The world is so extremely complex that people must depend on abstraction to make sense of it. Your eyes see millions of pixels flashing dozens of times each second, but your brain interprets them as one moving image. You’ve seen thousands of leafy objects in your life, but you know what to expect from plants, trees, and salads without having to investigate each object in detail.
We instinctively group our experiences to make predictions and save time; your business should be doing the same thing with its stores and products.
Should you treat each of your fashion business’s stores as a unique entity? Or should you treat each store identically? On closer inspection, neither option is ideal; the first increases costs whereas the second decreases revenue.
Clustering offers a more practical third option. By comparing the defining characteristics of each of your stores, including floor space, local fashion preferences, annual revenue, and inventory turnover, you can group your stores into meaningful clusters that provide a shorthand to guide your preseason and in-season planning.
If you’re about to launch a new store, your existing store clusters can help you predict the new store’s performance and identify its needs. What merchandising and sales strategy will be more effective? How high should you set sales targets? Find out which cluster the new store belongs to, and then look at the performance of other stores in that cluster.
You can determine how similar fashion products are by listing each product’s unique combination of attributes, like its type, color, fabric, collar, sleeves, and buttons. Generally, pairs of products with more attributes in common are more similar than pairs with fewer common attributes.
Multidimensional product clustering looks for groups of products that have several features in common. These clusters can help your brand align its product catalog with your customers’ preferences. They can also help you forecast demand for new products, which don’t have any historical sales data available for analysis. Find a cluster similar to the new product and use it to generate an approximate forecast.
Conclusion: Prepare for swift and decisive action
Advanced analytics and machine learning continue to make clustering and demand planning much faster and more accurate. For more information on how these technologies can simplify your overall strategy and individual plans for each channel and store, download our eBook, “Predictive demand planning across the fashion supply chain”.
About the author
Combining human centered AI with deep domain expertise, Algo’s analytics enriched supply chain intelligence platform helps suppliers and retailers plan, collaborate, simulate and execute a more efficient supply chain.