River Logic
Supply chain planning software
Supply chain management software
- Features
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- Energy and utilities
- Manufacturing
- Agriculture, fishing, and forestry
What is River Logic
River Logic is a supply chain planning platform focused on optimization-based modeling for end-to-end supply chain and business planning. It is used by supply chain, operations, and finance teams to evaluate scenarios such as network design, sourcing, production planning, inventory policies, and profitability trade-offs. The product emphasizes prescriptive analytics using mathematical optimization to recommend plans under constraints, rather than only forecasting or reporting. It is typically applied to complex, multi-echelon supply chains where cost, service, and capacity constraints must be balanced.
Optimization-driven planning models
The platform centers on mathematical optimization to generate recommended plans based on constraints and objectives (e.g., margin, service, capacity, emissions). This supports prescriptive decision-making for problems like production allocation, distribution flows, and sourcing trade-offs. It is well-suited to situations where rule-based planning or spreadsheet models struggle to capture cross-functional constraints. The approach can help quantify the impact of decisions across the full network rather than within a single function.
End-to-end scenario analysis
River Logic supports what-if analysis across supply, manufacturing, distribution, and demand assumptions in a single model. Teams can compare scenarios such as supplier disruptions, capacity expansions, product mix changes, or policy shifts and see impacts on cost and service. This is useful for integrated business planning and strategic planning cycles where multiple stakeholders need a consistent set of assumptions. The scenario orientation aligns with planning processes that require frequent re-evaluation of constraints and objectives.
Handles complex constraints
The modeling approach is designed to represent multi-echelon networks, shared resources, and detailed operational constraints. This can include capacity limits, lead times, bills of material, transportation lanes, and service requirements. For organizations with complex manufacturing and distribution footprints, this level of constraint representation can reduce reliance on manual overrides. It also supports more defensible trade-off decisions by making constraints explicit in the model.
Higher modeling effort required
Optimization-based planning typically requires more upfront model design, data preparation, and validation than lighter-weight planning tools. Organizations may need specialized skills to define objectives, constraints, and scenario structures that reflect real operations. Model maintenance can become an ongoing effort as networks, products, and policies change. This can lengthen time-to-value for teams expecting rapid configuration with minimal modeling.
Data integration can be complex
To produce reliable recommendations, the platform depends on consistent master data and transactional inputs from ERP, WMS/TMS, and demand sources. Integrating and reconciling data across systems (items, locations, capacities, costs, lead times) can be non-trivial. Data quality issues can materially affect optimization outputs and user trust. Some organizations may need additional integration tooling or services to operationalize data pipelines.
Not a full SCM execution suite
River Logic primarily addresses planning and decision optimization rather than day-to-day execution workflows. Companies may still require separate systems for procurement execution, order management, warehouse operations, transportation execution, and supplier collaboration. As a result, it often functions as a planning layer that must be connected to operational systems for execution and monitoring. This can increase overall solution architecture complexity.
Seller details
River Logic, Inc.
Plano, Texas, United States
2000
Private
https://www.riverlogic.com/
https://x.com/riverlogic
https://www.linkedin.com/company/river-logic
