Question

In the fashion business, faux pas can be costly. In order to hem back the risk writes The Wall Street Journal (Sept. 9, 2013), some retailers are increasingly turning to trend forecasting and analytics (the topic of Chapter 4). For an average annual fee of $7,000-$15,000, customers get access to forecasts of fashion trends and data offering ideas for colors, fabrics, and cuts. Fashion companies use the data to plan their latest collection or show.

“Fashion forecasters have always been used but they’re more accessible now because of the technology,” says a Marks & Spencer exec. “They are important, not always to lead but to re-evaluate and help confirm you’re on the right track.”

Forecasters claim to save their clients travel expenses, the cost of freelancers paid to photograph trendy people, and time spent trawling the vast cache of fashion data on the Internet. “We can’t get rid of risk but we can mitigate risk,” says the CEO of the forecasting firm Stylesight.

“Forecasters take the information and package it in a way that speaks the language of the retailers and manufacturers. Then it’s our job to decide what makes sense for our business; we have to filter it again,” says Kohl’s VP. “Fashion moves so quickly. Companies like Stylesight, which are updated every day, are really useful in order to make sure we have the right information. They offer us an industry eye on all of the information, broken down by print, color, and classification like sweaters of woven tops.”

Retailers say the information forecasters provide has become an important part of how they tap consumers, who spend less, shop online more and demand the latest outfits in increasingly tight time frames.

Discussion questions:

1. Why do large retailers like Macy’s and Kohl’s need forecasts of fashion demands?

2. What forecasting techniques discussed in Chapter 4 can be applied to this problem?

Answer 1

Answer 1. The use of sales and demand forecasting in the fashion and apparel industry is continuously increasing for the last few years due to high competition, and having seasonal trends in the industry. Fashion forecasting is a very important and essential tool to understand future demand and consumer preferences in terms of design, color, fabric, and cuts.

Demand forecasting refers to the tools and techniques to make a correct prediction for the demand and consumer preferences about a product or service offered by a particular manufacturer or retailer.

Demand forecasting plays an important role in the prediction of demand, consumer preferences, product price management, etc. But demand forecasting in the fashion and apparel industry is very critical due to the high volatility in demand, short life-cycle of the product, day-to-day changing consumer preference, seasonal effect, etc. But like other industries, the fashion and apparel industry is not untouched by using demand forecasting and using analytics. We will discuss the need and importance of demand forecasting in the fashion industry in the below points:

a. Product forecasting: Demand forecasting and analytics are very useful in understanding the future trend of product in terms of design, print, size, fabric, material, color and pattern in the fashion industry. The taste and preference of the customer are everyday changing and the industry has to make sure that what do their customer need actually?   Each brand and manufacture will like to know what is selling in the market and how to attract more customers by providing the best product to their customers. This will help brands and manufacturers in planning the best selling collection in the market.

b. Sales forecasting: One of the other advantages of demand forecasting and analytics in understanding the sales and revenue for their brand and for a particular product. Forecasting methods can help in projecting sales for a particular brand and product by using historical sales data, responses from the customers for a particular product, current trend analysis.

c. Supply chain and inventory management: Demand forecasting and analytics for any product or brand can help in managing supply chain and inventory planning. This is one of the most important uses of demand forecasting in the fashion industry. Using demand forecasting, brands and manufacturers will make sure that they have enough inventory as per the consumer demand in the market. There would be a huge loss in having incorrect inventory in stores and warehouses. Companies can also plan for the supply chain as per the demand they have predicted.

d. Economic forecasting: Economic conditions such as GDP, GNP, per capita income, etc plays an indirect role in changing in demand in the fashion industry. As we can see that developed countries spend more on fashion and apparel more as compared with spending by developing countries. Per capita income of consumers also affects the demand for any brand and manufacturers.

Answer 2. Forecasting techniques in the fashion industry

As we have discussed the need and advantages of using forecasting and analytics methods in the fashion and apparel industry. Now we will discuss the various forecasting techniques used in the fashion industry in below points,

(Note: It is not mentioned what techniques are discussed in Chapter 4, so here I am explaining the main techniques are used in the fashion industry worldwide)

a. Qualitative methods: These methods are little time consuming and used mostly when there is very little historical data. These methods are based on opinions, judgments and one to one process. There are no mathematical methods are used in qualitative methods but only depend on salesforce experience. Below are the sub-methods are used as qualitative method technique,

• Consumer Servey- Online and offline surveys from existing and prospective consumers.
• Executive opinion- Executive and experts forecast the demand for a particular product.
• Delphi method- In this method, a group of experts and sales team forecast the demand.

b. Quantitative methods: These methods are based on statistical and mathematical forecasting and unbiased in nature. In this various time series methods are used to understand the demand for the fashion industry. Below are the sub-methods are used in quantitative forecasting methods,

• Simple Average Mean method: In this method, we forecast the demand by analyzing historical data. The demand for any product or brand will equal the average sales of all previous historical data.
• Simple Moving average: This demand forecast method is not useful in seasonal product demand. In this method, the future demand for a product is calculated by the average demand from a series of foregoing periods and calculate the future demands.
• Trend protection: The linear regression method is used in this technique.
• Weighted moving average method: In this method, the demand for the future period is calculated by the weighted average of the most recent period.
• Exponential Smoothing method: In this method: This is one of the time series forecasting method that uses past historical data using exponentially decreasing weights.
• Naive Method: This is one of the easiest quantitative methods of forecasting. In the method, the demand of the next period will be same as in the current month sale.