In the illustration here above, we have a lead time equal to 4 days, which is used as the horizon for the forecast. Lokad’s forecasting engine requires a present variable to be provided, precisely because it’s not always possible to remove the ambiguity by just inspecting the historical data itself, as it’s not always possible to differentiate between lack of demand and truncated sales data for the last few hours of the dataset. As historical data tend to be retrieved in batches, there might be a small discrepancy. In practice the “present” does not always reflect the real wall-clock time, instead it represents the end of the historical data. A segment covering the future starting from the “present”Ī demand forecast typically starts from the “present” date and moves onward until the full horizon is covered. Moreover, the engine is also capable of processing an uncertain horizon represented by a probability distribution.īe aware that agreements with suppliers frequently involve lead times expressed in business days, thus those quantities need to be recalculated as calendar days. Lokad’s forecasting engine addresses this problem by directly taking a horizon expressed in days as input for its demand forecasts. However, when weekly or monthly forecasts are used, there is no longer a canonical way of adjusting the projected demand to the horizon of interest. Daily forecasts are frequently deemed as too erratic, and thus weekly or even monthly forecasts are used instead. This horizon is then consumed as an input by the demand forecasting engine.Ĭlassic forecasting methods tend to complicate the problem further because of their own limitations. We refer to this “consolidated” lead time as the forecasting horizon. When a supply chain decision need to be optimized, all those lead times need to be taken into account as whole. There are manufacturing lead times, transportation lead times, ordering lead times, etc. The term lead time can refer to many things. After next day delivery with semi-closed days.A segment covering the future starting from the “present”.If your business is using an app natively supported by Lokad, then Lokad auto-computes the applicable lead times automatically based on your historical data. In particular, we suggest not to trust the “official” lead times given by suppliers, as they frequently over or underperform those values. Lead times are best computed based on your past purchase orders, looking at the delays between the orders and the deliveries. In this page, we illustrate how the lead time should be measured in practical situations. The lead time,as needed for inventory optimization purposes, involves some subtleties. A proper measurement of the lead time is required no matter which forecasting technology is used. The lead time is a variable that is fundamental to properly compute how much inventory is needed to cover the future demand.
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