2010 IIE/Rockwell Student Simulation Competition

16th Annual IIE/Rockwell Student Simulation Competition

Members: Jin-Yong YimSeung-Min NohChan-Bum Jung

Faculty Advisor: Dr. Seong-Yong Jang

Objectives#1

Scenario 1#2

Scenario 2#3

IT‘s Recommendations#4

Additional Problems

#5 Conclusion

#6

Objectives

Objectives#1

Aims to The Successful Consulting

Minimize Total TravelDistance & Time

Minimize The Scrap

Identify Optimal # of Oper-ators

& Their Utilization1 2

3

Scenario 1

Scenario 1

1st Recommendation

Positioning of Machines

2nd Recommendation

Optimal # of Operators

#2

1. Positioning of Machines

Generate Machines’ Layout Al-ternatives

Step 1

Compare Each Alternative

Step 2

Recommend the Best Lay-out

Step 3

1 2 3

Positioning#2

From-ToChart

CellGrouping

ParetoRule

Step 1 : Generate Machines’ Layout Alternatives

From-To Chart Method1

Layout Alternative 1

Travel DistanceMachine NJ LocationTravel DistanceFrequency

FrequencyMachine NJ Location

#2

NJ01 NJ02 NJ03 NJ04 NJ05 NJ06 NJ07 NJ08 NJ09 NJ10 NJ11 NJ12 Total

M #1 133,212 1,938 18,624 5,568 44,340 0 12,699 230,753 2,142 33,147 70,866 22,248 575,537

M #2 217,355 29,365 12,597 541 128,960 41,940 62,118 31,456 29,992 298,840 59,520 30,705 943,389

M #3 134,853 7,329 8,239 8,239 88,179 2,144 16,128 101,460 13,884 35,464 26,598 16,224 458,741

M #4 233,916 1,656 4,092 0 95,535 2,283 4,852 0 1,298 124,740 31,185 1,760 501,317

M #5 187,179 19,775 5,526 0 163,275 43,260 17,820 15,264 64,584 37,544 49,818 15,552 619,597

From-To Chart Method (Frequency X Travel Dis-tance)1


Machine

Location

···

Rank Machine # Contribution Volume Rank Machine # Contribution Volume

1 2 943,389 17 9 428,625

2 26 705,687 18 14 424,318

3 7 669,169 19 10 407,977

4 16 652,457 20 11 381,077

5 5 619,597 21 15 366,854

6 1 575,537 22 22 357,430

7 8 572,260 23 6 320,774

8 29 546,944 24 20 308,723

9 13 536,634 25 18 288,907

10 17 528,823 26 28 223,439

11 4 501,317 27 33 219,898

12 19 462,063 28 21 196,969

13 25 459,644 29 12 181,573

14 3 458,741 30 24 117,676

15 23 435,284 31 32 66,174

16 27 430,465 32 31 11,311

#2

From-To Chart Method1Alternative


Masters Molding Facility Layout

Molding Machines

Material / Die Locations

Dryer Corral

Order and Dryer Boards

Die Trucks

Finished Parts

Lift Trucks

Key

2

726

NJ05

NJ01

5

16

1

3 4

6 9

8

10

11

17

15

14

13

12

20

18

19

31

32

33

2829

21

22

23

24

30

25

27 NJ12

NJ02

NJ03

NJ04

NJ09

NJ08

NJ07

NJ06

NJ112

NJ01

26

NJ05

7

NJ10NJ10

#2


Cell Grouping Method2


indicate that a machine assigned to a cell is not requiredby all parts assigned to that cell.

indicate that a part assigned to a cell requires processingby a machine not in the cell.

Voids

Exceptional elements

What is the Cell Grouping Method ?

#2



Evaluation of groupingsGiven a P-M matrix, what makes one partitioning better than another?

#2



Grouping efficiency & Grouping efficacy

#2

Ronald G. Askin, and Charles R. Standridge. (1993). “Modeling and Analysis of Manufacturing Systems.” John Wiley & Sons, Inc.

Reference



Example

#2



Only …

If Frequency >= 60, Then Value = 1

#2

Chosen Alternative


Group C Group B Group A


#2

Masters Molding Facility Layout

Molding Machines

Material / Die Locations

Dryer Corral

Order and Dryer Boards

Die Trucks

Finished Parts

Lift Trucks

Key

Cell Grouping Method2Alternative


3

5

1725 26

NJ05

NJ01

2

22

23

1829

7

16

27

4

14

13

10

21

28

831

243219

11

20

NJ12

NJ10

6 15

3330

12

1

9

NJ02

NJ03

NJ11

NJ04

NJ09

NJ08

NJ07

NJ06

3

5

1725 26

NJ05

NJ01

2

22

23

1829

7

16

27

Group A

4

14

13

10

8

19

NJ12

NJ10

Group B

12

1

9

NJ02

NJ03

NJ11

Group C

#2


Pareto Rule Method3


2 26 7 16 5 1 8 29 13 17 4 19 25 3 23 27 9 14 10 11 15 22 6 20 18 28 33 21 12 24 32 310

100000

200000

300000

400000

500000

600000

700000

800000

900000

1000000Pareto Contribution Volume Analysis

Machine #

Cont

ribut

ion

Volu

me

Group A (20%)

Group B

(30%) Group

C

(50%)

#2

Pareto Rule Method3


14

28 29

NJ05

NJ0113

25

22

19

32

5 4

11

17

20

12

30

10

1

16

24

3

6

1518

8

21

26

7

9

23

27 NJ12

NJ02

NJ03

NJ04

NJ09

NJ08

NJ07

NJ06

NJ11

NJ10

NJ05

NJ01

33

312

231

14

28

22

32

3311

20

12

31

10

24

6

1518

21

9

14

28

22

32

3311

20

12

10

24

6

1518

21

9

31

2

337

733

14

28

22

32

11

20

12

10

24

6

1518

21

9

1

NJ08

115

15

Total Travel Distance

1992

1934

1881

1832

19921934 (-58)1881 (-111)1832 (-160)

Priority1. Proximity2. Space3. Contribution volume

Alternative

#2

Step 2 : Compare Each Alternative

Compare Alternatives

Cost Estimation & Total Travel Distance & Time

The Initial From-To Chart Cell Grouping Pareto Rule0

500

1,000

1,500

2,000

2,500

1,000,000

3,000,000

5,000,000

7,000,000

9,000,000

$ 5,964,000

$ 4,513,000

$ 1,568,000

1992 miles(602 hr) 1785 miles

(539 hr)1794 miles

(542 hr)1832 miles

(554 hr)

Cost ($)

Travels for Quality Check were not CONSIDERED !!

The Pareto Rule MethodAs The Best Layout in Scenario

1

#2

2. Optimal # of Operators

Optimal # of Operators

Step 110 12

1415 !!

Operators’ Utilization

Step 2

80%93%

Operators#2

# of Operators

16 17 18 19 2075%

80%

85%

90%

95%

100%

105%

84%87%

91%

95%99%

Quality Control Satisfaction

(22,000 hrs)

# of Operators

The

Leve

l of Q

ualit

y Co

ntro

l Sat

isfac

-tio

n

(19,800 hrs)

Step 1 : Optimal # of Operators

#2

Operators‘ Utilization

Step 2 : Operators’ Utilization

Operators’ Utiliza-tion

20%

40%

60%

80%

100%

10 Operators 18 Operators

73 %82 %

#2

Scenario 2

Scenario 2

1st Recommendation

Adding DedicatedMaterial Handlers?

2nd Recommendation

Adding DedicatedQuality Resources?

#3

Scenario 2

Dedicated Material Handler’s Utilization

: appx. 10%

Total # of Operators Required including One Material Handler

: 18 (Not Reduced)

1st Recommendation

Adding Dedicated Material Handlers?

A Dedicated Material Handler

17 Operators(Average)

0% 25% 50% 75% 100%

10%73.5%

Efficiency was not im-proved

NOT

RECOMMENDED !!

#3

Scenario 2

# of Quality Resources Required

: 5 (Based on Quality Control Level)

Total # of Operators Required

: 18 (Not Reduced)

2nd Recommendation

Adding Dedicated Quality Resources?

Efficiency was not im-proved

NOT

RECOMMENDED !!

#3

IT‘s Recommendations

IT‘s Recommendation

New Construction (Arena 3D Player Animation)

#4


1. Remove the dryer transporting time

2. Reduce travel time with more open space by eliminating the dryer cor-ral

3. Lift trucks are no longer necessary because raw materials are located on the 2nd floor

4. Shorten travel distance due to more open space on the 2nd floor

5. Save money by decreasing the total number of operators in the plant

6. Cost of changing the layout is moderate compared to the other three machines’ layout alternatives

Advantages of IT’s Recommendation

#4


Cost Segmentation of IT’s New Rec-ommendation

These figures were estimated based on the cost value of Korean molding compa-nies.

$ 1,364 X 33 = appx. $ 45,000

(Fixed Dryer Hoppers)

(Iron Floor )

$ 392 X 1530 Square ft = appx. $ 600,000

(Oven Dryer )

$ 5,000 X 1= appx. $ 5,000

Cost

TotalCost

$ 45,000Dryer

$ 600,000Floor

$ 5,000Oven

appx.$ 650,000$ 45,000$ 645,000

#4


1 Extract the scraps from the finished goods

3 Insert the scraps into the mixer machine

4 Insert raw materials into the mixer machine

5 Mix the scraps with the raw materials(Recycling)

6 Produce new raw materials

Scrap

Rawmaterial

Scrap

#4

2 Crush the scraps using a disintegrator

RawMaterial

Scrap

Conclusion

Conclusion

0

500

1,000

1,500

2,000

2,500

0

2,000,000

4,000,000

6,000,000

8,000,000

10,000,000

$ 5,964,000

$ 4,513,000

$ 1,568,000 $ 650,000

1992 miles(602 hr) 1785 miles

(539 hr)1794 miles

(542 hr)1832 miles

(554 hr) 1585 miles(476 hr)

Cost ($)

IT’s Recommendation

#5

Additonal Problem

Additional Problems #6

WorkStation 1TRIA(7, 9, 12)(min)

WorkStation 2TRIA(4, 8.5, 15)(min)

WorkStation 3TRIA(5.6, 9.8, 17)(min)

Bubbles, cracks, etc Further assembly steps

Additional Assembly Line

(2%) * Rejection Rate

Wastes

(7.7%)Rework(7%)Rework


WorkStation 1

WorkStation 2

WorkStation 3

# in Queue(PC) 0.010(Max.2) 0.005(Max.2) 0.007(Max.2)

Utilization(%) 9.36 10.10 10.98

# of scrapped parts

Average cycle time(for the parts that are not rejected at any workstation)

Maximum cycle time(for the parts that are not rejected at any workstation)

# of times a rejected part was rejected

Collected Statistics

52 pieces

25.881 minutes

38.958 minutes

5 times


Comparison of Alternatives Time (min)

The initial cycle time 25.881

The cycle time by the queue priority 26.586 The cycle time by creating new re-worksta-tions 25.514

Collected Statistics

WorkStation 1TRIA(7, 9, 12)(min)

WorkStation 2TRIA(4, 8.5, 15)(min)

WorkStation 3TRIA(5.6, 9.8, 17)(min)

Re-WorkStation 1The processing time is in-

creased by 50%

Re-WorkStation 2The processing time is in-

creased by 50%

Q & A

Appendix

Appendix (Pareto Rule)

Select the candidate machine in close proximity to the Group A - machine’s most critical two NJ locations

Rule 1.

Select the candidate machine which can fit for the Group A - machine’sspace

Rule 2.

Select the candidate machine which is less important by the total contributionvolume than the other candidate machines in Group C

Rule 3.

Appendix (Cell Grouping)

Selected Values of 40 or more

Too Large number of 1’s



Too Small number of 1’s



Grouping efficiency = 0.811 Grouping efficacy = 0.500

Alternative 1


Alternative 2

Grouping efficiency = 0.756 Grouping efficacy = 0.463


Appendix (Cell Grouping Method)

Step 1

( Machine-part matrix )

The direct clustering algorithm

( Ordered machine-part matrix )

Example


Step 2

( Column-sorted machine-part matrix )

Sorting the columns to move towardthe left all columns having a 1 in thefirst row

Step 3

Sorting the rows by moving upwardrows having a 1 in the first column

( Row-sorted machine-part matrix )


Step 4

( Formation of two cells )

The machine can be groupedinto 2 cells

Unfortunately, it is not alwaysthe case that cells can be formedwithout conflicts existing

( Formation with conflicts existing )

Appendix (Node Method-Floyd Algorythm)

M# NodeM1 V58M2 V89M3 V83M4 V73M5 V80M6 V84M7 V91M8 V85M9 V86

M10 V24M11 V25M12 V38M13 V37M14 V63M15 V18M16 V17M17 V42M18 V43M19 V60M20 V15M21 V14M22 V52M23 V51M24 V98M25 V11M26 V48M27 V49M28 V53M29 V56M30 V57M31 V21M32 V90M33 V76

Place NodeNJ1 V71　 V99NJ2 V67NJ3 V68NJ4 V74NJ5 V10　 V94NJ6 V8NJ7 V2NJ8 V6NJ9 V77

NJ10 V30NJ11 V29NJ12 V95　　

Board V65　　Lift1 V7Lift2 V33　　Die1 V5Die2 V75Die3 V96　　Dryer V62　　Store V70

Appendix (Node Method-Floyd Algorythm)

The Floyd–Warshall algorithm compares all possi-ble paths through the graph between each pair of vertices.

Therefore, we can define shortestPath(i, j, k) in terms of the following recursive formula:

Appendix (Node Method-Floyd Algorythm)Start End Total Dis P a t h

V2 V5 810 V2 V3 V4 V5 　　　　　　　　　　　　　　　　　V2 V6 972 V2 V3 V4 V5 V6 　　　　　　　　　　　　　　　　V2 V7 1,336 V2 V3 V4 V5 V6 V7 　　　　　　　　　　　　　　　V2 V9 1,453 V2 V3 V4 V5 V6 V7 V8 V9 　　　　　　　　　　　　　V2 V10 1,124 V2 V3 V4 V5 V6 V7 V10 　　　　　　　　　　　　　　V2 V13 1,148 V2 V3 V4 V15 V14 V13 　　　　　　　　　　　　　　　V2 V14 1,101 V2 V3 V4 V15 V14 　　　　　　　　　　　　　　　　V2 V16 846 V2 V3 V97 V18 V17 V16 　　　　　　　　　　　　　　　V2 V17 835 V2 V3 V97 V18 V17 　　　　　　　　　　　　　　　　V2 V24 1,335 V2 V3 V4 V15 V14 V13 V12 V24 　　　　　　　　　　　　　V2 V25 1,535 V2 V3 V97 V18 V19 V20 V21 V22 V23 V25 　　　　　　　　　　　V2 V27 1,409 V2 V3 V97 V18 V19 V20 V34 V33 V32 V31 V30 V29 V28 V27 　　　　　　　V2 V28 1,395 V2 V3 V97 V18 V19 V20 V34 V33 V32 V31 V30 V29 V28 　　　　　　　　V2 V31 1,203 V2 V3 V97 V18 V19 V20 V34 V33 V32 V31 　　　　　　　　　　　V2 V32 1,118 V2 V3 V97 V18 V19 V20 V34 V33 V32 　　　　　　　　　　　　V2 V35 1,693 V2 V3 V97 V18 V19 V20 V34 V33 V32 V31 V30 V36 V35 　　　　　　　　V2 V38 1,658 V2 V3 V97 V18 V19 V20 V34 V33 V32 V31 V30 V36 V37 V38 　　　　　　　V2 V39 1,690 V2 V3 V97 V18 V19 V20 V34 V33 V32 V31 V30 V36 V37 V38 V39 　　　　　　V2 V41 1,861 V2 V3 V97 V18 V19 V20 V34 V33 V32 V31 V30 V36 V37 V38 V39 V40 V41 　　　　V2 V42 1,450 V2 V3 V97 V18 V19 V20 V34 V33 V44 V43 V42 　　　　　　　　　　V2 V45 1,378 V2 V3 V97 V18 V19 V20 V34 V33 V44 V45 　　　　　　　　　　　V2 V46 1,389 V2 V3 V97 V18 V19 V20 V34 V33 V44 V45 V46 　　　　　　　　　　V2 V48 980 V2 V3 V97 V18 V19 V48 　　　　　　　　　　　　　　　V2 V49 994 V2 V3 V97 V18 V19 V48 V49 　　　　　　　　　　　　　　V2 V55 758 V2 V1 V58 V55 　　　　　　　　　　　　　　　　　V2 V56 809 V2 V1 V58 V55 V56 　　　　　　　　　　　　　　　　V2 V57 768 V2 V1 V58 V55 V57 　　　　　　　　　　　　　　　　V2 V59 1,008 V2 V1 V58 V55 V54 V59 　　　　　　　　　　　　　　　V2 V60 1,218 V2 V1 V58 V55 V54 V59 V60 　　　　　　　　　　　　　　V2 V63 990 V2 V1 V58 V55 V54 V53 V63 　　　　　　　　　　　　　　V2 V64 1,444 V2 V1 V58 V55 V54 V59 V60 V61 V64 　　　　　　　　　　　　V2 V65 1,411 V2 V1 V58 V55 V54 V59 V60 V61 V64 V65 　　　　　　　　　　　V2 V66 1,431 V2 V1 V58 V55 V54 V59 V60 V61 V64 V65 V66 　　　　　　　　　　V2 V67 1,559 V2 V1 V58 V55 V54 V53 V63 V62 V69 V68 V67 　　　　　　　　　　V2 V69 1,215 V2 V1 V58 V55 V54 V53 V63 V62 V69 　　　　　　　　　　　　V2 V71 1,639 V2 V1 V58 V55 V54 V53 V63 V62 V69 V68 V70 V71 　　　　　　　　　V2 V72 1,769 V2 V1 V58 V55 V54 V53 V63 V62 V69 V68 V70 V71 V72 　　　　　　　　V2 V74 1,996 V2 V1 V58 V55 V54 V53 V63 V62 V69 V68 V70 V71 V72 V73 V74 　　　　　　V2 V75 2,299 V2 V1 V58 V55 V54 V53 V63 V62 V69 V68 V70 V71 V72 V73 V74 V75 　　　　　V2 V76 1,664 V2 V1 V58 V55 V54 V53 V63 V62 V69 V68 V70 V76 　　　　　　　　　V2 V82 1,753 V2 V1 V58 V55 V54 V53 V52 V83 V79 V81 V82 　　　　　　　　　　V2 V83 1,346 V2 V1 V58 V55 V54 V53 V52 V83 　　　　　　　　　　　　　V2 V84 1,346 V2 V1 V58 V55 V54 V53 V52 V83 V84 　　　　　　　　　　　　V2 V85 1,599 V2 V3 V97 V18 V19 V20 V34 V33 V44 V43 V86 V85 　　　　　　　　　V2 V88 1,675 V2 V3 V97 V18 V19 V20 V34 V33 V44 V43 V86 V87 V88 　　　　　　　　

Appendix (Appointment of Workers)

( The Current System) ( Alternative 1 – From-to Chart Method)

Appendix (Appointment of Workers)

( Alternative 2 – Cell Grouping Method) ( Alternative 3 – Pareto Rule Method)

Appendix ( Data Fitting )

Appendix ( Pictures)

( A Material Hand Cart) ( A Fixed Dryer Hopper)

Appendix ( Pictures)

( A Material Warehouse) ( An Oven Dryer)

Presentations & Public Speaking

2010 IIE/Rockwell Student Simulation Competition