org 3

CT Cabin CT

Average St dev Count 0.90 conf Average St dev Count 0.90 conf

year 1 1456 443 1000 18 1190 406 1000 17

year 2 936 212 1500 7 742 205 1500 7

year 3 757 163 2500 4 587 157 2500 4

year 4 642 135 4500 3 488 132 4500 3

year 5 562 115 5500 2 420 112 5500 2

year 6 512 102 6000 2 375 99 6000 2

Org 2

CT Cabin CT


year 1 1521 455 1000 19 1245 422 1000 17

year 2 998 227 1500 8 796 219 1500 7

year 3 822 189 2500 5 644 184 2500 5

year 4 696 147 4500 3 536 144 4500 3

year 5 614 128 5500 2 464 125 5500 2

year 6 561 116 6000 2 418 113 6000 2

Org 1

CT Cabin CT


year 1 1580 450 1000 18 1293 417 1000 17

year 2 1074 259 1500 9 862 247 1500 8

year 3 879 201 2500 5 689 193 2500 5

year 4 751 163 4500 3 582 158 4500 3

year 5 671 147 5500 3 514 143 5500 2

year 6 616 138 6000 2 466 134 6000 2 2-paired sample analysis

90% max min

Org3-2 -40 -69

Org3-1 -97 -127

Org2-1 -42 -73


13

Costs

Personnel cost

Org 3

Average Var Std dev 0.90 conf

Year 1 5581 113265 337 43

Year 2 6863 314022 560 72

Year 3 7513 230882 481 62

Year 4 8908 135684 368 48

Year 5 9791 149043 386 50

Year 6 10161 181278 426 55

Year 7 10295 239443 489 63

Org 2


Year 1 5474 79900 283 36

Year 2 6607 189170 435 56

Year 3 7213 138247 372 48

Year 4 8636 192782 439 57

Year 5 9612 171272 414 53

Year 6 10100 193209 440 57

Year 7 10257 210639 459 59

Org 1


Year 1 5442 92331 304 39

Year 2 6607 232463 482 62

Year 3 7308 148618 386 50

Year 4 8850 132675 364 47

Year 5 9940 164773 406 52

Year 6 10483 239161 489 63

Year 7 10686 281192 530 68

2-paired sample analysis

90% max min

Org3-2 627 -280

Org3-1 440 -498

Org2-1 227 -632

Indirect personnel cost

Org 3


Year 1 1889 5704 76 10

Year 2 2275 17954 134 17

Year 3 2451 15696 125 16

Year 4 2890 10458 102 13

Year 5 3167 14601 121 16

Year 6 3280 18684 137 18

Year 7 3325 22342 149 19

Org 2


Year 1 1725 3857 62 8

Year 2 2058 12938 114 15

Year 3 2229 12695 113 15

Year 4 2662 14381 120 15

Year 5 2959 17756 133 17

Year 6 3108 14465 120 16

Year 7 3155 12898 114 15

Org 1


Year 1 1759 7187 85 11

Year 2 2118 23433 153 20

Year 3 2338 15800 126 16

Year 4 2840 12699 113 15


14

Year 5 3164 17069 131 17

Year 6 3314 22538 150 19

Year 7 3368 26678 163 21


90% max min

Org3-2 320 75

Org3-1 191 -84

Org2-1 -14 -274

Hiring cost

Org 3


Year 1 2389 40205 201 26

Year 2 115 16538 129 17

Year 3 219 24285 156 20

Year 4 655 47456 218 28

Year 5 186 17494 132 17

Year 6 76 8435 92 12

Year 7 1 81 9 1

Org 2


Year 1 2060 32009 179 23

Year 2 117 17208 131 17

Year 3 253 25884 161 21

Year 4 594 55448 235 30

Year 5 221 32997 182 23

Year 6 114 14849 122 16

Year 7 0 0 0 0

Org 1


Year 1 2263 49073 222 29

Year 2 134 21765 148 19

Year 3 308 37824 194 25

Year 4 684 42635 206 27

Year 5 295 33326 183 24

Year 6 151 19572 140 18

Year 7 0 0 0 0


90% max min

Org3-2 82 -1

Org3-1 20 -75

Org2-1 -24 -112

Holding cost

Org 3

Average Count Variance Std dev 0.90 conf

year 1 733 1000 52296 229 9,3 year 2 468 1500 12173 110 3,7 year 3 377 2500 7151 85 2,2 year 4 317 4500 4970 70 1,4 year 5 276 5500 3572 60 1,0 year 6 250 6000 2829 53 0,9

Org 2


15



Org 1




90% max average min

Org3-2 -21 -28 -35

Org3-1 -50 -58 -65

Org2-1 -22 -30 -37

Personnel

Assemblers

Org 3


Year 1 80 75 9 1

Year 2 86 79 9 1

Year 3 95 53 7 1

Year 4 113 41 6 1

Year 5 120 39 6 1

Year 6 122 55 7 1

Year 7 122 55 7 1

Org 2


Year 1 76 48 7 1

Year 2 80 45 7 1

Year 3 88 30 5 1

Year 4 107 56 8 1

Year 5 114 46 7 1

Year 6 118 46 7 1

Year 7 118 46 7 1

Org 1


Year 1 77 53 7 1

Year 2 81 50 7 1

Year 3 91 32 6 1

Year 4 110 40 6 1

Year 5 119 48 7 1

Year 6 124 58 8 1

Year 7 124 58 8 1


16


90% max min

Org3-2 13 -3

Org3-1 10 -6

Org2-1 3 -10

Testers

Org 3


Year 1 30 9 3 0

Year 2 31 8 3 0

Year 3 35 6 2 0

Year 4 45 8 3 0

Year 5 47 10 3 0

Year 6 49 10 3 0

Year 7 49 10 3 0

Org 2


Year 1 31 10 3 0

Year 2 32 8 3 0

Year 3 37 8 3 0

Year 4 47 12 3 0

Year 5 51 11 3 0

Year 6 52 7 3 0

Year 7 52 7 3 0

Org 1


Year 1 29 10 3 0

Year 2 32 13 4 0

Year 3 37 11 3 0

Year 4 48 11 3 0

Year 5 52 12 3 0

Year 6 54 12 3 0

Year 7 54 12 3 0


90% max min

Org3-2 1 -5

Org3-1 1 -6

Org2-1 3 -4

Support personnel

Org 3


Year 1 37 6 2 0,32

Year 2 39 5 2 0,30

Year 3 42 5 2 0,30

Year 4 51 5 2 0,27

Year 5 54 6 2 0,31

Year 6 55 6 2 0,32

Year 7 55 6 2 0,32

Org 2


Year 1 34 4 2 0,26

Year 2 35 4 2 0,25

Year 3 39 5 2 0,30

Year 4 47 6 3 0,32

Year 5 51 5 2 0,30

Year 6 53 4 2 0,24

Year 7 53 4 2 0,24


17

Org 1


Year 1 34 8 3 0,36

Year 2 36 7 3 0,35

Year 3 41 5 2 0,29

Year 4 51 5 2 0,30

Year 5 54 6 2 0,32

Year 6 56 7 3 0,35

Year 7 56 7 3 0,35


90% max min

Org3-2 6 1

Org3-1 3 -2

Org2-1 0 -5

Summary per organisation

Org 1

#

personeel Cost # machines Cost/machine

Year 1 141 15.190 € 10 1.519 €

Year 2 149 16.945 € 15 1.130 €

Year 3 168 20.952 € 25 838 €

Year 4 209 29.203 € 45 649 €

Year 5 225 31.699 € 55 576 €

Year 6 234 32.122 € 60 535 €

Year 7 234 14.055 €

160.166 € +

Org 2

#


Year 1 141 14.877 € 10 1.488 €

Year 2 147 16.297 € 15 1.086 €

Year 3 164 19.934 € 25 797 €

Year 4 200 27.478 € 45 611 €

Year 5 216 29.438 € 55 535 €

Year 6 223 29.828 € 60 497 €

Year 7 223 13.412 €

151.264 € +

Org 3

#


Year 1 147 14.796 € 10 1.480 €

Year 2 156 16.277 € 15 1.085 €

Year 3 172 19.596 € 25 784 €

Year 4 209 26.731 € 45 594 €

Year 5 221 28.343 € 55 515 €

Year 6 227 28.540 € 60 476 €

Year 7 227 13.620 €

147.904 € +

Mid demand scenario

Cycle time

Org 3

CT Cabin CT


year 1 1435 414 1000 17 1178 379 1000 15

year 2 934 218 1500 7 743 212 1500 7

year 3 783 175 1500 6 612 170 1500 6

year 4 688 145 2500 4 529 141 2500 4

year 5 612 128 4500 2 462 124 4500 2


18

year 6 544 110 5500 2 404 107 5500 2

Org 2

CT Cabin CT


year 1 1507 427 1000 17 1236 398 1000 16

year 2 992 236 1500 8 790 223 1500 7

year 3 841 181 1500 6 662 176 1500 6

year 4 748 156 2500 4 581 153 2500 4

year 5 667 145 4500 3 511 141 4500 3

year 6 597 126 5500 2 449 122 5500 2

Org 1

CT Cabin CT


year 1 1577 438 1000 18 1298 412 1000 17

year 2 1066 239 1500 8 851 229 1500 8

year 3 913 213 1500 7 722 207 1500 7

year 4 812 180 2500 5 635 176 2500 5

year 5 724 159 4500 3 560 155 4500 3

year 6 653 144 5500 2 497 140 5500 2 2-paired sample analysis

90% max min

Org3-2 -38 -75

Org3-1 -97 -139

Org2-1 -44 -79

Costs Personnel cost

Org 3


Year 1 5569 104625 323 42

Year 2 6800 254666 505 65

Year 3 7102 301079 549 71

Year 4 7391 251451 501 65

Year 5 8728 169412 412 53

Year 6 9719 162606 403 52

Year 7 9940 177198 421 54

Org 2


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Year 2 6561 200925 448 58

Year 3 6786 221748 471 61

Year 4 7098 164204 405 52

Year 5 8387 154953 394 51

Year 6 9393 208549 457 59

Year 7 9701 187134 433 56

Org 1


Year 1 5469 82393 287 37

Year 2 6576 215031 464 60

Year 3 6867 221038 470 61

Year 4 7312 151008 389 50

Year 5 8686 145138 381 49

Year 6 9758 141879 377 49

Year 7 10078 224522 474 61


19


90% max min

Org3-2 709 -177

Org3-1 547 -404

Org2-1 259 -647


Org 3


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Year 3 2317 19026 138 18

Year 4 2403 16448 128 17

Year 5 2824 14894 122 16

Year 6 3121 13417 116 15

Year 7 3192 16196 127 16

Org 2


Year 1 1720 6495 81 10

Year 2 2051 14709 121 16

Year 3 2108 14850 122 16

Year 4 2190 11802 109 14

Year 5 2588 11017 105 14

Year 6 2883 15715 125 16

Year 7 2991 18068 134 17

Org 1


Year 1 1767 9987 100 13

Year 2 2114 23966 155 20

Year 3 2191 25782 161 21

Year 4 2330 18719 137 18

Year 5 2780 12577 112 14

Year 6 3107 10119 101 13

Year 7 3203 22802 151 19


90% max min

Org3-2 327 91

Org3-1 202 -59

Org2-1 -13 -262

Hiring cost

Org 3


Year 1 2373 31003 176 23

Year 2 112 15241 123 16

Year 3 39 6762 82 11

Year 4 116 11834 109 14

Year 5 681 62733 250 32

Year 6 209 18975 138 18

Year 7 0 0 0 0

Org 2


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Year 3 39 5568 75 10

Year 4 142 14526 121 16

Year 5 601 45810 214 28

Year 6 229 28168 168 22

Year 7 1 52 7 1


20

Org 1


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Year 2 135 23216 152 20

Year 3 62 9500 97 13

Year 4 242 23816 154 20

Year 5 651 48328 220 28

Year 6 298 42808 207 27

Year 7 3 570 24 3


90% max min

Org3-2 85 14

Org3-1 29 -56

Org2-1 -20 -106

Holding cost

Org 3


year 1 724 1000 45835 214 8,7

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year 4 342 2500 5713 76 2,0

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Org 1


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year 2 535 1500 15292 124 4,1

year 3 457 1500 12186 110 3,7

year 4 405 2500 8835 94 2,4

year 5 360 4500 6892 83 1,6

year 6 323 5500 5634 75 1,3


90% max average min

Org3-2 -20 -29 -38

Org3-1 -50 -61 -71

Org2-1 -23 -31 -40

Personnel

Assemblers

Org 3


Year 1 80 58 8 1

Year 2 86 65 8 1

Year 3 88 68 8 1

Year 4 93 58 8 1

Year 5 112 44 7 1

Year 6 119 42 6 1

Year 7 119 42 6 1


21

Org 2


Year 1 76 44 7 1

Year 2 80 46 7 1

Year 3 81 48 7 1

Year 4 86 42 6 1

Year 5 105 52 7 1

Year 6 112 40 6 1

Year 7 112 40 6 1

Org 1


Year 1 77 46 7 1

Year 2 81 53 7 1

Year 3 84 50 7 1

Year 4 91 31 6 1

Year 5 107 55 7 1

Year 6 117 48 7 1

Year 7 117 48 7 1


90% max min

Org3-2 14 -1

Org3-1 11 -4

Org2-1 4 -10

Testers

Org 3


Year 1 29 11 3 0

Year 2 31 9 3 0

Year 3 31 8 3 0

Year 4 34 6 3 0

Year 5 43 10 3 0

Year 6 47 10 3 0

Year 7 47 10 3 0

Org 2


Year 1 30 11 3 0

Year 2 32 8 3 0

Year 3 33 7 3 0

Year 4 35 5 2 0

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Year 6 50 8 3 0

Year 7 50 8 3 0

Org 1


Year 1 29 13 4 0

Year 2 32 13 4 0

Year 3 32 12 4 0

Year 4 36 11 3 0

Year 5 47 9 3 0

Year 6 51 11 3 0

Year 7 51 11 3 0


90% max min

Org3-2 1 -5

Org3-1 1 -6

Org2-1 2 -3


22

Support personnel

Org 3


Year 1 37 6 3 0,32

Year 2 38 6 2 0,30

Year 3 39 5 2 0,30

Year 4 41 6 2 0,30

Year 5 50 5 2 0,29

Year 6 53 4 2 0,27

Year 7 53 4 2 0,27

Org 2


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Year 2 35 4 2 0,27

Year 3 35 4 2 0,26

Year 4 37 4 2 0,25

Year 5 46 5 2 0,27

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Year 7 50 5 2 0,29

Org 1


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Year 2 36 7 3 0,34

Year 3 37 7 3 0,35

Year 4 40 6 2 0,31

Year 5 50 4 2 0,27

Year 6 53 6 2 0,32

Year 7 53 6 3 0,32


90% max min

Org3-2 6 1

Org3-1 3 -1

Org2-1 0 -5


Org 1

# personeel Cost # machines Cost/machine

Year 1 141

15.197 € 10

1.520 €

Year 2 149

16.845 € 15

1.123 €

Year 3 152

15.969 € 15

1.065 €

Year 4 167

20.012 € 25

800 €

Year 5 204

28.317 € 45

629 €

Year 6 221

30.920 € 55

562 €

Year 7 221

13.284 €

140.543 € +

Org 2


Year 1 140

14.794 € 10

1.479 €

Year 2 147

16.186 € 15

1.079 €

Year 3 149

15.227 € 15

1.015 €

Year 4 159

18.751 € 25

750 €

Year 5 196

26.484 € 45

589 €


23

Year 6 211

28.633 € 55

521 €

Year 7 212

12.693 €

132.769 € +

Org 3


Year 1 146

14.696 € 10

1.470 €

Year 2 155

16.181 € 15

1.079 €

Year 3 158

15.310 € 15

1.021 €

Year 4 168

18.453 € 25

738 €

Year 5 206

25.817 € 45

574 €

Year 6 219

27.713 € 55

504 €

Year 7 219

13.132 €

131.300 € +

Low demand scenario

Cycle time

Org 3

CT Cabin CT


year 1 1449 438 1000 18 1180 402 1000 16

year 2 975 235 1000 10 776 229 1000 9

year 3 836 186 1000 8 656 181 1000 7

year 4 753 153 1500 5 586 149 1500 5

year 5 681 143 2000 4 522 138 2000 4

year 6 634 131 2000 4 481 127 2000 4

Org 2

CT Cabin CT


year 1 1525 458 1000 19 1246 419 1000 17

year 2 1041 237 1000 10 833 230 1000 9

year 3 895 206 1000 8 706 201 1000 8

year 4 810 173 1500 6 635 170 1500 6

year 5 743 158 2000 5 576 155 2000 4

year 6 692 150 2000 4 533 145 2000 4

Org 1

CT Cabin CT


year 1 1606 491 1000 20 1312 450 1000 18

year 2 1103 266 1000 11 882 255 1000 10

year 3 963 224 1000 9 764 218 1000 9

year 4 869 186 1500 6 684 180 1500 6

year 5 800 179 2000 5 623 173 2000 5

year 6 745 168 2000 5 578 164 2000 5


90% max min

Org3-2 -39 -85

Org3-1 -97 -149


24

Org2-1 -36 -86

Costs Personnel cost

Org 3


Year 1 5563 99890 316 41

Year 2 6644 288426 537 69

Year 3 6724 290199 539 69

Year 4 6833 280180 529 68

Year 5 7031 289040 538 69

Year 6 7269 240247 490 63

Year 7 7332 235515 485 63

Org 2


Year 1 5482 86366 294 38

Year 2 6438 250944 501 65

Year 3 6467 245863 496 64

Year 4 6540 232937 483 62

Year 5 6745 217258 466 60

Year 6 6932 210033 458 59

Year 7 7014 223556 473 61

Org 1


Year 1 5459 103598 322 42

Year 2 6480 304179 552 71

Year 3 6516 303713 551 71

Year 4 6592 308563 555 72

Year 5 6839 263591 513 66

Year 6 7120 206925 455 59

Year 7 7194 209299 457 59


90% max min

Org3-2 895 -388

Org3-1 909 -567

Org2-1 592 -758


Org 3


Year 1 1885 6282 79 10

Year 2 2220 21247 146 19

Year 3 2237 19685 140 18

Year 4 2259 17344 132 17

Year 5 2303 18933 138 18

Year 6 2348 18538 136 18

Year 7 2360 19427 139 18

Org 2


Year 1 1734 4505 67 9

Year 2 2021 16130 127 16

Year 3 2031 15496 124 16

Year 4 2047 14040 118 15

Year 5 2092 14392 120 15

Year 6 2140 15212 123 16

Year 7 2160 15021 123 16

Org 1


Year 1 1766 8188 90 12


25

Year 2 2079 28966 170 22

Year 3 2089 27058 164 21

Year 4 2108 26486 163 21

Year 5 2175 25650 160 21

Year 6 2254 22767 151 19

Year 7 2278 22479 150 19


90% max min

Org3-2 361 35

Org3-1 310 -64

Org2-1 118 -268

Hiring cost

Org 3


Year 1 2365 34815 187 24

Year 2 23 3844 62 8

Year 3 7 1027 32 4

Year 4 38 6607 81 10

Year 5 77 11036 105 14

Year 6 38 6225 79 10

Year 7 0 0 0 0

Org 2


Year 1 2066 36738 192 25

Year 2 14 1818 43 6

Year 3 8 1586 40 5

Year 4 45 9312 96 12

Year 5 83 11373 107 14

Year 6 57 8394 92 12

Year 7 0 0 0 0

Org 1


Year 1 2267 60860 247 32

Year 2 27 5795 76 10

Year 3 6 941 31 4

Year 4 48 6921 83 11

Year 5 113 13268 115 15

Year 6 62 9479 97 13

Year 7 0 0 0 0


90% max min

Org3-2 82 -3

Org3-1 50 -42

Org2-1 7 -78

Holding cost

Org 3


year 1 733 1000 51928 228 9,3

year 2 486 1000 12486 112 4,6

year 3 418 1000 9484 97 4,0

year 4 375 1500 6250 79 2,6

year 5 338 2000 5614 75 2,2

year 6 313 2000 4555 67 1,9

Org 2



26

year 1 770 1000 55966 237 9,7

year 2 522 1000 14951 122 5,0

year 3 448 1000 11162 106 4,3

year 4 404 1500 7922 89 3,0

year 5 370 2000 6855 83 2,4

year 6 343 2000 5938 77 2,2

Org 1


year 1 809 1000 63657 252 10,3

year 2 553 1000 19044 138 5,6

year 3 482 1000 13582 117 4,8

year 4 434 1500 9409 97 3,2

year 5 398 2000 8682 93 2,7

year 6 371 2000 7707 88 2,5


90% max average min

Org3-2 -21 -32 -43

Org3-1 -49 -63 -76

Org2-1 -18 -31 -43

Personnel

Assemblers

Org 3


Year 1 80 54 7 1

Year 2 82 61 8 1

Year 3 82 60 8 1

Year 4 85 68 8 1

Year 5 88 55 7 1

Year 6 91 46 7 1

Year 7 91 46 7 1

Org 2


Year 1 76 46 7 1

Year 2 77 45 7 1

Year 3 77 46 7 1

Year 4 79 50 7 1

Year 5 82 48 7 1

Year 6 84 52 7 1

Year 7 84 52 7 1

Org 1


Year 1 77 61 8 1

Year 2 78 60 8 1

Year 3 79 63 8 1

Year 4 80 67 8 1

Year 5 85 47 7 1

Year 6 87 44 7 1

Year 7 87 44 7 1


90% max min

Org3-2 16 -4

Org3-1 15 -8

Org2-1 8 -13

Testers

Org 3


27


Year 1 30 10 3 0

Year 2 30 9 3 0

Year 3 30 9 3 0

Year 4 30 8 3 0

Year 5 31 8 3 0

Year 6 32 8 3 0

Year 7 32 8 3 0

Org 2


Year 1 31 10 3 0

Year 2 31 10 3 0

Year 3 31 9 3 0

Year 4 31 9 3 0

Year 5 33 8 3 0

Year 6 33 7 3 0

Year 7 33 7 3 0

Org 1


Year 1 30 12 3 0

Year 2 30 11 3 0

Year 3 30 11 3 0

Year 4 31 11 3 0

Year 5 32 11 3 0

Year 6 33 10 3 0

Year 7 33 10 3 0


90% max min

Org3-2 2 -5

Org3-1 3 -5

Org2-1 5 -4

Support personnel

Org 3


Year 1 37 6 3 0,32

Year 2 37 6 2 0,31

Year 3 37 5 2 0,30

Year 4 38 5 2 0,29

Year 5 39 5 2 0,30

Year 6 39 5 2 0,30

Year 7 39 5 2 0,30

Org 2


Year 1 34 5 2 0,28

Year 2 34 5 2 0,27

Year 3 34 4 2 0,26

Year 4 34 4 2 0,26

Year 5 35 4 2 0,27

Year 6 36 4 2 0,26

Year 7 36 4 2 0,26

Org 1


Year 1 35 8 3 0,37

Year 2 35 8 3 0,36

Year 3 35 8 3 0,36

Year 4 35 8 3 0,36

Year 5 37 7 3 0,35

Year 6 38 6 2 0,32

Year 7 38 6 2 0,32


28


90% max min

Org3-2 6 0

Org3-1 5 -1

Org2-1 2 -5


Org 1


Year 1 142 15.316 € 10

1.532 €

Year 2 143 14.118 € 10

1.412 €

Year 3 144 13.434 € 10

1.343 €

Year 4 146 15.264 € 15

1.018 €

Year 5 154 17.098 € 20

855 €

Year 6 158 16.847 € 20

842 €

Year 7 158 9.472 €

101.550 € +

Org 2


Year 1 140 14.919 € 10

1.492 €

Year 2 141 13.695 € 10

1.369 €

Year 3 142 12.981 € 10

1.298 €

Year 4 144 14.687 € 15

979 €

Year 5 149 16.319 € 20

816 €

Year 6 153 15.991 € 20

800 €

Year 7 153 9.173 €

97.765 € +

Org 3


Year 1 147 14.775 € 10

1.477 €

Year 2 149 13.742 € 10

1.374 €

Year 3 150 13.144 € 10

1.314 €

Year 4 153 14.749 € 15

983 €

Year 5 158 16.170 € 20

808 €

Year 6 162 15.914 € 20

796 €

Year 7 162 9.692 €

98.186 € +


29

Appendix VII: Orthogonal array experiment The orthogonal experiment that was used for the sensitivity analysis, used an orthogal array that balances out all parameters. The array

can be found below. For an example of the balance in parameters, a closer look at parameter 3 for example can be taken. All

parametes can be set at low mid and high levels (1, 2 and 3 in the table). A high level (3) of parameter 3 occurs exactly three times and

when the settings of the other parameters is examined, these are always 1, 2 and 3 for all three occurences of parameter 3 high. When

the effects of these three runs are averaged and devided over the overall average (all nine runs), one obtains the sole effect of

parameter 3 high, because all other parameters have been balanced out. The same exercise can be done for all other parameters and

all settings (low, mid and high).

Parameter (right) / Run

(down) 1 2 3 4

1 1 1 1 1

2 1 2 2 2

3 1 3 3 3

4 2 1 2 3

5 2 2 3 1

6 2 3 1 2

7 3 1 3 2

8 3 2 1 3

9 3 3 2 1

Parameter 1, 2, 3 and 4 in this array represent the input criteria (scalability, process improvement, coordination need, controllability) in the case of this study. The low, mid and high settings for these criteria correspond to -10% and +10% deviations. The actual numbers can be seen in the table below.

1 (low) 2 (mid) 3 (high)

1 scalability(training costs) -10% 0% 10%

2

process improvement (learning curve 86% 84% 82%

3

coordination need (support personnel ratio’s)

TL/production 10,8 12 13,2

GL/TL 5,4 6 6,6

VIS/testers 1,701 1,89 2,079

pp/tl 1,71 1,9 2,09

4 controllability

shape scale shape scale shape scale

test 0,671684 1,95345 0,671684 2,1705 0,671684 2,38755

assy 0,671684 0,651149 0,671684 0,723499 0,671684 0,795848


30

Appendix VIII: Verification & Validation The verification of a simulation model is the check on a correct translation from model to simulation. Validation is a check of

reality to the model. Validation is difficult to do in this case given the fact that actual production of NXE machines has only

recently started and as such there is no data with which to compare the simulation results. Consequently, only verification

has been conducted. Verification was made difficult by the fact that some interactions in the simulation resulted in different

numbers as were calculated. Of course, a simulation would not have been needed if this were not the case. Where these

differences occur, an explanation of why these numbers are different will be given.

First the theoretical cycle time of the different work centers and fasy have been calculated and compared with simulation

cycle times.

reir ws mf mid fasy sq pre pack&pack Total CT

Theoretical 72 242 272 439 946 472 180 1870

Simulation 69 240 284 449 1163 460 180 2087

Deviation 4,17% 0,83% -

4,41% -

2,28% -

22,94% 2,54% 0,00% -11,60%

Theoretical cycle times have been calculated based on a shortest path calculation. As can be seen the deviations are in

general quite small, except for the fasy part. Al modules come together at fasy, which is the reason for its large deviation.

Module have to wait for each other before fasy can continue. This causes delays which can also be expected in reality. A

shortest path calculation however does not take this in to account. It was checked whether this really was the explanation of

the deviation, by dropping all restrictions on fasy (i.e. all modules were present) which resulted in a simulation cycle time that

was close to the theoretical cycle time.

The learning curve was verified next. The table below contains a calculation of the individual work centers cycle time

corrected for an average cycle time reducement that is in accordance with the number of machines that are started each

year.

Year reir ws mf mid fasy sq pre

pack&pack # machines average CT reduction

Simulation cycle time

Theoretical cycle time

2011 year 1 46 161 190 301 779 308 121 10 0,67 1521 1398

2012 year 2 34 118 139 220 570 225 88 15 0,49 998 1023

2013 year 3 28 96 114 180 465 184 72 25 0,40 822 835

2014 year 4 23 82 97 153 395 156 61 45 0,34 696 710

2015 year 5 21 72 85 135 349 138 54 55 0,30 614 626

2016 year 6 19 65 77 121 314 124 49 60 0,27 561 563

As can be seen, the theoretical cycle time starts lower than the simulation cycle time, but ends up pretty close to the actual

simulation time. The supposed explanation for this is the fact that approximately half the machines that are built in simulation

are actually finished in the next year. The average cycle time is then still higher for the simulation then for the theoretical

calculation. This number decreases over the course of the simulation because cycle time also reduces. This has the effect

that relatively more machines are started and finished in the same year causing theoretical and simulation cycle times to

converge.

The next table demonstrates the number of manpower resources that are expected. These theoretical numbers were

attained by dividing the theoretical work load per year (due to machines) by the number of yearly work hours an employee

has.

Year reir ws mf mid fasy sq pre pack&pack theoretical

total Simulation total Capacity utilization

2011 year 1 2 2 2 3 16 3 2 31 107 0,29

2012 year 2 2 2 2 3 18 4 2 33 113 0,29

2013 year 3 2 3 3 5 24 5 2 43 125 0,35

2014 year 4 2 4 5 7 37 7 3 65 153 0,42

2015 year 5 2 4 5 8 40 8 3 70 165 0,42

2016 year 6 2 4 5 8 39 8 3 68 171 0,40


31

As can be seen quite large differences are obtained between simulation numbers and theoretical numbers. What has to be

kept in mind however is the fact that resource utilization tends to be very low at ASML, which is especially so during the early

years, because a number of steps require 4 or 2 employees to work on a machine simultaneously. When the number of

machines are still very small, the chance of having no work is quite large which causes relatively large spikes incapacity

demand to occur, resulting in a low utilization. As larger production volumes are demanded, the law of large numbers kicks

in which tends to average out the workload and increase utilization. Another thing has to be kept in mind with these small

numbers, which is the fact that the simulation tends to overestimate the number of employees needed, because it was a

demand that no starvation of processes due to resource shortages could occur. This causes the simulation to increase the

number of employees to a higher then perhaps necessary level. These utilization rates then are more dramatic then is to be

expected, but still conform of what could be expected.

The last table contains the number of cabins that are needed. This is an important variable for ASML, because factory workspace is

very expensive. This capacity is fixed on the short term and building more capacity takes years, so a careful forecast for how many

cabins are needed is important. The theoretical number of cabins is calculated by multiplying the number of machines per year with the

average cabin cycle time and dividing this by the total available hours in a year. The average number of simulation cabins is remarkable

similar to the theoretical cabins. What needs to be noted however is that the actual required number of cabins has a standard deviation

of approximately 1, due to the variability of cycle times. This means that the total cabins required to satisfy cabin demand at any given

moment needs to be 2 or so more.

Year Cabin

CT #

machines

# theoretical

cabins

# simulation

cabins

2011 year 1 1190 10 2 2

2012 year 2 742 15 2 3

2013 year 3 587 25 3 3

2014 year 4 488 45 4 4

2015 year 5 420 55 5 5

2016 year 6 375 60 5 5


32

Appendix IX: Criteria for ASML

Van Aken

Van Aken [3] uses 7 criteria, which he describes in a short but clear manner.

1. Strategy

His first criteria is a strategic fit with the organisational structure. He mentions that a common held belief is that structure

must follow strategy. He argues that this is not always true and that sometimes the reverse can also be true, when an unique

structure might enable an organisation to compete in a certain market. In that case, strategy follows structure. In general

however it is true that structure must follow from a well thought out strategy. A fit between strategy and structure would then

be a criteria for a good organisation.

2. Situation

His second criteria relates to an organisation and its relationship with it’s environment. With environment is meant the lega l,

financial, government, suppliers and customers it is influenced by in one way or another. This environment is characterized

by its complexity and its turbulence. Complexity requires specialization in an organisation and turbulence might require an

organisation to decrease the level of formalization and centralization in order to be flexible. In this sense an organisation can

also be judged on the fit it has with its environment.

3. Internal controllability

The internal controllability of an organisation relates to the extent to which an organisation is able to control it’s internal

processes. Van Aken argues that the extent to which a certain design is able to do this depends largely on a relative simple

and clear distribution of tasks, responsibilities and authority.

4. Efficiency

The efficiency of a structure depends on the output divided by the amount of resources that is required for the output. This

depends on the specialization in certain areas that enables more efficient working and the amount of coordination that is

required to enable cooperation between specialists. Coordination requires time and money and so decreases efficiency. Not

enough coordination however causes miscommunication and waste and is also costly. A good balance is required.

5. Responsivity

The timeliness to which employees and managers in the primary process of an organisation are able to adapt to changes

and disturbances in their work is called responsivity.

6. Quality of output

The output of an organisation is either a product or a service. The extent to which a customer is satisfied by a service or

product depends on the extent to which his needs are filled. This means that an organisation has to deliver the right kind of

service / product on the one hand and on the other hand, that that service or product is of good quality.

7. Quality of labor

An organisation structure can be more or less considerate to employees needs. Employees needs are dependent a.o. on the

extent to which they have challenging, non-monotonous and diverse work and the extent to which they have interaction with

other people.

Jägers

Jägers [10] uses 4 criteria. His book is written around a framework for designing organisations. Consequently, he devotes an

entire chapter to these criteria.


33

1. Efficiency

His first criteria relates to the efficiency with which an organisation operates. Efficiency is defined as the extent to which an

organisation is able to accomplish the same output with varying amounts of resources.

2. Flexibility

Jägers makes a distinction between strategic and operational flexibility. Strategic flexibility is needed when the requirements

an environment demands of an organisation are both numerous and constantly changing. Operational flexibility is needed

when requirements are numerous (thus complex) but do not change (a lot). The overall requirements don’t change, but

specific demands can require a rescheduling of organisation demands. For example, a production order can be canceled or

suddenly rescheduled. Some flexibility is still needed in that case, but not to the extent as for strategic flexibility.

3. Satisfaction

Satisfaction is the extent to which organisation members’ needs are met by working in the organisation. Needs are employee

dependant and the extent to which these needs need to be satisfied is employee dependant as well.

4. External need satisfaction

An organisation is depended on a number of other organisations and external individuals for its survival. These organisations

and individuals have needs and expectations from an organisation. The extent to which these needs and expectations are

met is thus important for an organisations survival. Typical external organisations and persons include government,

shareholders, (potential) employees, customers, suppliers, etc.

Keuning

Keuning [11] has only one paragraph devoted to his criteria. Moreover these are stated in very general terms so it can be

hard to determine the precise definition he has in mind. However the terms are similar to those used by the other authors, so

the precise meaning can be guessed from their definitions.

1. Efficiency

The efficiency of the organisation . Specifically with respect to the economy, technology and resources.

2. Satisfaction

The satisfaction of employees. (fit to people)

3. Needs satisfaction

The satisfaction of external institutions and persons. Specifically the clients and company stakeholders. (fit to clients and

stakeholders)

4. Survival

The (long term) survival of the organisation. (fit to strategy and to market structure)

De Sitter

De Sitter [17] is the only author that specifically focuses on production/manufacturing organisations. He claims these

organisations have one goal: maximum productivity. Productivity can be divided into a number of functional requirements.

These can be seen as criteria. There are 4 functional requirements.

1. Production flexibility

The extent to which multiple producttypes can be manufactured with minimal cycle time.

2. Production control


34

Production control is the reliability of cycle times. Production variability is always present in organisations but cycle times can

vary from constant and perfectly predictable to highly erratic with large variation. Production control is also the reliability of

delivering constant quality.

3. Production innovation

Production innovation is the extent to which a production organisation is able to think of new products that are desired by

customers and is able to design and produce these in short time frames.

4. Production quality of labor

Quality of labor is dependent on the extent to which personnel is able to satisfy job demands and have low stress levels.

They also need to feel involved and important. Lastly, the possibilities to learn and develop one self is also an important

requirement. Since de Sitter writes from a dutch point of view, he also sees labor relationships as a criteria for an

organisation.

Defining the criteria

As can be seen from the above related criteria, there is a large overlap in the criteria, but there are also criteria that are used

by one author but not by the other. For instance the criteria of van Aken of internal controllability is not found with the other

authors. It could be related to process control (cycle time variability), but he also has a specific criteria for that purpose

(responsivity) What van Aken means with it is more of a qualitative measure of the transparency of an organisation. (how

clear is the authority-, responsibility- and task distribution to all members of an organisation) While other authors also

mention its importance, they do not include it as an explicit criteria.

The criteria were compared and grouped together in a table. Where needed some criteria were cut up in to different criteria

in order to match them properly with other authors’ criteria. The criteria from different authors that overlapped, were given

names that reflect their definition. The result can be seen in the table below.


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Van Aken Jägers Keuning De Sitter Summary Definition

Strategy Survival

(fit to strategy)

Strategy fit Structure follows from ambitions

and mission of an organisation as

stated in its strategy.

Situation (fit with

environment)

External

needs

satisfaction

Survival (fit to

market structure

and to economy)

Environment

fit

Environment is defined as

constituting all the external

individuals, organisations and

institutions (excluding customers)

on which an organisation is

somehow dependant. An

organisation needs to be able to

satisfy environment needs.

Situation (fit with

technology)

Efficiency

(fit to technology)

Technology fit The extent to which an

organisation is able to adapt to

changes in environment needs.

Internal controllability Transparency The extent to which an

organisation is able to satisfy

customer needs.

Production flexibility Production

flexibility

The extent to which an

organisation is able to produce

the same amount of output with a

varying amount of resources.

Efficiency Efficiency Efficiency (fit to

economy &

resources)

Efficiency The clarity of the task-,

responsibility- and authority

distribution within an organisation.

Responsivity

(responsiveness to

changes in

environment)

Flexibility

(strategic and

operational)

Innovativeness

(strategic product

development)

External

flexibility


organisation is able to deliver its

service / product in a constant and

timely manner.

Responsivity

(responsiveness to

process disturbances)

Controllability

(reliable CT)

Controllability The extent to which an

organisation is able to leverage its

technology through its structure.

Quality of output (with

respect to customer)

Needs satisfaction

(fit to client)

Customer fit The extent to which an

organisation is able to produce a

constant and high quality product /

service.

Quality of output (with

respect to product)

Controllability

(Quality control)

Quality

management


organisation is able to satisfy

employee’s needs.

Quality of labor Satisfaction Satisfaction

(fit to people)

Quality of labor Quality of

labor

The extent to which a production

organisation is able to produce

varying volumes and types of

products.

Needs Satisfaction

(fit to stakeholders)

Quality of labor

relationships (labor

agreements)


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The concepts on the right side were given a definition, which can also be seen in table 1. The bottom most criteria of “Needs

satisfaction (fit to stakeholders)” and “Quality of labor (labor agreements)” were ignored because they are outside the scope

of this project.

This list of criteria was discussed with the project stakeholders at ASML and it was decided that the criteria that had an

external focus would not be used in the evaluation. This is because the production facility is very isolated from outside

customer influences in many ways and where it is not it is not in the organisations influence sphere to change anything about

it. The customer fit for instance is not inside the production organisations control. This is determined by the design of the

machines which is done by Development and Engineering. External flexibility would also apply more to D&E for the same

reason, but this is also not an important criteria for ASML, because customer demands have been more or less constant

over the last 20 years. (cheap & smaller line width) The fit with environment further is also not inside the organisations

responsibilities. The strategic fit then finally is obviously important as well, but is the responsibility of the main ASML board,

which is not inside this projects scope.

The criteria that remain were also evaluated on usefulness. Transparency was not used either, because only the production

tasks will be distributed differently. The management functions, support organisation and production engineering functions

remain the same. Consequently there will not be any large differences between the organisations with respect to this criteria.

The same reasoning applies to technology fit. This criteria would furthermore be very difficult to operationalise. Or in other

words, it would be difficult to determine how each organisation would score on this criteria. The production flexibility criteria

was also decided to be left out partly. The NXE organisation only has to produce one machine type, so the need for flexibility

with respect to the production of different product types is non-existent. Its flexibility with regards to its volume however is

important for ASML. This flexibility was subsequently termed scalability. Lastly, efficiency was decided to be to vague and all

encompassing. This is probably due to the more broader focus the authors had in general. Efficiency was subsequently split

up in productivity, process improvement and coordination need. Productivity is the number of machines that can be

produced by a similar number of resources. Process improvement is a quite important concept at ASML that refers to the

learning curve machines typically go through. The first machines will usually take much longer to finish then the last one. The

difference can be quite dramatically and given the large material investment ASML has to make in each machine, the time it

spends in its factory is a significant part of its cost, which makes this an important extra criteria to add. The last aspect of

efficiency is coordination need. This is similar to van Aken’s original definition of efficiency, where he writes about the need

to balance specialisation and generalisation.

Criteria Definition

Literature Controllability The extent to which an organisation is able to deliver its service / product in a

constant and timely manner.

Quality management The extent to which an organisation is able to produce a constant high quality

product / service.

Quality of labor The extent to which an organisation is able to satisfy employee’s needs.

Eff

icie

ncy

Productivity The number of machines ASML can produce per given time unit with the same

resources.

Process improvement The extent to which the organisation structure enables internal efficiency

process improvement.

Coordination need The amount of coordination that is required between organisational

departments in order to carry out their tasks.

ASML Scalability The speed with which an organisation is able to expand and contract in a

certain time frame with a minimum of problems.


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A large specialisation will probably increase individual workers efficiency, but might cause organisational inefficiencies

because people have difficulty communicating (miscommunications). It might also require more and longer communication to

coordinate activities, eventually also resulting in wasted time.

These changes resulted in the criteria as defined in the table above.

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