Inventory $%&'()*+,)-,./012%)&3&4'5&* &67'*$895&5:$3 1;%)&*30&*3<='(59> >?'<)*1+@:.; 951/A:1-%)&.;
9/8/2008
Types of Inventory
Raw materials Purchased parts and supplies Labor In-process (partially completed) products Component parts Working capital Tools, machinery, and equipment Finished goods
chaiyot pom mba cmu 11
1
Inventory Costs
305+65
Carrying Cost $@:9B0>@:C95ภ:;1ภE4;<ภF: 1B@5 $@:Gภ=<* $@:=&ภ14,HC $@:7;?ภ<5I<C Ordering Cost $@:9B0>@:C95ภ:;'<)*K%H& 1B@5 $@:1=(5+:* $@:L5'@* $@:1&ภ':;'<)*K%H& Shortage Cost $@:1',CG&ภ:'+,).-@':-:;N3&4'5&* $/:-30&*ภ:;AOภ$0: AOภ$0:&:>.7K%H&>:ภ$O@PL@* 9/8/2008
chaiyot pom mba cmu 11
EOQ
7;(-:S
2
Inventory Control Systems
9B0ภ4< Independent Demand ABC Classification Model Basic Model : Economic Order Quantity (EOQ) Fixed-order-quantity system (Continuous) constant amount ordered when inventory declines to predetermined level Fixed-time-period system (Periodic) order placed for variable amount after fixed passage of time 9/8/2008
chaiyot pom mba cmu 11
3
ABC Classification System 9B0[A<ภภ:;4;([:;G=CL0&P3ภ3@:* (Management by Exception) 9B0.=0ภ/0:*L/:* ;/-N\* &67ภ;S81$;%)&*><ภ; L&*+,)-;, :$:P2*
Demand volume & value of items vary Classify inventory into 3 categories Class % of Units % of Dollars A 5 - 15 70 - 80 B 30 15 C 50 - 60 5 - 10 9/8/2008
chaiyot pom mba cmu 11
4
ABC Classification Example Cost 60 350 30 80 30 20 10 320 510 20
Usage 90 40 130 60 100 180 170 50 60 120
Part 9 8 2 1 4 3 6 5 10 7
Value 30,600 16,000 14,000 5,400 4,800 3,900 3,600 3,000 2,400 1,700 $ 85,400 Class A B C
9/8/2008
Value Quantity Cumulative 35.8 6.0 6.0 18.7 5.0 11.0 16.4 4.0 15.0 6.3 9.0 24.0 5.6 6.0 30.0 4.6 13.0 43.0 4.2 18.0 61.0 3.5 10.0 71.0 2.8 12.0 83.0 2.0 17.0 100.0
Items 9,8,2 1, 4, 3 6, 5, 10, 7
chaiyot pom mba cmu 11
% Value % Units 71 15 16.5 25 12.5 60
5
The Inventory Order Cycle Demand rate
Inventory Level
Order qty, Q
Reorder point, R 0
9/8/2008
Lead time Order Order Placed Received chaiyot pom mba cmu 11
Lead Time time Order Order Placed Received 6
EOQ Model Cost Curves Slope = 0 Annual cost ($)
Total Cost
Minimum total cost
Carrying Cost = CcQ/2 Ordering Cost = CoD/Q Optimal order Qopt
9/8/2008
chaiyot pom mba cmu 11
Order Quantity, Q 7
EOQ With Noninstantaneous Receipt 0123 EPQ (Economic Production Quantity Inventory level
Maximum inventory level
Q(1-d/p) Begin Order Q (1-d/p) 2
0 Order receipt period 9/8/2008
Average inventory level
receipt
End Order
Time
receipt chaiyot pom mba cmu 11
8
Inventory level
Reorder Point With A Safety Stock
Q
Reorder point, R
Safety stock
0 9/8/2008
LT
Time chaiyot pom mba cmu 11
LT 9
Objectives in Scheduling
Scheduling ภ:;4;([:;+;<2C:ภ;+,)-, &CO@&C@:*>g:ภ<= .=0Pภ@ $5 1$;%)&*><ภ; '()*&g:5/C $/:-'?=/ภ.795ภ:; hA(3'(5$0:/4;(ภ:; +g:&C@:*.;9[0305+653)g:'6= 1/A:'<H5+,)'6= 17j5;?C?'6=+0:Cภ:;L&* ภ:;/:*Ph5ภ@&5A*-%&hA(3 9/8/2008
Meet customer due dates Minimize job lateness Minimize response time Minimize completion time Minimize time in the system Minimize overtime Maximize machine or labor utilization Minimize idle time Minimize work-in-process inventory
chaiyot pom mba cmu 11
10
Scheduling Function By Process Type Process Industry linear programming EOQ with noninstantaneous replenishment Mass Production assembly line balancing Project project -scheduling techniques (PERT, CPM) 9/8/2008
chaiyot pom mba cmu 11
11
Scheduling Batch/Job Shop Operations Batch Production many planning steps aggregate planning master scheduling material requirements planning (MRP) capacity requirements planning (CRP) Scheduling determines machine/worker/job assignments resource/requirement matchings 9/8/2008
chaiyot pom mba cmu 11
12
Difficulties Of Job Shop Scheduling Variety of jobs (customers) processed Distinctive routing and processing requirements of each job/customer Number of different orders in the facility at any one time Competition for common resources 9/8/2008
chaiyot pom mba cmu 11
13
Responsibilities of Production Control Department 1. Loading - Check availability of material, machines & labor 2. Sequencing - Release work orders to shop & issue dispatch lists for individual machines 3. Monitoring - Maintain progress reports on each job until it is complete 9/8/2008
chaiyot pom mba cmu 11
14
EFGHIIJKLMNOMPกR1JSR Scheduling Assignment Method Job Sequencing 1 Machine Multiple Machine Gantt chart 9/8/2008
MNOTFUVRPWXORกFIJ1FYZRก1 [\GPM0]\ W^_P Job shop MNOTFUVRPWXORกFIJ1FYZRก1 `KTaU`a\V0`RZE\RVกFP MNOกbX3V Johnson MNOกSRกFIdGR`d2I0PORX3VVRP chaiyot pom mba cmu 11
15
9/8/2008
Wd12L3VTFภ1 A VRP A 11
B
C
14
6
B
8
10
11
C
9
12
7
chaiyot pom mba cmu 11
16
9/8/2008
Wd12L3VTFก1 A VRP ก 5
B
C
8
0
X
0
2
3
d
2
5
0
chaiyot pom mba cmu 11
17
9/8/2008
Wd12L3VTFก1 A VRP ก 5
B
C
6
0
X
0
0
3
d
2
3
0
chaiyot pom mba cmu 11
18
9/8/2008
Wd12L3VTFก1 A VRP ก 5
B
C
6
0
X
0
0
3
d
2
3
0
chaiyot pom mba cmu 11
19
9/8/2008
Wd12L3VTFก1 A VRP ก 3
B
C
4
0
X
0
0
5
d
0
1
0
chaiyot pom mba cmu 11
20
9/8/2008
Wd12L3VTFก1 A VRP ก 3
B
C
4
0
X
0
0
5
d
0
1
0
chaiyot pom mba cmu 11
21
9/8/2008
TFV0GFU fgOI1h0R1 [`NRZ
WNKZVM0`\
X3PH犧―P
[VXeR
800
1100
1200
`RPi
500
1600
1300
1a\VW123V
500
1000
2300
chaiyot pom mba cmu 11
22
TFV0GFU fgOI1h0R1 [`NRZ
WNKZVM0`\ X3PH犧―P [VXeR 800
1100
1200
1000
`RPi
500
1600
1300
800
1a\VW123V
500
1000
2300
1000
Eaj犧・R
0
9/8/2008
0 chaiyot pom mba cmu 11
0
1iZ3V
0 23
TFV0GFU fgOI1h0R1 [`NRZ
WNKZVM0`\ X3PH犧―P [VXeR
`RPi
0
1a\VW123V
0
Eaj犧・R
0
9/8/2008
0
300
1iZ3V
400
200
1100
800
300
500
1800
1000
0 chaiyot pom mba cmu 11
0
0 24
TFV0GFU fgOI1h0R1 [`NRZ
WNKZVM0`\ X3PH犧―P [VXeR
`RPi
0
1a\VW123V
0
Eaj犧・R
200
9/8/2008
0
100
1iZ3V
200
0
900
600
100
300
1600
800
0 chaiyot pom mba cmu 11
0
0 25
TFV0GFU fgOI1h0R1 [`NRZ
WNKZVM0`\ X3PH犧―P [VXeR 0
0
100
0
`RPi
0
800
500
100
1a\VW123V
0
200
1500
800
Eaj犧・R
300
9/8/2008
0 chaiyot pom mba cmu 11
0
1iZ3V
0 26
TFV0GFU fgOI1h0R1 [`NRZ
WNKZVM0`\ X3PH犧―P [VXeR
`RPi
0
100
0
0
700
400
100
1a\VW123V
0
100
1400
700
Eaj犧・R
0
9/8/2008
100
1iZ3V
0 chaiyot pom mba cmu 11
0
0 27
Sequencing Rules ( 1 Station)
FCFS - first-come, first-served LCFS - last come, first served SPT - shortest processing time DDATE - earliest due date SLACK - smallest slack (due date - todayms date) - (remaining processing time) RWK - remaining work on all operations 9/8/2008
chaiyot pom mba cmu 11
28
B(H5*:5
A B C D E
9/8/2008
1/A:+,)9B0
/<5$;4ภg:[5='@*
3 4 2 6 1
5 6 7 9 2
chaiyot pom mba cmu 11
29
First-Come First-Served B(H5*:5
A B C D E
1/A:+,)9B0
3 4 2 6 1
/<5$;4กg:[5='@*
1/A:ก;?P'ก:;.[A
5 6 7 9 2
WGeRก1iH[กR1p0eX3VVRP1G`=3+7+9+15+16 = 50 GFP d\RWqeKLZWGeRก1iH[กR1p0eX3VVRP = 50/5 = 10 GFP WqeK9/8/2008 LZHE\eiVRPe\RNOR (0+1+2+6+14)/5 chaiyot pom = mba4.6 cmu 11GFP
0+3=3 3+4=7 7+2=9 9+6=15 15+1=6
30
Shortest Operating Time B(H5*:5
E C A B D
1/A:+,)9B0
1 2 3 4 6
/<5$;4กg:[5='@*
2 7 5 6 9
1/A:ก;?P'ก:;.[A
WGeRก1iH[กR1p0eX3VVRP1G`=1+3+6+10+16 = 36 GFP d\RWqeKLZWGeRก1iH[กR1p0eX3VVRP = 36/5 = 7.2 GFP WqeK9/8/2008 LZHE\eiVRPe\RNOR (0+0+1+4+7)/5 = 2.4 GFP chaiyot pom mba cmu 11
0+1=1 1+2=3 3+3=6 6+4=10 10+6=16
31
Due Date B(H5*:5
E A B C D
1/A:+,)9B0
1 3 4 2 6
/<5$;4กg:[5='@*
2 5 6 7 9
1/A:ก;?P'ก:;.[A
WGeRก1iH[กR1p0eX3VVRP1G`=1+4+8+10+16 = 39 GFP d\RWqeKLZWGeRก1iH[กR1p0eX3VVRP = 39/5 = 7.8 GFP WqeK9/8/2008 LZHE\eiVRPe\RNOR (0+0+2+3+7)/5 = 2.4 GFP chaiyot pom mba cmu 11
0+1=1 1+3=4 4+4=8 8+2=10 10+6=16
32
Last come first served B(H5*:5
E D C B A
1/A:+,)9B0
1 6 2 4 3
/<5$;4กg:[5='@*
2 9 7 6 5
1/A:ก;?P'ก:;.[A
0+1=1 1+6=7 7+2=9 9+4=13 13+3=16
WGeRก1iH[กR1p0eX3VVRP1G`=1+7+9+13+16 = 46 GFP d\RWqeKLZWGeRก1iH[กR1p0eX3VVRP = 46/5 = 9.2 GFP WqeK9/8/2008 LZHE\eiVRPe\RNOR (0+0+2+7+11)/5 = 4 GFP chaiyot pom mba cmu 11
33
Summary กb FCFS SOT Due Date LCFS
WGeR1G`VRPW[1sT WGeRWqeKLZVRPW[1sT 50 36 * 39 46
10 7.2 7.8 9.2
*
d\RWqeKLZdGR`e\RNOR 4.6 2.4 * 2.4 * 4.0
* best values 9/8/2008
chaiyot pom mba cmu 11
34
Johnsonms Rule Example Machine Center 1 6 3 18 15 16 10
Job A B C D E F B 9/8/2008
A
F chaiyot pom mba cmu 11
Machine Center 2 12 7 9 14 8 15 D
C
E 35
[tRPKJKL 1 [tRPKJK 2
3 7 03
6 12 9
10 15
19 34
[tRPKJKL 1 B A F D [tRPKJK 2 B A 0 3
9/8/2008
10
15 14
22
chaiyot pom mba cmu 11
18 9 52
F 37
C D
16 8 68
76
E C
E
51 52 61 68
76
36
Gantt Chart Key: Completed Activity
Job 32B
3
Behind schedule
Planned Activity
Facility
Job 23C
2
Ahead of schedule Job 11C
Job 12A
1
On schedule
1
2
3
4
5
6
8
9
10 11 12
Days
Todayâ&#x20AC;&#x2122;s Date 9/8/2008
chaiyot pom mba cmu 11
37
Gantt Chart Solution
9/8/2008
chaiyot pom mba cmu 11
38
Quality Control Approaches Statistical process control (SPC) Monitors production process to prevent poor quality input
Process
AS
Acceptance sampling (AS) Inspects random sample of product to determine if a lot is acceptable 9/8/2008
chaiyot pom mba cmu 11
SPC
AS output
39
Statistical Process Control
Take periodic samples from process Plot sample points on control chart Determine if process is within limits Prevent quality problems
9/8/2008
chaiyot pom mba cmu 11
40
Variation Common Causes Variation inherent in a process Can be eliminated only through improvements in the system Special Causes Variation due to identifiable factors Can be modified through operator or management action
9/8/2008
chaiyot pom mba cmu 11
41
Types Of Data Attribute data Product characteristic evaluated with a discrete choice Good/bad, yes/no Variable data Product characteristic that can be measured Length, size, weight, height, time, velocity 9/8/2008
chaiyot pom mba cmu 11
42
Variables MNO X-bar , R chart Attribute MNO p 犧：I c chart
Control chart
Process Control Chart Upper control limit
Process average
Lower control limit 1 9/8/2008
2
3
4
5
6
Sample number
chaiyot pom mba cmu 11
7
8
9
10 43
A Process Is In Control If No sample points outside limits Most points near process average About equal number of points above & below centerline Points appear randomly distributed 9/8/2008
chaiyot pom mba cmu 11
44
Development Of Control Chart Based on in-control data If non-random causes present discard data Correct control chart limits 9/8/2008
chaiyot pom mba cmu 11
45
Control Charts For Attributes p Charts Calculate percent defectives in sample c Charts Count number of defects in item 9/8/2008
chaiyot pom mba cmu 11
46
p-Chart UCL = p + zσp LCL = p − zσp p(1− p) σp = n p = average % defective in sample n = sample size 9/8/2008
chaiyot pom mba cmu 11
47
p-Chart Example 20 samples of 100 pairs of jeans Sample # # Defects Proportion Defective 1 6 .06 2 0 .00 3 4 .04 w w w 20 18 .18 200 9/8/2008
chaiyot pom mba cmu 11
48
p-Chart Calculations total defectives total sample observations 200 = 20(100) = 0.10
p=
p (1− p ) 0.10(1− 0.10) = 0.10 + 3 = 0.190 n 100 p (1− p ) 0.10(1− 0.10) LCL = p − z = 0.10 − 3 = 0.010 n 100
UCL = p + z
9/8/2008
chaiyot pom mba cmu 11
49
Example p-Chart 0.2
Proportion defective
0.18 0.16 0.14 0.12 0.1 0.08 0.06 0.04
20
18
16
14
12
..
10
8
6
4
2
0
0.02 0 Sample number
9/8/2008
chaiyot pom mba cmu 11
50
c-Chart Total # defects Process average = c = # samples Sample standard deviation = σ c = c UCL = c + z σc LCL = c - z σc
9/8/2008
chaiyot pom mba cmu 11
51
c-Chart Example Count # of defects in 15 rolls of denim fabric Sample # 1 2 3 w 15 9/8/2008
# Defects 12 8 16 w 15 190 chaiyot pom mba cmu 11
52
c-Chart Calculations 190 = 12.67 15 UCL = c + z σc = 12.67 + 3 12.67 = 23.35 c=
LCL = c - z σc = 12.67 − 3 12.67 = 1.99
9/8/2008
chaiyot pom mba cmu 11
53
Example c-Chart 24 21
15 12 9 6 3
14
12
10
8
6
4
2
0 0
.
Number of defects
18
Sample number 9/8/2008
chaiyot pom mba cmu 11
54
Control Charts For Variables Mean chart (X-Bar Chart) Uses average of a sample Range chart (R-Chart) Uses amount of dispersion in a sample 9/8/2008
chaiyot pom mba cmu 11
55
Range (R) Chart UCL = D4 R LCL = D3 R 竏然 R= k R = range of each sample k = number of samples 9/8/2008
chaiyot pom mba cmu 11
56
R-Chart Example Sample 1 2 3 w 10 9/8/2008
1 5.02 5.01 4.99 w 5.01
Slip-ring diameter (cm) 2 3 4 5.01 4.94 4.99 5.03 5.07 4.95 5.00 4.93 4.92 w w w 4.98 5.08 5.07 chaiyot pom mba cmu 11
5 4.96 4.96 4.99 w 4.99
x 4.98 5.00 4.97 w 5.03 50.09
R 0.08 0.12 0.08 w 0.10 1.1557
3Ď&#x192; Control Chart Factors Sample size n 2 3 4 5 6 7 8 9/8/2008
x-chart A2 1.88 1.02 0.73 0.58 0.48 0.42 0.37 chaiyot pom mba cmu 11
R-chart D3 0 0 0 0 0 0.08 0.14
D4 3.27 2.57 2.28 2.11 2.00 1.92 1.86
58
R-Chart Calculations â&#x2C6;&#x2018; R 1.15 R= = = 0.115 k 10 UCL = D4 R = 2.11(0.115) = 0.243 LCL = D3 R = 0(0.115) = 0
9/8/2008
chaiyot pom mba cmu 11
59
Example R-Chart 0.3 0.25 Range
0.2 0.15 0.1 0.05 0 1
2
3
4
5
6
7
8
9
10
Sample number
9/8/2008
chaiyot pom mba cmu 11
60 Ch 4 - 28
x Chart Calculations + x2 +L+ xn 50.09 x 1 x= = = 5.01 cm n
10
UCL = x + A2 R = 5.01+ ( 0.58) (.115) = 5.08 LCL = x â&#x2C6;&#x2019; A2 R = 5.01â&#x2C6;&#x2019; ( 0.58) (.115) = 4.94 x = average of sample means R = average range value 9/8/2008
chaiyot pom mba cmu 11
61
x-Chart Example ∑ x 50.09 x= = = 5.01cm n 10 UCL= x + A2 R = 5.01+ (0.58)(0.115) = 5.08 LCL= x − A2 R = 5.01− (0.58)(0.115) = 4.94 9/8/2008
chaiyot pom mba cmu 11
62
Sam ple average
Example x-Chart 5.10 5.08 5.06 5.04 5.02 5.00 4.98 4.96 4.94 4.92 1
3
5
7
9
Sample number
9/8/2008
chaiyot pom mba cmu 11
63
Control Chart Patterns UCL
UCL
LCL
LCL
Sample observations consistently below the center line
9/8/2008
Sample observations consistently above the center line
chaiyot pom mba cmu 11
64
Control Chart Patterns UCL
UCL
LCL
LCL
Sample observations consistently increasing 9/8/2008
chaiyot pom mba cmu 11
Sample observations consistently decreasing 65
Control Chart Patterns UCL
UCL
LCL
LCL
Sample observations consistently below the center line 9/8/2008
Sample observations consistently above the center line chaiyot pom mba cmu 11
66
Control Chart Patterns 1. 8 consecutive points on one side of the center line. 2. 8 consecutive points up or down across zones. 3. 14 points alternating up or down. 4. 2 out of 3 consecutive points in zone A but still inside the control limits. 5. 4 out of 5 consecutive points in zone A or B. 9/8/2008
chaiyot pom mba cmu 11
67
Results Of Pattern Test 2 of 3 consecutive points in zone A Samples 3 and 4 Process should be checked
9/8/2008
chaiyot pom mba cmu 11
68
Sample Size Determination Attribute control charts 50 to 100 parts in a sample Variable control charts 2 to 10 parts in a sample
9/8/2008
chaiyot pom mba cmu 11
69
Process Capability Range of natural variability in process Measured with control charts. Process cannot meet specifications if natural variability exceeds tolerances 3-sigma quality specifications equal the process control limits. 6-sigma quality specifications twice as large as control limits 9/8/2008
chaiyot pom mba cmu 11
70
Process can meet specifications
PROCESS
Process cannot meet specifications
9/8/2008
Natural control limits
Design specifications
Design specifications
Natural control limits
Design specifications
Natural control limits
PROCESS
PROCESS
Process Capability
chaiyot pom mba cmu 11
Process capability exceeds specifications
71
Acceptance Sampling Accept/reject entire lot based on sample results Not consistent with TQM of Zero Defects Measures quality in percent defective
9/8/2008
chaiyot pom mba cmu 11
72
Sampling Plan Guidelines for accepting lot Single sampling plan N = lot size n = sample size (random) c = acceptance number d = number of defective items in sample If d <= c, accept lot; else reject 9/8/2008
chaiyot pom mba cmu 11
73
Producerms & Consumerms Risk TYPE I ERROR = P(reject good lot) ι or producerms risk 5% is common TYPE II ERROR = P(accept bad lot) β or consumerms risk 10% is typical value 9/8/2008
chaiyot pom mba cmu 11
74
Quality Definitions Acceptance quality level (AQL) Acceptable fraction defective in a lot Lot tolerance percent defective (LTPD) Maximum fraction defective accepted in a lot 9/8/2008
chaiyot pom mba cmu 11
75
Operating Characteristic (OC) Curve Shows probability of lot acceptance Based on sampling plan quality level of lot Indicates discriminating power of plan 9/8/2008
chaiyot pom mba cmu 11
76
Operating Characteristic Curve 1.00
{
Probability of acceptance, Pa
Îą = 0.05
0.80
OC curve for n and c 0.60
0.40
0.20
β = 0.10
{ 0.02 0.04
9/8/2008
AQL
0.06
0.08
0.10
0.12
Proportion defective chaiyot pom mba cmu 11
0.14
0.16
LTPD
0.18
0.20 77
Double Sampling Plans Take small initial sample If # defective < lower limit, accept If # defective > upper limit, reject If # defective between limits, take second sample Accept or reject based on 2 samples Less costly than single-sampling plans 9/8/2008
chaiyot pom mba cmu 11
78
Multiple (Sequential) Sampling Plans Uses smaller sample sizes Take initial sample If # defective < lower limit, accept If # defective > upper limit, reject If # defective between limits, resample Continue sampling until accept or reject lot based on all sample data 9/8/2008
chaiyot pom mba cmu 11
79
Choosing A Sampling Method An economic decision Single sampling plans high sampling costs Double/Multiple sampling plans low sampling costs 9/8/2008
chaiyot pom mba cmu 11
80
กR1ISR1aV1Fก R (maintenance) *:5+,)30&*+g:12%)&;<กF:'I:21$;%)&*><ก; &67ก;S8 9[0.=0-:3;m:5PA?9[0ก:;hA(3;:4;%)5
GhGF PRกR1 BM -Breakdown Maintenance PM - Preventive Maintenance PM - Productive Maintenance CM - Corrective Maintenance MP - Maintenance Prevention TPM - Total Productive Maintenance TPM - Total Productive Manufacturing 9/8/2008
chaiyot pom mba cmu 11
81
P ZIRZกR1ISR1aV1Fก R \3`W`2L3W[KZ d\RMNOT\RZ ^ 3VกFPp`\M0OW[KZ W^eKLZPJUHJP p`\EO3VISR1aV1Fก R d\RMNOT\RZMPกR1ISR1aV1Fก R d\RMNOT\RZWNhV^ 3VกFP d\RMNOT\RZW`2L3NSR1aUW[KZ0RZ 9/8/2008
chaiyot pom mba cmu 11
^1h`R 82
GFEta^1i[Vd TPM WY2L3eUกR1[g]W[KZJKLZhLVM0]\ 6 ^1iกR1 Wd12L3VTFก1XFUXO3V ^1FIEF V^1FIHE\V Wd12L3VWUhPEFGW^e\R dGR`W1sGeUeV X3VW[KZMPก1iIGPกR1 fefehEPO3ZMPN\GVH1ก 9/8/2008
chaiyot pom mba cmu 11
83