(Thomassey et al., 2002). The effectiveness of the supply chain optimisation depends on
the forecast accuracy of the finished product sales (Graves et al., 1998).
The fashion industry is characterised by aggressive competition levels, a large
marketplace sourcing high-variety, high-margin, short product life cycles in a global
context, high level of impulse purchasing and unpredictable demand (Bruce et al., 2004;
Mason et al., 2007; Cao et al., 2008). The short life cycle of products implies that
available sales data are reduced (Thomassey et al., 2002). Demand forecasting in
apparel industry is very complex. Indeed, a wide range of textile item references exists,
and their historic sale data are often short and particularly perturbed by numerous
factors, which are neither strictly controlled nor identified (DeToni and Meneghetti,
2000). These factors can depend on the item (colours, prices, etc.), distributor (number
of stores, merchandizing, etc.), customers (fashion, etc.) or external factors (weather,
holidays, etc.). These factors have different impacts on sales and not always available.
In the apparel industry, the lead times from retailer order to delivery are quite long.
As a result of these long lead times, the risks of having too little or too much inventory
increases if the retailers have to place the orders long before the season. In addition to
long lead times, product proliferation is increasing the risks a manufacturer faces. As the
product proliferation increases, the variability of demand for each time also increases
(Tan, 2001). The long supply pipeline makes the lead time of textile-apparel supply chain
relatively long and uncertain in response to the volatile characteristics of fashion markets.
So, the coordination in textile-apparel supply chain becomes even more important
(Cao et al., 2008). In order to reduce stocks and to limit stock out, apparel companies require
specific and accurate demand forecasting systems (Thomassey et al., 2005).
The last decade apparel industry has learnt that the supply chain can be made
more efficient in order to decrease lead times and related demand forecast errors
(Jacobs, 2006). A demand forecasting system is required to respond to the versatile
fashion market and the needs of the distributors. Nowadays, due to the specific constraints
of the apparel sales (numerous and new items, short life time), existing forecasting models
are generally unsuitable or unusable (Thomassey and Fiordaliso, 2006).
An apparel demand forecasting system requires; to quickly react to a significant
variation of trend and seasonality, to identify and to smooth purely random events, to
perform forecasting on short historic sales data and to take into account the influences
of explanatory variables such as: product features, marketing strategy, distribution
area, distribution mode, competitive environment, space-time environment, consumer
environment, economic situation (Thomassey et al., 2002).
2.2 Forecasting in supply chain management
To have an available decision-making system is becoming a crucial issue for
organisations in a constantly fluctuating environment where the economic uncertainty
needs mathematical models. Forecasting the expected demand for a certain period of
time with one or more products is one of the most relevant targets in an enterprise.
Despite the need for accurate forecasting to enhance the commercial competitive
advantage, there is no standard approach (Efendigil et al., 2009).
In practice and in literature, various demand forecasting techniques have been
studied and used. Most of these techniques are based on statistical methods such as
moving average, time series analysis, exponential smoothing, Box-Jenkins method, and
casual models. These methods assume that historical data are recorded in the past and