- The Quantitative Supply Chain Manifesto
- The Lokad test of supply chain performance
- An overview of quantitative supply chain
- Generalized probabilistic forecasting
- Decision-driven optimization
- Economic drivers
- Data preparation
- The Supply Chain Scientist
- Timeline of a typical project
- Project deliverables
- Assessing success
- Antipatterns in supply chain

- Inventory forecasting
- Prioritized ordering report
- Old forecasting input file format
- Old forecasting output file format
- Choosing the service levels
- Managing your inventory settings
- The old Excel forecast report
- Using tags to improve accuracy
- Oddities in classic forecasts
- Oddities in quantile forecasts
- Stock-out's bias on quantile forecasts
- Daily, weekly and monthly aggregations

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As far as supply chains are concerned, a

// 'T' is of type [Id, *] table T = forecast.periodic( hierarchy: H1, H2, H3, H4 category: C1, C2, C3, C4 horizon: 56 // number vector in days present: (max(Orders.Date) by 1) + 1 demandDate: Orders.Date demandValue: Orders.Quantity censoredDemandDate: OOS.Date) show table "Periodic" with Id T.ForecastDate T.Mean T.Sigma

The table

`T`

returned by the forecasting engine does not have the type `[Id, Date, *]`

but the type `[Id, *]`

instead. This is a stipulation imposed by Envision, and dates, as they exist in the `Day` or `Week` table, cannot be introduced dynamically during the execution.The

`horizon`

argument is a number vector - or just a scalar number - that can be defined for each item. The horizon is expressed in days. The forecasting engine does not have any dependency on the horizon: using a longer horizon does not have any impact on earlier forecasts. The horizon is only used to truncate the forecasts and keep the verbosity of the results under control.The other arguments are identical to their counterparts that we previously introduced with the probabilistic demand forecasts.

The daily values

`T.Mean`

returned by the forecasting engine are fractional, possibly smaller than 1. Under the hood, the forecasting engine uses probabilistic forecasting models that are projected as their daily averages.Each daily value is also associated with

`T.Sigma`

, which represents an estimation of the square root of the variance of the forecasts. The variance is obtained as the second central moment similarly derived from the probabilistic forecasting model computed by the forecasting engine.It is notable that using the MSE as a loss can generate fractional values for the demand forecast. Thus, for example, the projected daily demand for a slow mover can be a value between 0 and 1.

The forecasting engine generates

Thus, as a rule of thumb, we suggest not using the