ABSTRACT
This paper focuses on the application of Taguchi Robust Design to Lean Production System to analyse
Industrial production wastes. The purpose of this study is to present robust design approach that reduces and possibly eliminate losses to the society that arise due to variations leading to wastes in production processes.
Past literatures were reviewed and applied to case study Innoson Vehicle Manufacturing Company (IVM) for the purpose of this study. The application of the Taguchi robust design approach to IVM motor production processes revealed that over-production, excess inventory, defects, and over-processing are the major wastes that are impeding their progress and profitability. The successful application of Taguchi robust design using
Design Expert software to analyse two production periods produced varying results, and also revealed Overproduction and Excess inventory as the highest forms of wastes in the firm. In IVM’s first three months of production, over-production had the highest percentage of waste contribution of 87.96, followed by excess inventory, defects, and over-processing with 5.57, 4.77, and 1.71 respectively, although the same findings were made in the last modelling, but the values of their percentage waste contributions varied. The modelling of
IVM’s last three months of production produced better results as it yielded the highest optimization value of
90.44%, against that of the first analysis which had 82.30%. The work showed that to guard against logistics problems as well as shortfalls that may arise from the inability of their suppliers to meet up with demands, the company should strictly maintain 43 inventories, and also manufacture 43 vehicles every month, and subsequently adjust whenever there is a significant change in demand. Finally, a deduction was made that for business of production to be competitive and profitable in companies in the twenty first century auto market, steps to reduce and possibly eliminate all forms of wastes by implementing full Taguchi robust design approaches of Lean Production System is imperative.
© Emerging Academy Resources
KEYWORDS: Defects, Orthogonal Arrays, Loss Function, Wastes, Over-Production, Excess Inventory
__________________________________________________________________________________________
INTRODUCTION
Schonberger et al (2000) pointed out that Lean
Production System is an approach to manufacturing which aims at achieving greater results with fewer resources. According to Ohno (1988) the best approach of improving productivity is for manufacturers to produce only the exact amount of products they require with the minimum number of employees, he explained that efficiency is sensible only when it achieves cost reduction.
Apart from identifying and eliminating wastes, LPS enables organizations to be more profitable through the application of fewer resources to manufacture more quality products at a faster rate, thereby leading to competitive advantage and customer satisfaction.
On the other hand, the Taguchi robust design is a method for designing experiments to investigate how different parameters affect the mean and variance of a process performance characteristic that defines how well the process is functioning. According to Fraley etal (2007) Taguchi method involves reducing the variation in a process through robust design of experiments.
Phadke (1989) observed that Taguchi method is a
“scientifically disciplined mechanism for evaluating and implementing improvements in products, processes, materials, equipment, and facilities.
The Philosophy of Taguchi Robust Design
The philosophy of Taguchi is summarized by his quality loss function (see figure 1). The function states that any deviation from the target value leads to a quadratic loss in quality or customer satisfaction.
Mathematically, the function may be expressed as:
45

Research Journal in Engineering and Applied Sciences 1(1):45-50
Application of Taguchi Robust Design as Optimized Lean Production System in Manufacturing Companies
Figure 1:Taguchi's quality loss function.
(Source: http://www.weibull.com/DOEWeb/taguchis_robust_p arameter_design_method.htm) value of , represents the quality loss and is a constant.
Taguchi (1987), Achanga and Okogbaa (2006),
Allen, Robinson, and Stewart (2001), Albino and
Okogba (2001), Box, Bisgaard, and Fung (1988) also worked on Lean Production System and Taguchi
Robust Design.
Application of Taguchi Robust Design to IVM
The seven wastes that LPS aims to eliminate which bedevils manufacturing companies and reduces their profitability and throughput are Transport, Inventory,
Movement, Waiting, Over-production, Overprocessing, and Defects. However, after a detailed research on IVM manufacturing activities and processes, it was observed that the company’s successes are being impeded by four wastes: Defects
(A), excess inventory (B), over-production(C), and over-processing (D). where represents the performance parameter of the system, represents the target or the nominal
Table 1: Six Months Production Chart of IVM
Sold Vehicles Defects Months Available
Inventory
Dec. 2010
Jan. 2011
Feb. 2011
60
59
45
Mar. 2011 47
April 2011 44
May 2011 45
June 2011 30
Manuf.
Vehicles
50
52
41
43
42
42
28
31
37
40
38
39
40
41
2
1
1
0
7
3
1
Excess
Inventory
4
2
3
2
10
7
4
Over
Production
9
3
2
0
19
15
7
Over Processing
3
0
1
4
5
1
4 production processes of Innoson Vehicle
Manufacturing Company (IVM). The design expert
1
2
3
4
5
6
7
8
9
Analysis of the IVM’s First Three Months
Production Processes
The Design Expert (DX8) software is applied to model the first three months (December 2010,
January 2011, and February 2011) wastes in the
Table 2: The Design layout
Standard Run Factor 1
A:Defects
(No. of vehicles)
Factor 2
B:Excess
Inventory
(No.of vehicles)
5
3
8
4
2
6
7
9
1
7
7
7
3
3
3
1
1
1
10
7
4
10
7
4
10
7
4
19
15
7
15
7
19
7 and the Taguchi orthogonal array,
19
15
Factor 4
D:Overprocessing
(No. vehicles)
5
1
4
4
5
1
1
4
5
74.6
60.3
49.7
62.9
61.4
77.8
50.1
83.5
62.3
is used with the values of table 1 to generate the design layout of Table 2.
Factor 3
C:Overproduction
(No.of vehicles) of
Response 1
Optimize
(Cost of Wastes)
Half-Normal Plot of Wastes in IVM
Figure 2 can be used to choose significant effects.
Large and significant values appear at the upper section of the graph. The effects plot of figure 1 revealed that over-production (C) and Excess
Inventory (B) are the two major wastes that affects
Innoson Vehicle Manufacturing Company, followed by Defects (A) and Over-processing (D).
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Research Journal in Engineering and Applied Sciences 1(1):45-50
Application of Taguchi Robust Design as Optimized Lean Production System in Manufacturing Companies
Design- Expert® Sof tw ar e
Optimize
A : Def ects
B: Excess Inventory
C: Over -pr oduction
D: Over -pr ocessing
95
90
80
70
50
30
20
10
0
0.00
Half-Normal Plot
B
A
2.47
4.95
7.42
C
9.89
The Effects List gives the calculated values of the sum of squares and the mean squares of all the four wastes in Innoson Vehicle Manufacturing Company in tabular form. Again the values validated the earlier findings that Over-production is the most significant waste, followed by Excess Inventory, Defects, and
Over-processing. The effects list also provided the percentage waste contributions of the various identified wastes, which also laid credence to the earlier results. As can be seen on Table 3, Overproduction had the highest waste contribution of
87.95, followed by Excess inventory, Defects, and
Over-processing, with 5.57, 4.77, and 1.71 respectively.
| Normal Effect|
Figure 2: Half-Normal plot showing the major wastes in IVM
Table 3: The Effects List
Wastes Df
A – Defects 2
B – Excess Inventory 2
C – Over-production 2
D – Over-processing 2
Sum of Squares
52.49
61.31
967.86
18.81
Mean Square
26.24
30.65
483.93
9.40
% Contribution
4.76952
5.571
87.9595
1.70898
Analysis of Variance (ANOVA)
The Analysis of Variance (ANOVA) is a very important aspect of the modelling as it indicated that the model is significant.
Table 4: Analysis of Variance Results
Source
Model
B – Excess Inventory
C – Over-production
Residual
Cor Total
Sum
Squares
1029.17
61.31
967.86
71.29
1100.46 of Df
4
2
2
4
8
Mean Square
257.29
30.65
483.93
17.82
As Over-production, and Excess Inventory are the only wastes that appeared on the table, it means that they are the only significant wastes that affects IVM heavily. This implies that the impacts of Defects and
Over-processing are negligible.
Model Graph And Interaction Effects
Design-Expert® Software
Optim ize
Optim ize = 82.3
LSD: 12.3556
Design Points
X1 = B: Excess Inventory = 7
Actual Factors
A: Defects = 7
C: Over-production = 19
D: Over-process ing = 5
90
80
70
60
50
40
10
One Factor
7
B: Excess Inventory
4
F value
14.44
1.72
27.15 p-value
Prob > F
0.0120
0.2891
0.0047
Significant
Figure 3 shows that when excess inventory is set at 7 with other factors as shown graphically the optimize value is 82.3%. Although the value is high, but the company should persevere on its continuous improvement, as the aim of LPS is total elimination of all wastes that are inherent in production processes and hence achieve an optimization value of 100%.
The factorial analysis based on two factors B and C and on two levels of factors (high and low) using design expert gave a predictive model for optimizing the performance characteristics as:
Optimize = +64.73-2.2 B [1] +3.67B [2]
+13.9C [1]-2.90C [2] (2)
As the aim of Optimum Manufacturing is the detection of all wastes inherent in manufacturing processes and subsequent elimination, having confirmed that the two most significant wastes in
Innoson Vehicle Manufacturing Company are Overproduction and Excess inventory their optimization points have been set for the different values of the wastes as can be seen in the appendix
Figure 3: Model Graph for Excess Inventory
47

Research Journal in Engineering and Applied Sciences 1(1):45-50
Application of Taguchi Robust Design as Optimized Lean Production System in Manufacturing Companies
The final equations in terms of actual factors gives the optimization values of the different significant wastes, it implies that while all efforts are geared towards waste reduction, that when Excess inventory is 10 and Over-production is 19 that an optimization value of +76.43333 was achieved. And also when
Excess Inventory is 4 and Over-production is 7 an optimization value of +52.26667 was also achieved.
Analysis of IVM’s Last Three Months of
Production
Having obtained the above results from the values of the first three months of production in IVM with
Taguchi robust design, the analysis revealed that the vehicle manufacturing firm must reduce the values of wastes inherent in their production processes if they hope to remain relevant in auto manufacturing, and also compete favourably with foreign companies.
This therefore enabled the authors to advise the
Production Manager and the entire management of the company on the need to reduce their inventory and over-production. This made them to overhaul their entire production mechanisms as well as their supply chain, which led to a considerable reduction in wastes as could be seen in Table 5. The following analysis which is aimed at comparing the levels of wastes with the earlier months is based on the production outputs of the months of April, May, and
June 2011.
Table 5: IVM’s Last Three Months Production Chart
Months Defects Excess
Inventory
Over
Production
Over
Processing
5
6
7
8
9
1
2
3
4
April
2011
May
2011
June
2011
Standard
1
1.1
0
2
3
2.1
3
2
0
0
1
4
Entering the values of the four wastes of the above three months as inputs, the Design Expert 8.0 software evaluated the design layout as shown in
Table 6.
Table 6: The Design Layout
Run
3
7
5
9
4
1
2
8
6
1
1
1
1.1
1.1
1.1
0
0
0
Factor
1
A:Defe cts
(No. of vehicle s)
2
3
2.1
2
3
2.1
2
3
2.1
Factor 2
B:Excess
Inventor y (No. of vehicles)
3
0
3
2
3
2
0
2
0
Factor 3
C:Overproductio n
(No. of vehicles)
1
1
4
0
0
1
4
4
0
Factor 4
D:Overprocessin g
(No. of vehicles)
Respons e 1
Optimize
(Cost of
Wastes)
74.6
60.3
49.7
62.9
61.4
77.8
50.1
83.5
62.3
Just as in the first IVM analysis, the half normal plot
(Figure 4) revealed that Over-production (C) and
Excess Inventory (B) are the two major wastes that affects Innoson Vehicle Manufacturing Company, followed by Defects (A) and Over-processing (D).
48
Desi gn-Expert® Software
Opt i mi ze
A: Defects
B: E xcess Inven tory
C: O ver-producti on
D: O ver-processi ng
95
90
80
70
50
30
20
10
0
Half-Normal Plot
B
A
C
0.00
1.41
2.83
4.24
5.65
7.07
8.48
9.89
|Normal Eff ect|
Figure 4: Half-Normal plot showing the major wastes
The effects list of the last three months of the period under review in IVM provided the sum of squares of the four identified wastes, their mean squares, and their percentage of waste contributions. As could be observed from the Table, Over-processing has a percentage waste contribution of 10.68%, followed by Defect, Excess inventory, and Over-production with 15.72%, 33.44%, and 40.16% respectively.
Although the various percentage of waste contributions differ from those obtained during the first three months of production in IVM, the results validated the earlier results, as Over-production has the highest value, while Over-processing maintained the lowest.
Table 7: The Effects List
Term df
Defects (A) 2
Excess
Inventory
Over-
2
2 production
Overprocessing
2
Sum of
Squares
172.99
367.98
443.63
117.53
Mean
Square
86.50
183.99
221.82
58.76
%
Contribution
15.72
33.44
40.16
10.68
Analysis of Variance
The analysis of variance indicated that the model is significant, and also calculated the sum of squares, and the mean square.
Table 8: Analysis of Variance
Source
Model
Sum of
Squares
Df Mean
Square
F value
pvalue
Prob
>F
1029.17
4
367.98
257.29 14.44 0.0120
2 183.99 1.72 0.2891
Significant
B – Excess
Inventory
C – Overproduction
443.63 2 221.82
27.15
0.0047
Residual 68.43
Cor Total 880.04
4 16.42
8

Research Journal in Engineering and Applied Sciences 1(1):45-50
Application of Taguchi Robust Design as Optimized Lean Production System in Manufacturing Companies
The appearance of only Over-production and Excess inventory on the Table shows that they are the only two wastes in IVM that are significant, also the
Model F-value of 14.44 implies the model is significant.
The Design Expert software gave a predictive model for optimizing the performance characteristics as:
The highest optimization value of 90.442 was obtained when Excess inventory and Over-production has values of 3. This implies that for best optimization that the company should maintain an
Excess inventory of 3, as well as aim to over-produce just 3 vehicles in their every month of production, this will enable them to guard against logistics problems as well as shortfalls that may arise from the inability of their suppliers to meet up with demands.
DISCUSSION OF RESULTS
Although Innoson Vehicle Manufacturing Company claimed that they were implementing Lean
Production System, their production charts in Table 1 proved otherwise as the wastes in their first three months of production were very high. The application of robust design revealed that the two most prevalent and significant wastes in the company are Overproduction and Excess inventory; this therefore enabled the author to advise them to drastically reduce the wastes by overhauling their production processes.
As LPS is a journey of continuous improvement,
IVM immediately began to reduce the wastes from
March 2011, and was able to record a considerable improvement in April, May, and June 2011. The application of robust design and the analysis of the last three months yielded better output as it led to the best optimization equation and optimization parameter setting.
The major similarity in the two factorial models is that they identified the same rate of occurrence of all the wastes in IVM as Over-production remained the most significant waste, followed by Excess inventory,
Defects, and Over-processing. However, although
Over-production and Over-processing maintained the highest and lowest values of waste percentage contributions respectively, their values varied as
Over-production had 87.96% in the first model and
40.16 in the last model, while Over-processing had
1.71% and 10.68% in the first and last models respectively.
The two models of factorial analysis were rated significant by the software, and also rated Overproduction and Excess inventory as the only significant wastes in the company, but as could be seen in Tables 3 and 7, they had different values for the wastes’ sum of squares, and mean squares.
Two different equations in terms of coded factors were obtained from the two different analyses, but most importantly the analysis of the company’s last three months of production produced the highest optimization value of 90.44%, against that of the first analysis which had 82.30% hence validating the need for application of the methodology of this study.
CONCLUSION
The following conclusions are drawn:
1.
More prevalent wastes and their rate of contributions in the IVM are identified.
2.
The major aim of Taguchi robust design to reduce and possibly eliminate loss to the society that arise due to variations and wastes in production processes are achieved.
3.
Optimum parameter setting of wastes for enhanced industrial income is establishes.
4.
Finally, a deduction was made that for business of production to be competitive and profitable in the companies in the twenty first century auto market, steps to reduce and possibly eliminate all forms of wastes by implementing full Taguchi robust design approaches of Lean Production
System is imperative.
REFERENCES
Achanga, P. And Okogbaa, G. (2006). Critical
Success Factors for Lean Implementation within
SMEs. Journal of Manufacturing Technology
Management, Vol. 17, Iss. 4, pg. 460 – 471
Albino, V. And Okogbaa, G. (2001). Computer-
Aided Design, Engineering, & Manufacturing:
Systems Techniques & Applications, Optimization
Methods For Manufacturing. Vol. IV, CRC Press
LLC,
Allen, J. Robinson, C. and Stewart, D. (2001). Lean
Manufacturing: A Plant Floor Guide. Society of
Manufacturing Engineers, Dearborn, Michigan
Box, G., Bisgaard, S., and Fung, C. (1988). Quality
Practices in Japan. Quality Progress Publications,
Okinawa, Japan
Fraley, S., Joel, C., and Falk, J. (2011). Design of
Experiments via Taguchi Methods: Orthogonal arrays. Retrieved October 22, 2011, from http://www.scribd.com/doc/36563150/Design-of-
Experiments-via-Taguchi-Methods-Using-
Orthogonal-Arrays
Ohno, T. (1998). Toyota Production System: Beyond
Large Scale Production. Productivity Press, New
York
Phadke, M. (1989). Quality Engineering Using
Robust Design. Prentice Hall, UK
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Research Journal in Engineering and Applied Sciences 1(1):45-50
Application of Taguchi Robust Design as Optimized Lean Production System in Manufacturing Companies
Schonberger, D., Philip, K., Matthew, R. And
Roberts C. (2000). Technology Management handbook. CRC Press LLC, pg. 81
Taguchi, G. (1987). The Taguchi Approach to
Parameter Design. Quality Progress Publications,
Okinawa, Japan
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Retrieved October 18, 2011, from http://www.weibull.com/DOEWeb/taguchis_robust_p arameter_design_method.htm
APPENDIX
Final Equation in Terms of Actual Factors:
Excess Inventory 10
Over-production
Optimize=+76.43333
19
Excess Inventory
Over-production
Optimize=+59.63333
10
15
Excess Inventory 10
Over-production 7
Optimize=+51.53333
Excess Inventory
Over-production
Optimize=+82.30000
4
19
Excess Inventory 7
Over-production 15
Optimize=+65.50000
Excess Inventory
Over-production
Optimize=+57.40000
Excess Inventory
7
7
7
Over-production
Optimize=+77.16667
19
Excess Inventory 4
Over-production 15
Optimize=+60.36667
Excess Inventory 4
Over-production 7
Optimize=+52.26667
Final Equation in Terms of Actual Factors:
Excess Inventory
Over-production
Optimize=+79.67777
Excess Inventory
Over-production
Optimize=+64.66668
2
3
2
2
Excess Inventory 2
Over-production 0
Optimize=+49.73333
Excess Inventory
Over-production
Optimize=+90.44200
Excess Inventory
Over-production
Optimize=+69.40000
3
3
3
2
50
Excess Inventory 3
Over-production 0
Optimize=+57.35555
Excess Inventory 2.1
Over-production 3
Optimize=+80.43333
Excess Inventory 2.1
Over-production 2
Optimize=+60.36667
Excess Inventory 2.1
Over-production 0
Optimize=+49.97777