Optimizing Fashion Branding Strategies: Management of Variety of Items and Length of Lifecycles in a Stochastically Fluctuating Market

Abstract

In the fashion and apparel industry, which is characterized by demand fluctuation, increasing number of clothing retailers offer a greater number of products in smaller lines, continuously changing the products in their storefronts. A relevant example is Zara, a Spanish clothing retailer, which achieves 15 days magic. That is, it takes only 15 days for Zara to carty out the entire process, from conceptual design to well-produced and packaged products in the retail stores. According to Ferdows et al. (2004), Zara has also hit on a formula for supply chain success by dividing each year into 20 seasons, each featuring five to six colors with five to seven sizes. This retailing model, which is called fast fashion, is also adopted by Sweden-based H&M, Japan-based World Co., and Spain-based Mango (see Passariello 2008; Rohwedder and Johnson 2008). Demand fluctuation gives rise to numerous problems concerning forecasting, production planning, inventory management, production system management, and timely distribution. Therefore, information systems such as Quick Response (QR) systems, Electronic Data Interchange (EDI), Point of Sale (POS) systems, and Management Information Systems (MIS) have been implemented to monitor demand fluctuation. Although these techniques have been applied to forecast consumers' wants for products, it is difficult for clothing retailers to organize and analyze the collected data, that is, to integrate data with theory and make quantitative decisions. Consequently, forecasting demand for fashion products still relies heavily on clothing retailers' intuition. The present study attempts to lay out a stochastic dynamic model to investigate how a clothing retailer should introduce and withdraw the products. More precisely, we formulate market fluctuation as a stochastic process and attempt to make clear how to manage the number of 'seasons' in a fluctuating market In academic research, it is of growing interest to investigate how firms should overcome the fluctuation of markets with pioneering works such as Eisenhardt (1989); Williams (1994); Fines (1998), and Choi and Cheng (2010). Eisenhardt (1989) demonstrated that firms must embed flexibility in strategic actions in order to survive in fast changing markets. Williams (1994) and Fines (1998) further revealed that firms' strategies in fast-changing markets must be characterized by rapid product changes. Choi and Cheng (2010) focused on QR and made clear the latest applications of QR in business, as well as how to improve the effectiveness of QR using innovative methods. The purpose of the present chapter is to push forward these analyses. The theory we will utilize is optimal stopping theory, which emphasizes the importance of flexibility and has been used to develop strategies in various stochastically fluctuating markets. McDonald and Siegel (1986) demonstrate that firms should 'wait and see' until uncertainty is resolved. Dixit (1989) examines the strategies of a firm that intends to enter a foreign market. Farzin et al. (1988) investigate the timing of IT investment. Bentolila and Bertoia (1990) consider the management of employment and lay-off'. Leahy (1993); Caballero and Pindyck (1996), and Baldursson and Karatzas (1997) analyze the effect of stochastic fluctuations on the economy. (PsycINFO Database Record (c) 2016 APA, all rights reserved)