Order Quantity Forecasting for the Fashion Industry

Table of Contents

1 Introduction

2 Problems of Fashion Retailing

2.1 Fashion Business

2.1.1 Characteristics of Fashion and Sports Equipment Products

2.1.2 Problems of Fashion Purchasing

2.1.3 Recent Developments in Fashion Purchasing

2.2 Research Questions and Goals of the Thesis

2.3 Outline of the Thesis

3 Forecasting

3.1 Fundamentals

3.2 Sales Forecasting

3.3 Sales Forecasting for Fashion Products

4 Regression Analysis

4.1 Fundamentals

4.2 Parametric Regression Analysis

4.2.1 Quality of the Estimated Regression Model

4.2.2 Nonlinear Regression Analysis

4.3 Nonparametric Regression Analysis

4.3.1 Binning and Local Averaging

4.3.2 Kernel Estimation

4.3.3 Local Polynomial Regression

4.3.4 Quality of the Estimated Nonparametric Regression Model

4.3.5 Nonparametric Multiple Regression Analysis

5 Data Mining

5.1 Fundamentals

5.1.1 Knowledge Discovery from Data

5.1.2 Measurement Scales

5.2 Preprocessing of Data

5.2.1 Data Cleaning

5.2.2 Data Transformation

5.2.2.1 Normalisation of Interval and Ratio Scaled Data

5.2.2.2 Normalisation of Ordinal Data

5.2.2.3 Normalisation of Alpha Variables

5.2.3 Data Reduction

5.3 Data Warehousing

5.3.1 Data Warehousing for Fashion Retailers

6 Economical Quality of Forecasts

6.1 Product Costing and Pricing

6.2 Costs of Overstocking and Understocking

6.3 Evaluating the Economical Quality of Forecasts

7 Application of Forecast

7.1 Calculating Regression Analysis by MATLAB

7.2 Preliminary Examination of Data

7.2.1 Data Description

7.2.2 Data Preprocessing after Export from Data Warehouse

7.2.3 Examinations of Sizes over Time

7.2.3.1 Examination of Sizes during the Season

7.2.3.2 Examinations of Sizes for the same Season over several Years

7.2.4 Examinations of Sizes over Stores

7.3 Forecast and Evaluation Process

7.4 Modelling

7.4.1 Univariate Approach

7.4.2 Multivariate Approaches

7.4.2.1 Surface Fitting Using a Parametric Polynomial Regression Model

7.4.2.2 Surface Fitting Using a Custom Equation Regression Model

7.4.2.3 Surface Fitting Using a Nonparametric Lowess Regression Model

7.5 Forecasting

7.5.1 Evaluation of the Forecast

7.5.1.1 Hypothesis 1: Actual Sales Data Represents the Demand of Products

7.5.1.2 Hypothesis 2: Actual Sales Data is Biased by Original Order

7.5.1.3 Conclusions on Hypotheses

7.6 Practical Challenges

7.7 Suggesting an Implementation

8 Conclusions

8.1 Results of the Thesis

8.1.1 General Results

8.1.2 Data Understanding

8.1.3 Data Modelling

8.1.4 Forecasting

8.1.5 Implementation

8.1.6 Summary of Results

8.2 Future Research

Research Objectives and Themes

The primary goal of this thesis is to enhance long-term order quantity forecasting for fashion products by utilizing historical sales data through regression analysis. It addresses the complexities of fashion retailing—such as seasonality, long lead times, and specific product attributes—and seeks to evaluate the economic benefits of improved forecasting methods to increase company efficiency.

• Application of regression analysis in fashion retailing
• Methodologies for data preprocessing and data warehousing
• Evaluation of forecasting accuracy and economic impact
• Mathematical modeling of size distributions in fashion retail

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Summary of Chapters

1 Introduction: Provides an overview of the challenges in fashion retailing and outlines the need for accurate demand forecasting to improve cost structures.

2 Problems of Fashion Retailing: Discusses the characteristics of fashion products, such as seasonality and long lead times, and identifies the core challenges in purchasing.

3 Forecasting: Reviews general forecasting techniques and differentiates between subjective and model-based methods, specifically for fashion contexts.

4 Regression Analysis: Details parametric and nonparametric regression models as tools for numeric prediction in data mining.

5 Data Mining: Explores fundamental data mining concepts, including data cleaning, transformation, and warehousing necessary for accurate analysis.

6 Economical Quality of Forecasts: Establishes a framework for evaluating the monetary benefit of forecasts, focusing on overstocking and understocking costs.

7 Application of Forecast: Demonstrates the practical application of regression models using MATLAB, including data preprocessing and evaluation against real sales data.

8 Conclusions: Summarizes the results of the thesis, discusses findings regarding forecast implementation, and proposes directions for future research.

Keywords

Sales Forecast, Regression Analysis, Fashion Purchasing, Data Mining, Order Quantity, Forecasting Techniques, Data Warehousing, Nonparametric Regression, Parametric Regression, Fashion Retail, Inventory Costs, Overstocking, Understocking, Decision Support System, MATLAB

Frequently Asked Questions

What is the core focus of this thesis?

The thesis focuses on improving the long-term forecasting of order quantities for seasonal fashion and sports equipment products using regression analysis techniques.

What are the primary challenges addressed in the work?

The main challenges are the specific characteristics of fashion products, including short life cycles, long lead times, seasonality, and the complexity of managing variations in sizes, colors, and cuts.

What is the primary goal of the research?

The goal is to determine the conditions under which long-term order quantity forecasts can be improved based on historical sales data, ultimately aiming to reduce inventory costs and prevent overstocking or understocking.

Which scientific methods are applied?

The author uses both parametric and nonparametric regression analysis, specifically utilizing tools like the MATLAB surface fitting tool to model sales data and calculate demand forecasts.

What does the main body of the work cover?

The main body covers the theory of forecasting, regression analysis, data mining and warehousing, the development of an economic evaluation method for forecasts, and a practical application study using real-world test data.

Which keywords best characterize this work?

Key terms include Sales Forecast, Regression Analysis, Fashion Purchasing, Data Mining, and Order Quantity.

Why are standard regression models sometimes insufficient for this data?

Standard linear models may fail due to complex, nonlinear relationships between variables; thus, the author explores nonparametric methods like Lowess to better fit the specific sales patterns of fashion items.

What is the significance of the "Hypothesis 2" in the application chapter?

Hypothesis 2 assumes that actual sales data might be biased by the original order (due to limited supply or display constraints) and suggests that a revised forecast based on sub-category demand models can better capture true consumer potential.

How does the author propose implementing these findings?

The author proposes the development of a Decision Support System (DSS) integrated into the retailer's IT landscape, where the system provides baseline forecasts that purchasers can adjust based on their expert knowledge and market trends.