Logility NDO allows you to utilize multiple solvers to analyze several characteristics of your supply chain. The solvers can be accessed via the magic sextant and this article will explain each option in more detail.
Parameters
The solvers allow you to alter the service role that you want to solve for. You can solve for either any of the facility types in your model (Plant, Warehouse, etc.) individually, all of them together, or for just the facilities that you've selected. When selecting a service role, lanes are automatically created from all facilities of that type (or the ones selected) to all customers. Note that these facilities will need to be able to supply products or procure products from another service facility in the model in order to directly serve customers.
The solvers offer five optimization metrics to choose from:
- Landed Cost - the total cost of all model elements is minimized while still attempting to meet as much demand as possible.
- If there is not enough supply to meet demand, the solver will still attempt to satisfy as much demand as possible, rather than reporting infeasibility.
- Customer Service Time - The weighted average customer service time will be minimized.
- Customer Service Distance- only the facility->customer service driving distance is minimized,
- End-to-End Service Time - the whole network is considered, so here both facility->facility service times as well as facility->customer service times will be minimized.
- Emissions (CO2) - The total sum of all emissions will be minimized.
In addition, in a fashion similar to that of service role, you can select any service mode of transportation to solve for in addition to selecting "Current" which will create lanes of the same mode as the ones already going to or coming from a facility.
Solve for...
The Solve for... solver allows you to optimize your current network as is. This solver can be useful for seeing just how optimized your network is once you've established a baseline model or for re-optimizing your network after adding new nodes, among many other use cases.
Solve for Best X
The Solve for Best X solver allows you find the "x" best locations for servicing your supply chain. For example, if you have 10 facilities in your model and choose to solve for the best 3 facilities, the solver will analyze your model and select the best 3 facilities which satisfy the selected optimization metric, effectively "opening" those facilities and "closing" the remaining ones. For the Best X, Next X, and Target Service Time solvers, you are able to choose whether to use the facilities present in the model, or to incorporate Starboard's candidate list of Greenfield facilities (~115 locations).
Solve for Next X
The Solve for Next X solver allows you find the next best "x" locations for servicing your supply chain. This solver becomes available when you have one or more facility nodes selected. To recast the Best X example, if you select facility A and have 10 other facilities in your model and choose to solve for the best 3 facilities, the solver will analyze your model and select the best 3 facilities for satisfying the selected optimization metric in addition to facility A.
Solve for Target Service Time
The Solve for Target Service Time solver allows you to expand on the other solver's capabilities for optimizing for customer service time goal. Like the other solvers, you can specify the number of service facilities to open, but, in addition, you can also specify the target number of service days. The solver will prioritize satisfying demand with the target service day threshold, and then minimize cost from there.
NOTE: Is is possible for the overall weighted service time to go up as the objective is to get as much demand within a target not to reduce the overall service time.
Solve for Emissions Limit (Cap)
The Solve for Emissions Limit solver allows you to expand on the other solver's capabilities by targettting a maximum or cap to emissions. Like the other solvers, you can specify the number of service facilities to open, but, in addition, you can also specify the target number of service days. The solver will prioritize satisfying demand with the target service day threshold, and then minimize cost from there.
