Samaneh Beheshti-Kashi

Robust production planning in fashion apparel industry under demand uncertainty via conditional value at risk

This paper presents a mathematical model for robust production planning. The model helps fashion apparel suppliers in making decisions concerning allocation of production orders to different production plants characterized by different lead times and production costs, and in proper time scheduling and sequencing of these production orders. The model aims at optimizing these decisions concerning objectives of minimal production costs and minimal tardiness. It considers several factors such as the stochastic nature of customer demand, differences in production and transport costs and transport times between production plants in different regions. Finally, the model is applied to a case study. The results of numerical computations are presented. The implications of the model results on different fashion related product types and delivery strategies, as well as the model’s limitations and potentials for expansion, are discussed. Results indicate that the production planning model using conditional value at risk (CVaR) as the risk measure performs robustly and provides flexibility in decision analysis between different scenarios.

A survey on retail sales forecasting and prediction in fashion markets

Sales forecasting is an essential task in retailing. In particular, consumer-oriented markets such as fashion and electronics face uncertain demands, short life cycles and a lack of historical sales data which strengthen the challenges of producing accurate forecasts. This survey paper presents state-of-the-art methods in the sales forecasting research with a focus on fashion and new product forecasting. This study also reviews different strategies to the predictive value of user-generated content and search queries.

Forecasting of Seasonal Apparel Products

Demand forecasting of fashion apparel products has to cope with serious difficulties in order to get more accurate forecasts early enough to influence production decisions. Demand has to be anticipated at an early date due to long production lead times. Due to the absence of historical sales data for new products, standard statistical forecasting methods, like, e.g., regression, cannot easily be applied. This contribution applies selected methods into improve forecasting customer demand of fashion or seasonal apparel products. We propose a model which uses retailer pre-orders of seasonal apparel articles before the start of their production to estimate later, additional post-orders of the same articles during the actual sales periods. This allows forecasting of total customer demand based on the pre-orders. The results show that under certain circumstances it is possible to find correlations between the pre-orders and post-orders of those articles, and thus better estimate total demand. The model contributes to the improvement of production volumes of apparel articles, and thus can help reduce article stock-outs or unwanted surpluses.

The Usage of Social Media Text Data for the Demand Forecasting in the Fashion Industry

The fashion industry faces different challenges in the field of demand forecasting. Factors such as long delivery times in contrast to short selling periods requires precise demand figures in order to place accurate production plans. This paper presents firstly the idiosyncrasies of the fashion industry and shows current fashion forecasting approaches. Then, the idea of applying social media text data within the demand forecasting process is presented by showing works of integrating user generated content in different application fields. Following the research question on the predictive value of social media text data for the fashion industry, the research objective and the methodology are formulated in a last step.

Social Media Analytics for Decision Support in Fashion Buying Processes

The Web 2.0 and the emergence of numerous social media services enable individual users to publish and share information on the one hand and to discuss diverse topics online on the other hand. Accordingly, different research streams have emerged in order to tackle the diverse phenomena related to social media. Social media analytics as an interdisciplinary research field has arisen and integrates the different approaches of structural attributes, opinion/sentiment-related as well as topic/trend-related approaches. This research follows topic- and trend-related approaches with the methods content and trend analysis on social media text data. These methods might be applied on different domains including the fashion industry. This research focusses on the fashion industry for three reasons. Firstly, this industry is a highly consumer-oriented industry, and these consumers themselves are the users of social media services. Secondly, the industry faces challenges in meeting the demand of the customer on time. Thirdly, in the last years, fashion blogs have gained increased relevance from the consumers and the industry. Accordingly, the fashion blogs may contain information for supporting decision maker in the industry, to perform their tasks such as meeting the demand with a lower degree of uncertainty. The objective of this chapter is to explore the potential added value of social media analytics for fashion buying processes, not only by presenting an abstract approach, but more by conducting experimental analyses on a fashion blog corpus covering a 5 year time period. Based on the topic detection and tracking research which origins from the intelligent information retrieval, a research approach is presented by integrating a text mining process, on the detecting and tracking of fashion features and topics in the blog corpus. A fashion topic may refer to different features such as a colour, silhouette or style. While for the topic detection task, the feature colour is focussed, the topic tracking includes topics on silhouette, style, colour and decorative applications. The analyses have shown that it is possible to detect single colour and co-occurred colour occurrences. In addition, it was demonstrated that it is possible to track fashion topics over a 5 year time period in a fashion post corpus. The fashion buyer might have an added value for his activities by quantifying the individual perceptions through the application of the presented approach.

Big Data Analytics: A Case Study of Public Opinion Towards the Adoption of Driverless Cars

With the growth of textual data and the simultaneous advancements in Text Analytics enabling the exploitation of this huge amount of unstructured data, companies are provided with the opportunity to tap into the previously hidden knowledge. However, how to use this valuable source, still is not unveiled for various domains, such as also for the transportation sector. Accordingly, this research aims at examining the potential of textual data in transportation. For this purpose, a case study was designed on public opinion towards the adoption of driverless cars. This case study was framed together with the Danish road directorate, which is, in this case, the problem owner. Traditionally, public opinion is often captured by means of surveys. However, this paper provides demonstrations in which public opinion towards the adoption of driverless cars is examined through the exploitation of newspaper articles and tweets using topic modelling, document classification and sentiment analysis. These analyses have for instance shown that Text Analytics may be a supplementary tool to surveys, since they may extract additional knowledge which may not be captured through the application of surveys. In this case, the Danish Road Directorate can use these result to supplement their strategies and expectations towards the adoption of driverless cars by incorporating the public’s opinion more carefully.

Big Textual Data in Transportation: An Exploration of Relevant Text Sources

With the emergence of Big Data and growth in Big Data techniques, a huge number of textual information is now utilizable, which may be applied by different stakeholders. Formerly unexplored textual data from internal information assets of organisations, as well as textual data from social media applications have been converting to utilizable and meaningful insights. However, prior to this, the availability of textual sources relevant for logistics and transportation has to be examined. Accordingly, the identification of potential textual sources and their evaluation in terms of extraction barriers in the Danish context has been focussed in this paper.

The Fashion Trend Concept and Its Applicability to Fashion Markets and Supply Chains

This paper focuses on different aspects of fashion markets. In a first step, fashion levels will be classified; followed by definitions on fashion trends, and the suggestions on a fashion trend concept. In order to fill this concept and support decision-making processes along the supply chain, such as the catching of actual fashion trends, it is required to fill this concept with relevant information on different product features. Social media text data is considered as one relevant source. Showing previous researches, we assume that for instance fashion weblogs can be used for extracting this information. In a further step, we describe different fashion markets, namely fast fashion and luxury, in order to examine the applicability of the approach to real-life markets and their supply chain processes. The paper concludes by formulating hypotheses on a potential application of the approach.

Fashion Supply Chains and Social Media: Examining the Potential of Data Analysis of Social-Media Texts for Decision Making-Processes in Fashion Supply Chains

Fashion companies often face challenges in meeting the demand of consumers since often production plans have to be placed before exact knowledge of the demand is available. Since the industry is a highly consumer- and trend-oriented industry, predicting the customers demand is crucial for the company’s success. Nowadays, these customers have been empowered through the Web 2.0 and are able to publish opinions and experiences on various social-media applications. At the same time, these consumers are members of the fashion supply chain. This paper considers a typical fashion supply chain and focusses on the role of the buyer, whose function resides with the retailer. The buyer plays a crucial role since she or he is responsible for the trend monitoring and selection of future fashion collections. The objective of this paper is to examine if social-media text data shared by means of fashion blogs contains color information and if these color comments correspond to real-world customer demand. For this purpose, 232 blog posts were collected, analyzed, and compared to qualitative information on colors provided by a real-world clothing company. The analysis shows that it is indeed possible to discover color information from fashion blogs. Moreover, it revealed that the information identified in the blogs correspond with real-world customer demand.