“What happens if this node goes down? What happens if this gets delayed? What if… We can set that framework now — with Optilogic we have the ability to look at scenarios at a granular level.”
Cosmic Frog Cost-to-Serve Planning Features
Intelligent Greenfield Analysis
Cosmic Frog’s Intelligent Greenfield engine enables you to assess the high-level structure of your network’s manufacturing processes, identify the best locations for distribution centers, and make optimized sourcing decisions for your customers. This results in minimized fixed and variable costs for each facility, while still respecting capacity and service level constraints.
Example of a Greenfield study with a new product (orange) being sourced from two facilities:
Supply Chain Network Design
Cosmic Frog’s feature-rich network optimization engine produces a hyper-detailed model of how your supply chain will respond to new product introductions. Use this tool to explore questions like:
- What production process should we use?
- How will the new product impact the existing production line?
- How will different packaging sizes impact development cost, transportation cost, and work center capacity?
- Where should we stock inventory for the new product?
- How will various new product inventory policies impact inventory levels throughout our network?
- How will new products impact cost to serve and profitability for every product line and customer segment?
Supply Chain Simulation
Cosmic Frog’s discrete-event simulation engine produces a highly reliable representation of your network’s reaction to new product introductions by considering how supply chain elements interact at a granular level. Simulation also accounts for variability in your data and provides statistical metrics for your predefined KPIs.
How it works:
- Feed a probabilistic demand forecast into your model and analyze expected outcomes via KPIs like cost, lead time, and service level.
- If no probabilistic forecast is available, use a point forecast and add symmetric variability (like triangular or normal), then analyze the expected results.
- Alternatively, use the point forecast and lock certain decisions like sourcing or inventory, then vary demand systemically or stochastically to visualize how your network reacts to changes across the various scenarios. This approach helps validate the forecast for new products that lack data.
- You can also add congestion to your network to measure the impact of queuing on product and transportation times.