The digital transformation is more important than ever for fashion retail. And that’s not just because everything in today’s society somehow has to be digital. Strictly speaking, digital solutions have the potential, especially in fashion retail, to be the driving factor in solving central challenges facing the industry.
The number of SKUs is increasing and increasing and…
For example, the ever-increasing number of SKUs in the industry is increasingly presenting retailers with very specific problems. In short: the complexity increases along with the number of SKUs that a retailer has to manage. The sheer volume of products for which prices and stocks have to be planned in the stores meanwhile push even the most sophisticated Excel construct to its limits.
Variety of different channels and POS
Another key driver of the increasing complexity is the large number of different sales channels and points of sale, which can be found above all in the omnichannel model. Combined with the already mentioned high number of SKUs, it becomes a real challenge to have the optimal stock at the optimal selling price for every product, everywhere and at all times.
Changed consumer behavior
Last but not least, retailers have to deal with changing consumer behavior. Consumers are better informed than ever before. They check prices online and appreciate the availability and convenient service they get from the big online pure players. In order not to lag behind in the market, retailers need to stock the right quantity of goods at the right price for every channel, every time.
What contribution can AI and machine learning make?
The digital possibilities to master the challenges described are of course diverse today. Strictly speaking, Excel is also a “digital solution”. So why does the use of AI and machine learning have the greatest potential? Or to put it another way: “What contribution can AI and machine learning make in the field described?
Unlike Excel and other spreadsheet applications, AI and machine learning are designed to deal with large amounts of data or big data. The high number of SKUs and sales channels does not pose any major challenges for the AI.
But how can the optimal price and inventory decisions be automated with the help of AI? The machine learning algorithm creates a central demand forecast using the retailer’s historical item and transaction data as well as other external data sources such as weather data or competitor prices. Various scenarios will be compared with each other, taking into account the retailer’s individual business strategy, in order to determine the optimal price or the optimal storage quantity. This creates a self-learning system that is constantly being optimized and has prices and stocks under control, allowing retailers to concentrate on the essential factors of their business.