Increasing Allocation Accuracy Is Key to Efficient Fulfillment
As we look in the rearview mirror at 2020, one thing is abundantly clear — retail will never be the same. The COVID pandemic has accelerated digital transformation by as much as three to five years.
In 2021, the ability to react to changes in customer demand will be even more important as the needs and expectations of consumers continue to change so dramatically. For example, the big metro area and resort stores — that previously were top selling locations — are no longer carrying the brand, or consumers continuing to make more online purchases due to either their limited ability or lack of desire to shop in-store. Yet, a study conducted by Forrester shows that 73% of brands are not prepared to predict changing customer demand.
Although many retailers started implementing more advanced and intelligent tools to better support their omnichannel strategies, they focused their initial efforts on the online shopping experience rather than on inventory planning and fulfillment. Now that those technologies are in place, retailers and brands need to immediately turn their focus to building their back-end omnichannel systems and supply chain processes.
Given that online sales for brands has grown so quickly in the past 12 months, some having more than doubled, traditional allocation and forecasting will end up costing retailers millions of dollars in unnecessary labor and shipping costs. Why? Because now, a retailer must anticipate more than just what to carry in the store for in-person shopping. They must also predict what online orders will need to be fulfilled by the store, as well as the onslaught of online returns, from consumers who are geographically close by.
One apparel retailer that was already experiencing a far greater increase in shipping costs as compared to the growth of their online sales decided to start addressing this problem. They implemented a more advanced and accurate forecasting system that determined their store allocations right before the 2020 holiday season. By better predicting their customer demand down to the Item/Store level, including what customers would be ordering online, they were able to significantly reduce split shipments as compared to their 2019 holiday season, and doubled their ship completes. That translated into millions of dollars saved in reduced shipping costs and improved sell-through.
This solution involved a sophistication of demand forecasting that has never been required before because retailers had often been able to “get by” with not having a highly accurate prediction of what product to allocate to each store. On average, over 40% of in-store purchases are impulse purchases. In other words, if the consumer was not specifically going to the store looking for that exact item in that exact size and color, they may well still make a purchase of a different item that was physically available at that time.
But with the increase of online sales, retailers must be far more accurate in anticipating what consumers will want to buy and when, down to the exact item/color/size. Additionally, they need to be able to predict where those orders will be shipped to in order to accurately allocate product to their distribution centers and stores in order to maximize the efficiency of their labor force and minimize shipping costs. And due to the increase in returns of online sales, they need to reforecast and re-allocate frequently to avoid markdowns and clearance at the end of the season, which can hurt sell-through and margin.
Although there are other omnichannel strategies that retailers will need to address over the coming months, in the end, it all comes back to the age-old wisdom of having the right product, in the right place, at the right time. And a more accurate allocation of a retailer’s product, both pre-season and in-season, is the key to improving fulfillment efficiency and the maintenance, if not the improvement, of their bottom line.
Kaushik Katari is the chief products officer for antuit.ai, responsible for the development of AI-powered SaaS solutions that are easy to implement and deliver significant margin impact for retailers.
