Design engines

A new home for the Coupa Supply Chain documentation

Starting with Supply Chain 42, our documentation will be located on Coupa Compass. The help here will continue to be accessible for the foreseeable future, but will no longer be updated. If you need a Compass user account, contact your company’s Designated Support Contact (DSC).

You can access new and updated Supply Chain documentation in the following location on Compass:

https://compass.coupa.com/en-us/products/supply-chain-design-and-planning

Design engines

You can make use of a set of powerful Design engines:

Technology	Design engine	Description
Network Optimization (Network Optimization can use both regular design engines and the Next generation solver)	Network Optimization	Use Network Optimization to investigate cost optimization, site location and capacity constraints. Network Optimization determines the optimal network structure, flows, and policies for any given set of sites, customers, products, demand (customer or site), transportation policies, production policies, site and customer sourcing policies, and inventory policies.
	NO Decomposition	The decomposition method allows you to break a large model into smaller pieces and solve them more efficiently. For large models, this method can help reduce both solving time and memory usage significantly. Model decomposition extracts solvable sub-models from the original model by analyzing the complex relationship across all supply chain elements. After solving all the sub-models, the solution of the original model is obtained by merging the output of all the sub-models.
	Greenfield Analysis	Use Greenfield Analysis to determine the best geographic locations of potential “greenfield" facilities given a set of customers. The best locations are those that minimize the total cost of shipping each customer’s demand over a distance given by their latitude/longitude coordinates.
	Network Optimization Infeasibility Diagnosis	Infeasibility diagnosis determines if a network optimization will be infeasible or not. If so, it guides you on how to resolve the infeasibility by providing information in the Constraint Summary output table or in the Customer Flows, InterSite Flows and Production Process Flows output tables.
	Cost to Serve Analysis	You can use cost to serve analysis to review the landed cost of the overall flow of product from production to customer receipt for Network Optimization. When you run Cost to Serve Analysis, the Cost to Serve Details output table is populated with the lane by lane flow per Customer-Product-Period.
	NO and CTS Analysis	This problem type runs Network Optimization, followed by Cost to Serve Analysis.
	Rapid Solve	This problem type applies the selected Rapid Scenario items to an existing Base Scenario, enabling you to investigate changes to scenarios quickly.
Transportation Optimization (Transportation Optimization can use both regular design engines and the Next generation solver)	Transportation Optimization	Transportation Optimization addresses vehicle routing problems requiring detailed transportation representation. Generally, a vehicle routing problem refers to a number of similar but unique transportation problems involving consolidation of shipments into multi-stop routes.
	Driver Scheduling	Use Driver Scheduling to assign a sequence of routes to specific drivers, minimizing costs within the applicable constraints. Driver class definitions determine the equipment types the driver supports, scheduling constraints, such as maximum duty time per shift, and costs associated with the driver.
Inventory Optimization (Inventory Optimization can use both regular design engines and the Next generation solver)	Safety Stock Optimization	Use Safety Stock Optimization to determine the optimal location and quantity of safety stock inventory, which ensures against both unknown demand and unknown lead time variability.
	Service Level Optimization	This optimization type selects the optimal service level for each finished good within a user-defined product set at a customer-facing site to achieve a specific objective. It requires that Safety Stock Optimization has already been run since safety stock placement must already be determined.
	Rolling Horizon Modeling	Rolling Horizon Modeling models future forecasts, providing time-phased safety stock and inventory policies. Rolling horizon modeling makes the safety stock placement decision in the "strategic horizon", and lets you set time-phased safety stock level in the "planning horizon". It requires that Safety Stock Optimization has already been run since safety stock placement must already be determined.
	Rolling Horizon Validation	Rolling Horizon Validation provides service levels, fill quantity rates and ready rate values by site-product, based on the time-phased safety stock and inventory policies determined by Rolling Horizon Modeling.
	Safety Stock + Service Level Optimization	Use this optimization type to run Safety Stock Optimization followed by Service Level Optimization.
	Safety Stock Optimization + Rolling Horizon Modeling	Use this optimization type to run Safety Stock Optimization followed by Rolling Horizon Modeling.
	Inventory Simulation	Inventory Simulation allows you to simulate the Safety Stock Optimization results by focusing on only recommended optimal policies. You can then see the performance of the optimized safety stock policy parameters.
	Safety Stock Optimization Infeasibility Diagnosis	If Safety Stock Optimization returns an infeasibility, you can use Safety Stock Optimization Infeasibility Diagnosis to detect what sets of constraints are causing the infeasibility.
Simulation	Simulation	Similar to Classic Simulation, Simulation provides a faster solution. This Simulation engine supports additional functionality not available in Classic Simulation. These features include Material requirements planning (MRP) and State Management. Refer to Features in Simulation for the set of features supported by release.
Simulation	Classic Simulation	Discrete event Simulation represents the operation of the network as a chronological sequence of events, with the ability to incorporate variability factors as inputs to the model. It is the representation of specific, defined events scheduled over a period of time.

Cloud design engines

The Supply Chain platform provides excellent price performance by enabling you to solve multiple what-if scenarios in a fraction of the time it would take to run them locally. A number of design engine servers are available on the Supply Chain platform, with specifications similar to those shown below.

Component	Coupa Cloud Solver Design Engine Servers**
Component	Default	Enhanced	Premium
CPU Cores	4 Core 2.5 GHz	8 Core 2.5 GHz	16 Core 2.5 GHz
Logical Processors	8	16	32
RAM	64 GB	128 GB	256 GB
Hard Drive	SSD	SSD	SSD

**Specifications represent an example hardware setup for Coupa's cloud solver. The Enhanced and Premium options are available for an additional charge. Contact Coupa for information about available design engine servers.

The engine names in the platform include the release number, such as "R40 - Enhanced".

Keep in mind that solve hours are consumed at a higher rate when using the larger design engines.

Advantages
- No IT Support Required. The cloud option is immediately available to existing Supply Chain Guru X desktop users.
- Unlimited Power On Demand. Solve requests are added to a queue, which automatically launches additional servers as needed.
- Built-in Parallel Processing. The cloud solver automatically processes multiple what-if scenarios in parallel that would otherwise be run sequentially on local hardware.
- No hardware to maintain. No operating system updates or hardware to manage.
- Collaboration and Knowledge Management. The cloud enables publishing and sharing of models under controlled security permissions within the organization.
Disadvantage
- IT Approval Required. Most IT organizations must verify that Coupa provides adequate security controls, which requires additional time prior to the actual implementation process.

Next generation solver

As Coupa continues to innovate and deliver faster solutions to our customers, a next generation (Next Gen) deployment of the solver is the default when running models in Modeler. The Next Gen solver offers the same Network Optimization, Inventory Optimization, and Transportation Optimization technology, with improvements to the processing of models.

Benefits of the Next Gen solver include:

Improved performance – In many cases, models solve faster with the Next Gen solver than the previous solver. This may not affect models that already run quickly but is more evident with longer running models.
“Round Robin” processing of requests – The Next Gen solver will loop through requests by customer. This prevents cases where a customer sends a large number of requests that effectively block the next customer’s requests from starting right away. The solver executes a request from the first customer, then moves on to the next customer and executes a request. This continues for all customers in the queue. Once it has been through all customers, it loops back to the first customer and will continue to take requests in this manner as long as there is available memory and CPU. As resources are freed up, it processes additional requests.
Removal of strict limitation on the maximum number of design engines – The Next Gen solver will not have the same cap of the available number of design engines that can be accessed at one time. As a result, the Next Gen solver can scale more effectively than the previous solver when large numbers of requests are made.

Using the Next Gen solver:

In Modeler, open the Launch Pad, and select the Technology (“Network Optimization”, "Inventory Optimization", or "Transportation Optimization").
Ensure the Enable Next Generation Solving Engine switch is on. To use the standard design engine solver, turn off the switch.
Select your scenario(s) and optionally select a Solving Engine.
Click Run. Your scenarios run using the new deployment.

You can review the status of your solves in the Scenario Queue in Modeler and on the Models tab in Queue Management. Next Gen solve have a Run Type of "Solve Task".

Last modified: Tuesday July 30, 2024

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