As demand becomes more fragmented and economic constraints tighter, the way in which a company allocates its inventories becomes decisive. Finding the right balance between product availability and control of tied-up capital is no longer a matter of intuition, but of rigorous management. Yet many organizations continue to operate according to uniform or fixed rules, generating overstocks on the one hand, and shortages on the other. This article takes a structured look at the different methods ofstock allocationand how they contribute to overall supply chain performance.
What is stock allocation and why is it important?
Stock allocation or product distribution is the decision as to “who gets what, when and in what quantities”. It takes place at the junction between planning and execution: aggregated forecast demand is translated into concrete volumes by site, channel or zone, then converted into physical movements. Allocation concerns both available stock and future deliveries: how should a supplier’s delivery be broken down, with what priorities, and according to what criteria of service, margin or risk of expiry?
Complexity increases with the number of SKUs, the diversity of point-of-sale formats, the multiplicity of channels and the heterogeneity of purchasing behavior in different territories. Relevant allocation converts theoretical stock into stock that is actually accessible, where and when the customer buys.
Product availability remains one of the major determinants of satisfaction. Every break in stock translates into lost sales, a damaged image and, ultimately, a decline in customer loyalty. Symmetrically, overstocking exposes us to carrying costs, depreciation and markdowns. Optimized allocation shifts the balance: fewer out-of-stocks, fewer surpluses, more sales captured for the same stock, and often a significant reduction in working capital requirements. It also reduces inter-site transfers, those late corrections that consume transport, time and energy, and gives sales agility by enabling rapid reallocation to areas that are “taking off”.
The different stock allocation methods
Stock allocation can follow a number of logics, more or less sophisticated, depending on the maturity of the organization, the availability of data and the complexity of the distribution network. Some methods favor simplicity and stability, while others rely on data and artificial intelligence for continuous adjustment. Each has its place, provided it is used in the right context.
Fixed rules
This is the most intuitive method, and the most widespread in organizations in their initial structuring phase. It is based on pre-determined scales: for example, allocating an identical quantity to each store, or breaking down the quantities in proportion to the outlet’s surface area, sales or category. This logic ensures rapid execution and simple understanding, but ignores the real variability of demand. High-traffic outlets find themselves out of stock, while others accumulate surpluses. Apparent logistical efficiency comes at the price of lost sales and useless tied-up capital.
Allocation based on forecast demand
A more advanced stage consists in sizing allocation volumes according to anticipated demand by product and by site. Forecasting then becomes the heart of the process. Based on historical data, seasonal effects and local trends, forecasts project future demand over a given horizon. Allocation is based on these projections to adjust quantities to each destination, in proportion to their potential. This method brings distribution closer to actual needs, and reduces global overstocking.
However, it requires reliable data and robust forecasting models, capable of correcting for the effects of disruptions, promotions or one-off events. An erroneous forecast immediately translates into an inappropriate allocation; the quality of forecasting therefore directly conditions the chain’s performance.
The control point
In a high-recurrence network, the initial allocation is often supplemented by automatic replenishment mechanisms. The order point triggers a replenishment as soon as the available stock falls below a defined threshold. This simple principle protects against breakage between two allocation cycles. Its relevance lies in the correct calibration of the threshold: it must absorb the variability of demand during the delivery lead time, without generating excessive overstocking. Coupled with frequent, reliable deliveries, this system maintains availability while lightening operational management.
ABC analysis
ABC analysis prioritizes products according to their contribution to value, often measured by sales or margin. Strategic A items benefit from fine-tuned allocation and close monitoring; B items benefit from standard management; C items benefit from simplified management. This logic also applies to sales outlets, by allocating more analysis and stock resources to high-potential sites. By concentrating efforts on the elements that contribute most, the company improves efficiency without making management unnecessarily complex. ABC provides an indispensable basis for prioritizing any balanced allocation strategy.
Economic Order Quantity (EOQ)
EOQ (Economic Order Quantity) seeks to strike a balance between the cost of procurement and the cost of ownership. It determines the optimum lot size to minimize total inventory management costs. In the context of allocation, it helps to calibrate the volumes shipped to each site and to plan the frequency of shipments. This approach works well on products with regular demand and stable logistics costs. In volatile environments, QEC remains a useful benchmark, but needs to be revised frequently, as an erroneous forecast or fluctuating lead time can negate its benefits.
Just-in-Time (JIT)
JIT is based on a simple idea: deliver exactly what you need, when you need it. In terms of allocation, this means more frequent restocking and smaller quantities, to limit local stock while guaranteeing availability. This method presupposes excellent logistical reliability and short lead times, as the slightest supply incident results in a stock-out.
It is particularly effective for fast-moving products and local networks. JIT improves reactivity and frees up capital, but requires disciplined execution and perfect synchronization between warehouses, carriers and points of sale.
First In, First Out (FIFO)
FIFO means that the oldest items are sold first. This is an essential rule for perishable products or those subject to obsolescence. When applied to allocation, it means directing replenishments and transfers to sites capable of quickly disposing of older batches. This guarantees healthy stock rotation, limits losses and ensures regulatory compliance in sectors such as food and healthcare. FIFO is often part of a broader approach to physical flow optimization, where warehouse management systems (WMS) guarantee traceability and consistency of outputs.
Last In, First Out (LIFO)
LIFO, on the other hand, favors newer units. It is rarely used in distribution, except when products evolve rapidly and the most recent version has the greatest commercial value. This method can be found in certain industrial environments or for short-cycle technological products. In allocation, LIFO must be used with caution: without rigorous management, it runs the risk of allowing older inventories to age, generating costly write-downs.
Dynamic and predictive allocation
The most recent evolution consists of integrating optimization and machine learning models capable of automatically calculating the best possible allocation according to dozens of parameters: expected demand, margin, logistical constraints, risks of disruption, transfer or storage costs. These algorithms continuously reassess allocation as actual sales deviate from forecasts. They learn from past deviations and adapt to external events. The result is an agile, data-driven allocation that reduces disruptions, avoids surpluses and maximizes the value created.
Stock allocation: how to find the right balance?
No single method is sufficient. The most successful companies orchestrate these approaches in a complementary way: forecasting to anticipate, ABC to prioritize, QEC and reorder point to pace, JIT to lighten, FIFO to control rotation, and predictive models to continuously adjust.
The challenge is not to choose a single recipe, but to build an allocation architecture adapted to the reality of the network, capable of evolving with demand and market constraints.
Common stock allocation mistakes to avoid
Many mistakes are made right from the first allocation. Distributing stock uniformly, or without taking into account the potential of each outlet, creates imbalances right from the start. Some sites find themselves overloaded, others out of stock, forcing multiple transfers and incurring unnecessary costs. Conversely, giving priority to the most insistent stores rather than those where demand is real creates unfairness and reduces overall performance. The most efficient companies define clear rules, based on objective data such as sales volume or delivery frequency, and review them regularly.
Another mistake is to freeze the allocation once it has been decided. Even the best plan has to evolve with reality. Without frequent monitoring of sales and rapid reallocation mechanisms, discrepancies widen and problems set in. Simple monitoring – regular comparison between forecasts and actual sales, detection of major discrepancies, rapid decisions – can already prevent many a shortage or overstock. Automating the simplest adjustments frees up time to concentrate on more sensitive cases.
Forgetting operational constraints is also a source of malfunction. Allocating more stock than actual warehouse capacity, ignoring delivery frequency or transport times, often leads to returns, breakage or additional handling costs. Allocation must therefore always be aligned with logistical capacity and reality on the ground.
Finally, certain events can disrupt demand: heatwaves, strikes, competitor launches or regulatory changes. The most reactive companies keep an active watch, prepare scenarios in advance and know how to quickly adjust their plan when the situation evolves. The aim is not to anticipate everything, but to act quickly and effectively when necessary.
Stock allocation, a continuous process to enhance performance
Stock allocation is not an isolated calculation, but a continuous process that puts the right products in the right place at the right time. Simple rules may suffice to start with, but integrating forecast demand, taking into account differences between zones and formats, and then moving on to more dynamic allocation, enables us to go further in terms of performance. The choice of method depends on the level of organization and constraints of each company, but the objective remains the same: to serve customers better, limit unnecessary transfers, reduce costs and improve profitability.
Successful allocation depends above all on the quality of data, the clarity of rules and the ability to adjust decisions rapidly. With modern tools and a more integrated vision of planning, it becomes a real competitive lever. Companies that make it a pillar of their strategy quickly see the results: fewer out-of-stocks, less overstocking, more sales and a smoother supply chain.
