'Recent Trends in Utilization of
Fiber, Yarn, Fabrics and Computer Technology in Fashion Designing and
Management'
Recent trends in fiber, yarn
and fabric management in Textile-Apparel-Distribution network require a very
accurate estimation of consumption pattern and sourcing management to minimize
their costs and satisfy their customers. For such strategy, fashion designers
rely more on fashion forecasting system to respond to the versatile textile
market. However, the specific constraints of the textile consumption and sales
(numerous and new items, short lifetime) complicate the forecasting procedure
and distributors prefer to use intuitive estimation methods of the sales rather
than the existing forecasting models. In this paper we firstly discuss the different
aspects used for improving the accuracy of fashion product development and
forecasting process, use of computer technology in management of fiber, yarn
and fabrics for estimation of their consumption. We also demonstrate a decision
aid system, based on neural networks, which automatically performs item sales
forecasting of textile items with two models being demonstrated for silk fabric
consumption and cotton-textiles fabric consumption. Performances of our model
are evaluated using real data released by authorized agencies. Network
forecasted results show excellent correlation with real data emphasizing the
its advantages over traditionally, statistical time series methods like moving
average, auto-regression or combinations of them used for forecasting apparel
and fashion product sales.
Keywords: Fashion forecasting, 2-D and 3-D cad system, Neural
Network forecasting model, time series, Texture mapping.
Introduction
Introducing new products of Apparels made from different variations of
fibers, yarns and fabrics can provide a competitive advantage as well as a
long-term financial return on investment. New product development is thus
vitally linked to a company's competitive strategy and consequently to the new
product sales forecasting of apparels and textiles that provides quantitative
information on the expected return from development
efforts. Sales forecasting of apparels and textiles attempts to decrease
uncertainty, providing input for the decisions made about introducing of new
products and offering feedback for the new product development process. The
significant correlation between new product development and new product sales
forecasts makes new product forecasting extremely important.
Traditionally, statistical
time series methods like moving average, auto-regression or combinations of
them are used for forecasting fashion trends and sales of fashion products.
Since these models predict future sales only on the basis of previous sales,
they fail in environments where sales are heavily influenced by exogenous
variables, such as economic conditions, climatic conditions, competitive
activities, advertising and several factors. Although traditional statistical
models characterize the above mentioned factors, they are essentially linear in
nature and don't characterize the nonlinear nature of apparel sales and analyse
fibre, yarn, fabric consumption trends. Fuzzy logic, artificial neural networks
coupled with latest applications of computer technology for fashion design and
management and genetic algorithms(Gas) offer an alternative approach by taking
into account both endogenous and exogenous variables and allowing arbitrary
non-linear approximation functions derived(learned directly from the previous
data).
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About the Authors
The authors are associated
with Central Silk Technological Research Institute (CSTRI), Central Silk Board,
Ministry of Textiles, Madivala, Bangalore, India and Department of Electronics
and Communication University Visveshvaraya College of Engineering (UVCE), Bangalore University K. R. Circle, Bangalore, India
