DISPATCH & REPLENISHMENT PLANNING
SOLUTIONS DESIGNED TO SYSTEMATICALLY ESTIMATE MARKET DEMAND
Ensure the right level of inventory of finished goods across a multi echelon supply chain by defining dynamic buffer levels across in the outbound logistics considering demand variability, supply variability, average lead times and average demand
Description
Dispatch Replenishment Planning is a Short Term Planning solution which is run on a day to day basis. DRP Module allow the Logistics / Supply Chain team to keep an optimal level of inventory in the Supply Chain. The tool uses statistical methods / DDMRP / TOC based DBM to dynamically calculate Reorder points at different levels of the Supply Chain. These Reorder points are continuously checked against the inventory. Based on the Inventory Replenishment strategy, the inventory at the Factory/Mother Warehouse is dispatched to those depots/hubs in the supply chain that most need it. The tool allows for categorization of different types of SKUs so that different Dispatch strategies can be adopted. In addition, the product also allows rule- based truck loading/containerization based one or two fill parameters.
Features
Inventory Planning Strategies
- Multi-Echelon Inventory Planning
- Multi-Time Period Inventory Planning
Replenishment Methods
- Periodic Replenishment
- Continuous Replenishment
- Item Classification-Based Replenishment Strategies
Dynamic Reorder Point Calculations
- Statistical Methods
- Demand-Driven Material Requirements Planning (DDMRP)
- Distribution-Based Management (DBM)
Dispatch Optimization
- Load Building for Truck
- Dispatch Order Creation
- Prioritization of Direct Dispatches
- SKU Priority
Load Building for Truck
- Efficient consolidation of inventory items for optimal truck loading.
- Utilization of space optimization techniques to maximize truck capacity.
Priority-based Dispatch Orders
- Identification and prioritization of urgent or time-sensitive orders.
- Allocation of resources to fulfill high-priority orders first.
- Utilization of SKU priority rankings to determine dispatch order.
Some Customer Cases
On the quantitative side, we have integrated the powerful Statistical and Graphical Modelling libraries of Python. We have also connected to ML / AI libraries of WML / Google / AWS to enables the application of a variety of statistical and ML/AI techniques for different situations
Consumer Electrical Company
Enhanced visibility and transparency via S&OP suite for a consumer electrical manufacturer
Speciality Tyre Company
Implemented Demand Forecasting and Demand Aggregation for the smooth function for the manufacturing unit
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