Leveraging Machine Learning to Tune your Omni-Inventory Strategy
“What you do today can improve all your tomorrows.” – Ralph Marston
The new omnichannel world has not only affected how retailers engage with their customers, but also how retailers plan their inventory. For many retailers, inventory has become quite undisciplined, coming and going from new and different channels at all hours. And that creates a lot of challenges, because if you remember from our previous articles, retailers generally measure omnichannel demand two ways. Either they count everything as retail demand for whatever location fulfills an order or sells directly to a customer, or they look at in-store sales as one demand stream and e-commerce as another. The problem is neither approach is nuanced or tailored to the multiple-choice fulfillment options consumers have now. Both the lift and shift resulting from adding new fulfillment experiences needs to be accounted for to truly understand the demand on your inventory. (For more on managing the omnichannel lift and shift impacts on demand, click here.)
Without insight into those factors, organizations are forced to make educated guesses around demand forecasting. And that makes it nearly impossible for them to effectively have their inventory in the right place at the right time using conventional planning methods. That means they have to work harder to meet consumer expectations, which drives up costs. It’s time we work smarter. It’s time we use inventory as a strategic lever to make omnichannel profitable.
And that’s where Omni Inventory Optimization (OIO) comes in.
The need for a solution like OIO, which takes a more sophisticated view of demand and how it affects inventory, is evident. But understanding demand at the most granular level is only a start to optimizing inventory. The right solution should also have the ability to learn and self-adjust over time, based on how consumers are taking advantage of the various fulfillment options available to them.
Let’s say you’re ready to roll out a new fulfillment experience. Usually you would begin with a pilot program or test market. OIO is ideal for this. As the program launches, initial adoption expectations would be used to model inventory needs. Once the program is live, OIO watches how consumers react to the program and how demand is affected in both the DC and stores. This is where OIO intelligence comes into play. OIO constantly monitors and learns so it can automatically self-tune the inventory distribution based on actual adoption. Every week that passes, the solution continues to fine-tune the inventory plan. It analyzes the demand data, improves the strategy and adjusts inventory placement. Without any additional effort from your team, OIO continually optimizes inventory across your fulfillment initiatives.
As an organization, you can be assured that you are always meeting demand in the most efficient way possible – regardless of how your customer engages with your brand. That precision and accuracy allows you to easily expand successful programs and maintain inventory optimization.
To get a sense of exactly how OIO works, here’s an example:
Let’s say you’ve recently launched buy-online, pickup-in-store in three test markets. Just prior to launch, your demand planners adjusted the inventory plan to ensure the three stores had sufficient product to service any customers who want to try the new fulfillment option. Given that this is a new program and one of the first omni-fulfillment strategies you’ve tested, the planners had to make some assumptions. In this case, they estimated there would be a 10% lift in new demand and 5% shift of existing customers to the new fulfillment option. When they applied this model to their inventory plan, it seemed like a safe, but conservative approach to start with.
Here is where OIO gets to work. Two weeks after launch, the technology detects positive news. The demand planners were partially correct. Overall, there was nearly a 15% lift in total demand from offering buy-online, pickup-in-store. That means stores likely need additional inventory to ensure shoppers can purchase the items they desire. However, OIO has also learned that close to 10% of existing shoppers shifted their buying preferences to the new fulfillment option. So, now there are opportunities to reduce the amount of expected demand for the e-commerce DC and therefore possibly reduce inventory overall.
Thanks to OIO’s ability to self-learn and self-tune, the retailer was able to increase sales, boost customer loyalty and reduce inventory.
It’s an omnichannel world. OIO helps you leverage inventory to thrive in the new environment. With self-learning capabilities, it allows you to constantly improve how you meet consumer preferences and demand. With every new fulfillment option, OIO helps you pilot and expand programs with the most accuracy possible. The results are better experiences for the omnichannel shopper, along with lower costs, greater capital savings and reduced inventory across the network.
That’s what it means to truly optimize inventory.
Read our whitepaper to learn more about Omnichannel Inventory Optimization.