Inventory management and replenishment are at the heart of supply chain performance. Too much stock ties up cash, increases inventory value, adds to warehousing costs and complicates warehouse logistics management. Conversely, too little stock leads to out-of-stock situations, lower service levels, delivery disruptions and a weakened relationship with the end customer.
To arbitrate between these risks, many companies structure their supplies and inventories around the ROP, also known as the replenishment point. replenishment point or order point, also known as alert stock, used as the central trigger for replenishment orders.
The ROP defines the stock level at which a new order must be placed in order to cover demand during the supply lead time, while allowing for a safety margin. This replenishment point, sometimes formalized as an order point, structures purchasing decisions. But this seemingly simple principle can be applied to a wide variety of replenishment methods. Fixed-date replenishment, just-in-time, dynamic thresholds or just-in-time logic: each of these approaches mobilizes the replenishment point and the reorder point in a specific way, with strong operational implications.
This article provides an overview of the main POR-related replenishment methods, before exploring the growing role of artificial intelligence and automation in the calculation, adjustment and execution of replenishment thresholds.
Replenishment : Why does it all start with POR?
Before analyzing the different replenishment methods associated with the POR, it is essential to recall the role of the replenishment point in inventory management. As an ordering point, the ROP is used to guarantee sufficient stock to ensure the continuity of goods flows between the upstream and downstream parts of the supply chain. Here, the replenishment point and the reorder point serve as operational benchmarks.
Classically, the calculation of the reorder point is based on the demand forecast, average consumption, supplier delivery time and a level of variability measured by the standard deviation. The reorder point acts as an inventory control tool: when available stock reaches this reorder point, an order must be placed.
In an organization equipped with ERP, MRP or inventory management software, the order point and replenishment point can generate an alert, a purchase proposal, or even an automatic order. The replenishment point is then integrated into a supply management dashboard, used by the stock manager, procurement officer or supply chain manager.
The ROP, as a replenishment point and order point, is not an isolated method, but a cross-functional flow management mechanism, whose effectiveness depends on the logistics process, stock reliability and the quality of inventory tracking.
What are the main POR-related replenishment methods?
Fixed-date supply
Fixed-date procurement, also known as periodic procurement, involves placing orders at regular intervals defined in the logistics planning schedule. At each deadline, the quantity ordered is adjusted to bring the average stock level down to a target stock level.
This method facilitates logistics planning and transport pooling. The replenishment point acts as a safeguard: if the minimum stock level is exceeded before the replenishment point, an emergency order is triggered.
Just-in-time replenishment (replenishment)
The aim of just-in-time production is to limit stocks by maintaining a level close to that which is strictly necessary. The reorder point is therefore positioned very low. This order point reduces financial immobilization, but increases dependence on supplier lead times.
Dynamic threshold methods
Dynamic threshold methods allow ROP to evolve according to forecasts, actual consumption and operational events. An increase in sales, a promotion or a change in the product lifecycle leads to an automatic adjustment of the reorder point.
They rely on advanced management software, integrated with supply chain management, capable of continuously adjusting stock levels and rotation, while limiting out-of-stocks and the accumulation of large stocks.
Just-in-time (JIT) approach
Just-in-time takes just-in-time logic to its extreme, aiming for stock levels close to zero. Deliveries are synchronized as close as possible to the moment of consumption. In pure JIT logic, supplies are triggered by actual demand rather than by a stock threshold. However, even JIT systems maintain very low ROPs as the ultimate safety net.
The contribution of artificial intelligence to calculating POR
Artificial intelligence is profoundly transforming the way POR is calculated and used. Traditional methods are based on averages and simplifying assumptions. They struggle to simultaneously integrate the complexity of sales behavior, fine-grained seasonality and external factors.
Machine learning algorithms, on the other hand, analyze massive volumes of data to identify complex patterns. They take into account detailed sales history, seasonal variations, calendar effects, past promotions and their actual impact. They can also integrate external data, such as weather, local events or certain macroeconomic trends.
Thanks to the use of artificial intelligence, the point of replenishment (POR) becomes more precise and responsive. By analyzing massive volumes of data, algorithms can better predict future trends in a context of constant variability. What’s more, AI facilitates the rapid detection of trend changes, enabling replenishment thresholds to be adjusted even before imbalances become apparent. This optimizes inventory management, reducing the risk of stock-outs or overstocking.
Automate alerts and trigger orders
AI is not limited to calculation. It also automates monitoring and execution. AI monitors stock levels in real time, and constantly compares the observed situation with recalculated thresholds.
When the POR is exceeded, an alert is automatically generated. In the most advanced organizations, this alert is transformed directly into an order proposal, integrating the optimal quantity, the relevant supplier and logistical constraints. Grouping rules can be used to consolidate multiple requirements to optimize transport costs.
In some cases, the process is fully automated. Orders are transmitted directly to suppliers via EDI interfaces or APIs, without human intervention. Teams can then concentrate on complex cases, exceptional arbitration and overall management.
Real-time analysis and continuous adaptation
One of the major benefits of AI lies in its capacity for dynamic adaptation. ROP is no longer recalculated on a fixed date, but continuously. Each new piece of information can influence the threshold.
An acceleration in sales over a few days may be enough to raise the ROP. A gradual slowdown leads to an automatic drop. Scheduled events, such as promotions or assortment changes, are integrated upstream to adjust thresholds before their impact.
In food retailing, some players go a step further by integrating weather forecasts. A predicted heat wave may lead to an early increase in POR for certain categories, while a spell of bad weather may lead to a more cautious approach.
This capacity for permanent adjustment transforms ROP into a genuine operational management tool, capable of reconciling high service levels with inventory control in increasingly volatile environments.
Aligning method, data and execution around POR
POR-related replenishment methods cover a wide spectrum, from simple calendar-based sourcing to dynamic approaches driven by artificial intelligence. The replenishment point remains a central benchmark, but its role and implementation vary greatly depending on the strategy adopted.
As supply chains become more complex and demand more uncertain, static approaches are showing their limits. The integration of dynamic thresholds, the automation of alerts and the contribution of AI make it possible to exploit the full potential of POR.
The challenge for companies is no longer simply to calculate a threshold, but to choose the replenishment method most consistent with their organization, logistical constraints and economic objectives. It is in this alignment between method, data and execution that POR reveals its full value.
