Keywords: Fuzzy Logic, Textile Industry,
Production Optimization.
1
FASHION
BUSINESS DYNAMICS
The
fashion segment in
The
most important events in the fashion industry are fairs. A fair is a place
where the wholesale buyers select products from many brands and places their
orders. One Brand is supported by many other enterprises from the textile
segment which provide the main brand with different kinds of services and raw
material in a supply chain operation.
At
the end of these events, many products will have been selected in client’s
orders. These products will compose the shop window in the next season. The
production starts in the plant based on current sales and sales projections.
For
the next 45 days the sales representatives take up sales from their offices and
showrooms. They will collect orders and send them to the manufactures.
In 60
days, a first group of product is delivered. During the next two months other
deliveries will complete the orders.
In
the manufactures, by the end of the event, the production manager will unify a
complete list of orders. From a complete collection, 25% of products must
immediately be manufactured based on a bet. This bet is a judgment made by
designers and production managers that cross sales projection to define a lot
size that must be produced.
The
bet is necessary to make the delivery possible for some product, 15 days after
the orders have been emitted by sales representative.
Owing
to the fact that the minimum lot must be respected (SLACK, 1997), many products
will not be manufactured according to the sales results.
The
production managers expect that the projection of sales, that rules the
production of 25% of the clothes collection, become real, otherwise the
products will increase stocks.
The
storage increase is a problem to be solved in many industrial segments,
especially in the fashion industry where the season’s change brings
obsolescence to the product mix. When the season finishes, the product sales
value will decrease, increasing financial losses.
|
Fig. 1 – Product value decrease along time in
fashion business industry. Source: LODE, 2011. |
2
THE
MOTIVATION TO CREATE A DECISION MAKING TOOL
This
case study started as a contribution to help managers to make a better bet. The
storage increase is a problem that hinders the profit results and is the reason
why many firms have doors closed.
While
the designers think that the business is, basically, to create clothes collections,
production managers have the challenge to optimize the supply chain and sales
to turn investment into profits, the main core business. The fashion industries
would not be a reality without the professional management approach and many
other tools that support it.
The
desire algorithm was developed to solve the problem of storage increase in
fashion industries but the research has shown the algorithm's potential in
applications to other industries. It can measure the customer’s attractiveness
to any product, it can also define priority markets in launches and prices
policies by ranking fuzzy numbers maximizing and minimizing sets (CHEN, 1985).
The
main objective is to maximize premium-selling price from manufacture and reduce
the stock levels. The price markup represents the value achieved for a monetary
unit invested in a product. The value decreases along the time until it
achieves a stock value, as seen below in the markup table:
Table 1 – The
Markup common in the fashion business industries.
|
PRICE |
PREMIUM |
CLEARANCE |
STORAGE |
|
MARKUP |
2,5 |
1,8 |
0,8 |
|
PRODUCTION COST ($
100) |
$ 250 |
$ 180 |
$ 80 |
|
PROFIT/ LOSSES |
$ 150 |
$ 80 |
- $ 20 |
3
WHY
THE USE OF FUZZY LOGIC IS INDICATED?
Lotfi
Zadeh presented fuzzy logic to the world in
About
fuzzy logic applications, Terano (1992) says: “The outstanding feature of fuzzy
sets is the ability to express the amount of ambiguity in human thinking and
subjectivity (including natural language) in a comparatively undistorted
manner.”
The
fuzzy logic was selected to this work because it is a typical case of
imprecision (ROSS, 2010). The facts that compose the value perception by
customers are the first attributes that must be identified. These facts must be
translated in criteria and a group of linguistic expressions.
During
the researches, the fuzzy logic was used to build a model that could select in
a collection, which of the products would have commercial success. Other models
for selection and decision based on fuzzy logic have been very successful in
locational studies (
The
conception using fuzzy logic gave us the freedom to deal with ambiguity and
imprecision (ROSS, 2010), common in fashion business environment, in a product
evaluation and obtain as a result some secure information to direct manager
decisions.
4
POTENTIAL
OPPORTUNITIES
When
this work started, the main claim was to create an algorithm that could preview
the costumer desire as a solution to a problem in the textile industry, more
specifically in clothes production ruled by the fashion calendar and tendency.
Understanding the demand for products in fashion
industry is very difficult (ABRANCHES, 1990). In fact, it is a judgment made by
experienced professionals that capture the fashion tendencies or, by the use of
intense marketing and advertising budget, they turn it real (CHURCHILL, 2000).
While
a fashion tendency is created by big brands and budgets, another group,
composed by small and medium size manufactures, maintains their brands
observing the whole market.
The
fashion designers usually work in research for tendencies and in new products
development based on research on other collections, redesigning successful
products.
As
problems, the tendencies changes every season, new materials and techniques are
developed and the final products have a short life cycle (PORTER, 2004) where
the main value is depreciated in few months.
A
research in fashion business tendency could minimize the deflection from the
main tendency, but the diversity and creativity make the industry rich by the
value perception in original product ideas. That is innovation (KOTLER, 2005).
So,
what should be the methodology to create original products and launch them in
the market with success?
In
fact, this work does not answer this question but helps the brands understand
what product should be definitively excluded from a collection and which of
them should receive more attention in a bet based production. Moreover, it can
help managers to minimize the quantity of product in storage by adjusting price
rules based on value perception by customers group (BLACKWELL, 2005).
By
the use of a GIS tool and a regional research, using the desire algorithm, the
brands can establish a regional sequence to launch their products and the price
levels that will be well received by the market. Minimize losses and offer
valued products to the market, in a scenario that investments are maximized,
are the basic premise (SLACK, 1997). The challenge to plan a large-scale
production in a segment where the historic information does not exist is too
big.
How
do we preserve a short industry from a competitive disadvantage (PORTER, 2004)
configured by high levels in storage?
The
Desire’s algorithm provides a tool to analyze value to goods in many industrial
segments and helps product launches and distribution rules. In the actual phase
it can help as a research tool to test value perception of many goods in
markets. It can also be applied in different ranges of regional areas where the
product value is considered higher than others. At this point, the distribution
of goods in a large-scale production and price's policies can be solved by
maximum profits.
The
textile industry in
Especially
in
A
profit can be obtained in a logistic operation where the products are delivered
first where it achieves a higher value perception. Of course it should be used
together with other tools such as: GIS, ERP systems in which all operational
information are computed.
As
the customer accepts the products, achieving an equivalent value and sales
results are consequence. This is why the algorithm is called Desire’s
Algorithm, because it measures value perception (CHURCHILL, 2000) from the
customer in a price and its attractiveness. The industry, in this context,
gains a different tool to provide researches before launching a new product or
a new model.
In a
direction for sustainability, followed globally, tools and models that reduce
the impacts on the environment are absolutely welcome.
The
cotton production uses much water, electric energy and manpower. From the raw
material to the end product, the industrial process causes many environmental
impacts. If the environmental cost is added, the problem grows. It represents a
loss that should be accounted (CLEMENTE, 1998).
When
the raw material usage is maximized in a succeeded production, which entire lot
is sold in a better price, the environmental resources consumed are well
applied. Once textile industry uses caustic soda, sulphur and other waste,
maximizing resources is absolutely important in a sustainability world
panorama.
5
THE
METHODOLOGY
To
build an algorithm to the textile industry it is absolutely necessary to have
an interaction with many professionals in the fashion business environment.
A
group of interviews was done in a medium size factory with designers,
production managers, buyers, sales representatives, photographers, fashion
producers and customers.
The
main group of criteria was pointed by these professionals and adapted to the
linguistic expressions used in the fashion segment.
Five criteria are used in desire algorithm. In this
case, the criteria are: desirability, colour, price, versatility and modeling.
These criteria were considered important drivers of buying decision-making.
The
criteria are balanced by the use of the modifiers: moderate important,
important and very important.
To
complete the algorithm, the appraiser is considered in the product evaluation.
This fact is relevant because different professionals have distinct interaction
with the conception and commercialization of the product.
The
algorithm of desire, in the final conception, is a decision-making tool that
helps managers to decide which product must be manufactured and receive more
attention in a typical bet. Minimizing
the bet error represents reducing investment in bad product and maximizing
investment in a product well evaluated.
Finally,
the model presented in this work will show how the main index is created, the
ranking of decisions with four outputs and simulation of expected returns. The
appraiser opinion will not be computed although it is a very simple
mathematical operation. It would increase the index from 576 to 4032 options of
possible results.
6
THE
DESIRE’S ALGORITHM
The
algorithm modeled for fashion business segment, in the textile industry, was
developed and applied in a cooperative work with professionals in a small
factory, which guides the case study.
The
style department, represented by designers, had discussed about the main
factors that customers would consider to select a group of products in their
orders, from a mix of 400 products.
The
model was created to analyze many products in a collection and present to
managers information about the attractiveness between products and customers,
considering a determined price in a determined place (BLACKWELL, 2005).
Fuzzy
logic comprehension is absolutely necessary to build the model. The experience
to turn linguistic expression in value, in this case, fuzzy numbers that
represent a region of values is the main point to obtain results from the
imprecise information (ROSS, 2010).
It is
based in 5 criteria for basic evaluation of the attractiveness power for a
determinate product: desirability, color, price, modeling and versatility.
The
criteria are influenced by modifiers which give the criteria an extra weight:
very important, important and moderately important. Each criteria has its own
parameters represented by linguistic variables translated in fuzzy sets.
The
desire’s algorithm considers different kinds of professional as appraisers and
the mathematical model understands these opinions with different weights
through the fuzzy sets. The appraisers listed to this model were: internal
designers, external designers, fashion producers, sales manager, visitor,
customers and sales representatives.
Basically,
criteria, modifiers, and appraisers compose the mathematical model. The fuzzy
sets are presented in the following table:
Table 2 – Five
criteria represented by fuzzy sets used in the Desire’s Algorithm.
|
CRITERIAS |
||||
|
C2 - Color |
C4 – Versatility |
C5 – Modeling |
||
|
Unwanted (0, 0, 3, 4) |
Don’t Like (0, 1, 2, 3) |
Cheap (8, 9, 10, 10) |
Versatile Bit (1, 2, 2, 3) |
Do Not Dress Well (1, 2, 2, 3) |
|
Indifferent (2, 3, 4, 5) |
Indifferent |
Coherent (5, 6, 7, 8) |
Versatile (5, 6, 7, 8) |
Dresses Well (5, 6, 7, 8) |
|
Desirable (5, 6, 7, 8) |
Like (5, 6, 8, 9) |
Expensive (3, 4, 5, 6) |
Very Versatile (7, 8, 10, 10) |
Dress Perfectly (7, 8, 10, 10) |
|
Very Desirable (7, 8, 10, 10) |
Much Like (8, 9, 10, 10) |
Very Expensive (0, 1, 2, 3) |
|
|
Table 3 – Fuzzy
Sets for criteria used in the Desire’s Algorithm.
|
FUZZY SETS FOR CRITERIAS |
||||
|
C1 – Desirability |
C2 - Color |
C3 - Price |
C4 – Versatility |
C5 – Modeling |
|
|
|
|
|
|
Table 4 – Three
modifiers and fuzzy sets used in the Desire’s Algorithm.
|
MODIFIERS |
CRITERIAS |
GRAFIC |
|
M1 - Very Important (7, 8, 10, 10) |
C1 – Desirability C2 - Color |
|
|
M2 – Important (5, 6, 7, 8) |
C3 - Price |
|
|
M3 - Moderately Important (3, 4, 5, 6) |
C4 – Versatility |
6.1 Processing Data
Step 1 - Apply the Modifier On Criteria for each
response
[C1 (d, a, b, c) M1 (d, a, b, c)] = C1’ (d, a, b, c)
[C2 (d, a, b, c)
M1 (d, a, b, c)] = C2’ (d, a, b, c)
[C3 (d, a, b, c)
M2 (d, a, b, c)] = C3’ (d, a, b, c)
[C4 (d, a, b, c)
M3 (d, a, b, c)] = C4’ (d, a, b, c)
[C5 (d, a, b, c)
M3 (d, a, b, c)] = C5’ (d, a, b, c)
Step
2 – Sum the Criteria to achieve the Fuzzy Rate
C1’ (d, a, b, c)
C2’ (d, a, b, c)
C3’ (d, a, b, c)
C4’ (d, a, b, c)
C5’ (d, a, b, c) = Fuzzy Rate (d, a, b, c)
Step 3 – Sum the Fuzzy Rate Line
d + a + b + c =
Attractiveness Index
Step 4 – Calculate all possible combination of
Criteria and normalize results.
A
list of 576 Attractiveness Index will be found as the result of crossing all
response alternatives:
Figure 1 – Curve
plotted by the 576 different possibilities of answers in product evaluation.
Table 5 –
Desire’s Algorithm - evaluation of price for the same product.
|
STEPS |
PRODUCT 1 |
PRODUCT 2 |
PRODUCT 3 |
PRODUCT 4 |
|
|
MODIFIED
CRITERIA C
(d, a, b, c) |
C1’
(D, A, B, C) |
VERY DESIRABLE (49, 64, 100, 100) |
VERY DESIRABLE (49, 64, 100, 100) |
VERY DESIRABLE (49, 64, 100, 100) |
VERY DESIRABLE (49, 64, 100, 100) |
|
C2’
(D, A, B, C) |
LIKE (35, 48, 80, 90) |
LIKE (35, 48, 80, 90) |
LIKE (35, 48, 80, 90) |
LIKE (35, 48, 80, 90) |
|
|
C3’
(D, A, B, C) |
VERY EXPENSIVE (0, 6, 14, 24) |
EXPENSIVE (15, 24, 35, 48) |
COHERENT (25, 36, 49, 64) |
CHEAP (40, 54, 70, 80) |
|
|
C4’
(D, A, B, C) |
VERSATILE (15, 24, 35, 48) |
VERSATILE (15, 24, 35, 48) |
VERSATILE (15, 24, 35, 48) |
VERSATILE (15, 24, 35, 48) |
|
|
C5’
(D, A, B, C) |
DO NOT DRESS WELL (3, 8, 10, 18) |
DO NOT DRESS WELL (3, 8, 10, 18) |
DO NOT DRESS WELL (3, 8, 10, 18) |
DO NOT DRESS WELL (3, 8, 10, 18) |
|
|
FUZZY SUM |
|
(102, 150, 225, 280) |
(117, 168, 260, 304) |
(127, 180, 274, 320) |
(142, 198, 295, 336) |
|
ATTRACTIVENESS
INDEX |
(d
+ a + b + c ) |
757 |
849 |
901 |
971 |
|
ATTRACTIVENESS
INDEX / 1211 |
FINAL
RESULT |
0,62510 |
0,70107 |
0,74401 |
0,80182 |
6.2 Algorithm Outputs
Now
it’s time to take decisions based on risk analysis. As outputs, the decisions
options are listed in 4 categories divided by intervals:
Do
Not Produce: Attractiveness Index – Less than 0,55 (194 results)
The
algorithm considers that the product is not good enough to be manufactured. In
other words, the product was rejected by the appraisers’ opinion and represents
a high-risk investment. It will increase losses in the product mix.
Low Attraction: Attractiveness Index
– Between 0,55 and 0,65 (148 results)
The
product can be manufactured but represents a high-risk investment. The
premium-selling price will not be achieved, a large part of the lot will be
sold in the clearance-selling and another part will end up in the storage.
The
production managers shall take a look at the production cost and check if a
price change would increase the product attraction.
Medium Attraction: Attractiveness Index – Between 0,65
and 0,75 (129 results)
The
product will be well sold. This product will probably have a good
premium-selling and a short quantity of products will be sold in a
clearance-selling. These products are very important in the product mix and
represent a medium risk investment with major probability of success.
High Attraction: Attractiveness
Index – More than 0,75 (105 results)
The
product is very well accepted in a premium-selling price and a residual part of
the lot will be completely sold in the clearance-selling. These products are
premium and represent the best manufacture option. It represents the minimum
risk in investment portfolio.
6.3 Analyzing Results
As
seen below, based on results exhibited in table 5, the output can be analyzed
for evaluation of attractiveness.
Observe
that the price policies can be reviewed in this example. The medium attraction
result appears in two products and the customer can accept the expensive price.
The manager can take the decision to maintain the expensive price and exceed
the markup margin and, in the clearance selling, change price from to the
coherent, maintaining the attractiveness.
Table 6 –
Desire’s Algorithm - evaluation of attractiveness.
|
DESIRE’S ALGORITHM |
PRODUCT 1 |
PRODUCT 2 |
PRODUCT 3 |
PRODUCT 4 |
|
FINAL RESULT |
0,62510 |
0,70107 |
0,74401 |
0,80182 |
|
OUT PUT |
LOW ATTRACTION |
MEDIUM ATTRACTION |
MEDIUM ATTRACTION |
HIGH ATTRACTION |
6.4 Expected Value in Bet
Table 7 –
Attractiveness output and respective sales probability.
|
MARKUP |
PREMIUM 2,5 |
CLEARANCE 1,8 |
STOCK 0,8 |
EXPECTED
VALUE |
|||
|
OUTPUT |
|||||||
|
DO NOT PRODUCE |
0% |
$ 0,00 |
30% |
$ 0,54 |
70% |
$ 0,56 |
$ 1,10 |
|
LOW ATTRACTION |
20% |
$ 0,50 |
50% |
$ 0,90 |
30% |
$ 0,24 |
$ 1,64 |
|
MEDIUM ATTRACTION |
50% |
$ 1,25 |
40% |
$ 0,72 |
10% |
$ 0,08 |
$ 2,05 |
|
HIGH ATTRACTION |
80% |
$ 2,00 |
20% |
$ 0,36 |
0% |
$ 0,00 |
$ 2,36 |
Table
7 presents the simulation of sales divided by the different output. As the
attraction between customers and products grows the sales results increase the
expected value. The premium price is achieved with more intensity in the high
attraction group.
This
model is experimental. It has been tested with great results for some brands in
The
old method to process the bet is a prediction about future sales based in the
launch sales. The productions managers usually do the bet multiplying launch
sales by two and half times.
7
CONCLUSIONS
The
fuzzy model is more sensible to treat data and produces a straight approach to
the customer’s desire. It is a tool that helps the production managers to give
a better look at the production prediction. It can anticipate the problems that
cause losses and consequently reduce profit.
Observe
that the best result in sale's probability achieves an average of
As
production manager, the selection of a good portfolio of products, represented
by a successful collection, must be done based in profit possibilities. That is
the main point that must be achieved.
8
FINAL
CONSIDERATIONS
As
final consideration, the fuzzy model called Desire’s Algorithm is a tool that
must be developed and the researches done until the moment show that crossing
GIS information with the algorithm results can be very important to the fashion
business development.
The
use of new interfaces based on applications made for tablets are being studied
as a way to collect customer’s opinions in fashion fair and events. The
regional opinion can be collected in sales points and appraisers from many
parts of the country can be added to generate a regional view of acceptance of
determined product mix.
REFERENCES
ABRANCHES, G. P. (1990) Manual da gerência de confecção: a indústria de confecções de
estrutura elementar – VOLUME I. Rio de Janeiro: SENAI-CETIQT: CNPQ : IBICT:
PADCT: TIB.
BLACKWELL, R. D. (2005) Comportamento do consumidor.
CHEN, Shan-Huo.
(1985) Ranking Fuzzy Numbers with
maximizing and minimizing set. Fuzzy Sets and Systems. North-Holland:
Elsevier Science Publishers.
CHURCHILL, G. A. (2000) Marketing: Criando valor para o cliente. São Paulo: Saraiva.
CLEMENTE, A. (1998) Projetos Empresariais e Públicos.
DORIA, F.; DORIA, P. (1999) Comunicação: dos fundamentos à internet. Rio de Janeiro: Renavan.
HASS, R.;
MEIXNER, O.(2002) An illustrated Guide to the Analytic Hierarchy Process.
KOTLER, P. (1996) Administração
de Marketing: análise, planejamento, implementação e controle. 4a ed. São
Paulo: Atlas.
KOLTLER, P. (2005) O Marketing sem segredos: Philip Kotler responde as suas dúvidas. Porto
Alegre: Bookman.
LIANG, Gin-Shuh. WANG, Mao-Jiun A
fuzzy multi-criteria decision-making method for facility site selection. INT. J.
NARASIMHAN, R. (1979)
A fuzzy subset characterization of a site selection problem. Decision Sciences, v. 10, n. 4p.
618-628.
NARASIMHAN, R.
(1991) A fuzzy subset characterization of a site selection problem. PROD. RES. V. 29, n. 11, p. 2313-2330.
PORTER, M. E. (2004) Estratégia Competitiva: técnicas para a análise de indústrias e da
concorrência. 3a re-impressão.
ROSS, T. J. (2010)
Fuzzy Logic with engineering
applications. 3rd edition.
SLACK, N. et al.
(1997)
Administração da Produção. São
Paulo: Atlas.
TERANO, T. (1992)
Fuzzy Systems Theory and Its Aplication.