Order Quantity Forecasting for the Fashion Industry
Master's Thesis from the year 2010 in the subject Computer Science - Applied, grade: 1, Fachhochschule Salzburg (Information Technology und Systems Management), language: English, abstract: Precise order quantity forecasting for fashion retailers is difficult, because of the specific nature of fashion products namely long lead times, seasonality, and product attributes such as sizes, colours, and cuts. This thesis contributes to order quantity forecasting for fashion products by the use of regression analysis. For this purpose, forecasting techniques in general, and parametric as well as nonparametric regression analysis in articular are presented. This is followed by fundamentals of data mining, specifically data preprocessing and data warehousing, in order to be able to apply regression analysis on historical sales data. Furthermore, to examine the quality of forecasts a method for evaluating the economical benefit of order quantity forecasting was developed. As a next step, the presented methods for forecasting were applied to historical sales data. Therefore, sales data was analysed, regression models were applied and forecasts were calculated and evaluated finally. This thesis is concluded by suggesting a forecasting implementation and by discussing the contributions to order quantity forecasting.
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actual sales data Additionally alpha variables Anorak Alpine applied Backhaus binning calculated Chapter coefficient of determination colour and cut confidence bounds contribution margin cost price CPFR curve fitting custom equation data cube data mining data preprocessing data set data warehouse demand dependent dimension distribution evaluating example fashion and sports fashion products fashion purchasing fashion retailers forecasting methods Hartung and Elpelt hierarchy levels Hypothesis implementation independent variables Kamber kernel estimation lowess MATLAB measures multivariate nonlinear nonlinear regression nonparametric multiple regression nonparametric regression nonparametric regression analysis normalisation OLAP OLTP outliers parameters parametric regression analysis polynomial regression product sub-category Anoraks Pyle regression analysis regression coefficients regression model sales forecasting sales potential sales price sales quantities sizes over stores Ski Boots specific sports equipment products stand.dev star schema statistical significance sum of squares Table thesis toolbox total order quantity univariate approach variance winter season zero β β