Before running a Greenfield Optimisation, please visit the Optimisation Overview article in the Knowledge Base, which covers the different optimisation types available in SimPath.
The minimum required to optimise a Scenario is a configured Forecast and Operations. Refer to the Knowledge Base for guidance on each.
Configuring Costs is optional but recommended. It unlocks cost-based optimisation across individual cost dimensions and Total Cost, surfacing tradeoffs that Asset-Distance optimisation alone cannot reveal.
A Greenfield Optimisation answers the question: if you could redesign your network from scratch, what would it look like? When you mark a Location Type as Greenfield, the optimiser is free to open new facilities of that type in the countries you specify, and may or may not retain the original locations.
As long as at least one Location Type is set to Greenfield, the optimisation is classified as Greenfield. You can combine Greenfield with other constraint types as needed.
The Optimise button is enabled whenever a Scenario has sufficient information to run. Click it, give your optimisation a name, select your optimisation metric and click Create. In the video example, our optimisation dimension is Total Cost.
This page is where you tell the optimiser what changes it is allowed to make to find better networks.
You can mark any Location Types as Greenfield. In this example, we want to understand whether more optimal Factory configurations exist, so Factories are set to Greenfield with the United Kingdom selected as the target country. All other Location Types are constrained. You can select any target countries appropriate for your network.
Since Factories sit between Suppliers and Distribution Centres, changes to Factory locations may shift processing volumes downstream. To give the optimiser more freedom to explore, Distribution Centres and Treatment Centres are constrained to Optional with 15% Flex Capacity.
Constraints and Flex Capacity can be configured at the location type level or on individual locations.
When you are ready, click Save.
From the Optimisations page, click Optimise and monitor progress. Once complete, the results are available for inspection and download.
In this example, 317 networks were generated in 30 seconds. Ten of those networks achieved a lower Total Cost than the Base Network. Comparing the Base Network against the lowest Total Cost Optimised Network shows an overall saving of 6.6%, driven by reductions in facility and transport costs, despite a slight increase in processing costs.
The comparative map shows that the base network Factories in the Midlands and South are suboptimally located. These are removed and replaced by two better-positioned Factories near Stoke-on-Trent and southeast of London, which are closer to their downstream Distribution Centres. This is the primary driver of transport savings.
Processing volumes increase at the Yorkshire Factory and across several Distribution Centres in England. The Northern Factory sees roughly a third of its volume redistributed. If you have watched the Consolidation video in the Knowledge Base, this result will be familiar.
The Transport tab shows the largest savings on the Supplier-to-Factory route (6%) and the Factory-to-Distribution Centre route (13%), confirming that the Base Network Factories were creating transport overhead. Although volume redistribution increases last-mile transport costs to Retailers, the upstream savings more than offset this.
Facility cost savings come from having fewer Factories and downsizing some Distribution Centres, consolidating operations in more optimal locations and leveraging economies of scale at Factories.
SimPath makes the interplay between transport and facility costs transparent. Greenfield Optimisation lets you explore the art of the possible for long-term, strategic supply chain transformation.