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Inventory level of an item which signals the need for a replenishment order. The reorder point is classically viewed as the sum of the lead demand plus the safety stock. At a more fundamental level, the reorder point is a quantile forecast of the future demand. The calculation of an optimized reorder point typically involves the lead time, service level, and the demand forecast. Relying on a

The concept we describe here under the name of "reorder point" is also known as ROP, reorder level, or reorder trigger level.

The reorder point is an important concept not only for inventory optimization but for The extrapolation is typically based on the assumption that the forecast error follows a normal distribution. Our guide about safety stocks describes in detail how a plain

- Converges
*too*quickly toward zero, much faster than empirical distributions observed in retail and manufacturing. - Is perfectly
*smooth*while demand goes into*integral*steps. The negative impact this smoothness is the strongest on**intermittent demand**. - Is not suited for
**high service levels**(in practice values above 90%). Indeed, the further away from the median (50%), the less accurate the*normal*approximation.

- Service levels are above 90%.
- Demand is intermittent, with less than 3 units sold per period (day, week, month depending on the aggregation).
- Bulk orders, i.e. a single client purchasing more than 1 unit at once, represent more than 30% of the sales volume.

In practice, the reorder point error (see section below) is typically reduced by more than 20% if any one of those three condition is satisfied. This improvement is mostly explained by the fact that the extrapolation used to turn a

Reducing the pinball loss for your inventory can only be achieved through better forecasts (quantile or extrapolated). As a rule of thumb, a reduction of 1% of the pinball loss will generate between 0.5% to 1% of safety stock reduction while preserving the same frequency of stock out.

With this, it becomes possible to The process might appear a bit puzzling because we apply the term

The Microsoft Excel sheet here above illustrates how to assess your

**Product name:**for readability only.**Service level:**the desired probability of*not*hitting a stock-out.**Lead time:**the delay to complete a replenishment operation.**Reorder point:**the threshold (frequently called*Min*) that triggers the replenishment. Reorder points are the values being*tested for their accuracy*.**Day N**: the number of units sold during this day. The layout chosen in this sheet is handy, because then, it become possible to compute the*lead demand*through the`OFFSET`

function in Excel (see below).

Then, the sheet includes two

**Lead demand:**that represents the total demand between the very start of*Day 1*and the end of*Day N*(where N is equal to the lead time expressed in days). Here, the`OFFSET`

function is used to make a sum over a varying number of days using the lead time as argument.**Pinball loss:**that represent the accuracy of the reorder point. This value depends on the lead demand, the reorder point and the service level. In Excel, we are using the`IF`

function to distinguish the case of over-forecasts from the case of under-forecast.

For consistency of the analysis, the input settings (reorder points, service levels and lead times) need to be extracted at the same time. Based on the conventions we follow in this sheet, this time can be either at the very end of Day 0 or just before the beginning of Day 1. Then, those settings are validated against

The pinball loss function has been known for decades. If you agree with the hypothesis that the reorder point should be defined as a value that covers the demand with a certain probability (the service level), then textbook statistics indicate that the pinball loss is the

You cannot assess the quality of the reorder point for a single item by looking at a single point in time. Unless your service level is very close to 50%, the pinball loss has a strong variance. As a result, you need to average the loss values over several dozens of distinct dates to obtain a reliable estimate when looking at a single item. However, in practice, we suggest instead to average losses over many items (rather than many dates). With an dataset containing more than 200 items, the pinball loss is typically a fairly stable indicator, even if you only consider a single point in time to perform the benchmark.

The reality of inventory management is that achieving 99.9% service level requires an enormous amount of inventory. Indeed, 99.9% means that you don't want to afford more than 1 day of stockout every 3 years. With the classical safety stock formula, using a very high service level does not generate massive stocks. However, using a very high service level

If your lead times are long and can be expressed in weeks rather than days, then, yes, you can use historical data aggregated in weeks, the approximation should be good. However, if your lead times are shorter on average than 3 weeks, then the discrepancy introduced by the weekly rounding can be very significant. In those situations, you really should consider daily aggregated data. Daily data might complicate a bit the data handling within the Excel sheet, because of data verbosity. However, in practice, the pinball loss is not intended to be computed within an Excel sheet except for Proof-of-Concept purposes. The one aspect that really matter is to feed the inventory optimization system with daily data.

Big and infrequent orders are found in

The situation can be complicated if the same order can be passed to

In order to model more precisely a

Since the local supplier has a smaller lead time, the second reorder point is lower than the first one. Intuitively, orders are made to the local supplier, only when it becomes highly probable that a stock-out will be hit and that it is too late already to order from the

Get optimized sales forecasts with our inventory forecasting webapp. Lokad specializes in inventory optimization through demand forecasting. Optimized reorder points - and much more - are native features of our forecasting engine tool.

- ABC analysis
- Backorders
- Container shipments
- Economic drivers
- Economic order quantity
- Fill Rate
- Financial impact of accuracy
- Inventory accuracy
- Inventory control
- Inventory costs (carrying costs)
- Inventory turnover
- Lead time
- Lead demand
- Min/Max Planning
- Minimal Order Quantities (MOQ)
- Multichannel Order Management
- Optimal service level formula
- Perpetual Inventory
- Phantom Inventory
- Prioritized Ordering
- Product life-cycle
- Quantitative supply chain
- Reorder point
- Replenishment
- Safety stock
- Service level
- Stock-keeping unit (SKU)
- Stock Reward Function

- Backtesting
- Continous Ranked Probability Score
- Data preparation
- Forecasting accuracy
- Forecasting methods
- Obfuscation
- Overfitting
- Pinball loss function
- Probabilistic forecasting
- Quantile regression
- Seasonality
- Time-series

- Supply Chain Antipatterns
- Devil's advocate
- The Non-Euclidian Horror
- The 100% service level
- The Jedi initiation

- Bundle Pricing
- Competitive Pricing
- Cost-Plus Pricing
- Decoy Pricing
- Long-term maintenance agreement pricing
- Long-term pricing strategies
- Odd Pricing
- Penetration Pricing
- Price Elasticity of Demand
- Price Skimming
- Repricing software (Repricer)
- Styling Prices for Retail