The impact of calendar days on the accuracy of forecasts
2 October 2020Classical treatment of forecast errors as determinants of the effectiveness of the demand forecasting process and their use when neural networks are applied
13 December 2021The impact of calendar days on the accuracy of forecasts
2 October 2020Classical treatment of forecast errors as determinants of the effectiveness of the demand forecasting process and their use when neural networks are applied
13 December 2021Building seasonal inventory ahead of holiday sales spike
The pre-holiday period is fast approaching, which for many companies means a significant increase in demand, but also the potential problem of suppliers or their own factories periodically lowering service levels. The reduced level of supplier order fulfillment during periods of increased demand is due, on the one hand, to the unavailability of production capacity to handle increased demand on an ongoing basis and, on the other hand, to insufficient inventory building in the period before a significant sales peak.
One method of dealing with such phenomena resulting in loss of potential sales and thus margin is to implement a seasonal stock building model allowing to build stock (from Anticipatory Stock) before a sales peak.
While the approach itself is obvious, its implementation is no longer. Indeed, to implement the model, it is necessary to capture the following elements:
– the demand that occurs during a sales peak – it tends to have different characteristics than the demand in pre-holiday periods
– demand what is in front of the peak – we need to handle him too, but we don’t want to do it with seasonal supplies
– when we can start accumulating seasonal stock – expiration dates can be a real limitation
– The point at which we should stop building it , because we don’t want to be left with too much stock after the season
The process of building seasonal inventory can adopt similar strategies whether it involves manufacturing or distribution companies.
In the case of manufacturing companies, the usual approach is to “flatten” production in order to maintain a constant production rhythm that takes into account at least the economic size of production batches, without introducing abrupt changes and changeovers.
In the case of distribution companies, the aforementioned flattening will allow the implementation of logistics operations taking into account available resources, i.e. without having to hire additional people or change their working hours.
A stocking model that allows “flattening” of production and supply with seasonal inventory is the Anticipatory Stock Policy. It assumes that the statistical forecast calculated for the seasonal inventory building period defined in the data and the high sales season period is appropriately distributed over the seasonal inventory building period. This ensures that demand volumes that take into account future sales peaks are taken into account for current stocking recommendations, thus ensuring that stock is built up in time to handle both current demand and increased purchases before the holidays.
If you’d like to learn more about how the use of machine learning and artificial intelligence can help you increase merchandise availability through the high sales season, contact us. We will be happy to help your company.
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