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Optimal supply chain management depends on accurate analytics that enables companies to predict product demand and minimize risks arising from outside factors.
Whether it's internal skills, skills found externally, or, as is increasingly the case, a combination of both, an analytics project at the service of the supply chain must include at least two essential steps before the system is put into production. Here are some of them.
Data preparation and data cleansing on time series in supply chain analytics
The first phase, which involves cleaning and prepping, deals with retrieving historical data. This seeks to purify the time series, which represent the starting point of any forecasting engine, from everything that has somehow altered demand in the past. The analysis of product substitution mechanisms that show links and similarities regardless of the brand is also part of this data preparation phase. Therefore, knowledge of the sector's dynamics and the related processes must go hand in hand with the statistical one that allows managing information and data to make them homogeneous and "digestible" by forecasting tools. Once this part is completed, the supply chain analytics project aims to find a model that can interpret the series and find the best forecast regarding accuracy and responsiveness to changes.
Forecast and data modeling in supply chain analytics
The second phase focuses on data modeling that must consider the two variables indicated above. On the one hand, accuracy makes it possible to identify which time series are stable; on the other hand, the ability to better react to sudden changes in demand puts in a position to readapt to specific sector-specific problems or to new scenarios such as those dictated, for example, by the regulatory restrictions launched to combat infections from Coronavirus. In supply chain analytics, models must understand both of these factors so that you can produce the best algorithm to adopt from the study of the trend and characteristics of the single demand and the single industry. Then, the models will translate the algorithm into the current statistical languages, typically R or Python, to be tested and compared with the time series to generate demand forecasting that not only optimizes the value chain but avoids the main inherent risks.
The three risks that a supply chain analytics model reduces
The forecasting exercise applied to supply chain analytics helps to overcome the challenge of any company working on a stock by warehouse, namely, producing the correct amount of goods in a given time horizon. The main risk to be countered, therefore, is that of lost sales, that is, of producing less than what is needed and of not being able to fully satisfy the demand or not being able to distribute what was made in the points of sale of the Deputies. In practice, this is the risk of stock out present in most markets, but exceptionally high in the field of high-margin goods in which a non-sale can coincide with the loss of the customer. The opposite risk is that of the leftover, producing excess inventory, thus immobilizing resources that are not disposed of. The third risk, finally, is that of not correctly balancing the entire supply chain and, therefore, of not having a forecast aligned between production capacity, supply, use of personnel, investments in machinery, etc. The suitable analytics model in the supply chain helps reduce all 3 of these risks.
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