IDEAL REPLENISHMENT PLANNING
Solution specialized in optimization of the ideal mix and quantity of product restocking for the retail points of sale. The suggestions for restocking quantities seek to maximize the relationship between revenue and demand met and minimize products losses due to validity or obsolescence, excess in stock and sales lost due to disruptions.
Suggestion of Supply Order of the Distribution Centers, Plant Stocks and other logistics facilities
Analytical dashboards for performance follow-up and proactive decision making in the supply chain management.
Optimization of stock availability in the CD’s to serve the sales channels, respecting the limitations of storage capacity and invested capital.
The machine learning algorithms are constantly adjusted to improve the accuracy of forecasts.
Those involved in logistics chain management have a view of operational risks and time for a proactive action.
The results of the operation are presented in interactive dashboards for further analysis.
Allows the comparison of sales histories to understand the factors that impact demand.
For the processing of the models, public databases variables that extend the horizon beyond the borders of the company are used.
“…reductions of disruptions in 56% of the stock by mix optimization.”
After the deployment of the neural networks, the store managers stopped making the replacement orders to the factory and today the algorithm decides the ideal quantity of supply for these stores without human intervention. All supply forecasts and orders calculated by the neural network follow by means of EDI directly to Kopenhagen and Brasil Cacau points of sale at the pre-established restocking order dates.
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