DEPOT DECISION TOOL
The automated inventory system along with AI intervention provides the better solution which meets the Ordnance Corps for handling the inventory for the Defense. The aim of this project is to demonstrate Artificial Intelligence (AI) in inventory management is the future enabler. AI will automate manually intense data management tasks.
Objectives
• To keep material cost under control by optimizing various costs indulged with inventories like purchase cost, carrying a cost, storage cost, etc.
• To reduce the timelines and ensure the inventory to be placed at the appropriate time frame using AI along with inventory management activities.
• To forecast the demand, manage supplier backorders and optimize inventory stock levels.
• To ensure a continuous supply of materials and to avoid overstocking and under-stocking of inventory. ERP implemented in a phased manner is needed to be integrated with AI to provide the solution. Our project servers the following functionality in an optimized network like • Asset Visibility • Forecasting
Asset Visibility
Our DEPOT Decision Tool consumes authoritative source data for both on-hand quantities and authorization data. It allows for complete visibility and asset posture across the Army. On-hand and transactional quantities are combined to show predictive on-hand across time. Daily updated Property Book Data can be updated periodically.
Forecasting
Identification of property excess/shortages across the Army. Redistribution of excess property across all commands. DEPOT level distributions carried out using the MIN-MAX Replenishment. Streamlining of DISPO instructions by ensemble the forecasting with RNN-Exponential smoothing. Recurrent Neural Networks Predict On-demand by the power to Store Relevant Information About Past History. Along with RNN - Simple Exponential Smoothing (SES) resolves the unflavored data with no clear trend or Seasonal Pattern to yearly, quarterly, weekly, day-wise, and even hourly base reports. The proposed sourcing decision and unforeseen plan prediction using time series analysis. The automation of ordnance inventory will help in increasing the visibility of its existing inventory and cut down on wastage by optimally using the available resources, avoiding the purchase of the same items by different agencies and reducing the maintenance costs.
Built With
- ai
- java
- language:-python(front-end)
- machine-learning
- mongodb
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