cpg-network-design

To optimize distribution center locations and minimize CPG logistics costs, you model facility locations, transportation expenses, and inventory levels. This balances fixed and variable costs while ensuring target service times are met for retail channels.

CPG network design is the process of configuring distribution networks to balance costs and service levels across channels. It handles omnichannel fulfillment by modeling integrated, dedicated, or hybrid networks to serve traditional retail and e-commerce.

Yes, you can use Python to model safety stock and cycle inventory across a multi-echelon network. By leveraging optimization and statistical modeling libraries, you determine optimal inventory levels to handle promotional demand variability and high SKU complexity.

To size distribution network capacity for seasonal promotional demand surges, you analyze demand variability and service requirements. Modeling promotional planning within the network ensures facilities handle sudden volume spikes without compromising delivery times.

Yes, PuLP and OR-Tools work for facility location optimization in supply chain logistics. These Python libraries provide the mathematical programming capabilities needed to model transportation costs, capacity constraints, and determine optimal DC placements.

To model transportation costs and service times for a retail network, you need customer demand data, potential DC locations, and plant capacities. This data enables geospatial analysis and optimization to generate actionable network configurations and performance KPIs.