Order Quantity Forecasting for the Fashion Industry

Thèse de Master , 2010 , 103 Pages , Note: 1

Informatique - Informatique appliquée

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.

Inhaltsverzeichnis (Table of Contents)

• Introduction
• Problems of Fashion Retailing
• Characteristics of Fashion and Sports Equipment Products
• Problems of Fashion Purchasing
• Recent Developments in Fashion Purchasing
• Research Questions and Goals of the Thesis
• Outline of the Thesis
• Forecasting
• Fundamentals
• Sales Forecasting
• Sales Forecasting for Fashion Products
• Regression Analysis
• Fundamentals
• Parametric Regression Analysis
• Quality of the Estimated Regression Model
• Nonlinear Regression Analysis
• Nonparametric Regression Analysis
• Binning and Local Averaging
• Kernel Estimation
• Local Polynomial Regression
• Quality of the Estimated Nonparametric Regression Model
• Nonparametric Multiple Regression Analysis
• Data Mining
• Fundamentals
• Knowledge Discovery from Data
• Measurement Scales
• Preprocessing of Data
• Data Cleaning
• Data Transformation
• Normalisation of Interval and Ratio Scaled Data
• Normalisation of Ordinal Data
• Normalisation of Alpha Variables
• Data Reduction
• Data Warehousing
• Data Warehousing for Fashion Retailers
• Economical Quality of Forecasts
• Product Costing and Pricing
• Costs of Overstocking and Understocking
• Evaluating the Economical Quality of Forecasts
• Application of Forecast
• Calculating Regression Analysis by MATLAB
• Preliminary Examination of Data
• Data Description
• Data Preprocessing after Export from Data Warehouse
• Examinations of Sizes over Time
• Examination of Sizes during the Season
• Examinations of Sizes for the same Season over several Years
• Examinations of Sizes over Stores
• Forecast and Evaluation Process
• Modelling
• Univariate Approach
• Multivariate Approaches
• Surface Fitting Using a Parametric Polynomial Regression Model
• Surface Fitting Using a Custom Equation Regression Model
• Surface Fitting Using a Nonparametric Lowess Regression Model
• Forecasting
• Evaluation of the Forecast
• Hypothesis 1: Actual Sales Data Represents the Demand of Products
• Hypothesis 2: Actual Sales Data is Biased by Original Order
• Conclusions on Hypotheses

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

This master thesis explores the challenges of order quantity forecasting for fashion retailers, a complex process due to factors such as long lead times, seasonality, and product variability. The thesis aims to develop and evaluate forecasting techniques using regression analysis, particularly parametric and nonparametric methods, to enhance order quantity accuracy for fashion products. It also examines the role of data mining, including data preprocessing and warehousing, in preparing historical sales data for regression analysis. Additionally, the thesis proposes a method for evaluating the economic benefit of accurate forecasting, contributing to a more comprehensive understanding of its practical implications for fashion retailers.

• Order Quantity Forecasting for Fashion Products
• Regression Analysis Techniques
• Data Mining for Fashion Retail
• Economic Evaluation of Forecasts
• Practical Applications of Forecasting Models

Zusammenfassung der Kapitel (Chapter Summaries)

The thesis begins by introducing the challenges of fashion retailing, highlighting the specific characteristics of fashion and sports equipment products. It then delves into the intricacies of fashion purchasing, exploring its complexities and recent developments. The research questions and goals of the thesis are presented, outlining the scope and objectives of the study. The thesis then provides a detailed overview of forecasting techniques, including fundamentals of sales forecasting and specific considerations for fashion products.

Chapter 4 focuses on regression analysis, covering both parametric and nonparametric approaches. It explores the fundamentals of regression analysis, discusses the quality of estimated regression models, and examines various nonparametric regression techniques, including binning, kernel estimation, and local polynomial regression. Chapter 5 delves into the principles of data mining, emphasizing the importance of knowledge discovery from data and the role of measurement scales. It then explores data preprocessing techniques, including data cleaning, transformation, and reduction, and discusses the significance of data warehousing, particularly for fashion retailers.

Chapter 6 focuses on the economic quality of forecasts, analyzing product costing and pricing, costs associated with overstocking and understocking, and methods for evaluating the economic benefit of accurate forecasting. The final chapter, Chapter 7, delves into the application of forecasting techniques, showcasing the use of MATLAB for regression analysis. It examines the preliminary examination of data, including data description, preprocessing, and analysis of sizes over time and stores. It then outlines the forecast and evaluation process, explores different modelling approaches, and concludes with an evaluation of the forecast, including hypotheses testing and conclusions on the findings.

Schlüsselwörter (Keywords)

The primary focus of this master thesis lies on order quantity forecasting for the fashion industry, employing regression analysis as a key technique. The thesis explores various aspects of data mining, including data preprocessing and warehousing, to support the application of regression analysis on historical sales data. Furthermore, the economic quality of forecasts is examined, highlighting the practical benefits of accurate forecasting for fashion retailers. Key terms and concepts include sales forecast, regression analysis, fashion purchasing, data mining, and order quantity.

• Université: Fachhochschule Salzburg (Information Technology und Systems Management)
• Note: 1
• Auteur: Peter Hirschbichler (Auteur)
• Année de publication: 2010
• Pages: 103
• N° de catalogue: V164751
• ISBN (ebook): 9783640801701
• ISBN (Livre): 9783640802296
• Langue: anglais
• mots-clé: Sales Forecast, Regression Analysis, Fashion Purchasing, Data Mining, Order Quantity