TAGUCHI’S QUALITY IMPROVEMENT ANALYSIS OF THE SME BREAD MANUFACTURING.
GLO BAL EN GIN EERS & TECH N O LO GISTS REVIEW
www.getview.org
TAGUCHI’S QUALITY IMPROVEMENT ANALYSIS OF THE SME
BREAD MANUFACTURING
HAERYIP SIHOMBING1, HAFIZ2, M.K., YUHAZRI 3, M.Y., KANNAN4, R.
1, 3
Faculty of Manufacturing Engineering
Universiti Teknikal Malaysia Melaka
Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, MALAYSIA
1
iphaery@utem.edu.my
3
yuhazri@utem.edu.my
2, 4
Faculty of Mechanical Engineering
Politeknik Merlimau Melaka
Pejabat Pos Merlimau, Melaka MALAYSIA
A BSTRA CT
The purpose of this study is to optimize the manufacturing process of the SME bread product using
Taguchi method. The study is focused on the quality problems occurred as the outcomes of the
process (quality of the bun produced) related to controllable factors of products design identified
(machine temperature and length duration time) for the improvement required. By implementing
the technique of analysis of variance (ANOVA), the composition of the controlled parameters, such
as machine temperatures and duration times, is therefore determined and constructed into
Orthogonal Array (OA) of L9(33) related to what the setting parameters produces the optimal
output. To improve the quality of the manufactured product, the setting of parameter recommended
in this study is A 2B2C2 or A 1B2C2 {(Low or High) ∩ 10minutes ∩ 200O C)}.
Keyw ords: Taguchi method, Bread Manufacture, Loss Function, S/N Ratio.
1.0
I NTRODUCTION
Accor ding to Adam et al ., (1986), quality is the degr ee to w hich a pr oduct or ser vice confor ms to a set of
pr edet er mined standards, especially to the character istics that deter mine its value in the mar ket and its
per for mance of the function to w hich it was designed. Based on this r eason, quality is the r esult of a compar ison
betw een w hat was r equir ed and pr ovided w hich it can be differ ent things to differ ent people and taken into
account of both objective and subjective inter pr etations against the needs and expectations of customer s
(Char ter ed Quality Institute, 2012). When it is r elated to pr oducts, Meir ovich et al ., (2007) under lined that the
quality is, in facts, the degr ee to w hich a pr oduct or ser vice’s design (specification) fits to customer needs and
expectations. While the confor mance to quality is r elated to the degr ee of how match betw een the featur es of a
specific pr oduct (ser vice) to its specification. This is w hy the customer s’ expectation involves the specification as
a quality of pr oduct or ser vice. And also the compar ison against the competitor s in the mar ketplace as w hich
Pr at and Tor t (1989) in their study discusses w hy the pet food manufactur ing companies need to implement the
quality impr ovement as cr ucial task in or der to sur vive in today’s mar ketplace.
Based on this consider ation, if the impr ovement tasks and pr ogr ams made ar e mainly in r educing the
var iability as w ell as other failur e of pr oducts thr ough the inspection in or der to maintain economic viability,
the facts that by such contr olling conducted to the quality of pr oduct is, cer tainly, r isked w ith the cost consumed
(Jur ans’ appr aisal and failur e cost). This is due to since a pr oduct must be efficient to manufactur e and
insensitive to variability on the factor y floor and in the field, the business or company should ther efor e focus
their concer n thr ough the development of ext ensive car efully planned exper imentation at the design stage of
pr oducts or pr ocesses. On this, by employing design of exper iments (DOE) of the quality engineer ing methods
pr oposed by Dr . Taguchi as one of the most impor tant statistical tools of TQM for designing high quality,
accor ding to Unal and Dean (1991), will minimizing the pr ocess var iation and r educing r ew or k, scrap and the
need for inspection systems at r educed cost.
In addition, although Design of Exper iments (DOE) is the method r elated to exper iment in ter ms to define
the systematic pr ocedur e car r ied out under contr olled conditions in or der to discover an unknow n effect,
Genichi Taguchi developed this concept to impr ove the quality of manufactur ed goods based on the concept of
" Unifor mity ar ound a tar get value." Related to this per spectives, Antony (2002) r epor ted about the successful
application of Taguchi method by many US and Eur opean manufactur er s over the last 15 year s in or der to
impr ove their pr oduct quality and pr ocess per for mance. This is due Taguchi concer t off-line quality contr ol
based on an under standing of the loss function, system design, parameter design and toler ance design, that
-
G. L. O. B. A. L E. N. G. I . N. E. E. R. S. . & . . T . E. C. H. N. O. L. O. G. I. S. T . S R. E. V. I. E. W
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enables the pr oduct development pr ocess is immediately pr oduces a quality pr oduct or pr ocess at the low est
possible cost and thus the online quality contr ol function dur ing manufactur ing and ser vice after the sale effor t
ar e ther efor e r educed.
In facts, in the Small and Medium Enter pr ise (SME) food manufacturing w her e the manufactur ing pr ocess
r uns in the tr aditional w ays (Dhungana, 2003) and (Zulkifly et al ., 2000), the var ieties quality of pr oducts ar e
always occur r ed. Ther e ar e also found the limited implementation of management system and quality contr ol
tow ar ds the pr oduct to meet the specification, beside the lack of skill and know ledge oper ator s. In facts, the
pr oblems occur r ed and found in this study ar e mostly due to setting the par ameter of the machine to meet the
specification of the end pr oduct. Since the company r uns their pr oduction system solely depend on the visual
inspection method to confirm the specification of end pr oduct (decision to finish the pr ocess of the br ead
cooking), then this condition effect to inconsistently quality of food pr oducts. Some of them ar e even over cooked
or incomplete cook. Based on this r eason, this study is car r ied to identify the par ameter factor s that affects to
the quality of food manufactur ing pr ocess and to deter mine the optimal par ameter factor s of food
manufacturing pr ocess by using Taguchi Method.
2.0
LITERATURE REVIEW
2.1
DOE Using Taguchi
Sukthomya and Tannock (2005) stated that Taguchi (1986) intr oduced a simplified and modified DOE
appr oach w hich has been w idely adopted by industr y. This is due to Taguchi method is a technique for
designing and per for ming the exper iments to investigate the pr ocesses (w her e the output depends on
many factor s such as var iables and inputs), w ithout having to slow and uneconomically r un the pr ocess
caused by all possible of combinations values (Dobr zañski et al., 2007). Taguchi for mulated both a
philosophy and methodology for the pr ocess of quality impr ovement that depends on statistical concepts,
especially statistically designed exper iment. On this, the concept design is consider ed to be the fir st phase
of the design str ategy. This phase gather s the technical know ledge and exper iences to help the designer to
select the most suitable one for the intended pr oduct (Bhar ti and Khan, 2010).
Refer to Amer ican Supplier Institute (1992), Taguchi developed an appr oach to design of
exper iments that addr essed the r ealities of industr ial design, pr oductivity and cost effectiveness based on
the r elationship betw een technology, var iation, cost and savings. Since the classical exper imental design
methods ar e time consuming w her e the exper iments must be per for med w hen the number of contr ol
factor s is high, Taguchi methods use a special design of or thogonal ar r ays to study the entir e facto space
w ith only a small number of exper iments (Bhar ti and Khan, 2010).
The development of Taguchi’s method is based on or t hogonal ar r ays (OA) r elated to statistics
(Weng et al., 2007). Or thogonal Ar r ay (OA) is one par t of the exper imental gr oup w ho only use par t of the
state total, w her e this section per haps only half, quar ter or an eighth of a full factor ial exper iment. An
optimal exper imental design should pr ovide maximum amount of infor mation by means of minimum
exper imental tr ials (Bor an and Hocalar , 2007). Chong et al ., (2009) stated that Or thogonal Ar r ay (OA) is a
statistical DOE that is mostly applied in the manufactur ing quality contr ol and r apid softw ar e testing for
faults detection.
OA is the matr ix of number s ar r anged in columns and r ow s. The Taguchi method employs a gener ic
signal-to-noise (S/ N) r atio to quantify the pr esent var iation. These S/ N r atios ar e meant to be used as
measur es of the effect of noise factor s on per for mance char acter istics. S/ N r atios take into account both
amount of variability in the r esponse data and closeness of the aver age r esponse to tar get. Ther e ar e
sever al S/ N r atios available depending on type of char acter istics: smaller is better , nominal is best (NB)
and lar ger is better . Refer to Cheng (2001), Taguchi has tabulated 18 basic or thogonal ar r ays that know n
as the standar d or thogonal ar r ays. Each or thogonal ar r ay uses a notation that indicates its number of
r ow s and columns, as w ell as the number of level in each column.
2.2
Manufacturing System
Today, the ear liest for ms of br ead w ould have been ver y differ ent fr om how w e can see in industr ialized
countr ies and it w ould pr obably be closest in char acter to the moder n flat br eads of the Middle East
(Cauvain, 1999). The common pr ocess of pr oducing br eads is mainly based on 3 steps: dough for mation,
fer mentation, and baking.
2.2.1 Dough formation
This pr ocess r equir es the mixing of flour , water , yeast, salt and other ingr edients. On this pr ocess,
the changes ar e associated with the for mation of gluten, w hich r equir es both the hydr ation of the
pr oteins in the flour and the application of ener gy thr ough the pr ocess of kneading. Accor ding to
© 2012 GETview Limit ed. All right s reserved
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Global Engineers & Technologist s Review , Vol.2 No.9
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Cauvain (1999), kneading is the development of gluten str uctur e in the dough thr ough the
application of ener gy dur ing mixing. Mixing the dough pr ovides tw o functions: homogeneous
distr ibution of components, and development of the glut en matr ix. Gluten is the skelet on of w heatflour dough and r esponsible for gas r etention w hich pr ovides the pr oduction of light loaf of br ead.
Mixing time var ies with the flour , dough temper atur e, dough consistency, and mixer .
2.2.2 Fermentation
Dur ation of the fer mentation pr ocess depends on the amount and the quality of the ingr edients.
Yeast is the most impor tant ingr edient that affects the fer mentation pr ocess. The r elationship
betw een dough development time and yeast level pr obably comes fr om the contr ibution that
enzymes pr esent in the yeast cells, viable or dead. They modify the pr otein str uctur es, w hich ar e
for ming w ith incr easing dough r esting time. Flour also contains enzymes, w hich can contr ibute to
dough development. Since the mechanism for dough development in fer mentation depends on
yeast activity, the temper atur e of the dough play a major r ole in deter mining the time at w hich full
development is achieved (Cauvain, 1999).
2.2.3 Baking process
Dur ing baking sever al changes take place both in the cr umb and cr ust. The br ow ning r eaction that
involves both ‘ car amelization’ of sugar s and ‘pr oteinaceousmater ials’ impar ts a deep color to the
cr ust. Ther mal decomposition of star ch and for mation of ‘dextr ins’ contr ibute to cr ust br ightness.
This is accompanied by for mation of flavor and taste components. At the same time changes take
place inside the loaf of br ead. At ear ly stages, the incr ease in temper atur e w ill enhances enzymatic
activity and gr ow th of yeast and bacter ia. At about 50OC-60 OC, the yeast and bacter ia ar e killed.
While on beyond temper atur e star ch gelatinizes, pr oteins coagulate, and enzymes ar e inactivated.
Steam is for med at ar ound 100ºC, at w hich the final volume and cr umb textur e of the br ead ar e set.
The inside of the loaf does not exceed 100ºC; how ever , in the cr ust much higher temper atur es ar e
attained. In the t emper atur e r ange of 100-150ºC, light and br ow n dextr in ar e for med w hich ar e
follow ed by car amel (Pomer anz, 2004).The br ead is allow ed to cool at r oom temper atur e and
r eady for packaging. Figur e 1 show the sequence pr oduction pr ocess in manufactur ing br ead.
Process 1
Mixing br eaded
Process 2
*Dough cutter
Process 3
Steam br ead i n
fer menti ng box
Process 4
Cook in I nfr ar ed food oven
Figure 1 : Pr ocess Machi ne (Flow )
3.0
RESULTS AND DISCUSSION
The study is car r ied out to identify and analyze the manufactur e pr oducts in the food manufactur ing industr y.
The industr ial visit is conducted to the factor y for clear ly under standing about the food pr ocess manufactur ing;
technically and pr actically, and to deeply know ing about the pr oblems and factor s affecting to quality. The
pur pose of the industr ial visit is to obser ve and under stand the cur r ent pr ocess of manufactur ing system,
pr oduct quality, and quality contr ol system, w hile the infor mation of food manufactur ing pr ocess flow and the
quality per for mance is gather ed for analysis.
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Global Engineers & Technologist s Review , Vol.2 No.9
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3.1
Study on Current Manufacturing System
This study is conducted on the cur r ent food manufactur ing system against the char acter istic behavior of
the selected contr ollable par ameter s applied in the food manufacturing pr oduction line pr ocess. The flow
pr ocess and the steps of manufactur ing as descr ibed below .
1. The fir st step is the mixi ng of the ingr edi ents to make the
dough of t he br ead.
1. Pr ocess 1: Mixing br eaded
•
This pr ocess evenly di str ibutes the i ngr edient s, develop
the glut en and to i niti ate fer ment ation
•
•
The machi ne has 3 alter native speeds scale.
•
To change the speeds, fir st tur n off t he motor , then move
the shifter handle to the desir ed speed.
Speed number 1 for slow speed is for heavy mixtur es. I n
many mi xing oper ation, i t is customar y to star t on speed
number 1 and t hen change to a hi gher speed as t he w or k
pr ogr esses.
•
2. Then, t he pr epar ed dough is divided i nto sever al amounts by
usi ng dough cutter machine.
3. The dough ar e then w ill ar r anged on the tr ay. The
composition of the dough i s pr oper ly ar r anged to make sur e
the dough w ill baked compl etely l ater .
3. The dough w hich has been pr epar ed on the tr ay w ill then on
hold in the fer ment ing box to ensur e t he dough is st eamed to
mai ntain the dough.
4. Next, t he br ead is baki ng i n i nfr ar ed food oven at sever al
times accor di ng to the setti ng.
5. The br ead i s allow ed to cool in r oom temper at ur e and
packagi ng for sell.
Speed number 3 for fast speed i s for li ght w or k as
w hipping cr eam, beati ng eggs and mixing t hi n batter s.
2. Pr ocess 2: Dough cutt er
•
This pr ocess is conti nued fr om the fir st pr ocess w hich
divide the dough to sever al quant ity that needed w it h set
up at t he machine button.
•
The capacity for one cycle pr ocess is 112 pcs. per min.
3. Pr ocess 3: Br ead fer menti ng box
•
The pr epar ed dough is tr ansfer r ed to the fer menting box.
This is r equir ed to keep t he bun i n good condition, fr esh
befor e tr ansfer to oven.
4. Pr ocess 4: I nfr ar ed food oven
•
This gas oven pr ovide platfor m type r evolving hot air oven
for cook pr ocess. The sur r ound hot air i n the oven i s
suppor t the cook pr ocess for sever al tr ays of buns i n the
oven.
To deter mine the r elationship betw een the quality of manufactur ed pr oduct and visual contr ol
par ameter of pr oduction line, the data collected is analyzed using Minitab softwar e. By car r ying out the
exper iment r elated to the pr ocess of contr ol par ameter design, the quantity, and location of r eject items
ar e identified and r ecor ded, w her e a step plan used is to analyze r esults and implement solutions (Antony
et. al ., 2001).
3.2
Experiment Flow Process
The exper iment is car r ied out w ith sever al of the pr ocess w hich beginning of mixing flour until finishes
cooking. The pr ocess flow of the pr oduction of the br ead ar e illustr ate in Table 1.
3.3
Bread I nspection
The declar ation of the br ead inspection is based on the visual inspection on the sur face of the br ead. The
inspection is doing manually for the appearance of the final pr oduct to confirmed the br ead is acceptable
and can be thr ough the next pr ocess, packaging. The color of the br ead (complete cook) and sur face peak
of final pr oduct ar e the measur able quality char acter istics of this pr oject. Ther e ar e other char acter istics
that can be measur ed, but both of the above quality character istics ar e consider ed for the most impor tant
cr iter ia of mar ket pr oducts by consumer s. The table 2 show s the accepted and r ejects condition for
quality char acteristics of the br ead.
Values of quality character istics (color of br ead sur face and sur face peak of pr oducts) ar e affected
by the conditions dur ing pr oduction pr ocess of br ead pr oducts. Diver sifying values of baking dur ation
and baking temper atur e of samples and the speed mixing type of ingr edients of samples significantly
affected quality character istics. For this exper iment consider ed, the above conditions ar e the factor s of
the exper imental pr ocedur e. Since the pur pose of this exper iment is the optimization of br ead pr oduct
pr ocess, any non-linear r elationship w as taken into consider ation. Her e, non-linear effects ar e to be
studied and it is necessar y to choose mor e than tw o levels for each factor s (Antony et al ., 2001). The
contr ol factor s and levels selected ar e show n in the table 3.
The exper imental pr ocedur e is car r ied out in a contr olled envir onment, as the exper iment
conditions r emained stable. This ensur es that ther e is no impact on r esults fr om exter nal factor s and the
only conditions dur ing the exper iment var ied in their values ar e thr ee contr ol factor s, that is, mixing type,
baking dur ation and baking temper atur e of pr ocess var ied in each exper iment tr ial. The quantities of
© 2012 GETview Limit ed. All right s reserved
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Global Engineers & Technologist s Review , Vol.2 No.9
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ingr edients r emained stable for all samples, based on the r ecipe that used alw ays the industr y for
pr oduction of par ticular br ead pr oduct. Figur e 2 show s the equipment used t o conduct the exper iment.
Table 1 : Pr ocess flow of the pr oduction of the br ead
No
1
Process
Mixing
flour
2
Picture
Descr iption
Mixing
i nclude
flour , egg and
w ater
No
6
Process
Place in the
mold
Picture
Descr iption
The dough i s placed
on the mold and
compr ess to fill the
mold.
Select
mixing
type
Mixing
type
divide
by
3
speed:
i.1 = Low
ii.2 = Medium
iii.3 = High
7
Divide the
dough
The
mold
is
inser t ing i nto dough
divider machi ne and
the
dough
w ill
ser ved follow the
w eight t hat measur e
pr eviously.
3
Mixing
dough
The i ngr edient is
mixi ng unti l the
ser ved as dough
8
Rub
margar ine
The mar gar i ne is
r ub on the tr ay
sur face to avoid the
br ead st ick w ith
tr ay sur face.
4
Rest
dough
The dough i s put
on the table for
10 mi nute and
cover by plastic
to
avoid
contamination.
9
Arrange on
tray and
place dough
in
fermenting
box
The dough i s placed
on the fer menti ng
box ar ound 1 hour
for steam pr ocess.
5
Scales the
dough
The dough is
scales
for
measur e
t he
appr opr i ate
w eight for divi de
the dough.
10
Cook in the
infrared
food oven
The br ead is cook in
the oven follow the
set up that ar r ange.
Table 2: Quali ty Char acter i stics Condi tion of the Br ead
Position
Unr ipe
Complete Cook
Overcook
Defect
TOP
BOTTOM
N/ A
Table 3 : Contr ol factor s and l evels of the exper i mental design
Factor
Labelled
Level
1
Level
2
Level
3
Mixing Type (rpm)
A
1
2
3
Baking Duration (min)
B
7
10
13
BakingTemperature ( oC)
C
195
200
205
© 2012 GETview Limit ed. All right s reserved
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Global Engineers & Technologist s Review , Vol.2 No.9
3.4
(2012)
The L9 Orthogonal Array Experiments
An Or thogonal Ar r ay (OA) is a matr ix of number s ar r anged in r ow s and columns. Each r ow r epr esents the
levels of the selected factor s of a given exper iment and each column r epr esents a specific factor s w hose
effects on the output. An OA help an exper iments plans easily constr ucted by assigning factor s to columns
of the OA, then matching the differ ent symbols of columns with the differ ent factor levels. OA have the
balanced pr oper ty that ever y factor setting appear the same number of times for ever y setting of all other
factor s in the exper iments.
In this r esear ch, the L9 (33) OA as show n in Table 4 is selected for the contr ollable factor s since it is
the most efficient or thogonal design to accommodate thr ee factor s at thr ee levels. The L9(3 3) ar r ay
specifies nine exper imental r un to be per for med. This means that nine exper imental tr ials w ith differ ent
combinations of the factor s should be conducted in or der to study the main effects. Thr ee controllable
factor s is identified w hich could affect the quality of the br ead manufactur ing pr ocess. Ther e ar e thr ee
par ameter s with thr ee levels each. The thr ee contr ollable factor s par ameter s w hich selected ar e mixing
type, baking dur ation (min) and baking temper atur e (OC). It is decided to test each contr ollable factor at
thr ee levels (Table 5).
Table 4 . Exper iment layout using
L9 (33 ) or thogonal ar r ay
Experiment Trial
A
Table 5 : Selected pr ocess par ameter s
and their r espective level s i n exper i mental desi gn
Parameters
B
C
Process Parameters Symbol
Level
1 (Low) 2 (Medium)
3 (High)
1
1
1
1
Mixing type
A
Low
Medi um
2
1
2
2
Baki ng dur ation (min)
B
7
10
13
3
1
3
3
Baki ng temper at ur e ( oC)
C
195
200
205
4
2
1
2
5
2
2
3
6
2
3
1
7
3
1
3
8
3
2
1
9
3
3
2
High
The sequence in w hich the exper iments w er e car r ied out w as r andomized to avoid any kind of
per sonal or subjective bias w hich may be conscious or unconscious. The Table 6 show s the combination
of pr ocess var iables for all nine exper iments that have been r un.
Table 6 : Taguchi L9 (33 ) or thogonal ar r ay design
Experiment
Trial
1
2
3
4
5
6
7
8
9
Mixing
type
Low
Low
Low
Medi um
Medi um
Medi um
High
High
High
Parameters
Baking duration Baking temperature
(min)
( oC)
7
195
10
200
13
205
7
200
10
205
13
195
7
205
10
195
13
200
© 2012 GETview Limit ed. All right s reserved
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Global Engineers & Technologist s Review , Vol.2 No.9
No
1.
2.
3.
4.
Equipment
(machine)
Mixing machine
Dough divi der
Fer menti ng box
Infr ar ed oven
Picture
(2012)
Specification
Model
Power (hp)
Bowl Capacity (liter)
Flour Capacity (kg)
Speed (rpm)
Gross Weight (kg)
Dimension (mm)
GF-201
½
20
3 kg
46/ 150, 88/ 316, 154/ 505
120 (90 net)
550 x 500 x 840
Model
Capacity
(pcs)
Power
(kw)
Voltage
(v / hz)
Dividing Weight (g)
Weight
(kg)
Dimension
(mm)
HDD-36
36
1.5
240 / 50
30 ~ 100
200
520 X 420 X 1400
Model
Layer
Power (Kw)
Voltage (v)
Weight (kg)
Dimension (mm)
FX-11
11
2.5K
240
48
500 x 755 x 1620
Model
Gas Pressure
(Pa)
Heat Load (mj / hr)
Voltage
(v / hz)
Weight
(kg)
Dimension
(mm)
GR-6
2800
135
240 / 50
480
1355 x 800 x 1780
Figure 2 : The li st of equipment used for exper i ment
4.0 DATA AND DISCUSSIONS
The r esult of br ead manufactur e pr ocesses in this exper iment is based on mean value of four samples at each set
of exper imental conditions for each pr ocess par ameter var iable. Table 7 show s the summar ized r esult and it is
found that exper iment number 4 is the best combination contr ol factor since the quantity of manufactur ed br ead
is gr eater in total accepted pr oduct.
4.1
Signal-to-Noise Ratio
The Taguchi’s method pr ovides the or thogonal ar r ay as a mathematical tool that allow s the analysis of the
r elationship betw een a lar ge number s of design par ameter s by using only a limited number of
exper iments r un. The tar get based on the conditions identified w hich r esulting the optimal pr ocess or
pr oduct per for mance. Her e, the S/ N r atio is the Taguchi advocates for measur ing the quality thr ough
or thogonal ar r ay based exper iments. The S/ N ratio is used to conver t the tr ial r esult data into a value for
the evaluation character istics as the optimum setting analysis.
In this study, the visual inspection against the output is to deter mine the quality char acter istics
consider ed of the br ead pr oduct. To analyze and deter mine w hich the optimal par ameter factor s for the
br ead manufacturing pr ocess, the r eject should be in minimum as the acceptable of output.
© 2012 GETview Limit ed. All right s reserved
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Global Engineers & Technologist s Review , Vol.2 No.9
(2012)
Table 7: Summarized results of manufactured bread quality (See Appendix)
Experiment
Trial
Total
accepted
1
2
3
4
5
6
7
8
9
12
13
0
14
15
7
11
11
12
Reject
Unr ipe
3
0
0
1
0
0
0
0
0
Defect Overcook
0
0
2
0
0
15
0
0
0
0
3
5
4
0
3
1
0
3
Total
3
2
15
1
0
8
4
4
3
The S/ N ratio selected for this study is lar ger -the-better quality char acter istics. The calculation and
equation for S/ N r atio is as following:
4.1.1 Larger- the- better:
S/ N = -10 log ( ∑ (1/yi2) / n)
Wher e,
yi = each obser vation value
n = number of obser vation (values at each tr ial condition)
The r esult is as Table 8.
Table 8: Calculat ion for S/ N r at io (Lar ger -t he-better )
n
1
1
1
1
1
1
1
1
1
y
3
2
15
1
0
8
4
4
3
y2
9
4
225
1
0
64
16
16
9
S/ N ratio
21.5836
22.2789
undefi ned
22.9226
23.5218
16.9020
20.8279
20.8279
21.5836
4.1.2 Main effect of signal-to-noise ratio (S/ N) response
The main effect plot is a plot of the mean r esponse values at each level of design par ameter or
pr ocess var iables. The sign of the main effect indicates the dir ection of the effect, w hether the
aver age r esponse value is incr eases or decr eases. The magnitude indicates the str ength of the
effect. If the effect of a design or pr ocess par ameter is positive, it implies that the aver age r esponse
is higher at high level then at low level of the par ameter setting. In contr ast, if the effect is negative,
it is means that the average r esponse at the low level setting of the par ameter mor e than at the
high level.
4.1.3 Main effect plot for signal-to-noise ratio
Figur e 3 show s S/ N gr aphs for the br ead manufactur ed exper iment r espectively. Basically, the
lar ger the S/ N ratio show s the better the quality. Based on this graph, it r eveals that the tr end of
the mixing type is less significant than other factor s. The S/ N r atio is slightly decr eased fr om low
setting to medium setting, but the S/ N r atio is slightly incr eased and appr oach to the mean line for
high type of mixing setting. For baking dur ation, the gr aph tr end begins w ith slightly incr eased
betw een 7 minute and 10 minute of baking pr ocess. The S/ N r atio value betw een 10 minutes and
13 minutes is squally decr eased and appr oach to the minimum of the S/ N r atio value. For baking
temper atur e, the S/ N r atio is smoothly incr eased betw een 195 oC and 200 oC and appr oach to the
maximum of S/ N r atio w hich then follow ed by slightly decr eased of baking temper atur e of 200 oC
to 205 oC. Based on the Figur e 3 and Table 9, it can be descr ibe that the baking dur ation is the main
factor influenced to the quality of the output of the br ead manufactur ing pr ocess. It show s that the
baking dur ation is the contr ol factor that has the most significant factor . The effect of the other
factor s (A and C) is of less significance.
Table 9 show s the aver age S/ N r atio values for the exper iment at thr ee levels setting of each
factor and the effect of each main effect on the S/ N ratio in r espectively. The mean of S/ N ratio for
per centage of the mixing type at level 1, level 2 and level 3 is 26.82, 26.40 and 26.48 in r espectively.
© 2012 GETview Limit ed. All right s reserved
13
Global Engineers & Technologist s Review , Vol.2 No.9
(2012)
Clear ly, the S/ N r atio of mixing type at level 1 appear s to be the best choice since it cor r esponds to
the highest aver age S/ N r atio. The mean S/ N r atio of baking dur ation is 26.75, 26.92 and 25.62 at
level 1, level 2 and level 3 in r espectively. The S/ N r atio for baking dur ation suggests that
par ameter at level 2 is better than at level 1 and level 3. Level 2 show s as the highest aver age of
S/ N r atio and is consider ed as the best choice. For S/ N r atio of baking temper atur e, same as
pr evious, level w ith highest S/ N ratio will be chosen so; the best choice is at level 2 w ith 26.95.
Fr om the analysis above, the optimum parameter s selected to optimize the quality of br ead
manufactur ed ar e A1B2C2.
The maximum-minimum value is equal to the r ange of S/ N ratio var iance due to the change
in the level setting. The lar ger the r ange, the mor e pow er ful impact the contr ol factor has on
quality. The r anking in Table 10 show that S/ N ratio of baking dur ation, w hich has r anking 1, has
r elatively str ong impacts and influence on the quality of br ead. S/ N ratio of mixing type and baking
temper atur e w hich has r anking 2 and 3 r espectively have r elatively w eak impacts. So, S/ N ratio of
baking dur ation should be str ictly contr olled for high quality of br ead dur ing the manufactur ing
pr ocess.
Instead, Table 9 show s that the most significant factor for the output of the pr oduct is the
contr ol factor towar d B (Baking dur ation). The effect of the other factor s (A and C) is of less
significant. Since the objective of this study is to optimize the br ead pr oduct pr ocess, the S/ N r atio
should be maximal in or der to minimize variability. Thus, factor B should be to set at level 2 in
or der to get the maximum r esult.
Table 9 : Response Table for Signal to Noise Ratios
Main Effects Plot for SN ratios
Data Means
Mixing t ype
Level
Baking durat ion
27.0
Mean of SN rat ios
26.5
26.0
25.5
Low
Medium
High
7
10
13
Baking temperature
27.0
1
2
3
Differ ence (Max –Mi n)
Rank
Mixing
type
A
26.82
26.40
26.48
0.42
3
Baking
duration
B
26.75
26.92
25.62
1.30
1
Baking
temperature
C
25.87
26.95
26.90
1.08
2
*Higher value
26.5
26.0
25.5
195
200
205
Signal-to-noise: Larger is better
Figure 3 : Main effect s plot for S/ N r atio of exper iment
4.1.4 Main effect plot for means
Figur e 4 show s the mean gr aphs for the br ead manufactur ed exper iment. Basically, the lar ger of
the S/ N r atio is as the better of quality. Figur e 4 show s the best levels for each contr ol factor s to
obtain the optimal accepted of the br ead pr oduct. Based on the gr aph, it show s that the tr end of the
mixing type is less significant compar ed to other factor s. The mean is slightly decr eased fr om low
setting to medium setting, but then slightly incr eased by appr oaching the mean line for high type of
mixing setting. For baking dur ation, the graph tr end begins with slightly incr eased betw een 7
minute and 10 minute of baking pr ocess. The mean value betw een 10 minute and 13 minute is
squally decr eased by appr oaching the minimum of the S/ N ratio value. For baking temper atur e, the
mean is smoothly incr eased betw een 195 oC and 200 oC by appr oaching the maximum of mean and
follow by slightly decr ease for baking temper atur e of 200 oC to 205 oC. Fr om the Figur e 4 and Table
10, it can be described that the baking dur ation is the main factor that influence the quality of the
output of the br ead manufactur ing pr ocess. It show s that the baking dur ation is the contr ol factor
that has the most significant factor . The effect of the other factor s (A and C) is of less significance.
Table 10 show s the aver age mean values for the exper iment r espectively at thr ee levels
setting of each factor and the effect of each main effect on the mean. The mean of mean values for
the mixing type at level 1, level 2 and level 3 is 21.93, 21.12 and 21.08 in r espectively. Clear ly, the
mean of mixing type at level 1 appear s to be the best choice since it cor r esponds to the highest
aver age mean. The mean of baking duration is 21.78, 22.21 and 19.24 at level 1, level 2 and level 3
in r espectively. This is means that for the baking dur ation, the par ameter at level 2 is better than at
level 1 and level 3. Same as pr eviously, the level w ith highest mean w ill be chosen as the best
choice, w hich is 22.26.
Based the analysis above, the optimum par ameter s selected to optimize the quality of br ead
manufactur ed is A1B2C2. Since the maximum-minimum value is equal to the range of mean variance
© 2012 GETview Limit ed. All right s reserved
14
Global Engineers & Technologist s Review , Vol.2 No.9
(2012)
due to the change in the level setting, the lar ger the r ange, the mor e pow er ful impact the contr ol
factor has on quality. The r anking in Table 11 show s that the baking dur ation has r anking 1 and
r elatively str ong impacts and influence on the quality of br ead. Thus, the mixing type and baking
temper atur e ar e the r anking 2 and 3 w her e they ar e r elatively give the w eak impacts..
Table 10 : Response Table for Means
Main Effects Plot for Means
Data Means
Mixing t ype
Baking durat ion
Level
22
Mean of Means
21
1
2
3
Differ ence (Max –Mi n)
Rank
20
19
Low
Mediu m
Bakin g t empe rat ure
Hig h
7
10
13
22
Mixing
type
A
21.93
21.12
21.08
0.85
3
Baking
duration
B
21.78
22.21
19.24
2.97
1
Baking
temperature
C
19.77
22.26
22.17
2.49
2
*Higher value
21
20
19
1 95
200
20 5
Figure 4 : Main effect s plot for Dat a Means of exper i ment
Table 10 show s that the most significant factor for the output of the pr oduct is contr ol factor
B (Baking duration). The effect of the other factor s (A and C) is of less significant. Since the
objective of this pr oject is to optimize the br ead pr oduct pr ocess, w hat is r equir ed is to the
maximum values in or der to minimize variability. Thus, factor B should be to set to Level 2 in or der
to get the maximum r esult.
4.2
Analysis of Variance (ANOVA)
The pur pose of the ANOVA is to investigate and r ecognize w hich pr ocess have the significantly affects to
the quality of the manufactur ed br ead. Fr om the analysis, it is used to identify w hich factor s ar e the most
impor tant in ter ms of quality character istic. Consequently, the impor tant factor s identified have to be
pr oper ly monitor ed dur ing the pr ocess for a consistently gather high quality of br ead pr oduct.
The ANOVA analysis is per for med by noting the sour ces of var iation in the left hand column, w hich
ar e the factor s under the exper iment. The descr iption of the label in ANOVA analysis is as follow :
Table 11 : List of ANOVA l abel
Label
DOF
SS
MS
F
P
Descr iption
Indicat es degr ee of fr eedom for the factor
Sum of squar e
- Squar ed deviation of a r andom var i able fr om means.
Mean squar e
- Sum of squar e divi di ng by the number of degr ee of fr eedom
associated w i th the factor s effects.
Result of the tr aditional Fi sher test for si gnificance
- Measur e effect of each factor or i nter act ion r el ative to the er r or .
Indicat es the per cent contr ibution of each factor .
Table 12 : ANOVA for S/ N r at io
Factor s
Mixing type
Baki ng dur ation
Baki ng temper at ur e
Residual er r or
Total
DOF
2
2
2
1
7
Seq SS
0.2295
2.0408
1.7530
1.4064
5.4297
Adj SS
0.01016
1.40689
1.75299
1.40641
Adj MS
0.00508
0.70345
0.87649
1.40641
F
0.00
0.50
0.62
Adj MS
0.00252
3.60445
4.69697
7.43794
F
0.00
0.48
0.63
P
0.996
0.707
0.667
Table 13 : ANOVA for Mean
Factor s
Mixing type
Baki ng dur ation
Baki ng temper at ur e
Residual er r or
Total
DOF
2
2
2
1
7
Seq SS
1.044
10.600
9.394
7.436
28.477
Adj SS
0.00505
7.20890
9.39395
7.43794
P
1.000
0.713
0.665
© 2012 GETview Limit ed. All right s reserved
15
Global Engineers & Technologist s Review , Vol.2 No.9
(2012)
At the end of the exper iment, the r esult show s that the combination of par ameter s factor s pr oduce
the differ ent output of the br ead manufactur ing pr ocess. The influences of the combination car r ied out
ar e illustr ated in Figur e 5 for all of 9 exper imental tr ial, w her e the defects detected at the each of the
matr ix ar ray location on the tr ay. It show s that the most high defect ar ea is at the b3 and c3 ar ea w hich
consist of 5 defects. The ar ea of b1 and c1 as the second highest defects ar ea w ith 4 defects occur red
dur ing the exper iment. Fr om the exper iment of factor arr angement in or thogonal ar r ay (OA), the location
of the defects r elated to the combination of setting par ameter factor , such as mixing type, baking dur ation
and baking temper atur e identified. It is found that the highest of defects ar ea occur r ed at the 2, 3, 6,7,8,9
ar r angement number of exper iment tr ial.
Figure 5 : Br ead locat ion for over all exper iment tr ial s
Based on the combination par ameter factor s, it is found that the setting par ameter of L-H-H , M-H-L
and H-L-L ar e having gr eater influences to the r ejected output at the cr itical location (b3 and c3). Since
the specific location found as the r epeatable defects location, the fur ther study r equir ed is by analyzing
the influence of the heat tr ansfer in the oven that may affect the quality of the br ead.
5.0 CONCLUSION
In this pr oject, the mixing type, baking dur ation and baking temper atur e is identified as the parameter factor s
that affects to the quality of food (br ead) manufactur ing pr ocess. The analysis using Taguchi‘s method show s
that the optimal r esult achieved w hen the parameter setting of the pr ocess is A1B2C2. This is means that the
mixing type should set to low , baking dur ation at 10 minutes and baking temper atur e at 200 0C. This optimum
pr ocess of contr olled par ameter s setting indicated that the baking dur ation is as the most significant factor .
Since the measur ement of the br ead quality char acter istic in this study is deter mined by visual inspection, the
development method to inspect the taste, w eight, surface peak, shape, textur e or str uctur e of br ead pr oducts ar e
also r equir ed for fur ther study. In addition, the fur ther study r equired is against the heat tr ansfer distr ibution of
the oven that is influenced by location of heat gener ator (micr ow ave) tow ar ds the objects.
ACKNOWLEDMENTS
The author s w ould like to thank CRIM-UTeM. This pr oject is suppor ted by CRIM thr ough PJP/ 2011/ FKP (11D)
S00878.
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© 2012 GETview Limit ed. All right s reserved
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Global Engineers & Technologist s Review , Vol.2 No.9
(2012)
1
2
3
L
L
L
L
M
H
Baking
Temper ature ( oC)
Baking Time
Dur ation ( min)
Mixing
Type
Exper imental
Tr ials
APPENDIX
Qty
b1
c1
ov
0
TOV
0
b2
c2
rp
3
TRP
3
L
a3
b3
0
M
b3
td
ta
ov
rp
df
3
12
0
0
2
TDF
TD
TA
TOV
TRP
TDF
0
b4
b5
b1
b2
c3
c4
c5
c1
c2
c3
df
a4
a5
a1
a2
a3
3
12
0
0
2
a4
a5
a1
a2
a3
a4
a5
b4
b5
b1
b2
b3
b4
b5
c4
c5
c1
c2
c3
c4
c5
td
ta
ov
rp
df
td
ta
2
13
15
0
0
15
0
TD
TA
TOV
TRP
TDF
TD
TA
2
13
15
0
0
15
0
a1
b1
b2
b3
b4
b5
b1
b2
b3
b4
b5
b1
b2
c1
ov
0
TOV
0
a2
a3
a4
a5
a1
a2
a3
a4
a5
a1
a2
c2
c3
c4
c5
c1
c2
c3
c4
c5
c1
rp
df
td
ta
ov
rp
df
td
ta
ov
1
0
0
14
0
0
0
0
15
5
TRP
TDF
TD
TA
TOV
TRP
TDF
TD
TA
TOV
1
0
1
14
0
0
0
0
15
5
c2
rp
0
TRP
0
a3
b3
c3
df
3
TDF
3
td
8
TD
8
ta
11
TA
7
c3
c4
c5
c1
c2
c3
ov
rp
df
td
ta
ov
rp
df
td
ta
ov
rp
df
0
0
4
4
11
1
0
3
4
11
3
0
0
TOV
TRP
TDF
TD
TA
TOV
TRP
TDF
TD
TA
TOV
TRP
TDF
0
0
4
4
11
1
0
3
4
11
3
0
0
c4
td
3
TD
3
c5
ta
12
TA
12
H
L
M
5
M
M
H
6
M
H
L
7
H
L
H
8
H
M
L
H
Tr ay
Total
a2
M
H
Pictur e
a1
4
9
Quality Char acter istics
( Sur face Inspection)
M
a4
b4
a5
a1
a2
a3
a4
a5
a1
a2
a3
a4
b5
c4
c5
b1
b2
c1
c2
b3
b4
b5
b1
b2
b3
b4
c3
c4
c5
c1
c2
a5
a1
a2
a3
a4
b5
b1
b2
b3
b4
a5
b5
© 2012 GETview Limit ed. All right s reserved
18
www.getview.org
TAGUCHI’S QUALITY IMPROVEMENT ANALYSIS OF THE SME
BREAD MANUFACTURING
HAERYIP SIHOMBING1, HAFIZ2, M.K., YUHAZRI 3, M.Y., KANNAN4, R.
1, 3
Faculty of Manufacturing Engineering
Universiti Teknikal Malaysia Melaka
Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, MALAYSIA
1
iphaery@utem.edu.my
3
yuhazri@utem.edu.my
2, 4
Faculty of Mechanical Engineering
Politeknik Merlimau Melaka
Pejabat Pos Merlimau, Melaka MALAYSIA
A BSTRA CT
The purpose of this study is to optimize the manufacturing process of the SME bread product using
Taguchi method. The study is focused on the quality problems occurred as the outcomes of the
process (quality of the bun produced) related to controllable factors of products design identified
(machine temperature and length duration time) for the improvement required. By implementing
the technique of analysis of variance (ANOVA), the composition of the controlled parameters, such
as machine temperatures and duration times, is therefore determined and constructed into
Orthogonal Array (OA) of L9(33) related to what the setting parameters produces the optimal
output. To improve the quality of the manufactured product, the setting of parameter recommended
in this study is A 2B2C2 or A 1B2C2 {(Low or High) ∩ 10minutes ∩ 200O C)}.
Keyw ords: Taguchi method, Bread Manufacture, Loss Function, S/N Ratio.
1.0
I NTRODUCTION
Accor ding to Adam et al ., (1986), quality is the degr ee to w hich a pr oduct or ser vice confor ms to a set of
pr edet er mined standards, especially to the character istics that deter mine its value in the mar ket and its
per for mance of the function to w hich it was designed. Based on this r eason, quality is the r esult of a compar ison
betw een w hat was r equir ed and pr ovided w hich it can be differ ent things to differ ent people and taken into
account of both objective and subjective inter pr etations against the needs and expectations of customer s
(Char ter ed Quality Institute, 2012). When it is r elated to pr oducts, Meir ovich et al ., (2007) under lined that the
quality is, in facts, the degr ee to w hich a pr oduct or ser vice’s design (specification) fits to customer needs and
expectations. While the confor mance to quality is r elated to the degr ee of how match betw een the featur es of a
specific pr oduct (ser vice) to its specification. This is w hy the customer s’ expectation involves the specification as
a quality of pr oduct or ser vice. And also the compar ison against the competitor s in the mar ketplace as w hich
Pr at and Tor t (1989) in their study discusses w hy the pet food manufactur ing companies need to implement the
quality impr ovement as cr ucial task in or der to sur vive in today’s mar ketplace.
Based on this consider ation, if the impr ovement tasks and pr ogr ams made ar e mainly in r educing the
var iability as w ell as other failur e of pr oducts thr ough the inspection in or der to maintain economic viability,
the facts that by such contr olling conducted to the quality of pr oduct is, cer tainly, r isked w ith the cost consumed
(Jur ans’ appr aisal and failur e cost). This is due to since a pr oduct must be efficient to manufactur e and
insensitive to variability on the factor y floor and in the field, the business or company should ther efor e focus
their concer n thr ough the development of ext ensive car efully planned exper imentation at the design stage of
pr oducts or pr ocesses. On this, by employing design of exper iments (DOE) of the quality engineer ing methods
pr oposed by Dr . Taguchi as one of the most impor tant statistical tools of TQM for designing high quality,
accor ding to Unal and Dean (1991), will minimizing the pr ocess var iation and r educing r ew or k, scrap and the
need for inspection systems at r educed cost.
In addition, although Design of Exper iments (DOE) is the method r elated to exper iment in ter ms to define
the systematic pr ocedur e car r ied out under contr olled conditions in or der to discover an unknow n effect,
Genichi Taguchi developed this concept to impr ove the quality of manufactur ed goods based on the concept of
" Unifor mity ar ound a tar get value." Related to this per spectives, Antony (2002) r epor ted about the successful
application of Taguchi method by many US and Eur opean manufactur er s over the last 15 year s in or der to
impr ove their pr oduct quality and pr ocess per for mance. This is due Taguchi concer t off-line quality contr ol
based on an under standing of the loss function, system design, parameter design and toler ance design, that
-
G. L. O. B. A. L E. N. G. I . N. E. E. R. S. . & . . T . E. C. H. N. O. L. O. G. I. S. T . S R. E. V. I. E. W
6
Global Engineers & Technologist s Review , Vol.2 No.9
(2012)
enables the pr oduct development pr ocess is immediately pr oduces a quality pr oduct or pr ocess at the low est
possible cost and thus the online quality contr ol function dur ing manufactur ing and ser vice after the sale effor t
ar e ther efor e r educed.
In facts, in the Small and Medium Enter pr ise (SME) food manufacturing w her e the manufactur ing pr ocess
r uns in the tr aditional w ays (Dhungana, 2003) and (Zulkifly et al ., 2000), the var ieties quality of pr oducts ar e
always occur r ed. Ther e ar e also found the limited implementation of management system and quality contr ol
tow ar ds the pr oduct to meet the specification, beside the lack of skill and know ledge oper ator s. In facts, the
pr oblems occur r ed and found in this study ar e mostly due to setting the par ameter of the machine to meet the
specification of the end pr oduct. Since the company r uns their pr oduction system solely depend on the visual
inspection method to confirm the specification of end pr oduct (decision to finish the pr ocess of the br ead
cooking), then this condition effect to inconsistently quality of food pr oducts. Some of them ar e even over cooked
or incomplete cook. Based on this r eason, this study is car r ied to identify the par ameter factor s that affects to
the quality of food manufactur ing pr ocess and to deter mine the optimal par ameter factor s of food
manufacturing pr ocess by using Taguchi Method.
2.0
LITERATURE REVIEW
2.1
DOE Using Taguchi
Sukthomya and Tannock (2005) stated that Taguchi (1986) intr oduced a simplified and modified DOE
appr oach w hich has been w idely adopted by industr y. This is due to Taguchi method is a technique for
designing and per for ming the exper iments to investigate the pr ocesses (w her e the output depends on
many factor s such as var iables and inputs), w ithout having to slow and uneconomically r un the pr ocess
caused by all possible of combinations values (Dobr zañski et al., 2007). Taguchi for mulated both a
philosophy and methodology for the pr ocess of quality impr ovement that depends on statistical concepts,
especially statistically designed exper iment. On this, the concept design is consider ed to be the fir st phase
of the design str ategy. This phase gather s the technical know ledge and exper iences to help the designer to
select the most suitable one for the intended pr oduct (Bhar ti and Khan, 2010).
Refer to Amer ican Supplier Institute (1992), Taguchi developed an appr oach to design of
exper iments that addr essed the r ealities of industr ial design, pr oductivity and cost effectiveness based on
the r elationship betw een technology, var iation, cost and savings. Since the classical exper imental design
methods ar e time consuming w her e the exper iments must be per for med w hen the number of contr ol
factor s is high, Taguchi methods use a special design of or thogonal ar r ays to study the entir e facto space
w ith only a small number of exper iments (Bhar ti and Khan, 2010).
The development of Taguchi’s method is based on or t hogonal ar r ays (OA) r elated to statistics
(Weng et al., 2007). Or thogonal Ar r ay (OA) is one par t of the exper imental gr oup w ho only use par t of the
state total, w her e this section per haps only half, quar ter or an eighth of a full factor ial exper iment. An
optimal exper imental design should pr ovide maximum amount of infor mation by means of minimum
exper imental tr ials (Bor an and Hocalar , 2007). Chong et al ., (2009) stated that Or thogonal Ar r ay (OA) is a
statistical DOE that is mostly applied in the manufactur ing quality contr ol and r apid softw ar e testing for
faults detection.
OA is the matr ix of number s ar r anged in columns and r ow s. The Taguchi method employs a gener ic
signal-to-noise (S/ N) r atio to quantify the pr esent var iation. These S/ N r atios ar e meant to be used as
measur es of the effect of noise factor s on per for mance char acter istics. S/ N r atios take into account both
amount of variability in the r esponse data and closeness of the aver age r esponse to tar get. Ther e ar e
sever al S/ N r atios available depending on type of char acter istics: smaller is better , nominal is best (NB)
and lar ger is better . Refer to Cheng (2001), Taguchi has tabulated 18 basic or thogonal ar r ays that know n
as the standar d or thogonal ar r ays. Each or thogonal ar r ay uses a notation that indicates its number of
r ow s and columns, as w ell as the number of level in each column.
2.2
Manufacturing System
Today, the ear liest for ms of br ead w ould have been ver y differ ent fr om how w e can see in industr ialized
countr ies and it w ould pr obably be closest in char acter to the moder n flat br eads of the Middle East
(Cauvain, 1999). The common pr ocess of pr oducing br eads is mainly based on 3 steps: dough for mation,
fer mentation, and baking.
2.2.1 Dough formation
This pr ocess r equir es the mixing of flour , water , yeast, salt and other ingr edients. On this pr ocess,
the changes ar e associated with the for mation of gluten, w hich r equir es both the hydr ation of the
pr oteins in the flour and the application of ener gy thr ough the pr ocess of kneading. Accor ding to
© 2012 GETview Limit ed. All right s reserved
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Global Engineers & Technologist s Review , Vol.2 No.9
(2012)
Cauvain (1999), kneading is the development of gluten str uctur e in the dough thr ough the
application of ener gy dur ing mixing. Mixing the dough pr ovides tw o functions: homogeneous
distr ibution of components, and development of the glut en matr ix. Gluten is the skelet on of w heatflour dough and r esponsible for gas r etention w hich pr ovides the pr oduction of light loaf of br ead.
Mixing time var ies with the flour , dough temper atur e, dough consistency, and mixer .
2.2.2 Fermentation
Dur ation of the fer mentation pr ocess depends on the amount and the quality of the ingr edients.
Yeast is the most impor tant ingr edient that affects the fer mentation pr ocess. The r elationship
betw een dough development time and yeast level pr obably comes fr om the contr ibution that
enzymes pr esent in the yeast cells, viable or dead. They modify the pr otein str uctur es, w hich ar e
for ming w ith incr easing dough r esting time. Flour also contains enzymes, w hich can contr ibute to
dough development. Since the mechanism for dough development in fer mentation depends on
yeast activity, the temper atur e of the dough play a major r ole in deter mining the time at w hich full
development is achieved (Cauvain, 1999).
2.2.3 Baking process
Dur ing baking sever al changes take place both in the cr umb and cr ust. The br ow ning r eaction that
involves both ‘ car amelization’ of sugar s and ‘pr oteinaceousmater ials’ impar ts a deep color to the
cr ust. Ther mal decomposition of star ch and for mation of ‘dextr ins’ contr ibute to cr ust br ightness.
This is accompanied by for mation of flavor and taste components. At the same time changes take
place inside the loaf of br ead. At ear ly stages, the incr ease in temper atur e w ill enhances enzymatic
activity and gr ow th of yeast and bacter ia. At about 50OC-60 OC, the yeast and bacter ia ar e killed.
While on beyond temper atur e star ch gelatinizes, pr oteins coagulate, and enzymes ar e inactivated.
Steam is for med at ar ound 100ºC, at w hich the final volume and cr umb textur e of the br ead ar e set.
The inside of the loaf does not exceed 100ºC; how ever , in the cr ust much higher temper atur es ar e
attained. In the t emper atur e r ange of 100-150ºC, light and br ow n dextr in ar e for med w hich ar e
follow ed by car amel (Pomer anz, 2004).The br ead is allow ed to cool at r oom temper atur e and
r eady for packaging. Figur e 1 show the sequence pr oduction pr ocess in manufactur ing br ead.
Process 1
Mixing br eaded
Process 2
*Dough cutter
Process 3
Steam br ead i n
fer menti ng box
Process 4
Cook in I nfr ar ed food oven
Figure 1 : Pr ocess Machi ne (Flow )
3.0
RESULTS AND DISCUSSION
The study is car r ied out to identify and analyze the manufactur e pr oducts in the food manufactur ing industr y.
The industr ial visit is conducted to the factor y for clear ly under standing about the food pr ocess manufactur ing;
technically and pr actically, and to deeply know ing about the pr oblems and factor s affecting to quality. The
pur pose of the industr ial visit is to obser ve and under stand the cur r ent pr ocess of manufactur ing system,
pr oduct quality, and quality contr ol system, w hile the infor mation of food manufactur ing pr ocess flow and the
quality per for mance is gather ed for analysis.
© 2012 GETview Limit ed. All right s reserved
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Global Engineers & Technologist s Review , Vol.2 No.9
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3.1
Study on Current Manufacturing System
This study is conducted on the cur r ent food manufactur ing system against the char acter istic behavior of
the selected contr ollable par ameter s applied in the food manufacturing pr oduction line pr ocess. The flow
pr ocess and the steps of manufactur ing as descr ibed below .
1. The fir st step is the mixi ng of the ingr edi ents to make the
dough of t he br ead.
1. Pr ocess 1: Mixing br eaded
•
This pr ocess evenly di str ibutes the i ngr edient s, develop
the glut en and to i niti ate fer ment ation
•
•
The machi ne has 3 alter native speeds scale.
•
To change the speeds, fir st tur n off t he motor , then move
the shifter handle to the desir ed speed.
Speed number 1 for slow speed is for heavy mixtur es. I n
many mi xing oper ation, i t is customar y to star t on speed
number 1 and t hen change to a hi gher speed as t he w or k
pr ogr esses.
•
2. Then, t he pr epar ed dough is divided i nto sever al amounts by
usi ng dough cutter machine.
3. The dough ar e then w ill ar r anged on the tr ay. The
composition of the dough i s pr oper ly ar r anged to make sur e
the dough w ill baked compl etely l ater .
3. The dough w hich has been pr epar ed on the tr ay w ill then on
hold in the fer ment ing box to ensur e t he dough is st eamed to
mai ntain the dough.
4. Next, t he br ead is baki ng i n i nfr ar ed food oven at sever al
times accor di ng to the setti ng.
5. The br ead i s allow ed to cool in r oom temper at ur e and
packagi ng for sell.
Speed number 3 for fast speed i s for li ght w or k as
w hipping cr eam, beati ng eggs and mixing t hi n batter s.
2. Pr ocess 2: Dough cutt er
•
This pr ocess is conti nued fr om the fir st pr ocess w hich
divide the dough to sever al quant ity that needed w it h set
up at t he machine button.
•
The capacity for one cycle pr ocess is 112 pcs. per min.
3. Pr ocess 3: Br ead fer menti ng box
•
The pr epar ed dough is tr ansfer r ed to the fer menting box.
This is r equir ed to keep t he bun i n good condition, fr esh
befor e tr ansfer to oven.
4. Pr ocess 4: I nfr ar ed food oven
•
This gas oven pr ovide platfor m type r evolving hot air oven
for cook pr ocess. The sur r ound hot air i n the oven i s
suppor t the cook pr ocess for sever al tr ays of buns i n the
oven.
To deter mine the r elationship betw een the quality of manufactur ed pr oduct and visual contr ol
par ameter of pr oduction line, the data collected is analyzed using Minitab softwar e. By car r ying out the
exper iment r elated to the pr ocess of contr ol par ameter design, the quantity, and location of r eject items
ar e identified and r ecor ded, w her e a step plan used is to analyze r esults and implement solutions (Antony
et. al ., 2001).
3.2
Experiment Flow Process
The exper iment is car r ied out w ith sever al of the pr ocess w hich beginning of mixing flour until finishes
cooking. The pr ocess flow of the pr oduction of the br ead ar e illustr ate in Table 1.
3.3
Bread I nspection
The declar ation of the br ead inspection is based on the visual inspection on the sur face of the br ead. The
inspection is doing manually for the appearance of the final pr oduct to confirmed the br ead is acceptable
and can be thr ough the next pr ocess, packaging. The color of the br ead (complete cook) and sur face peak
of final pr oduct ar e the measur able quality char acter istics of this pr oject. Ther e ar e other char acter istics
that can be measur ed, but both of the above quality character istics ar e consider ed for the most impor tant
cr iter ia of mar ket pr oducts by consumer s. The table 2 show s the accepted and r ejects condition for
quality char acteristics of the br ead.
Values of quality character istics (color of br ead sur face and sur face peak of pr oducts) ar e affected
by the conditions dur ing pr oduction pr ocess of br ead pr oducts. Diver sifying values of baking dur ation
and baking temper atur e of samples and the speed mixing type of ingr edients of samples significantly
affected quality character istics. For this exper iment consider ed, the above conditions ar e the factor s of
the exper imental pr ocedur e. Since the pur pose of this exper iment is the optimization of br ead pr oduct
pr ocess, any non-linear r elationship w as taken into consider ation. Her e, non-linear effects ar e to be
studied and it is necessar y to choose mor e than tw o levels for each factor s (Antony et al ., 2001). The
contr ol factor s and levels selected ar e show n in the table 3.
The exper imental pr ocedur e is car r ied out in a contr olled envir onment, as the exper iment
conditions r emained stable. This ensur es that ther e is no impact on r esults fr om exter nal factor s and the
only conditions dur ing the exper iment var ied in their values ar e thr ee contr ol factor s, that is, mixing type,
baking dur ation and baking temper atur e of pr ocess var ied in each exper iment tr ial. The quantities of
© 2012 GETview Limit ed. All right s reserved
9
Global Engineers & Technologist s Review , Vol.2 No.9
(2012)
ingr edients r emained stable for all samples, based on the r ecipe that used alw ays the industr y for
pr oduction of par ticular br ead pr oduct. Figur e 2 show s the equipment used t o conduct the exper iment.
Table 1 : Pr ocess flow of the pr oduction of the br ead
No
1
Process
Mixing
flour
2
Picture
Descr iption
Mixing
i nclude
flour , egg and
w ater
No
6
Process
Place in the
mold
Picture
Descr iption
The dough i s placed
on the mold and
compr ess to fill the
mold.
Select
mixing
type
Mixing
type
divide
by
3
speed:
i.1 = Low
ii.2 = Medium
iii.3 = High
7
Divide the
dough
The
mold
is
inser t ing i nto dough
divider machi ne and
the
dough
w ill
ser ved follow the
w eight t hat measur e
pr eviously.
3
Mixing
dough
The i ngr edient is
mixi ng unti l the
ser ved as dough
8
Rub
margar ine
The mar gar i ne is
r ub on the tr ay
sur face to avoid the
br ead st ick w ith
tr ay sur face.
4
Rest
dough
The dough i s put
on the table for
10 mi nute and
cover by plastic
to
avoid
contamination.
9
Arrange on
tray and
place dough
in
fermenting
box
The dough i s placed
on the fer menti ng
box ar ound 1 hour
for steam pr ocess.
5
Scales the
dough
The dough is
scales
for
measur e
t he
appr opr i ate
w eight for divi de
the dough.
10
Cook in the
infrared
food oven
The br ead is cook in
the oven follow the
set up that ar r ange.
Table 2: Quali ty Char acter i stics Condi tion of the Br ead
Position
Unr ipe
Complete Cook
Overcook
Defect
TOP
BOTTOM
N/ A
Table 3 : Contr ol factor s and l evels of the exper i mental design
Factor
Labelled
Level
1
Level
2
Level
3
Mixing Type (rpm)
A
1
2
3
Baking Duration (min)
B
7
10
13
BakingTemperature ( oC)
C
195
200
205
© 2012 GETview Limit ed. All right s reserved
10
Global Engineers & Technologist s Review , Vol.2 No.9
3.4
(2012)
The L9 Orthogonal Array Experiments
An Or thogonal Ar r ay (OA) is a matr ix of number s ar r anged in r ow s and columns. Each r ow r epr esents the
levels of the selected factor s of a given exper iment and each column r epr esents a specific factor s w hose
effects on the output. An OA help an exper iments plans easily constr ucted by assigning factor s to columns
of the OA, then matching the differ ent symbols of columns with the differ ent factor levels. OA have the
balanced pr oper ty that ever y factor setting appear the same number of times for ever y setting of all other
factor s in the exper iments.
In this r esear ch, the L9 (33) OA as show n in Table 4 is selected for the contr ollable factor s since it is
the most efficient or thogonal design to accommodate thr ee factor s at thr ee levels. The L9(3 3) ar r ay
specifies nine exper imental r un to be per for med. This means that nine exper imental tr ials w ith differ ent
combinations of the factor s should be conducted in or der to study the main effects. Thr ee controllable
factor s is identified w hich could affect the quality of the br ead manufactur ing pr ocess. Ther e ar e thr ee
par ameter s with thr ee levels each. The thr ee contr ollable factor s par ameter s w hich selected ar e mixing
type, baking dur ation (min) and baking temper atur e (OC). It is decided to test each contr ollable factor at
thr ee levels (Table 5).
Table 4 . Exper iment layout using
L9 (33 ) or thogonal ar r ay
Experiment Trial
A
Table 5 : Selected pr ocess par ameter s
and their r espective level s i n exper i mental desi gn
Parameters
B
C
Process Parameters Symbol
Level
1 (Low) 2 (Medium)
3 (High)
1
1
1
1
Mixing type
A
Low
Medi um
2
1
2
2
Baki ng dur ation (min)
B
7
10
13
3
1
3
3
Baki ng temper at ur e ( oC)
C
195
200
205
4
2
1
2
5
2
2
3
6
2
3
1
7
3
1
3
8
3
2
1
9
3
3
2
High
The sequence in w hich the exper iments w er e car r ied out w as r andomized to avoid any kind of
per sonal or subjective bias w hich may be conscious or unconscious. The Table 6 show s the combination
of pr ocess var iables for all nine exper iments that have been r un.
Table 6 : Taguchi L9 (33 ) or thogonal ar r ay design
Experiment
Trial
1
2
3
4
5
6
7
8
9
Mixing
type
Low
Low
Low
Medi um
Medi um
Medi um
High
High
High
Parameters
Baking duration Baking temperature
(min)
( oC)
7
195
10
200
13
205
7
200
10
205
13
195
7
205
10
195
13
200
© 2012 GETview Limit ed. All right s reserved
11
Global Engineers & Technologist s Review , Vol.2 No.9
No
1.
2.
3.
4.
Equipment
(machine)
Mixing machine
Dough divi der
Fer menti ng box
Infr ar ed oven
Picture
(2012)
Specification
Model
Power (hp)
Bowl Capacity (liter)
Flour Capacity (kg)
Speed (rpm)
Gross Weight (kg)
Dimension (mm)
GF-201
½
20
3 kg
46/ 150, 88/ 316, 154/ 505
120 (90 net)
550 x 500 x 840
Model
Capacity
(pcs)
Power
(kw)
Voltage
(v / hz)
Dividing Weight (g)
Weight
(kg)
Dimension
(mm)
HDD-36
36
1.5
240 / 50
30 ~ 100
200
520 X 420 X 1400
Model
Layer
Power (Kw)
Voltage (v)
Weight (kg)
Dimension (mm)
FX-11
11
2.5K
240
48
500 x 755 x 1620
Model
Gas Pressure
(Pa)
Heat Load (mj / hr)
Voltage
(v / hz)
Weight
(kg)
Dimension
(mm)
GR-6
2800
135
240 / 50
480
1355 x 800 x 1780
Figure 2 : The li st of equipment used for exper i ment
4.0 DATA AND DISCUSSIONS
The r esult of br ead manufactur e pr ocesses in this exper iment is based on mean value of four samples at each set
of exper imental conditions for each pr ocess par ameter var iable. Table 7 show s the summar ized r esult and it is
found that exper iment number 4 is the best combination contr ol factor since the quantity of manufactur ed br ead
is gr eater in total accepted pr oduct.
4.1
Signal-to-Noise Ratio
The Taguchi’s method pr ovides the or thogonal ar r ay as a mathematical tool that allow s the analysis of the
r elationship betw een a lar ge number s of design par ameter s by using only a limited number of
exper iments r un. The tar get based on the conditions identified w hich r esulting the optimal pr ocess or
pr oduct per for mance. Her e, the S/ N r atio is the Taguchi advocates for measur ing the quality thr ough
or thogonal ar r ay based exper iments. The S/ N ratio is used to conver t the tr ial r esult data into a value for
the evaluation character istics as the optimum setting analysis.
In this study, the visual inspection against the output is to deter mine the quality char acter istics
consider ed of the br ead pr oduct. To analyze and deter mine w hich the optimal par ameter factor s for the
br ead manufacturing pr ocess, the r eject should be in minimum as the acceptable of output.
© 2012 GETview Limit ed. All right s reserved
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Global Engineers & Technologist s Review , Vol.2 No.9
(2012)
Table 7: Summarized results of manufactured bread quality (See Appendix)
Experiment
Trial
Total
accepted
1
2
3
4
5
6
7
8
9
12
13
0
14
15
7
11
11
12
Reject
Unr ipe
3
0
0
1
0
0
0
0
0
Defect Overcook
0
0
2
0
0
15
0
0
0
0
3
5
4
0
3
1
0
3
Total
3
2
15
1
0
8
4
4
3
The S/ N ratio selected for this study is lar ger -the-better quality char acter istics. The calculation and
equation for S/ N r atio is as following:
4.1.1 Larger- the- better:
S/ N = -10 log ( ∑ (1/yi2) / n)
Wher e,
yi = each obser vation value
n = number of obser vation (values at each tr ial condition)
The r esult is as Table 8.
Table 8: Calculat ion for S/ N r at io (Lar ger -t he-better )
n
1
1
1
1
1
1
1
1
1
y
3
2
15
1
0
8
4
4
3
y2
9
4
225
1
0
64
16
16
9
S/ N ratio
21.5836
22.2789
undefi ned
22.9226
23.5218
16.9020
20.8279
20.8279
21.5836
4.1.2 Main effect of signal-to-noise ratio (S/ N) response
The main effect plot is a plot of the mean r esponse values at each level of design par ameter or
pr ocess var iables. The sign of the main effect indicates the dir ection of the effect, w hether the
aver age r esponse value is incr eases or decr eases. The magnitude indicates the str ength of the
effect. If the effect of a design or pr ocess par ameter is positive, it implies that the aver age r esponse
is higher at high level then at low level of the par ameter setting. In contr ast, if the effect is negative,
it is means that the average r esponse at the low level setting of the par ameter mor e than at the
high level.
4.1.3 Main effect plot for signal-to-noise ratio
Figur e 3 show s S/ N gr aphs for the br ead manufactur ed exper iment r espectively. Basically, the
lar ger the S/ N ratio show s the better the quality. Based on this graph, it r eveals that the tr end of
the mixing type is less significant than other factor s. The S/ N r atio is slightly decr eased fr om low
setting to medium setting, but the S/ N r atio is slightly incr eased and appr oach to the mean line for
high type of mixing setting. For baking dur ation, the gr aph tr end begins w ith slightly incr eased
betw een 7 minute and 10 minute of baking pr ocess. The S/ N r atio value betw een 10 minutes and
13 minutes is squally decr eased and appr oach to the minimum of the S/ N r atio value. For baking
temper atur e, the S/ N r atio is smoothly incr eased betw een 195 oC and 200 oC and appr oach to the
maximum of S/ N r atio w hich then follow ed by slightly decr eased of baking temper atur e of 200 oC
to 205 oC. Based on the Figur e 3 and Table 9, it can be descr ibe that the baking dur ation is the main
factor influenced to the quality of the output of the br ead manufactur ing pr ocess. It show s that the
baking dur ation is the contr ol factor that has the most significant factor . The effect of the other
factor s (A and C) is of less significance.
Table 9 show s the aver age S/ N r atio values for the exper iment at thr ee levels setting of each
factor and the effect of each main effect on the S/ N ratio in r espectively. The mean of S/ N ratio for
per centage of the mixing type at level 1, level 2 and level 3 is 26.82, 26.40 and 26.48 in r espectively.
© 2012 GETview Limit ed. All right s reserved
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Global Engineers & Technologist s Review , Vol.2 No.9
(2012)
Clear ly, the S/ N r atio of mixing type at level 1 appear s to be the best choice since it cor r esponds to
the highest aver age S/ N r atio. The mean S/ N r atio of baking dur ation is 26.75, 26.92 and 25.62 at
level 1, level 2 and level 3 in r espectively. The S/ N r atio for baking dur ation suggests that
par ameter at level 2 is better than at level 1 and level 3. Level 2 show s as the highest aver age of
S/ N r atio and is consider ed as the best choice. For S/ N r atio of baking temper atur e, same as
pr evious, level w ith highest S/ N ratio will be chosen so; the best choice is at level 2 w ith 26.95.
Fr om the analysis above, the optimum parameter s selected to optimize the quality of br ead
manufactur ed ar e A1B2C2.
The maximum-minimum value is equal to the r ange of S/ N ratio var iance due to the change
in the level setting. The lar ger the r ange, the mor e pow er ful impact the contr ol factor has on
quality. The r anking in Table 10 show that S/ N ratio of baking dur ation, w hich has r anking 1, has
r elatively str ong impacts and influence on the quality of br ead. S/ N ratio of mixing type and baking
temper atur e w hich has r anking 2 and 3 r espectively have r elatively w eak impacts. So, S/ N ratio of
baking dur ation should be str ictly contr olled for high quality of br ead dur ing the manufactur ing
pr ocess.
Instead, Table 9 show s that the most significant factor for the output of the pr oduct is the
contr ol factor towar d B (Baking dur ation). The effect of the other factor s (A and C) is of less
significant. Since the objective of this study is to optimize the br ead pr oduct pr ocess, the S/ N r atio
should be maximal in or der to minimize variability. Thus, factor B should be to set at level 2 in
or der to get the maximum r esult.
Table 9 : Response Table for Signal to Noise Ratios
Main Effects Plot for SN ratios
Data Means
Mixing t ype
Level
Baking durat ion
27.0
Mean of SN rat ios
26.5
26.0
25.5
Low
Medium
High
7
10
13
Baking temperature
27.0
1
2
3
Differ ence (Max –Mi n)
Rank
Mixing
type
A
26.82
26.40
26.48
0.42
3
Baking
duration
B
26.75
26.92
25.62
1.30
1
Baking
temperature
C
25.87
26.95
26.90
1.08
2
*Higher value
26.5
26.0
25.5
195
200
205
Signal-to-noise: Larger is better
Figure 3 : Main effect s plot for S/ N r atio of exper iment
4.1.4 Main effect plot for means
Figur e 4 show s the mean gr aphs for the br ead manufactur ed exper iment. Basically, the lar ger of
the S/ N r atio is as the better of quality. Figur e 4 show s the best levels for each contr ol factor s to
obtain the optimal accepted of the br ead pr oduct. Based on the gr aph, it show s that the tr end of the
mixing type is less significant compar ed to other factor s. The mean is slightly decr eased fr om low
setting to medium setting, but then slightly incr eased by appr oaching the mean line for high type of
mixing setting. For baking dur ation, the graph tr end begins with slightly incr eased betw een 7
minute and 10 minute of baking pr ocess. The mean value betw een 10 minute and 13 minute is
squally decr eased by appr oaching the minimum of the S/ N ratio value. For baking temper atur e, the
mean is smoothly incr eased betw een 195 oC and 200 oC by appr oaching the maximum of mean and
follow by slightly decr ease for baking temper atur e of 200 oC to 205 oC. Fr om the Figur e 4 and Table
10, it can be described that the baking dur ation is the main factor that influence the quality of the
output of the br ead manufactur ing pr ocess. It show s that the baking dur ation is the contr ol factor
that has the most significant factor . The effect of the other factor s (A and C) is of less significance.
Table 10 show s the aver age mean values for the exper iment r espectively at thr ee levels
setting of each factor and the effect of each main effect on the mean. The mean of mean values for
the mixing type at level 1, level 2 and level 3 is 21.93, 21.12 and 21.08 in r espectively. Clear ly, the
mean of mixing type at level 1 appear s to be the best choice since it cor r esponds to the highest
aver age mean. The mean of baking duration is 21.78, 22.21 and 19.24 at level 1, level 2 and level 3
in r espectively. This is means that for the baking dur ation, the par ameter at level 2 is better than at
level 1 and level 3. Same as pr eviously, the level w ith highest mean w ill be chosen as the best
choice, w hich is 22.26.
Based the analysis above, the optimum par ameter s selected to optimize the quality of br ead
manufactur ed is A1B2C2. Since the maximum-minimum value is equal to the range of mean variance
© 2012 GETview Limit ed. All right s reserved
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Global Engineers & Technologist s Review , Vol.2 No.9
(2012)
due to the change in the level setting, the lar ger the r ange, the mor e pow er ful impact the contr ol
factor has on quality. The r anking in Table 11 show s that the baking dur ation has r anking 1 and
r elatively str ong impacts and influence on the quality of br ead. Thus, the mixing type and baking
temper atur e ar e the r anking 2 and 3 w her e they ar e r elatively give the w eak impacts..
Table 10 : Response Table for Means
Main Effects Plot for Means
Data Means
Mixing t ype
Baking durat ion
Level
22
Mean of Means
21
1
2
3
Differ ence (Max –Mi n)
Rank
20
19
Low
Mediu m
Bakin g t empe rat ure
Hig h
7
10
13
22
Mixing
type
A
21.93
21.12
21.08
0.85
3
Baking
duration
B
21.78
22.21
19.24
2.97
1
Baking
temperature
C
19.77
22.26
22.17
2.49
2
*Higher value
21
20
19
1 95
200
20 5
Figure 4 : Main effect s plot for Dat a Means of exper i ment
Table 10 show s that the most significant factor for the output of the pr oduct is contr ol factor
B (Baking duration). The effect of the other factor s (A and C) is of less significant. Since the
objective of this pr oject is to optimize the br ead pr oduct pr ocess, w hat is r equir ed is to the
maximum values in or der to minimize variability. Thus, factor B should be to set to Level 2 in or der
to get the maximum r esult.
4.2
Analysis of Variance (ANOVA)
The pur pose of the ANOVA is to investigate and r ecognize w hich pr ocess have the significantly affects to
the quality of the manufactur ed br ead. Fr om the analysis, it is used to identify w hich factor s ar e the most
impor tant in ter ms of quality character istic. Consequently, the impor tant factor s identified have to be
pr oper ly monitor ed dur ing the pr ocess for a consistently gather high quality of br ead pr oduct.
The ANOVA analysis is per for med by noting the sour ces of var iation in the left hand column, w hich
ar e the factor s under the exper iment. The descr iption of the label in ANOVA analysis is as follow :
Table 11 : List of ANOVA l abel
Label
DOF
SS
MS
F
P
Descr iption
Indicat es degr ee of fr eedom for the factor
Sum of squar e
- Squar ed deviation of a r andom var i able fr om means.
Mean squar e
- Sum of squar e divi di ng by the number of degr ee of fr eedom
associated w i th the factor s effects.
Result of the tr aditional Fi sher test for si gnificance
- Measur e effect of each factor or i nter act ion r el ative to the er r or .
Indicat es the per cent contr ibution of each factor .
Table 12 : ANOVA for S/ N r at io
Factor s
Mixing type
Baki ng dur ation
Baki ng temper at ur e
Residual er r or
Total
DOF
2
2
2
1
7
Seq SS
0.2295
2.0408
1.7530
1.4064
5.4297
Adj SS
0.01016
1.40689
1.75299
1.40641
Adj MS
0.00508
0.70345
0.87649
1.40641
F
0.00
0.50
0.62
Adj MS
0.00252
3.60445
4.69697
7.43794
F
0.00
0.48
0.63
P
0.996
0.707
0.667
Table 13 : ANOVA for Mean
Factor s
Mixing type
Baki ng dur ation
Baki ng temper at ur e
Residual er r or
Total
DOF
2
2
2
1
7
Seq SS
1.044
10.600
9.394
7.436
28.477
Adj SS
0.00505
7.20890
9.39395
7.43794
P
1.000
0.713
0.665
© 2012 GETview Limit ed. All right s reserved
15
Global Engineers & Technologist s Review , Vol.2 No.9
(2012)
At the end of the exper iment, the r esult show s that the combination of par ameter s factor s pr oduce
the differ ent output of the br ead manufactur ing pr ocess. The influences of the combination car r ied out
ar e illustr ated in Figur e 5 for all of 9 exper imental tr ial, w her e the defects detected at the each of the
matr ix ar ray location on the tr ay. It show s that the most high defect ar ea is at the b3 and c3 ar ea w hich
consist of 5 defects. The ar ea of b1 and c1 as the second highest defects ar ea w ith 4 defects occur red
dur ing the exper iment. Fr om the exper iment of factor arr angement in or thogonal ar r ay (OA), the location
of the defects r elated to the combination of setting par ameter factor , such as mixing type, baking dur ation
and baking temper atur e identified. It is found that the highest of defects ar ea occur r ed at the 2, 3, 6,7,8,9
ar r angement number of exper iment tr ial.
Figure 5 : Br ead locat ion for over all exper iment tr ial s
Based on the combination par ameter factor s, it is found that the setting par ameter of L-H-H , M-H-L
and H-L-L ar e having gr eater influences to the r ejected output at the cr itical location (b3 and c3). Since
the specific location found as the r epeatable defects location, the fur ther study r equir ed is by analyzing
the influence of the heat tr ansfer in the oven that may affect the quality of the br ead.
5.0 CONCLUSION
In this pr oject, the mixing type, baking dur ation and baking temper atur e is identified as the parameter factor s
that affects to the quality of food (br ead) manufactur ing pr ocess. The analysis using Taguchi‘s method show s
that the optimal r esult achieved w hen the parameter setting of the pr ocess is A1B2C2. This is means that the
mixing type should set to low , baking dur ation at 10 minutes and baking temper atur e at 200 0C. This optimum
pr ocess of contr olled par ameter s setting indicated that the baking dur ation is as the most significant factor .
Since the measur ement of the br ead quality char acter istic in this study is deter mined by visual inspection, the
development method to inspect the taste, w eight, surface peak, shape, textur e or str uctur e of br ead pr oducts ar e
also r equir ed for fur ther study. In addition, the fur ther study r equired is against the heat tr ansfer distr ibution of
the oven that is influenced by location of heat gener ator (micr ow ave) tow ar ds the objects.
ACKNOWLEDMENTS
The author s w ould like to thank CRIM-UTeM. This pr oject is suppor ted by CRIM thr ough PJP/ 2011/ FKP (11D)
S00878.
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Global Engineers & Technologist s Review , Vol.2 No.9
(2012)
1
2
3
L
L
L
L
M
H
Baking
Temper ature ( oC)
Baking Time
Dur ation ( min)
Mixing
Type
Exper imental
Tr ials
APPENDIX
Qty
b1
c1
ov
0
TOV
0
b2
c2
rp
3
TRP
3
L
a3
b3
0
M
b3
td
ta
ov
rp
df
3
12
0
0
2
TDF
TD
TA
TOV
TRP
TDF
0
b4
b5
b1
b2
c3
c4
c5
c1
c2
c3
df
a4
a5
a1
a2
a3
3
12
0
0
2
a4
a5
a1
a2
a3
a4
a5
b4
b5
b1
b2
b3
b4
b5
c4
c5
c1
c2
c3
c4
c5
td
ta
ov
rp
df
td
ta
2
13
15
0
0
15
0
TD
TA
TOV
TRP
TDF
TD
TA
2
13
15
0
0
15
0
a1
b1
b2
b3
b4
b5
b1
b2
b3
b4
b5
b1
b2
c1
ov
0
TOV
0
a2
a3
a4
a5
a1
a2
a3
a4
a5
a1
a2
c2
c3
c4
c5
c1
c2
c3
c4
c5
c1
rp
df
td
ta
ov
rp
df
td
ta
ov
1
0
0
14
0
0
0
0
15
5
TRP
TDF
TD
TA
TOV
TRP
TDF
TD
TA
TOV
1
0
1
14
0
0
0
0
15
5
c2
rp
0
TRP
0
a3
b3
c3
df
3
TDF
3
td
8
TD
8
ta
11
TA
7
c3
c4
c5
c1
c2
c3
ov
rp
df
td
ta
ov
rp
df
td
ta
ov
rp
df
0
0
4
4
11
1
0
3
4
11
3
0
0
TOV
TRP
TDF
TD
TA
TOV
TRP
TDF
TD
TA
TOV
TRP
TDF
0
0
4
4
11
1
0
3
4
11
3
0
0
c4
td
3
TD
3
c5
ta
12
TA
12
H
L
M
5
M
M
H
6
M
H
L
7
H
L
H
8
H
M
L
H
Tr ay
Total
a2
M
H
Pictur e
a1
4
9
Quality Char acter istics
( Sur face Inspection)
M
a4
b4
a5
a1
a2
a3
a4
a5
a1
a2
a3
a4
b5
c4
c5
b1
b2
c1
c2
b3
b4
b5
b1
b2
b3
b4
c3
c4
c5
c1
c2
a5
a1
a2
a3
a4
b5
b1
b2
b3
b4
a5
b5
© 2012 GETview Limit ed. All right s reserved
18