- Containers
- Copacking
- Cross-docking
- Drop shipping
- Decision-driven optimization
- DDMRP
- Economic Drivers
- Initiative (Quantitative SCM)
- Kanban
- Lean SCM
- Manifesto (Quantitative SCM)
- Micro fulfilment
- Product life-cycle
- Resilience
- Sales and Operation Planning (S&OP)
- Supply Chain Management (SCM)
- Supply Chain Scientist
- Test of Performance
- Third Party Logistics (3PL)

- Backorders
- Bill of Materials (BOM)
- Economic order quantity (EOQ)
- Fill Rate
- Inventory accuracy
- Inventory control
- Inventory costs (carrying costs)
- Inventory Turnover (Inventory Turns)
- Lead demand
- Lead time
- Min/Max inventory method
- Minimum Order Quantity (MOQ)
- Phantom inventory
- Prioritized ordering
- Reorder point
- Replenishment
- Service level
- Service level (optimization)
- Stock-Keeping Unit (SKU)
- Stockout

The fill rate is the fraction of customer demand that is met through immediate stock availability, without backorders or lost sales. The fill rate differs from the service level indicator. The fill rate has a considerable appeal to practitioners because it represents the fraction of the demand that is likely to be recovered or better serviced if the inventory performance was to be improved. The fill rate is measured empirically by averaging the number of correctly serviced requests over the total number of requests.

Example: let’s consider a bookseller selling a school manual. There is 1 order per day on average. Let’s assume that on average, out of 20 requests for the book, 19 requests come from individual students who require only a single copy of the book. In addition to this, 1 request out of 20, comes from a school teacher (we are still looking at the average), and the teacher asks for 20 copies because she is buying for her whole classroom. If the bookseller keeps 10 book units in stock, and if we assume the lead time to be of 1 day, then the service level is 95% (19/20=0.95) as nearly all the students will be served with their book. However, the teacher’s order request will be systematically declined as the stock never gets big enough to cover a whole classroom. Thus, in this case, the fill rate is close to 50% (19/(19+20) ≈ 0.5) as the teacher’s request accounts for slightly more than half of the total demand.

The service level $\tau_1$ is written as: $$\tau_1(s) = \mathbf{P}(X \leq s)$$ The fill rate $\tau_2$ is written as: $$\tau_2(s) = \frac{\mathbb{E}[\text{min}(X,s)]}{\mathbb{E}[X]}$$ Indeed $\text{min}(X,s)$ represents the restriction that the available stock is imposing on the quantities to be serviced without delay. If the actual demand value $x$ is lower than $s$, then $x$ units get served without delay otherwise, only $s$ units get served without delay.

`fillrate()`

function, as illustrated with:
Demand = call forecast.demand( ... ) // code snipped FR = fillrate(Demand)The variable

`FR`

is also a distribution of probabilities, and represents the marginal fill rate increments. In other words, `FR`

contains the marginal contribution of each extra unit kept in stock in fulfilling the future demand.