In the strategical time frame, companies need to review periodically their supply chain design due to increasing disruption, shifts in the market and changing consumer behaviours.

Mathematical modeling supports supply chain leaders in making decisions that balance conflicting variables in complex networks.

Network design is the optimization of the location and function of supply, manufacturing and distribution networks to align with the corporate strategy and meet customer needs.

Scenarios that are addressed using mathematical models are:

🔷 Opening/closing of facility decisions
🔶 Supplier selection
🔷 Transport mode and lane optimization
🔶 Customer to distribution center allocation
🔷 Which products to store in which locations
🔶 Make-versus-buy analysis
🔷 Design of new product networks
🔶 Risk analysis
🔷 Reducing carbon footprint and supporting sustainability goals

Modelling these concepts is done using mixed integer programming (MIP) which is linear programming (LP) with some decisions variables being integers.

The linear programming does the balancing demand and supply. The integers model the network design decisions.

In fact the integers we use here are binaries, e.g. 1 to open a location - 0 to close a location etc.

Sometimes we need to use heuristics like genetic algorithms, simulated annealing, and tabu search help solve large, complex problems that are computationally expensive.

Network design involves modelling at an aggregated level and for a long term horizon.

We group SKUs, resources, customers on a monthly, quarterly or even yearly bucket.

Aggregations are not always straightforward. For example when grouping SKUs we need to group the BOMs which means creating a new set of master data. Same for production rates and transportation lanes.

The current state of the network is also modelled in a base scenario. This scenario is then compared with the simulation scenario. We check the cost-benefit and trade-off analysis of opening - closing a location, a transportation lane etc.

In network design the focus in on expanding or redesigning an existing supply chain network, e.g. optimizing facility locations, sourcing strategies, and logistics flows.

Green field analysis is a subset of network design focused on identifying optimal locations for new facilities when there are no pre-existing constraints. It does not consider existing infrastructure.

It is used when selecting the best location for a new warehouse, plant, or distribution center to check how best to expand operations into a new market.

Which software did you use for network design and green field analysis problems?