Fulfillment Forecasting: The Keys to Successfully Aligning to Localized Customer Demand
Market-based demand forecasting to help you predict localized demand and placement of inventory
Retailers are facing increasing pressure to offer affordable, reliable, and frictionless customer experiences. With the acceleration of unified commerce, businesses must be able to leverage inventory to its fullest potential at every location within the network, and create engaging and convenient customer experiences.
A large department store in North America faced challenges with soaring fulfillment costs and decreasing margins, both placing pressure on the business. The wanted to leverage stores to help fulfill orders but weren’t sure how to balance the inventory needed to satisfy in-store, omni-channel, and ship-from-store customer purchases. To address these issues, the company is implementing a first-of-its-kind solution, Blue Yonder’s Fulfillment Item Forecasting, to manage demand and fulfillment costs across labor, logistics, capacity, markdowns, and stockouts. Through this solution, a market-level e-commerce forecast apportions items to ideal nodes, enabling better inventory placement and resulting in a reduced cost to serve, lower carbon emissions, and faster delivery to customers.
This customer example is just the beginning of a composable approach to a new way of augmenting retail and grocery planning solutions, solutions that we call Fulfillment Forecasting. To explore these topics more, I recently hosted a Blue Yonder Live session with Erin Halka, Senior Director Solution Strategy, and Badri Krishnamachari, Corporate Vice President Retail Solutions. Read on below for a Part 2 summary of the conversation and then go hear it straight from them on the Blue Yonder Live. You can also check out Part 1 of this blog.
Market-Based Demand Forecasting
According to Badri Krishnamachari, “The key to successful fulfillment forecasting and successful omni-channel operations is really to get a good idea of what a demand would look like through unconstrained demand.”
Badri also highlights that the key to success lies in truly understanding demand, the fulfillment options available, and the inventory and resources needed to meet that demand. Not just looking at historical demand, trends and data, but also considering future fulfillment strategies that could involve meeting demand from different locations or opening up new stores or micro-fulfillment centers.
Understanding unconstrained demand involves considering assets such as inventory placement, that are dependent on order volume and customer fulfillment choices.
By having better predictions of where to place inventory ahead of customer purchases, regardless of the channel, leads to improved cost savings. This is especially key as companies deal with rising prices due customer expectations for flexible and fast fulfillment options.
The Importance of Inventory Placement
With limited supply and inventory being spread across more locations, it is crucial for businesses to have better predictions to place inventory in the right place to avoid missing sales or having excess inventory that leads to markdowns and margin erosion.
According to Erin Halka, “If the customer has visibility to pick up their goods in a store, that can help convert them into buying those goods. For retailers, it’s key to consider the proximity and the timeframe that you’re trying to offer those customers. You think about having inventory close to them to be able to satisfy those expectations. And this is a shift in mindset.”
One challenge is the regionalization effect that is happening. Companies need to think about how they can better service the market and make sure they have the right time to service customers with a 2-3 day window from any location, be it a store, micro-fulfillment center (MFC), or third-party logistics (3PL) provider.
Another challenge is the change in shopping behavior. Companies need to shift their thinking away from location-specific demand to market and customer-centric demand and assign that demand to the locations and channels that can fulfill it. Machine learning (ML) can help with this by picking up promotional and causal influences that are local in nature.
Traditional approaches to planning demand, supply, or assets tended to be fixated with location. However, this can be misleading as fulfilling locations can be dynamic in the case of e-commerce. Companies need to move away from traditional methods and embrace ML to better manage their inventory.
Optimizing Inventory Placement with Blue Yonder
Predicting future customer orders to guide placement decisioning is crucial, as is having the flexibility for decisioning that depends on the business needs. This means companies need to prioritize intelligence in guiding where and which locations to place inventory, based on current market demand.
With Blue Yonder’s Fulfillment Forecasting microservices, companies can optimize the total cost of omni-fulfillment by considering various cost factors, including labor, logistics, capacity, markdowns, and stockouts. By creating a market-level e-commerce forecast and dynamically apportioning the forecast to ideal nodes while respecting capacity constraints and fulfillment rules, companies can enable better inventory placement that plugs into existing replenishment systems without any changes needed.
This saves time, capital and carbon while protecting walk-in sales.