Say Goodbye to Inventory Challenges With Predictive Analytics

Anyone who has spent time paying attention to fashion trends over the course of a season knows that clothing products and styles change as quickly and as frequently as the winter wind.  To say that the lifecycle of any trend is short-lived would be an understatement.

At what seems like lightning speed, we've seen jumpsuits eclipse separates, earthy neutrals outshine pastels, and capes dominate the outerwear category. What's more, the moment retailers adjusted to accommodate these preferences, they undoubtedly shifted only to be resurrected in another season. If you are a clothing retailer and you make your name on having the most relevant style, how can you possibly be expected to keep up without making it your full-time job to predict mercurial trends?

Collecting and manipulating data makes that job easier than it's ever been before, with the help of sophisticated retail predictive analytical tools that can make inventory managers and supply chain analysts weep with joy. Problems such as out-of-stocks or having overstock after a high-selling season can be mitigated, if not completely eradicated. And by pulling all of your data and analyzing it together through an omnichannel approach, you will find new efficiencies.

Identify inventory challenges
In the retail world we often need to find a balance between assortment depth versus assortment diversity. On one hand, retailers want to show that they have a strong and successful business and have a lot of choices for their customers. However, they may not want to order the same amount of inventory for each product (or size, color, style, etc.) - a decision that will only leave them with fringe sizes and colors at the end of season as deeply discounted, dead stock.

Another factor that makes inventory management a lot more challenging is that styles never stay the same. Retail is a dynamic industry requiring dynamic decisions made in real time and based on the behavior of products and stores. So for example, a fashion retailer with 100 stores, 500 different styles with several colors and sizes would have to be able to make millions of decisions on a consistent basis to adapt to the ever-changing environment. A predictive analytics solution can do this in just minutes and produce actionable results every day. Proactively.

Another challenge fashion retailers face more often than other retailers is allocation. It doesn't make sense to send the exact same merchandise to every single store. Different stores will have their own demand, seasonality and geo-demographic diversity of consumers, as well as store shelf capacities, assortment rules and much more. Predictive analytics looks at all of these factors and recommends the optimal way for fashion retailers to allocate merchandise to stores in the best way possible.

Uncover hidden opportunities
Often, top sellers can be a combination of high-correlation items that wouldn't even perform as well separately. There are certain products that sell very well when combined with another product, such as paper for your new printer. Companies such as Amazon know this well and use predictive analytics technology for "suggestive selling," "cross-selling," or "up-selling." Moreover, this example would not work the opposite way, as you can't offer a printer every time someone buys paper. In order to find these hidden opportunities, a predictive analytics engine needs to go beyond traditional forecasts and look at your business as a whole. You would be surprised to see what a retail predictive analytics engine can uncover about your trends, and product correlations.

How about this most recent holiday season? How much dead stock did you end up with? You can be sure that any fashion retailers who weren't using predictive analytics solutions are deeply discounting the rest of their stock and considering what they can do to ensure those kinds of losses don't reappear the next time around.

There are 353 days until Christmas and counting — what's your plan?

Yan Krupnik is the business development manager at Retalon, a provider of predictive analytics for the retail industry. Since 2002, Retalon has optimized pricing, inventory management, merchandising, planning and marketing operations for retail organizations in a variety of industries.
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