- 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

Time series are one of the most basic and versatile mathematical tools used in business. Quite simply, a time series consists of a series of data points indexed on time. A time series can thus model anything from the evolution of a company’s sales, to that of their product’s prices, on a yearly, monthly, daily or even hourly basis. Time series are particularly intuitive, making them ideal for describing, visualising, modelling and finally forecasting a number of variables.

However, in the case of particularly discontinuous data, a bucket graph can be more appropriate.

When reading a time series, one must also pay close attention to the x axis. Some graphs focus on small value intervals in order to emphasize data variations, with the risk being that these variations be overestimated. Other phenomena, such as exponential growth, are also misrepresented by a linear scale on the x axis. One can therefore choose to use a logarithmic scale where the early stages of growth can be perceived just as well as the later ones.

There are three main types of forecasts, which each serve different purposes.

- Point forecasts intend to give the one “best” future value of a variable according to a specified error metric. Such is the case of a weather forecast for example, which for each day predicts a single temperature value. A point forecast doesn’t aim to faithfully represent this variable’s evolution (the reader knows full well that the temperature is likely to vary around its predicted value) but serves as a useful indication for the reader and a solid basis for their future choices.
- Probabilistic forecasts provide the full probability distributions of the future value. Confidence intervals are frequently used to visualize such forecasts. Such forecasts can for example be useful for speculative purposes.
- Generative forecasts make the variable’s evolution appear “natural” or “plausible”, allowing for a certain amount of contingency and random evolution. This “generative perspective” can be useful when running simulations.

Time series are thus a particularly versatile abstraction and basic statistical tool. However, their apparent simplicity can be misleading. A number of factors can either alter the way the data is presented or account for noticeable variations in the data. Knowing how data is collected, and being aware of the aforementioned factors is therefore essential.