- 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

In supply chain, the demand - or the sales - of a given product is said to

The first day of the year (January 1st) is marked with a gray vertical marker. The historical data appears in red while the Lokad forecast is displayed in purple. The seasonality can be visually observed as a similarity of the patterns from one year to the next; use the gray markers as references.

`Y(t) = S(t) * Z(t)`

where `S(t + 1 year) = S(t)`

If such a function

- Compute the deseasonalized time-series as
`Z(t) = Y(t) / S(t)`

. - Produce the forecast over the time-series
*Z(t)*, possibly through moving average. - Re-apply the seasonality indices to the forecast afterward.

Back to the initial problem of estimating the seasonal indices

`S(t) = AVERAGE( Y(t-1)/MA(t-1) + Y(t-2)/MA(t-2) + Y(t-3)/MA(t-3) + ... )`

where

The approach propose in this section is

**Time-series are short.**The lifespan of most consumer goods do not exceed 3 or 4 years. As a result, for a given product, sales history offers on average very few points in the past to estimate each seasonal index (that is to say the values of*S(t)*during the course of the year, cf. the previous section).**Time-series are noisy.**Random market fluctuations impact the sales, and make the seasonality more difficult to isolate.**Multiple seasonalities are involved.**When looking at sales at the store level, the seasonality of the product itself is typically entangled with the seasonality of the store.**Other patterns**such as trend or product life-cycle**also impact time-series**, introducing various sort of bias in the estimation.

A simple - albeit manpower intensive - method to address those issues consists of manually creating

Those

The forecasting technology of Lokad natively handles both seasonality and quasi-seasonality, so you don't have to

In order to overcome issues raised by the limited historical depth available for most time-series in retail or manufacturing, Lokad uses