A Single Source of Data Truth: Improving Inventory Allocation Powers Velocity
Getting inventory velocity right is one of the trickiest problems to solve in retail.. There are so many interrelated processes that it’s easy for velocity to get out of balance. This leads to a poor inventory turnover ratio, higher carrying costs, and an increase in dead stock. The answer lies in properly assessing and optimizing inventory allocation, so that SKUs are positioned at the right place at the right time, in alignment with demand across channels. When that happens, velocity is positively affected as conversion rates rise and inventory turn is improved.
Inventory velocity is crucial in supply chain management as it measures the speed at which inventory moves from source through consumption. Faster velocity equals greater efficiency, lower carrying costs, fewer stockouts, and improved customer satisfaction as the desired product is consistently available when they want it.
But having inventory velocity optimized doesn’t just happen. It takes a diligent team effort, strong analytical tools and capabilities, and innovative technology that can sync disparate data across enterprise systems to provide a true, dynamic inventory view.
A Symbiotic Relationship: The Role of Allocation Improvements drive Velocity
Inventory velocity and inventory allocation are deeply interconnected within supply chain management. Optimizing inventory velocity — how quickly items are sold and replaced — relies on strategic inventory allocation so that products are efficiently placed where demand is highest. This symbiosis improves turnover rates, lowers holding costs, and makes for better overall supply chain responsiveness.
Optimizing inventory allocation for an omnichannel brand – selling to owned stores, wholesale, and e-commerce, for example – calls for a sophisticated approach. It requires balancing demand forecasting, inventory visibility, and agile logistics. Advanced data analytics help to accurately predict demand and adjust stock levels dynamically across channels.
Effective inventory allocation optimizes stock levels and distribution, aligning supply with demand across channels and locations and ensuring a faster inventory turn. This involves a complex process of tracking stock levels daily, in every location, and across every channel. Demand signals and buying patterns are analyzed, which in turn drives strategic inventory reallocation as needed.
For instance, a particular pair of designer jeans might be flying off the shelves in Florida but overstocked at stores in Michigan.
Optimized inventory allocation prevents an excess of both stockouts (resulting in angry and possibly former customers) and costly overstocks. It also enhances greater supply chain efficiency, increasing your ability to respond quickly to changes in market conditions or consumer preferences. Efficient allocation also reduces the need for frequent transfers between locations or expedited shipments to fulfill orders, lowering transportation costs.
The “Black Box” Problem of Siloed Inventory Data
One major challenge facing retailers and brands looking to optimize their inventory allocation is how to get their arms around the issue of data silos. Inventory data resides in a number of places across the tech stack: the WMS, the OMS, the ERP, the POS, and the inventory management system.
For this reason, omnichannel inventory data is often described as a “black box” problem. Channel management teams can’t access inventory data because of where it resides, or can’t go into the system of record and make changes on a timely basis. Some brands’ channel teams, for example, find themselves relying on Excel spreadsheets to take a retailer’s “data dump” from an ERP and determine if it can be dropped into the WMS as a purchase order for fulfillment. In 2024, it’s not exactly a recipe for optimized inventory allocation in order to increase velocity.
This leads to significant inefficiency, creating too much unnecessary back-and-forth between teams to reach reconciliation. It also prevents them from developing an optimized allocation strategy; for example, knowing how many units of a SKU should go to stores vs. online vs. marketplaces like Walmart or Amazon, and when.
In another scenario, an omnichannel apparel brand, with owned stores, e-commerce, and wholesale, uses its ERP as the data system of record. The ERP makes demand-based allocation recommendations based on various inputs (sales history, forecasts, market trends, etc.), which then have to be customized. But the channel team can’t make changes in the ERP, so data is shared via spreadsheet with the data management team, who then uploads it back into the ERP to complete the changes. This clunky process is not exactly a model of efficient inventory allocation, preventing channel teams from seeing same-day results of their decisions.
With systems not “talking” to each other, there’s no single source of truth, a shared view of inventory-wide data. Thus, visibility becomes blurry or worse, making scaling a business – or even operating efficiently – extremely difficult.
When inventory data is segmented by channel, information can’t flow seamlessly between the silos. This leads to inconsistent inventory levels as viewed by different channel teams or functional areas. As each channel updates its own inventory, it creates lags that don’t reflect the in-the-moment status of stock levels. For instance, an item sold in-store might still appear available online – until the next data sync, which can often occur the following day.
Another issue with siloed data is a lack of flexibility that allows teams to deviate from an inventory plan set in the system. This means they can’t react to things like unexpected weather or a promotion that takes off. Stores may run short of certain SKUs that would have otherwise been allocated to support ship-from-store. A lack of shared, accessible, dynamic data makes it difficult to pivot and adjust the plan, so teams are constantly in scramble mode, patching it as best they can.
Three Ways to Achieve Inventory Allocation for Maximum Inventory Velocity
Given all the advances in machine learning algorithms, it should definitely NOT be the case that Microsoft or Google spreadsheets – stalwart as they may be – are still the number-one supply chain and inventory planning tool. Fortunately, they don’t have to be anymore.
Game-changing inventory management technology from Dropit sits atop the retail tech stack, acting as a data connector and reconciler between systems. Through API connections to an ERP (SAP, Oracle Netsuite, etc.), Dropit creates an instance of a “data lake,” runs it through its proprietary ML model, and creates an output for use as decision intelligence by channel teams.
This next-generation capability opens up a world of possibilities for optimizing inventory allocation, and by extension, improving inventory velocity and turnover.
1. Advanced Inventory Analytics
Dropit’s sophisticated data analytics takes a deep dive by processing a variety of inputs: historical and current sales data, market trends, customer behavior patterns, etc. By leveraging these insights, businesses can make better-informed decisions on inventory allocation, ensuring that the right products are available at the right time and place, in the right quantities.
Because Dropit’s analytics look agnostically at inventory data across wholesale, retail, online, and drop shipping, it can notify teams to position stocks to optimally execute orders across all three. In another scenario, Dropit’s analytics can trigger an update to fulfillment execution in the WMS. This leads to a pivot from mostly parcel for e-commerce to bulk pick for retail, before an order drops. Both labor and transportation are adjusted in advance, resulting in savings for both.
2. Dynamic Inventory Visibility
Dropit provides dynamic visibility into inventory levels across multiple channels and locations. This allows you to monitor inventory fluctuations and demand changes dynamically, enabling agile allocation decisions in response to shifting market dynamics and customer preferences.
Dropit’s technology eliminates inventory discrepancies that result in stockouts, when the system pulls e-commerce orders from “ghost SKUs” that don’t exist, in between data syncs to true-up counts. Now, inventory levels are refreshed throughout the day, and team members can execute orders with confidence that stock levels are maintained and buffers protected across channels.
3. Dynamic Allocation Algorithms
Dropit employs dynamic allocation algorithms that continuously optimize inventory distribution based on changing demand patterns and supply chain constraints. These algorithms consider various factors such as product characteristics, sales velocity, and lead times to dynamically adjust inventory allocation strategies, maximizing efficiency and minimizing stockouts.
While your ERP is analyzing lots of historical data for buying patterns and behaviors, Dropit can compare this against live order data, which constantly changes inventory levels. For store teams, this opens up the possibility of adjusting and rerouting inventory with greater precision based on a variety of external factors (weather, market conditions, etc.).
The Inventory Allocation “Black Box” Is Finally Busted Open
For too long, the lack of real-time data visibility has been a limiting factor holding back channel teams from optimized inventory allocation. This in turn has slowed inventory velocity, putting turn ratios out of kilter and causing overstocks that add costs, limit full-price sales, and kill margins.
One of Dropit's key strengths lies in its ability to connect with ERP, OMS, POS, and CRM systems, drawing from each to create a centralized data lake. By syncing data and leveraging machine learning algorithms, Dropit gives retailers access to the most up-to-date information, optimizing inventory flows.
Retailers get a comprehensive view of inventory and operational processes, enabling them to make informed decisions and drive business growth. This helps them achieve incremental improvements in inventory flow, increasing order to cash with better decision intelligence.
Dropit can optimize your inventory allocation and improve inventory velocity,
request a demo to learn more.
