Demand forecasting plays an important role in basic
Operations Management as an input for planning
activities. Poor forecasting effects are stock outs or high
inventory, obsolescence, low service level, rush orders,
inefficient resource utilization and bullwhip propagating
through the upstream supply chain. As such, demand
forecasting is a popular research topic and many models
for forecasting fashion products have been proposed in
the literature over the past few decades.
Typically, high performance companies focus on robust
demand forecasting approaches; however, the challenge
of demand forecasting varies greatly according to
company and industry. In the fashion industry, products
are usually characterized by long replenishment lead
times, short selling seasons and nearly unpredictable
demand and therefore, inaccurate forecasts [1]. All these
features make the issue of forecasting demand
particularly challenging. Companies in the fashion
industry have been trying to manage the demand for
many years, which has brought about the development of
a number of specific forecasting methods and techniques.
Much of this earlier work was intended to create insights
and tools for improving the demand forecasting of
fashion products. However, the reality that is now
gradually being accepted both by those who work in the
industry and those who research forecasting is that the
demand for fashion products cannot be forecast. Instead,
we need to recognize that fashion markets are complex
open systems that frequently demonstrate high levels of
‘chaos’. In such conditions, managerial efforts may be
better expended on devising strategies and structures that
enable products to be created, manufactured and
delivered on the basis of ‘real‐time’ demand [2].