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The company under study specializes in producing garment products. The production process of the company has so much waste, a long production time, a high cycle time, and a high defect rate, leading to low productivity, low quality, and late deliveries, affecting the competitive edge of the company. In this article we have discussed how Six Sigma can be applied to improve the company production process to reduce waste, the process production lead time, the cycle time, and the process defect rate and then to improve productivity and quality and finally increase the on-time delivery rate and the competitive edge of the company. The research methodology is based on Lean Six Sigma theory, with the platform of DMAIC procedure, including five steps: define, measure, analyze, improve, and control. The tools used in the steps of DMAIC procedure include cause and effect diagram, Pareto diagram, value stream management, work design, SMED, line balancing, Kanban systems, FIFO, autonomous maintenance, visual management, design of experiments, and control charts. After applying Lean Six Sigma tools, the company has reduced the production lead time by 89.21% from 279 to 30.1 min, reduced the production cycle time by 36% from 25 to 16 s, reduced the process defect rate by 37.45% from 14.9 to 9.32%, and then improved the on-time delivery rate.
Lean Six SigmaDMAICSIPOCcause and effect diagram CEDPareto diagramVSMwork designSMEDline balancingKanbanFIFOAMvisual managementDOEcontrol charts1. Introduction
The company under study specializes in producing garment products. The main products of the company include men vestons, jackets, shirts, and trousers. According to statistics, the average on-time delivery percentage of the company in the past is 85%. It is low and does not meet the expectation of the company leaders. Through analysis, the cause of the problem is due to a long production time, low productivity, and a high defect rate.
This paper uses Lean Six Sigma tools to solve the problem of low on-time delivery through reducing the production time, increasing productivity, and improving the defect rate. The research is carried out on a product with the highest demand on a line of the shirt garment production area.
2. Literature review and research methodology
Lean Six Sigma is the fastest way to improve time and quality; therefore, we can minimize operation costs, minimize capital investment, increase value, and increase customer satisfaction. Lean Six Sigma is an integrated approach of Lean Manufacturing and Six Sigma. If we use Lean Manufacturing alone, we cannot control the process and solve quality problems in the best way. If we use only Six Sigma, we cannot minimize the time, cost, and capital investment. Simultaneous use of Lean and Six Sigma tools helps us achieve high quality, high-speed production, and low cost with breakthrough improvements in a short time. Lean Six Sigma gives faster results than either Six Sigma or Lean Manufacturing.
The research methodology is based on Lean Six Sigma theory, with the platform of DMAIC procedure (1), including five steps:
Define
Measure
Analyze
Improve
Control.
The define step defines the problem, objective, and scope of the study. In this step, the objective is defined qualitatively. The measurement step collects data, maps the current state value stream, identifies current performance indexes, and sets objectives quantitatively. The analysis step finds out the root causes of the problem and then identifies solutions and tools to solve the problems. The improvement step implements solutions to improve the process in order to achieve the objectives. The control step controls the improved process to maintain improved results.
The tools used in the steps of DMAIC procedure include value stream management, work design, SMED, line balancing, Kanban, FIFO, autonomous maintenance, visual management, design of experiments (DOE), and control charts. All tools used can be found in references (1) and (2), along with articles (3) and (4).
3. Define
The problem that the company encounters is a low percentage of on-time delivery. The average on-time delivery percentage of the company is 85% and needs to be improved. Through analysis, the cause of the problem is due to a long production lead time, low productivity, a high cycle time, or a high defect per unit rate. The research is carried out on a product family with the highest demand (5, 6). The SIPOC diagram of the product family is as in Figure 1.
SIPOC diagram.
This paper focuses on the process of back finish with six stations as in Table 1.
Stations in back finish process.
Station
Station name
W1
Yoke assembly
W2
Press cut yoke
W3
Labeling
W4
Shoulder assembly
W5
Collar assembly
W6
Collar edging
The defined stage is summarized in the project charter as in Table 2.
Project charter.
Project charter
Research
Applying Lean Six Sigma to improve the garment production process of product family X.
Problem
Low on-time delivery rate.
Objectives
Increase the on-time delivery rate by - Reducing the lead time. - Increasing productivity. - Reducing the defects of the garment production process.
Scope
X shirt, back finish process.
VOC
Increase the on-time delivery rate.
CTQ
Defect per unit, lead time, cycle time.
4. Measure
The collected data in the back finish process, including number of workers n, change over time (COT), cycle time (CT), lead time (LT), and defect per unit (DPU), are shown in Table 3.
Collected data of back finish process.
Station
n
COT (min)
CT (s)
LT (s)
DPU (%)
W1
2
65
14.5
29
4.38
W2
1
0
17
17
0
W3
1
0
17
17
0.66
W4
1
65
25
25
4.65
W5
3
65
9
27
1.66
W6
2
65
18
36
3.51
Currently, the company works 6 days/week, 1 shift/day, 8 h/shift. The average break time is 40 min/day. The average COT is 65 min/day. The available producing time is as follows:
APT=8×60-40-65=375(min)
with the demand of 1500 pcs/day. The talk time TT is calculated as follows:
TT=375×60/1500=15(s)
Work in process inventory WIP and time in process TIP are calculated and shown in Table 4.
Data of WIP and TIP.
WIP
WIP
TIP (min)
WIP before W1
50
725
WIP between W1 and W2
112
1904
WIP between W2 and W3
16
272
WIP between W3 and W4
208
5200
WIP between W4 and W5
11
99
WIP between W5 and W6
283
5094
WIP after W6
25
411
Total
13705
With the data above, the current state map of the process is as in Figure 2.
Current value stream mapping.
From the map, the current indexes, their values, and performance assessment are as in Table 5.
Current indexes.
Index
Values
Assessment
LT (min)
279
Rather high and need to be reduced
PCE (%)
0.906
Very low due to waste, need to be increased.
CT (s)
25
Not meet TTc, need to be reduced
DPU (%)
14.9
Rather high and need to be reduced
The objectives of the research are set as in Table 6.
Objectives of research.
Index
Current value
Objective
LT (min)
279
30
CT (s)
25
16
DPU (%)
14.9
10
5. Analyze5.1. Analyze the long-time problem
The long-time problems are demonstrated by long LT and long CT, exceeding the TT. The balance chart is as in Figure 3.
Current balance chart.
The process is unbalanced, leading to the increase of WIP inventory, causing long LT. Besides, CT of the process is 25 s, higher than TT, which is 15 s; the current production rate does not meet the demand rate. The causes of the problem are analyzed through the fish bone diagram as in Figure 4.
The fish bone diagram shows the causes of long-time problems.
From the diagram, the causes, solutions, and solving tools are demonstrated in Table 7.
Solutions and tools for problems.
Problems
Causes
Solutions
Tools
Long LT
Waste in working motion
Eliminate waste in motion
Work design
High WIP
Balance the line
Line balancing
WIP out of control
Control the WIP inventory
Kanban, FIFO
CT > TT
Waste in working motion
Eliminate waste in motion
Work design
Work elements not distributed evenly
Reallocate work elements among stations
Line balancing
Long COT
Reduce the COT
SMED
5.2. Analyze the high-defect problem
The causes of high-DPU problems are analyzed through the fish bone diagram as in Figure 5.
The fish bone diagram shows the causes of defect problems.
The Pareto charts for the causes are as in Figure 6.
The Pareto chart analyzes the causes of high-defect problems.
The fish bone diagram for the skip stitch shoulder effect is as in Figure 7.
The fish bone diagram shows the causes of skip stitch shoulder defects.
The fish bone diagram for the skip stitch yoke effect is as in Figure 8.
The fish bone diagram shows the causes of skip stitch yoke defects.
From the fish bone diagrams, the causes, solutions, and solving tools for skip stitch shoulder and yoke defects are presented in Table 8.
Solutions and tools for skip stitch shoulder and yoke.
Causes
Solutions
Tools
The speed of the needle and rotary hook does not match
Define a suitable speed for the needle and rotary hook
Design of experiments
The stitch’s tension level is not suitable
Adjust the stitch’s tension level
Amount of taken thread is not suitable
Adjust a suitable amount of taken thread
The distance of the needle and the rotary hook is not suitable
Adjust a suitable distance of the needle and the rotary hook
The presser foot is not tight
Check and tighten the presser foot
Autonomous maintenance
Blunt or broken hooks
Check and replace hooks
Blunt or broken needles
Check and replace needles
The needle plate got scratched
Check and use a sand-papering needle plate
Old and low-quality thread
Sort out threads depending on their expiration
Thread storage system design
The needle size is not appropriate
Select the suitable needle size
Visualization
The fish bone diagram for the broken stitch shoulder defects is as in Figure 9.
The fish bone diagram shows the causes of broken stitch shoulder defects.
From the fish bone diagrams, the causes, solutions, and solving tools for broken stitch shoulder defects are presented in Table 9.
Solutions and tools for causes of defects.
Causes
Solutions
Tools
The stitch’s tension level is not suitable
Adjust the stitch’s tension level
Design of experiments
The speed of the needle and rotary hook does not match
Define a suitable speed for the needle and rotary hook
Scratched thread guild
Check and use a sand-papering thread guild
Autonomous maintenance
Needle plate got scratched
Check and use a sand-papering needle plate
Blunt or broken needles
Check and replace needles
Assemble the needle incorrectly
Check needle installation and give installation instruction
Low-quality thread
Sort out threads depending on their expiration
Thread storage system design
The needle size is not appropriate
Select the suitable needle size
Visualization
6. Improve6.1. Quick change over SMED
Change over time needs to be reduced to lift up TT. Based on (2), SMED is used by the following steps.
Step 1: Identify time elements.
Elements and their time are collected, as in Table 10.
Time of elements.
Elements
Time (s)
Changing thread
149
Changing needle
59
Positioning needle
116
Cleaning machine
302
Changing presser foot
125
Getting required resources
598
Returning previous resources
605
Getting instruction for new order
124
Preparing required documents
55
Preparing material
783
Running first test
301
Adjusting thread tension
183
Adjusting rotary hook
329
Adjusting sewing step
176
Total
3905
Step 2: Separate external elements
The internal and external elements are separated as in Table 11.
Separate external elements.
Internal
External
Elements
Time (s)
Elements
Time (s)
Changing thread
149
Getting required resources
598
Changing needle
59
Returning previous resources
605
Positioning needle
116
Getting instruction for new order
124
Cleaning machine
302
Preparing required documents
55
Changing presser foot
125
Preparing material
783
⋯
⋯
⋯
⋯
Total
1740
Total
2165
Step 3: Convert internal elements to external elements.
The internal elements are converted to external elements as in Table 12.
Convert internal elements to external.
Internal
External
Elements
Time (s)
Elements
Time (s)
Changing thread
119
Putting thread spool on rack
30
Changing needle
59
Getting required resources
598
Positioning needle
116
Returning previous resources
605
Cleaning machine
302
Getting instruction for new order
124
Changing presser foot
125
Preparing required documents
55
Running first test
301
Preparing material
783
⋯
⋯
⋯
⋯
Total
1710
Total
2195
Step 4: Streamline remaining elements
The remaining elements are streamlined as in Table 13.
Streamline remaining elements.
Internal
External
Elements
Time (s)
Elements
Time (s)
Changing thread
119
Putting thread spool on rack
30
Changing needle
59
Getting required resources
598
Positioning needle
72
Returning previous resources
605
Cleaning machine
302
Getting instruction for new order
124
Changing presser foot
125
Preparing required documents
55
Running first test
301
Preparing material
783
Adjusting thread tension
135
Adjusting rotary hook
157
Adjusting sewing step
95
Total
1240
Total
2195
After using SMED, COT, APT, and TT change as in Table 14.
Comparison of parameters before and after applying SMED.
Before
After
COT (s)
3905
1240
APT (s)
23,095
25,760
TT (s)
14.98
16.77
The balance chart of the process after using SMED is as in Figure 10.
Balance chart after applying SMED.
The process CT is 25 s, still higher than TT. Work design needs to be used to reduce the CT of each station to meet the TT.
6.2. Work design
Collecting the working elements of each station and their processing time is as in Table 15.
Collected data of working elements.
Station
No. of workers
Element
Time (s)
CT (s)
W1
2
Yoke assembly + folding
23
14.5
Peel back
3
Stitching size label
3
W2
1
Press cut yoke
15
17
Check
1
Mark shoulder
1
W3
1
Stitch main label
6
17
Mark
3
Wasted thread cutting
2
Make button hole x 2
6
W4
1
Shoulder assembly
25
25
W5
1
Neck assembly
27
9
W6
1
Collar edging
32
18
Wasted thread cutting
4
By analyzing and removing wasted motion, reallocating the work load of the right and left hands, designing support tools, and rearranging station layout, the results are that the CT reduced while the number of workers remained unchanged, as shown in Table 16.
Working elements after the improvement.
Station
No. of workers
Element
Time (sec)
CT (sec)
W1
2
Yoke assembly + folding
12
8
Peel back
2
Stitching size label
2
W2
1
Press cut yoke
12
14
Check
1
Mark shoulder
1
W3
1
Stitch main label
6
17
Mark
3
Wasted thread cutting
2
Make button hole x 2
6
W4
1
Shoulder assembly
19
19
W5
1
Neck assembly
22.5
7.5
W6
1
Collar edging
29
16.5
Wasted thread cutting
4
The balance chart after using work design is as in Figure 11.
Balance chart after applying work design.
The process CT is now 19 s, which still exceeds the TT, 16.8 s. Line balancing is used to reallocate work load of each station to meet demand.
6.3. Line balancing
Applying the line balancing model in (3), we reallocate the work load and worker for the stations with constraints of the element order and the goal of meeting TT. The results are as in Table 17.
Working elements data.
Station
Element
Time (s)
No. of workers
CT (s)
W1
Yoke assembly + folding
12
1
16
Peel back
2
Stitching size label
2
W2
Press cut yoke
12
1
14
Check
1
Mark shoulder
1
W3
Stitch main label
6
1
15
Mark
3
Wasted thread cutting
6
W4
Make button hole x 2
2
3
14.5
Shoulder assembly
19
Neck assembly
22.5
W5
Collar edging
29
2
16.5
Wasted thread cutting
4
The balance chart after using line balancing is as in Figure 12.
Balance chart after applying line balancing.
The process CT now is 16.5 s, which meets the TT, 16.77 s. On the other hand, the number of workers has reduced to 8, the number of stations has reduced to 5, and the line balancing efficiency has increased to 92.12%. The process is more balanced, but we still have to control WIP inventory.
6.4. WIP inventory control
With reference to (1), in order to control WIP, Kanban systems would be placed between stations 2 and 3 and between stations 4 and 5. FIFO lanes would be placed between stations 1 and 2 and between stations 3 and 4.
Applying Kanban models in (1), the numbers of Kanban cards, N, are calculated, as in Table 18, with the demand D of 1500 products, a lot size Q of 5, and α = 0.1. The formula of N is as below:
N=DL(1+α)Q
Calculation of Kanban card quantity.
Stations
LT = ST + PT + QT + MT
LT
N
ST
PT
QT
MT
W2–W3
23
75
15
20
0.0050
2
W4–W5
16
82.5
15
20
0.0049
2
According to models in (4), the FIFO lane sizes, S, are calculated as in Table 19.
Calculation of FIFO lane size.
Stations
CT
Standard deviation
Processing time
S
W1–W2
W1
16
3.2
120 s
8
W2
14
2.7
W3–W4
W3
15
4.5
120 s
8
W4
14.5
3.1
6.5. Autonomous maintenance
In order to solve the causes of machine failures, machines have been restored to their basic conditions. AM activities are then established by the following steps.
Step 1: A standard checklist for routine cleaning, inspection, and lubrication activities is established as in Table 20.
Standard checklist for routine cleaning, inspection, and lubrication.
Items inspected
Item pictures
Inspection objects
Tools
Symbols Inspection Cleaning Lubrication
1/ Sewing needles
Rust
Observe
2/ Rotary hooks
Dusts Errors Lubricant Rust
Observe Lubricant Wiper Grinder brush
3/ Needle plate sets
Dusts Errors Lubricant Rust
Observe Lubricant Wiper Grinder brush
4/ Feed dogs
Dusts Errors Lubricant
Observe Lubricant Wiper Grinder brush
5/ Threads
Errors
Observe
Step 2: Establish needle and thread inspection standards as in Table 21.
Needle and thread inspection standards.
Needle inspection
Inspection
Qualified
Unqualified
Solutions
Pinhead
Pinhead’s sharpness
Normal
Curved or broken
Remove
Body
Straightness and roughness
Normal
Curved or broken or rusted
Remove
Eye
Roughness and transfiguration
Normal
Broken or rusted or transfigurated
Remove
Thread inspection
Inspection
Qualified
Unqualified
Solutions
Fraying thread
Observe and touching
Normal
Fraying thread
Mark errors and replaces
Fastening color
Touching
Normal
Bleeding or shading color
Mark errors and replaces
Step 3: Design checking notes for cleaning, inspection, and lubrication as in Table 22.
Checking note for cleaning, inspection, and lubrication.
Machine
Standard information for cleaning, inspection, and lubrication
No.
Abnormal condition
Dates
Condition after solving problems
Worker’s name
1
2
3
6.6. Visual control
In order to solve needle-size-not-appropriate problems, a visual tool has been used by matching size needles with colors. Also, we put them in the same color boxes. Depending on needle sizes, the storage capacity of each box is determined as in Table 23.
Needle sizes and storage capacity of each box.
No.
Needle size
Color
Storage capacity
1
65/9
Yellow
972
2
75/11
Red
660
3
90/14
Blue
330
4
100/16
Green
300
6.7. Thread storage system design
A low-quality thread is one of the most important causes which lead to broken stitch problems. By using 5-Why analysis, the root cause is identified, that is, the storage time of the thread. The quality of long-time-storage threads has been reduced. To solve the problem, a thread storage system has been designed to determine the storage time as in Figure 13. This system contains three types of cards:
Thread storage system.
Time card: Cards indicate the storage date of the material.
Empty card: Cards attached on empty batches.
Emergency red card: This is the only card that is attached on the longest storage time batch, which is priority for using.
The operational process is as follows:
Import materials are stored in the empty batch (with empty cards).
Then, empty cards are replaced with time cards with storage dates of the batch.
Emergency cards with the longest storage time batch.
In case of stocking out batches (with emergency cards), they are replaced with empty cards.
6.8. Design of experiments
With reference to (1), DOE is implemented in the Yoke Station to minimize skip stitch errors and in the Shoulder Station to minimize skip stitch and broken stitch errors.
6.8.1. Yoke station
The input variables are needle eye stock, rotary hook distance D, and amount of taken thread A. The output variable is the skip defect rate DR. The variables are shown in Figure 14.
Input and output variables for DOE.
The levels of factors are defined as in Table 24.
Levels of factors for DOE.
Factors
Levels
Unit
A
2
3
4
5
Level
D
1
2
3
mm
With the number of repetitions of 3, the number of experiments is 36. The data are collected. From the collected data, the ANOVA is shown as in Table 25.
Result of ANOVA of Yoke Station.
General linear model
Analysis of variance
Source
DF
Seq SS
Contribution
Adj SS
Adj MS
F-Value
P- value
A
2
0.1769
91.60%
0.1769
0.0084
995.09
0.00
D
3
0.0095
4.95%
0.0095
0.0031
35.87
0.00
AD
6
0.0045
2.34%
0.0045
0.0007
8.47
0.00
Error
24
0.0021
1.10%
0.0021
0.000089
Total
35
0.193
100.00%
S
R-sq
R-sq (adj)
PRESS
R-sq (pred)
Model summary
0.0094281
98.90%
98.39%
0.0048
97.51%
Through ANOVA analysis, both factors and their interaction have affected defect rate. The contour plot of defect rate DR according to the amount of taken thread A and the distance of needle eye stock and rotary hook D is shown in Figure 15.
Contour plot of defect rate DR.
From the plot, the distance of needle eye stock and rotary hook D should be between 1.6 mm and 2.1 mm, and the amount of thread taken A should be set up from the level of 3.5 to 4.5. The value of the distance of 2 mm and the amount of thread taken of level are chosen.
6.8.2. Shoulder station
The input variables are needle eye stock, rotary hook distance D, amount of taken thread A, and thread tension T. The output variable is the skip defect rate DR. The variables are shown in Figure 16.
Input and output variables for DOE.
The levels of input factors are defined as in Table 26.
Levels of input factors for DOE.
Factors
Levels
Unit
A
2
3
4
5
Level
D
1
2
3
mm
T
3
4
5
Level
With the number of repetitions of 2, the number of replicas is 36. The data are collected. From the collected data, the ANOVA is shown as in Table 27.
Result of ANOVA of Shoulder Station.
General linear model
Analysis of variance
Source
DF
Seq SS
Contribution
Adj SS
Adj MS
F-Value
P-value
A
3
0.0259
4.35%
0.0259
0.0086
80.84
0.000
D
2
0.3369
56.55%
0.3369
0.1684
1575.4
0.000
T
2
0.2187
36.71%
0.2187
0.1093
1022.6
0.000
AD
6
0.0048
0.82%
0.0048
0.0008
7.62
0.031
AT
6
0.0017
0.29%
0.0017
0.0002
2.65
0.004
DT
4
0.0019
0.33%
0.0019
0.0004
4.63
0.207
ADT
12
0.0018
0.30%
0.0018
0.0001
1.41
Error
36
0.0038
0.65%
0.0038
0.0001
Total
71
0.5958
100.00%
S
R-sq
R-sq (adj)
PRESS
R-sq (pred)
Model summary
0.0094281
98.90%
98.39%
0.0048
97.51%
Through ANOVA analysis, both factors and their 2-factor interaction have affected the defect rate. The interaction plot is shown in Figure 17.
Display of the interaction plot.
From the plot, the lowest defect rate would be attained with a distance of 2 mm, a tension level of 3, and a thread taken level of 3.
The results of applying DOE are shown in Table 28.
Results of applying DOE.
Yoke station
Shoulder station
Distance
2 mm
Distance
2 mm
Taken thread
Level 4
Taken thread
Level 3
Tension level
Level 3
Defect rate
< 0.005
Defect rate
0.02
6.9. Future state map
After applying Lean Six Sigma tools, the future state map is as in Figure 18.
Future state map.
From the map, the system indexes are presented as in Table 29.
Index of the system.
Index
Value
LT (min)
30.1
PCE (%)
6.73
CT (s)
16.5
DPU (%)
9.32
A comparison of the performance indexes of the current state map CSM and the future state map FSM is shown in Table 30.
Comparing the performance indexes of CSM and FSM.
Index
CFM
FSM
% Improvement
Lead time (min)
279
30.1
89.21%
PCE (%)
0.903
6.73
745.29%
Cycle time (s)
25
16.5
34%
DPU (%)
14.9
9.32
37.45%
The result shows that all the performance indexes have been improved.
7. Control
In order to maintain improved results, the control charts of important system parameters, LT, CT, and DPU, would be developed. With the current data, the control charts are constructed and operated. The LT control chart is as in Figure 19.
LT control chart.
The CT control chart is as in Figure 20.
CT control chart.
The DPU control chart is as in Figure 21.
DPU control chart.
8. Conclusion
The research has implemented Lean Six Sigma to improve a garment production process. The research methodology is based on the platform of DMAIC procedure, including five steps: define, measure, analyze, improve, and control. The tools used in the steps of DMAIC procedure include CED, Pareto diagram, value stream management, work design, SMED, line balancing, Kanban, FIFO, autonomous maintenance, visual management, DOE, and control charts.
After applying Lean Six Sigma tools, the company has reduced the production lead time by 89.21% from 279 to 30.1 min, reduced the production cycle time by 36% from 25 to 16 s, reduced the process defect rate by 37.45% from 14.9 to 9.32%, and then improved the on-time delivery rate. The results show that the research has met the objectives with the advantages of following strict and scientific methodology and using many tools; among them are strong tools like DOE.
The research still had some disadvantages. The number of collected samples used in DOE is small. The control phase has not built up standard operating processes for controlling the whole process. The improvements have not been implemented to verify the effectiveness of research. These restrictions would guide the way for future research.
Author contributions
PNN is the thesis advisor of PH, PTN, and QD. PN has developed the models for the thesis. PH, PTN, and QD have collected and analyzed the data and run the models. PNN has composed the article based on the thesis. All authors contributed to the article and approved the submitted version.
We extend our heartfelt appreciation to everyone who has contributed to the completion of this research article, especially our families, Ho Chi Minh City University of Technology (HCMUT), and the scientific community, for their invaluable support.
Conflict of interest
The research is conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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