Using Predictive Analyics in Supply Chain Network Optimization
Supply Chain is actually a multi steps continuous process in which each step affects the whole chain, meaning that any failure or delay in one of the process’s steps can have a ripple effect through the whole system, preventing proper execution as expected.
As such, there is a need for visibility through the whole chain, in particular the sort of visibility, which can identify or predict faults in each step. This visibility suppose to be part of the integration between the steps in the chain or\and actions performed following the observation of events linked with mitigating strategies in both direction through the chain. And this leads to the application of predictive analytics.
The application of predictive analysis can improve target activities in the supply chain network management and optimization, like demand forecasting, inventory budget optimization and safety stock level recommendations, which in turn impact in-store availability, customer service levels, inventory costs and lost sales.
By numerous retesting of different mathematical forecasting models the best method is chosen and this typically contributes to a more accurate forecast along with the mathematics of optimization.
Predictive analysis also has a potential to minimise supply chain disruptions and risks by implementation of supply chain analytics dependent on forecasting models. Depending on the analytic platform used, predictive analysis can determine additional profit opportunities, recovery speed from external shocks and impact on profitability in case of a major supplier loss or decrease in demands.
Lately, more and more companies and organizations start implementing predictive analysis and according to an infographic from SAP, 86% of these companies see a real potential for a major positive impact on their organization in the future, and 68% already realized advantages achieved by the advanced perspectives given by the implementation of it.