What is Allocation?
In supply chain and retail, Allocation refers to the process of distributing available inventory across various destinations (such as stores, regions, or retail partners) when supply is limited or demand is variable. It answers the question: “Who gets how much of the product we have?” If you have 10,000 units available and multiple places that need stock, your allocation strategy decides, for example, that Retailer A gets 5,000, Retailer B gets 3,000, and Retailer C gets 2,000.
Allocation can also occur at a more granular level (like sending inventory to individual stores in a chain based on expected sales). Essentially, it’s about prioritizing and apportioning stock in a smart way, rather than just filling orders first-come-first-served. This term often comes up when demand exceeds supply (product shortage) or when launching a new product (limited initial supply) – you allocate what you have in a way that best meets business objectives.
Why Allocation matters in CPG
For consumer goods brands, especially those dealing with retail channels, allocation is a critical lever to optimize sales and maintain good retailer relationships. Here’s why it matters: demand is almost never perfectly equal to supply across all locations. There are times you simply don’t have enough product for everyone who wants it (think: a viral new toy around the holidays, or supply disruptions on a popular snack). Rather than letting it be a free-for-all, companies use allocation strategies to decide where to send the limited stock. Done well, allocation can maximize your overall sales and minimize lost opportunities. Done poorly, you might end up with empty shelves at a top account (lost sales and upset customers) while lesser outlets sit on excess inventory.
In a CPG context, Sales and National Account Managers care about allocation because they need to keep their key retail partners happy. If Walmart is your biggest client, you don’t want them to go out of stock while a smaller regional chain has product sitting in the backroom – that’s a misallocation. Supply Chain Leaders and operations teams typically design the allocation plan. They’ll use data like each retailer’s sell-through rates, current inventory, and promotional plans to decide who gets what. Demand Planners feed into this by providing the forecasted demand by account or region, which is essential input for allocation decisions. Executives get involved when things are really tight – for instance, in an extreme shortage, the C-suite might say, “Our strategic priority is to allocate to Retailer A first because that’s a flagship partner.”
Allocation is also important for ecommerce vs. retail balancing. If you also sell directly online, you might reserve some inventory for your own website vs. allocating everything to retailers. These decisions can affect long-term customer loyalty and margins (selling D2C might be a higher margin, but you also need to keep retail channels stocked to preserve those relationships).
Moreover, allocation isn’t only reactive (in crises); it’s used proactively. For example, during a product launch, you allocate initial shipments based on where you expect the highest demand. Or if one region’s stores consistently sell more of a certain product, you allocate a larger share of the production to that region’s warehouses. Modern analytics make this easier: tools like Alloy.ai’s Allocation Insights can highlight where each unit of inventory will have the greatest impact on revenue, guiding you to allocate inventory to the channels or locations that need it most. This ensures you get the biggest bang for each unit when supply is constrained.
Real-World Example
Imagine you’re a Supply Chain Director at a toy company heading into the holiday season, and you have a hot product that every retailer wants. Unfortunately, due to an unexpected factory delay, you only have 70% of the inventory you originally planned to have by December.
Now it’s allocation time – you must decide how to split that inventory amongst your retail partners. Here’s how this scenario might play out.
- First, you loop in the Demand Planning and Analytics team to get the latest data.
- Retailer A (a big-box chain) is projected to sell 40,000 units.
- Retailer B (another chain) forecasted 20,000 units.
- Your direct ecommerce channel is expecting 10,000 units.
- Total demand of 70,000 units.
- Total supply of 50,000 units.
- You don’t want any channel to go completely dry, but you also want to maximize overall sales. Using an Alloy.ai dashboard, you review each retailer’s sell-through velocity and current on-hand inventory.
- Retailer A is selling fastest and would lose the most sales if shorted too much (and they’re a key strategic account), so you decide to allocate a relatively higher portion to them – say 30,000 units.
- Retailer B gets 15,000.
- Your own D2C site gets 5,000 (with the thinking that direct customers might switch to buying at a retailer if your site runs out, whereas retailers can’t so easily get stock elsewhere).
- You’ve just made a tough call: Retailer B will only get 75% of what they wanted, which means some stockouts in late December for them. You communicate this plan to the National Account Managers for each retailer, so they can manage expectations. The NAM for Retailer B might negotiate some compromise, like scheduling another smaller shipment in January to make up for lost holiday sales.
- During this time, the IT/Data team is ensuring all the allocation data is reflected in the ERP and that retailer orders are adjusted accordingly (some manual intervention may be needed to split available inventory).
- As the season unfolds, you closely monitor the sell-through. Suppose Retailer A’s sell-through is even higher than expected and they’re about to stock out before Christmas, while Retailer B’s sales are slower (maybe they didn’t promote it as hard). You might re-allocate on the fly – pulling a few thousand units from B’s upcoming shipment to send to A instead. This kind of agility keeps overall sales as high as possible.
- After the dust settles, your Executive team looks at the outcome: thanks to smart allocation, you achieved an overall 90% fill rate across all orders and captured an estimated $5 million in sales that would have been lost if you had allocated evenly (which would have caused Walmart-sized holes on shelves). The execs also appreciate that no single account was completely starved – you maintained service to all, if not ideally, which helps preserve those relationships for the long term.
Key Metrics and KPIs for Allocation
- Fill Rate / Service Level: This measures what percentage of the demand or orders were filled. You can look at fill rate by retailer or channel. In allocation scenarios, you might have target service levels (e.g., “We promise to fulfill at least 80% of each retail partner’s orders even in a shortage”). After allocation, you’ll calculate actual fill rates – say Retailer A got 95% of their desired units, Retailer B got 75%. This metric is closely watched by both Supply Chain and Sales teams, and low fill rates might trigger penalty clauses or at least tense conversations with retailers.
- Lost Sales: The flip side of fill rate – how many sales did you not capture because your product wasn’t available? In our example, if demand was 70k units and we shipped 50k, then roughly 20k units’ worth of sales were lost. Analytics teams often put a dollar figure on this (“we lost $300k in revenue due to stockouts in December”). Tracking lost sales helps quantify the impact of allocation decisions and can inform future planning (e.g., “We should have expedited that extra production run; it would have paid off”).
- Weeks of Supply by Location: Before and after allocation, planners look at how many weeks of inventory each location or retailer will have. This helps ensure you’re not over-allocating to one place (where it would sit for 10 weeks) while another has 1 week and then goes empty. A balanced allocation might aim to give everyone, say, 3-4 weeks of supply cover, adjusted for expected sales rates. Analytics tools can rank which locations have the lowest weeks of supply – those likely need more stock ASAP.
- Allocation Attainment / Accuracy: This is an internal metric on how well the allocation plan was executed versus what was intended. For example, you planned to allocate 30k units to a retailer, but due to logistics or late changes, only 25k actually got there in time – that’s an attainment gap. Measuring this can highlight process issues (like if your allocation decisions aren’t being communicated through systems properly).
- Retailer In-Stock %: Ultimately, allocation success can be measured by the on-shelf availability at the retailer. In-stock percentage is the portion of stores that have the item in stock. If after allocation, Retailer A maintained a 98% in-stock through the season while others maybe were around 90%, that shows where the pain was felt. CPG companies often get reports or use third-party data to monitor in-stock rates at key retailers as the real-world outcome of their allocation strategy.
(Many CPG brands use software to optimize allocation. For instance, Alloy.ai’s Allocation Insights can highlight which orders or locations will have the biggest sales impact if they receive more stock. This data-driven approach helps your team allocate scarce inventory in a way that maximizes revenue and service levels, rather than relying on gut feel or politics.)
Related Terms
- Replenishment: Often used in tandem with allocation – once you allocate initial stock, replenishment is the ongoing process of refilling inventory. Allocation is “who gets what initially or in a crunch,” while replenishment (ideally automated based on min/max levels or demand) is the regular flow of goods. In constrained times, you might suspend normal replenishment and switch to allocation mode until things normalize.
- Order Fulfillment: This is the end-to-end process of delivering orders to customers (retailers in this case). Allocation affects fulfillment because it determines which orders can be fulfilled in full versus partial. Order fulfillment metrics like OTIF (On-Time In-Full) are directly impacted by allocation choices.
- Fair Share Allocation: A strategy where available inventory is split in proportion to something – e.g., each retailer gets a share of product proportional to their typical sales or forecast. It’s one approach to allocation (others might be priority-based or first-come). Fair share feels “equitable,” but may not maximize sales if one customer could sell far more given the stock. Companies debate these approaches during shortages.
- Constraint Planning: In demand and supply planning, this is planning under constrained supply. It involves deciding how to allocate limited production to various demands. It’s a more general term that covers allocation at the production level too (e.g., which products to make if a raw material is scarce). In CPG, constraint planning processes feed into allocation decisions when you have to ration finished goods.
CPFR (Collaborative Planning, Forecasting, and Replenishment): A practice where manufacturers and retailers work closely to plan and fulfill demand. Under CPFR, you might share data and jointly decide allocation during shortages to minimize impact. It’s related because when allocation is needed, the best outcomes often come from collaborating with the retailer (e.g., they might say “allocate more to these stores, we’ll drop promotion in others”). Collaboration can turn a tough allocation situation into a win-win or at least a mutually understood plan. (On that note, having a real-time data platform like Alloy.ai that both you and your retail partner trust can make CPFR and allocation discussions much smoother.)