Building a business based on data is no longer a trend, but a necessity. The “data is new money” principle also applies to retail. After all, advanced data analysis using AI enables automation and optimization of processes in the supply chain. The result? Increased sales and reduced costs. This can be seen in the fashion industry, burdened with many challenges in demand forecasting and order planning.
According to Statista, the fashion market in Poland will reach $15.6 billion in revenue in 2023. It is estimated that the market will grow at a rate of 6.05 percent per year (CAGR 2023-2027), with the eCommerce segment playing an important role in this growth. These metrics confirm the great potential of the industry, whose growth is driven by digitization and new technologies. However, what creates uncertainty are the echoes of the COVID-19 pandemic and broken supply chains, as well as the day-to-day challenges of demand forecasting, production planning and store replenishment.
Is it possible to forecast sales and demand in the fashion industry?
The difficulty of forecasting future business events stems directly from the peculiarities of the fast fashion industry. Rapid changes in trends, short life cycles of seasonal products and long lead times often give demand planners sleepless nights. At the same time, strong pressure from competitors and high customer expectations motivate the search for solutions that streamline an essential part of the order planning process, reduce or even eliminate demand forecast errors and erroneous decisions based on intuition rather than facts.
This is where artificial intelligence and machine learning come to the rescue. The potential of advanced data analysis can be used to optimize supply chain management and replenishment, forecast sales and demand, and generate production plans and orders to suppliers, among other things.
The path to achieving business goals through AI is not always obvious. In the fashion industry, with the help of historical data, we are able to take into account recurring trends, patterns or cuts that were more popular in a particular store or sales channel. However, diligent data collection is key, as it is in the data that answers to many questions are contained.
Michał Koziara, CEO 3Soft S.A. and co-founder of the Occubee platform
Lack of historical data – a challenge for forecasting
What if we do not have historical data? This situation affects many fashion retailers and is the aftermath of frequent new product and collection launches. In that case, forecasts generated from historical data for similar products can be a solution. For example, AI can estimate how popular a product with a particular cut will be, based on historical sales of products with the same cut.
The second aspect is the collection of data as soon as a product is launched, which allows artificial intelligence algorithms to start working almost immediately. Collecting sales data on the fly enables deep learning models to learn quickly and influence the quality of predictions as soon as the product appears on store shelves.
Inter-store transfers and in-store availability issues
Stock shortages and inter-store transfers are also a challenge for the fashion industry.
The fashion industry strives for a situation where in stores with high demand high availability of products is guaranteed. This can be achieved with the help of forecasts. The retailer then gains the opportunity to sell the customer the goods they are looking for – without sending them away to other stores or inviting them again after the product has been pulled from the warehouse to the store. In this way, the risk of lost sales is eliminated.
Michał Koziara, CEO 3Soft S.A. and co-founder of the Occubee platform
Online and offline sales – how to manage goods in the warehouse?
Currently, the leading demand in retailers’ business strategies is to develop the online channel. However, operating in an omnichannel environment makes retailers face a dilemma: how much product to allocate to the online channel and how much to the offline one? In the traditional approach, this division is carried out based on “rigidly” defined proportions. Then, however, it is not uncommon to come across overstocks in one channel and out-of-stocks in the other one for the same product.
With advanced data analysis, it is possible to better predict demand for a given channel, as well as to dynamically allocate goods across channels. For example: after observing a significant increase in sales in the online channel, on-the-fly machine learning models will cause goods initially reserved for the offline channel to be moved to the online one.
Data as a source of competitive advantage
Through AI data analysis in the fashion industry, it is possible to forecast sales both at the store and product level, and detect variability over time. In addition, by juxtaposing a sales forecast and store stock, it is possible to predict when the amount of stock in the store will drop to a level resulting in a product shortage, before such a drop occurs. In this way we buy time and gain the opportunity to replenish products in the stores, and, as a result, we avoid out-of-stocks, which ultimately contributes to increased sales.
While forecasting demand in the fashion industry is not the easiest thing to do, it makes good business sense. High competitiveness and increasing customer demands make it imperative for companies to develop in a data-driven spirit today in order to maintain their market position in the future. It’s worth noting that this does not only apply to the biggest players. Thanks to SaaS solutions such as Occubee, medium-sized and growing retailers can also benefit from the power of artificial intelligence.
Michał Koziara, CEO 3Soft S.A. and co-founder of the Occubee platform
