By Tsan-Ming Choi
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Extra resources for Analytical Modeling Research in Fashion Business (Springer Series in Fashion Business)
For apparel items, return rates are around 20 % for brick-and-mortar retailers and twice that for online sales (VandeVate and Bedir 2005; Martinez 2009). As pointed by Banjo (2013), behind the uptick in e-commerce, however, is a little known secret that as much as a third of all Internet sales gets returned. European online retailers experience product returns rates of 40 % or higher in product categories such as fashion. Mostard and Teunter (2006) stated that return rates of certain fashion items have been estimated to be as high as 74 %.
T test for comparing means). In Step 4, an essential task is to verify and validate the solution. , the computerized algorithm) runs smoothly as planned. Validation means that whether the model and the derived solution are consistent with the reality. Observe that for the testing in Step 4, we should employ the real data collected from the fashion companies. The testing results, if positive, can hence provide convincing support to the model and the solution under tests. Step 5: Establishing controls: In the optimization model for a real-world-related fashion business operations management problem, there are various model parameters.
In fact, for engineering analytical modeling research, data are the lifeblood as it directly drives the model development, the analysis, the testing, and also the implementation of the optimization tool. Poor data management will ruin everything. In the fashion industry, since not many companies are familiar with scientiﬁc operations management tools (see Raman 1999), we cannot take for granted that high-quality data are available. It is thus essentially important to collect the needed data and make sure the collected datasets are properly managed for model construction and further analytical studies.