Managing inventory across various sales channels can be a complex puzzle, as each channel has distinct characteristics. Retailers face a push-and-pull dynamic with each sales point. Ecommerce often experiences peak demand in shorter, unpredictable cycles, while physical locations require stable stock to meet customer expectations. On the other hand, marketplaces like Amazon demand quicker inventory turnarounds and adherence to specific stock and shipping protocols. In this scenario, how do retailers find a balance?
Without a well-planned allocation system, retailers risk overstocking in one area and understocking in another, leading to lost sales or excess storage costs. The key to effective multi-channel inventory management is understanding each channel’s unique demand and having the right strategy.
In this blog, we’ll explore multi-channel complexities, review inventory allocation strategies, and discuss technologies for governing inventory. Stay tuned to unlock retail success.
Inventory Allocation is the strategic process of distributing stock across multiple sales channels—such as ecommerce, physical stores, and marketplaces—to meet customer demand efficiently. This approach considers the unique demand patterns and distribution needs of each channel, helping retailers maintain optimal stock levels. By managing allocation effectively, businesses can minimize the risks of overstocking or stockouts, maximize profitability, and enhance customer satisfaction across channels.
Manual inventory allocation is straightforward and works well for smaller operations with limited SKUs. Typically, planners review historical sales data and adjust inventory based on expected seasonal demand. By using basic spreadsheets or ERP software, businesses can make adjustments at a lower cost, keeping processes simple and manageable. However, scaling this method is challenging; manual allocation across multiple locations can result in fragmented processes and higher labor costs.
Manual allocation can lead to inefficiencies for businesses with several channels or a large product range. In fact, U.S. retail operations have an average inventory accuracy of only 63%. This could, in part, be because manual allocation creates bottlenecks in high-demand periods, making it hard to achieve optimal distribution when it’s needed most.
When businesses begin to outgrow manual processes, they often turn to rule-based allocation systems. This approach uses predetermined rules, such as "allocate 40% to online and 60% to brick-and-mortar," to standardize stock levels across locations and channels. Rule-based allocation is particularly helpful for multi-channel retailers looking for a reliable way to manage high-demand products during peak seasons.
A key advantage of rule-based allocation is its scalability. Retailers can adjust rules seasonally or by channel, responding to changes in demand patterns. However, the rigidity of rule-based systems can sometimes lead to stockouts or excess inventory if demand shifts unexpectedly.
| Did you know? Fixing understocking and overstocking reduces 10% of inventory costs. |
Algorithmic allocation goes beyond rule-based systems. It uses algorithms to analyze historical sales data, demand forecasts, and other variables to inform stock decisions. This method enables retailers to automatically adjust inventory levels at individual locations, leading to faster fulfillment times and lower shipping costs. Algorithmic systems are a smart fit for multi-location retailers dealing with fluctuating demand and seasonality.
For instance, some systems consider product lifecycle stage or sales velocity, allocating fast-moving items to high-demand regions first. This approach is essential for businesses looking to scale operations without compromising on fulfillment speed and accuracy, especially as they expand across multiple sales channels.
Machine learning-based allocation represents the next evolution in inventory management, offering unprecedented flexibility and responsiveness. They adapt stock allocation on the fly by continuously analyzing historical data, sales patterns, and real-time customer demand. This technology can predict demand surges or slowdowns more accurately than traditional forecasting, minimizing stockouts and overstocks even during peak sales periods.
With this advanced level of insight, retailers can meet customer expectations faster, improve shipping efficiency, and reduce holding costs. In fact, retailers using machine learning-driven allocation have seen holding costs decrease by up to 30%. Given that real-time visibility and adaptability are key to meeting multi-channel demand, machine learning is becoming an essential tool for competitive retailers.
Demand forecasting is essential to prevent both stockouts and overstock situations, which can disrupt the customer experience and drain resources. For multi-channel retailers, it’s important to forecast demand individually for each channel by leveraging historical sales data, seasonal trends, and channel-specific metrics.
When accurately forecasted, retailers can have inventory allocated where it is most needed. Accurate forecasting helps anticipate customer needs, providing a foundation for smarter allocation and a streamlined supply chain.
As we’ve discussed, real-time visibility is essential to effective inventory allocation, particularly when working with multiple channels. Tools like ERP (Enterprise Resource Planning) systems, WMS (Warehouse Management Systems), and real-time analytics platforms allow IT teams to monitor stock levels, demand shifts, and ensure inventory visibility across channels.
With accurate, up-to-the-minute data, these tools enable companies to track inventory flow, detect fluctuations in demand, and make timely adjustments. Having real-time data at hand allows for a more responsive and efficient approach to multi-channel inventory accuracy.
Dynamic reallocation involves redistributing inventory based on the performance of each sales channel. For example, if a particular channel experiences a surge in demand, inventory can be shifted to support this high-performing channel.
Prioritizing channels based on performance data helps capture revenue opportunities without overextending inventory. This strategy allows retailers to be proactive in adjusting stock levels to meet demand, optimizing profitability and reducing the risk of stockouts.
Each channel experiences unique volatility and demand fluctuations, so setting safety stock levels is essential. Safety stock is a buffer that helps prevent stockouts but must be carefully calculated to avoid excessive inventory holding costs.
By analyzing each channel’s demand patterns, retailers can establish adequate safety stock levels that reflect the volatility and service expectations of that channel.
Efficient inventory strategies also require balancing operational costs with customer satisfaction. Retailers can keep service levels high without incurring excessive costs by optimizing storage and shipping costs while ensuring quick delivery times.
Finding this balance is especially critical for ecommerce, where fast delivery and consistent availability of products are expected. Allocating inventory strategically helps maintain customer loyalty and prevents lost sales due to stockouts, all while controlling costs associated with storage and logistics.
Seasonal changes and promotional events can significantly impact demand, requiring timely adjustments in inventory allocation. For instance, holiday seasons often drive higher demand in both ecommerce and physical channels, while certain promotions might create spikes in specific regions or online.
Strategic planning for these periods can help prevent excess inventory during off-peak times and ensure sufficient stock during high-demand periods. Incorporating seasonality into allocated inventory reduces the risk of lost sales due to stockouts and avoids post-season markdowns due to overstock.
Allocating inventory is not a one-time task but requires ongoing assessment and adjustment. Consumer behavior, market dynamics, and operational goals change over time, making it necessary to revisit inventory strategies regularly.
By continuously evaluating and refining inventory management practices, retailers can ensure that their allocation strategy aligns with current business objectives and customer expectations. Regular reviews keep inventory management practices relevant, efficient, and responsive to shifts in the retail landscape.
Deck Commerce’s Order Management System (OMS) provides retailers with the essential tools to achieve accurate, real-time inventory visibility across all sales channels, enhancing inventory management and fulfillment efficiency. With Deck Commerce OMS, businesses can streamline multi-channel inventory management by centralizing inventory data, dynamically adjusting stock levels, and proactively managing channel-specific demand patterns.
Deck Commerce OMS aggregates inventory across all channels, ensuring that both you and your customers have access to up-to-date, available-to-sell (ATS) inventory.
Inventory quantities are updated in real-time, so you can ensure that your sales channels reflect accurate stock levels at all times, whether you’re selling through ecommerce, physical stores, or marketplaces.
You can allocate specific percentages of your inventory to different sales channels, ensuring that stock is available where it's most likely to sell.
The ability to set safety stock levels per SKU ensures that you always have a buffer for high-demand items, reducing the risk of stockouts.
Deck Commerce OMS enables you to manage preorder and backorder quantities, keeping your operations flexible and responsive to customer demand.
Optimize your multi-channel inventory management with Deck Commerce OMS. Gain real-time visibility, enhance flexibility, and ensure products are available where your customers need them most—all while reducing operational costs. Schedule a demo today to see how Deck Commerce OMS can streamline your inventory allocation and drive your business forward!