Demand forecasting in fashion industry with seasonal perspective Assignment

Introduction:

Various models for projecting fashion goods have been developed in research over the past decades, and the study of demand forecasting is a major area of study.

High-performing businesses often prioritize accurate demand forecasting methods, but this may be difficult for any business, regardless of size or industry. Long replenishment lead periods, short selling seasons, and almost unpredictable demand make demand forecasting difficult, if possible, in the fashion sector. All of these factors contribute to the difficulty of predicting consumer demand. After years of striving to keep up with customer demand, fashion industry businesses have developed various forecasting methodologies and tools (Battistoni et al., 2013). Much of this previous work was to gain an understanding and develop techniques to enhance fashion product demand forecasts. Despite this widespread belief, it has become increasingly understood that anticipating the market for fashion commodities is impossible. On the contrary, we must acknowledge that the fashion industry's markets are highly complicated open processes that regularly exhibit high degrees of 'chaos. Under these circumstances, managers may benefit from developing strategies and organizational structures that facilitate the development, production, and distribution of goods in response to "real-time" consumer demand.

Companies have shifted their attention to the overage-underage trade-off, similar to that of a newsstand vendor, to combat the challenge of estimating demand. That's why fast fashion leaders like H&M and Zara can get their products to consumers quickly. Companies in the fashion sector have spent the last few years revamping their structure, focusing on strategy and inventory.

Therefore, it is important to ask if it is worthwhile to continue researching demand forecasting. Is there a way of thinking or doing things that would work better in this situation?

This study explores the current fashion industry backdrop in light of the demand forecasting methods established in recent years. New research on consumer behavior and the strategies of the leading suppliers and demand-oriented corporations in the fashion industry are pitted against the most relevant literature on demand forecasting from the past several years. The goal is to figure out which types of forecasting are most useful in the present situation.

The primary takeaway from this literature study should be a new method for demand forecasting. We have used a quantitative approach to analyze and interpret demand in fashion industry with seasonal perspective.

Fast fashion is characterized by several marketing elements, including a lack of predictability, a high proportion of impulsive purchases, a short product life cycle, and significant market demand swings (Fernie & Sparks, 1998). Therefore, to profit, fashion apparel stores must use a "speed to market" strategy to take advantage of trends that their rivals do not carry. It has also been underlined that merchants' profit margins improve when they are nimble and sensitive to the market by quickly incorporating consumer preferences into the design process in product development (Christopher, Lowson, and Peck, 2004). When we go back over time, we see that the greatest influence on the fashion business came from catwalks and shows. These trend exhibitions also catered mostly to the fashion industry's creatives, buyers, and executives. In contrast, the demystification of the fashion process began around 1999 when images from recent fashion shows began appearing in periodicals and on the web (Sydney, 2008). Therefore, conscientious shoppers were introduced to unique designs and trends that had been taken straight from the runways. Top Shop, H&M, Zara, Mango, New Look, and other fast fashion retailers were quickly copying these looks to entice customers and get their spin on the runway trends into stores within three to five weeks (Barnes & Lea-Greenwood, 2006). The fashion apparel industry has moved its focus from predicting future trends to using real-time data to understand consumers' needs and wishes better, building on the foundations of swift reactivity (Jackson, 2001). There is a risk of losing fashion-conscious customers due to lengthier lead times if designers must effectively predict future trends (Christopher, Lowson, and Peck, 2004) or replicate runway looks fast in the factory (Richardson, 1996). This potential risk can be completely removed by using real-time data.

United Kings's fashion industry:

The United Kingdom's fashion industry has received much credit for pioneering this novel approach (Barnes & Lea-Greenwood, 2006). Large shops in the UK, with rigid supply chains, have long dominated the fashion apparel business (Hines & Bruce, 2001). Price pressure from market powerhouses became an issue for UK-based garment producers and retailers in the 1990s. The UK's New Look and George, among others, have moved their product sourcing to the Far East to take advantage of the region's lower manufacturing costs and remain competitive. Consequently, retailers had to implement procedures like just-in-time (JIT), computer-integrated manufacturing (CIM), total quality management (TQM) in manufacturing, and an emphasis on shorter supply lines and quick response in the market due to the increased complexity of supply chains caused by widespread geographic separation (Bruce, Daly, and Towers 2004). So, keeping in mind the product's low price, shops in the UK started stocking more of it in more styles and colors. In addition to the traditional two-season buying cycle, they also implemented a mid-season buying period, which resulted in a "throwaway market" that offered trendy items at bargain prices. Since then, the "throwaway market" (today called quick fashion) has been the standard (Tokatli, Wrigley, and Kizilgu, 2008). In conclusion, the highly competitive and consolidated nature of the UK fashion industry paved the way for street fashion to increase brand visibility and speed to market (Birtwistle & Freathey, 1998). In the following parts, we will hear from retailers and shoppers about their experiences with fast fashion.Fashion demand from the supplier perspective:In today's retail climate, the apparel market is more dynamic and diversified than ever. The fashion garment business is shifting from a production-driven to a market-driven approach, and the advent of fast fashion is a symptom of this shift. The retail industry is slowly coming to terms with the fact that the keys to success in the current market are adaptability and speed. To stay ahead of the competition, the fashion apparel sector has been studying buyer-supplier relationships, rapid response, and supply chain management over the past two decades (Crewe & Davenport, 1991; Fiorito, May, and Straughne 1995; Sohal, Perry, and Pratt 1998; Perry & Sohal, 2000). Academic research into fast fashion has focused on its implications for business models that employ rapid response strategies to cut down on manufacturing delays (Bailey, 2001). The literature often links fast fashion to organizational pressure to shorten lead times and supply chain cooperation involving several parties (Barnes & Lea-Greenwood, 2006; Wensley, 1999). Quick response (Fernie & Azuma, 2004), just-in-time (Bruce, Daly, and Towers, 2004), and agile supply chains are just a few examples of the newer practices in the fashion industry that describe shorter, more flexible supply chains in response to the antiquated long buying cycles that have forced many retailers to improve responsiveness in reduced time (Bruce, Daly, and Towers 2004; Christopher, Lowson, and Peck 2004). These methods have traditionally been linked to vertical integration and emphasize cooperation, communication, and mutual trust among supply chain participants to increase productivity in a demand-driven market (Birtwistle, Siddhiqui, and Fiorito, 2003). Computer-aided design (CAD) and electronic data interchange (EDI) have helped merchants and manufacturers communicate better, resulting in shorter lead times (Bruce, Daly, and Towers, 2004).Fashion demand from a consumer perspective:Consumers' growing fashion awareness and sophistication necessitate that clothing shops respond to this trend by stocking their shelves with products that can be quickly delivered to stores and online shoppers (The Economist 2005). Fast fashion is gaining popularity as customers' spending and shopping habits diverge. Researchers should now examine the range of consumer behavior regarding fast fashion in light of these new advances. For the benefit of the retail fashion industry, the literature on fashion brands emphasizes several aspects of system management backed by supply chain theory. Fast fashion as a consumer-driven strategy has received surprisingly little attention in the academic literature. Consumers may now access more information and fashion trends worldwide in record time, leading to increased shopping frequency (Hoffman, 2007). Retailers in the fashion industry have to regularly refresh their product lines to keep up with the ever-increasing amount of competition in the industry, as putting more emphasis on stocking more recently updated products, responding more quickly to "new" fashion trends, and offering "refreshing" products rather than only focusing on manufacturing efficiencies (Barnes & Lea-Greenwood, 2006; Hines, 2001; Hoffman, 2007). Adding new phases to the already established seasons (the period during which fashion products are offered) in a fashion calendar was conceptualized to extend the range of clothing available in the market. Due to the increased demand, fashion garment manufacturers receive orders for smaller batch sizes with shorter lead times every three to five months (Tyler, Heeley, and Bhamra, 2006). For instance, Liz Claiborne created six rather than merely two seasons (Bailey, 2001). Partially in response to consumer lifestyle shifts and partly due to the necessity to meet customer demand for fashion items for specific events, the number of mid-seasons has shifted in recent years.

Structure of the fashion industry:

In the late 1980s, major stores controlled the fashion garment business, increasing competition (Barnes & LeaGreenwood, 2006). Other fashion apparel retailers transitioned from product- driven to buyer-driven chains, formed supplier relationships, and promoted their brands to compete (Tyler, Heeley, and Bhamra 2006). This increased revenues from high-value research, design, sales, and marketing, allowing them and the manufacturers to link with international factories (Gereffi, 1999, p. 43). The fashion garment business built infrastructure in the late 1980s to promote responsiveness (rapid response) through shorter lead times and low costs. After that, offshore production and processes in the fashion garment business became a trend, resulting in a cost advantage. Outsourcing leads to lengthier lead times, complex supply chains due to geographic distances, inconsistent and variable processes at both ends of the chain, and complex import/export procedures (Birtwistle, Siddhiqui, and Fiorito, 2003; Bruce & Daly, 2006). Outsourcing manufacturing to low-wage nations was deceiving because the savings were often minor relative to obsolescence, forced markdowns, and inventory-carrying expenses (Christopher, Lowson, and Peck 2004). Tyler, Heeley, and Bhamra (2006) cited product development as the reason for extended fashion garment lead times. All the essential participants in a supply chain (fashion and textile designers, retail buyers, and manufacturers) worked sequentially, resulting in high expenditures, poor communication, and reworks owing to faulty product development. Fashion retailers should have quickly translated trends into the market, hurting revenues (Fiorito, May, and Straughne 1995). Rapidly changing lifestyles and consumer fashion and clothing choices worsened the situation. All these flaws led the sector to restructure to improve performance (Taplin, 2006). Just-in-time procedures and lower lead periods are examples of 1990s restructuring. From 1994 to 1995, 60% to 72% of US fashion apparel merchants implemented a QR strategy (Jones, 1995). In recent years, outsourcing manufacturing to low-wage countries and demand-driven flexible supply chains have proved that quick responsiveness is achievable even over large distances.

Over the past two decades, the fashion business has been undergoing significant change as a result of a number of external factors: Cost-cutting is essential, and the fashion industry is no exception. Like many others, the industry has found that sourcing materials and moving production to developing countries with lower labor costs yields significant savings while maintaining or improving competitive advantage. In addition, many businesses are adopting this strategy to achieve a significant cost advantage in the manufacturing and retail sectors. However, in many cases, lead times have increased dramatically as a result of the need to source goods and components from outside (Chan, 2010).

The market has shifted from catering to the masses to becoming increasingly specialized. In addition, customers' preferences and expectations shift rapidly and differently in the fashion sector. In today's market, consumers have high expectations that businesses will provide them with both the products and services they need. Fast fashion, for instance, has expanded its share of the clothing market because consumers have come to demand more options and more regular design updates. An organization's retail outlets play an important part in forming consumer perceptions of the brand and shaping their overall happiness. One of the most important factors in determining whether a customer is happy or not is the quality of the service they receive, as pointed out by Rayman et al.
Finally, technology has had numerous effects on the fashion industry, one of which is a huge increase in the instantaneous awareness of new trends and brands, which has led to an increase in client demand. It has also helped retailers, wholesalers, and manufacturer's better share data and collaborate on strategic moves. Hence, this research analyzes the demand forecasting in fashion industry with seasonal perspective.

Literature Review:

Forecasting approaches:

Both research and practice on the topic of demand forecasting have garnered a lot of attention because it is one of the most difficult problems facing retailers, wholesalers, and suppliers in any sector. The forecasting methods' applicability and value in the fashion sector are in question. Conventional methods of demand forecasting, such as linear models, are optimized for steady, high-volume demand and fail to account for irregular, unpredictable, or lumpy patterns of demand. Performance is expected to vary greatly between attribute levels, as has been discussed by a number of writers. Even more so if the demand pattern is highly lumpy or erratic, as is common in the fashion industry and typically results in subpar performance when using statistical methods.

Fast fashion:

On top of that, as shown by Gutierrez et al. (2008). It is possible that conventional time-series approaches miss out on catching nonlinear patterns in data. Expert systems, such an "Artificial Neural Network (ANN)", are a sensible option for getting over these restrictions. ANN has been used to great effect by numerous writers and there have even been some novel applications to the fashion industry's demand side. While the Model has been shown to produce reliable forecasts, the authors noted that the time spent on forecasting is a significant limitation on its practical use. This is because the depth or diversity of the data substantially increases the learning time required for ANN.

An Overview of the Fashion and Apparel Sector:

Changes in the global business climate over the past two decades have significantly impacted the fashion apparel industry. Looking back at fast fashion's history can shed light on where the industry is headed in future research. The evolution of the fashion industry since the '90s is the subject of the following sections. Decline in factory output The fashion industry's success up until the mid-1980s was predicated on the mass production of standardized styles that did not frequently change due to the design constraints of the factories, such as Levi's 501 jeans and a man's white shirt. However, there were exceptions, such as the rapidly changing haute couture (Brooks, 1979). Consumers of the era seemed less concerned with looking good and more interested in getting their money is worth out of their purchases. According to Bailey and Eicher (1992), the import of women's clothing focused on current trends has increased dramatically since the 1980s, when it was rather rare. As customers became more concerned with being fashionable, the demand for timeless staples like white t-shirts and blue jeans declined (Bailey, 2001). The hosiery industry, for example, made basic hosiery in various colors and textures to match any dress (Donnellan, 1996). Because of the market's inability to sell fashion items during the anticipated season, markdowns have increased due to the shift away from fashion-oriented clothing (OTA, 1987). Malone (1998, 1999) added weight to this point by showing that there are better ways to make money in the fashion industry than mass-produced fashion items.

Fashion Demand:

Since customers accept fashion trends for only a short period (Sproles, 1979), it is clear that the fashion industry has a short life cycle. Since the 1980s, a typical fashion garment's life cycle has included four phases: debut and adoption by fashion leaders, growth and increase in public acceptance, mass conformity (maturity), and decline and obsolescence. Likewise, the fabric exhibitions, fashion shows, and trade fairs of the time were the backbone of the fashion calendar, with their basic pattern of Spring/Summer and Autumn/Winter ranges often resulting in the development of a seasonal range in one full year. In the early 1990s, however, stores began consumers' preferences and the demand for novelty cause them to seek out new styles at regular intervals (Sproles & Burns, 1994). Forecasting and product planning have shifted due to consumers' ever-changing wants, with increased emphasis on producing low-quantity, high- quality copies of popular patterns and styles seen in fashion publications and on the runway (Christopher, Lowson, and Peck, 2004). Varied generations have different ideas on what constitutes "throwaway" clothing. For instance, compared to the baby boomer generation, who like to buy fewer but higher-quality items, the millennial generation prefers to buy more low- quality, cheap, and trendy garments (Crewe & Davenport, 1992). Fast fashion is seen as wasteful by more traditional shoppers because rather than purchasing one high-quality item to fulfill a wardrobe need, fast fashion encourages shoppers to buy several low-quality items and then discard the older items as quickly as the new ones are introduced (Sydney, 2008). According to the findings of this study and in agreement with Barnes and Lea-Greenwood (2006), it is clear that fast fashion is a consumer-driven strategy in addition to a supplier-driven one.

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