GITNUXSOFTWARE ADVICE
Supply Chain In IndustryTop 10 Best Distribution Network Design Software of 2026
Top 10 distribution network design software ranked for logistics planners, with criteria, strengths, and tradeoffs. Includes ToolsGroup Network Design.
How we ranked these tools
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Framework: Features 40% · Ease 30% · Value 30%
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For teams running frequent scenario-driven DC footprint and allocation studies with tight constraints, ToolsGroup Network Design is the most dependable fit, while Optilogic suits distribution planners who want repeatable cloud-based network scenarios, and if you code exact optimization models in-house, Gurobi Optimizer can be the strongest way to run runs.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
ToolsGroup Network Design
Scenario management that keeps optimization inputs and constraint settings aligned for repeatable network comparisons.
Best for: Fits when teams run frequent scenario-driven DC footprint and allocation studies with tight constraints.
AnyLogistix
Editor pickGIS boundary overlay connects demand and candidate sites to geographic constraints during network configuration.
Best for: Fits when network analysts need GIS mapping and repeatable scenario comparisons for network design.
Gurobi Optimizer
Editor pickTunable MIP search parameters and callbacks enable custom feasibility handling during solve.
Best for: Fits when teams need code-driven network models and repeatable scenario optimization runs.
Comparison Table
ToolsGroup Network Design
enterpriseSupply chain planning and optimization with network design features.
Scenario management that keeps optimization inputs and constraint settings aligned for repeatable network comparisons.
ToolsGroup Network Design is built around network modeling workflows that translate business rules into an optimization run, then apply post-optimization validation to check key feasibility and cost drivers. Scenario management supports controlled what-if studies so teams can compare alternative center placements, allocation patterns, and service-level constraint settings without rebuilding models each time.
A practical tradeoff is that model fidelity depends on how well lane costing, capacity assumptions, and constraint parameters are parameterized before each run. It fits best when a planning team needs repeatable network studies fed by ERP item and order signals and refined with lead-time variability and boundary constraints in later iterations.
- +Scenario comparison supports controlled what-if studies across network alternatives
- +Constraint-driven modeling captures service requirements with lane and capacity logic
- +Repeatable run pipelines fit ongoing planning cycles with minimal rework
- +Solver-driven results tie directly to allocation and location decisions
- –Model setup requires disciplined parameterization of costs, capacities, and constraints
- –Advanced customization demands more analyst time than simple spreadsheets
- –Iteration loops slow when data lineage and IDs are inconsistent across sources
- –Complex multi-objective tradeoffs can require careful configuration choices
Supply chain planning teams
Quarterly DC footprint scenario planning
Shortlisted network designs with tradeoffs
Transportation planning analysts
Lane costing and allocation rule testing
More accurate transportation cost baselines
Show 2 more scenarios
Network engineering groups
Greenfield and brownfield modeling
Clearer capex versus operating tradeoffs
Model new versus existing facility constraints and quantify feasibility and cost differences.
Demand planning teams
SKU rationalization and center coverage
Fewer centers with maintained service
Incorporate demand clustering inputs and re-run allocation to validate coverage effects.
Best for: Fits when teams run frequent scenario-driven DC footprint and allocation studies with tight constraints.
AnyLogistix
enterpriseSupply chain network design and analytics software built on AnyLogic.
GIS boundary overlay connects demand and candidate sites to geographic constraints during network configuration.
AnyLogistix fits organizations that need end-to-end network modeling from location and lane assumptions to facility and service outcomes. GIS boundary overlay helps connect site candidates and demand geographies without manual coordinate work. CSV and Excel demand import supports quick demand clustering inputs and SKU rationalization inputs when the source system exports flat files.
A key tradeoff is reliance on structured input preparation for lane costing, capacity, and constraints, which can slow early iterations compared with tools that infer more from live systems. AnyLogistix works best when analysts can standardize input templates and run many scenario comparisons, such as reshaping DC footprint and service constraints across weeks or quarters.
- +GIS boundary overlay reduces demand-to-location mapping work
- +CSV and Excel import supports fast demand and lane input templates
- +Scenario runs support repeated network strategy comparisons
- +Lane-based assumptions help align outputs to transportation costing logic
- –Structured input preparation is required for constraints and costing
- –API-driven automation depth is less prominent than built-in workflow runs
- –Complex multi-echelon inputs need careful staging before optimization
- –Model governance and audit trails depend on disciplined run management
Network planning analysts
DC footprint optimization with constraints
Clear strategy shortlists
Supply chain operations teams
Greenfield vs brownfield network redesign
Lower transition risk
Show 2 more scenarios
Demand planning leads
Demand clustering inputs from flat files
Faster scenario iteration
Imports clustered demand from spreadsheets and recalculates network options across strategy variants.
Transportation planning teams
Lane rate feed for costing logic
More consistent total cost estimates
Runs scenario comparisons using lane-based cost inputs tied to the modeled network flows.
Best for: Fits when network analysts need GIS mapping and repeatable scenario comparisons for network design.
Gurobi Optimizer
enterpriseMathematical optimization solver used for network design modeling.
Tunable MIP search parameters and callbacks enable custom feasibility handling during solve.
Gurobi Optimizer fits distribution design teams that need control over the full model, including greenfield vs brownfield facility variables, capacity limits, and inbound versus outbound flow balancing. The API supports building linear and mixed-integer constraint systems and objective terms that match lane-based transportation costing and transshipment structures. Automation typically comes from scripted runs that load model inputs, set parameters, and write solution outputs for downstream scenario reporting.
A common tradeoff is that Gurobi Optimizer is an engine rather than a GUI design tool, so users must build or integrate the network modeling interface, data import, and validation layer. It works best when an engineering team already has demand, network candidate sets, and cost matrices in structured form and needs repeatable scenario harnesses for strategic and tactical planning horizons.
- +High-performance MIP solving for large distribution network models
- +API-first modeling for precise constraints and objective control
- +Scenario runs via programmatic parameters and repeatable solve scripts
- +Heuristic options provide feasible plans during search
- –Requires custom modeling and UI integration for network design workflows
- –Model size growth can slow solves when binaries and arcs explode
- –Solution validation and data quality checks are typically external
Supply chain optimization engineers
Greenfield DC siting with capacities
Actionable facility decisions per scenario
Network planning analytics teams
Brownfield allocation and lane rerouting
Lower cost with controlled service levels
Show 2 more scenarios
Planning ops teams
What-if comparisons across demand clusters
Faster scenario decision cycles
Run structured solves for clustered demand inputs and export consistent solution artifacts for review.
Decision support developers
Integration with ERP cost matrices
Repeatable optimization in production
Generate models from imported cost and capacity tables and automate solve outputs to planning systems.
Best for: Fits when teams need code-driven network models and repeatable scenario optimization runs.
o9 Solutions
enterpriseCloud-native integrated planning platform with network design modules.
Scenario comparison harness that links network design outputs to downstream planning steps within the same planning workflow.
o9 Solutions focuses on distribution network design within a broader planning suite that connects network modeling with demand signals and operational constraints. The workflow centers on scenario creation for strategic versus tactical horizons, with optimization outputs intended to feed subsequent planning steps.
- +Scenario workflows support recurring network strategy comparisons
- +API-oriented integrations help connect network decisions to planning data
- +Constraint handling covers service-level and capacity requirements
- +Cross-functional inputs help keep network plans consistent with demand signals
- –Network modeling depth can lag specialist network optimization engines
- –Effective governance depends on disciplined configuration of scenarios and data feeds
- –Some modeling controls require technical admin work
- –Large scenario sets can slow iteration without a clear run strategy
Best for: Fits when planning teams need scenario-managed network design feeding ongoing demand and operations planning.
Oracle SCM Network Design
enterpriseNetwork design and optimization within Oracle Supply Chain Management Cloud.
Scenario execution and comparison are tightly aligned with Oracle SCM planning data staging, supporting repeatable network runs for distribution footprint decisions.
Oracle SCM Network Design generates distribution network scenarios that connect facility choices with transportation and service-level constraints. It is designed for network modeling workflows that feed strategic and tactical planning horizons, including capacitated facility siting and lane-based cost inputs.
- +Strong scenario comparison for distribution network tradeoffs
- +Capacity-aware facility siting with lane-level transportation cost handling
- +Designed to fit Oracle SCM planning data and execution workflows
- +Repeatable runs via staged model inputs and controlled execution
- –Model setup requires disciplined data preparation and mapping
- –Limited coverage of non-Oracle data pipelines without additional work
- –Heuristic controls can feel less transparent than solver-first tooling
- –Scenario volume management becomes a bottleneck with large SKU sets
Best for: Fits when Oracle SCM planning teams need controlled network modeling and scenario comparison with service constraints.
SAP Integrated Business Planning
enterpriseSupply chain planning suite including network design capabilities.
Scenario-based network design runs that stay connected to SAP planning versions and downstream planning outputs for execution alignment.
SAP Integrated Business Planning centers distribution network design inside a broader planning suite that connects scenario planning with SAP ERP execution. It supports facility and network configuration trade-offs using optimization runs backed by supply and demand inputs, then translates results into executable planning outputs through SAP integration.
- +Executes network design outputs within SAP planning and execution workflows
- +Supports constraint-driven scenario comparison across strategic planning horizons
- +Uses an integration-first approach for master data alignment across systems
- +Provides governance-friendly planning structure for large organizational setups
- –Network design modeling depth can be limited compared with specialized network optimization tools
- –Implementation requires disciplined configuration across planning versions and data readiness
- –API access for automation and external optimization workflows can be restricted
- –Location master and cost inputs often need careful preprocessing for usable lanes
Best for: Fits when an enterprise already standardizes on SAP planning, needs governed network scenarios, and wants execution-ready outputs.
Optilogic
enterpriseCloud-based supply chain design and network optimization platform.
Integrated scenario comparison keeps network structure, constraints, and lane costing aligned across repeated what-if runs.
Optilogic focuses on distribution network design tied directly to facility and transportation decision variables, with scenario comparison built around what-if runs. The tool supports capacity-aware facility location-allocation and lane-based transportation costing inputs, then evaluates service-level constraints during optimization runs.
- +Scenario comparison harness supports repeatable network what-if iterations
- +Lane-based transportation costing aligns with lane rate feed inputs
- +Capacity-aware facility siting reduces infeasible facility-location plans
- +Mixed planning runs can combine strategic structure and tactical constraints
- –Requires setup discipline to keep constraints consistent across scenarios
- –Integration depth depends on external feed mapping for master data objects
- –Solver configuration options can be limiting for advanced hybrid heuristics
- –Post-optimization simulation validation workflow is not as detailed as specialized add-ons
Best for: Fits when teams need repeatable network design scenarios with capacity and service constraints tied to lane costs.
IBM Decision Optimization
enterpriseDecision optimization engine for supply chain network modeling.
IBM Decision Optimization’s optimization modeling workflow integrates with automation-friendly run orchestration for repeatable scenario comparisons.
IBM Decision Optimization is a distribution network design software that couples an optimization modeling workflow with a mixed-integer programming solver geared for planning decisions. It supports facility location-allocation and capacitated facility siting inputs across strategic and tactical horizons, including greenfield versus brownfield modeling with service-level constraints.
- +Supports mixed-integer programming formulations for multi-constraint network design
- +Facility location-allocation modeling covers capacitated siting and allocation choices
- +Scenario runs enable what-if sensitivity analysis across planning assumptions
- +Integration-oriented optimization workflow fits ERP and order history ingestion
- –Model setup and data conditioning require disciplined configuration and governance
- –Heuristic vs exact optimization control can add workflow complexity for teams
Best for: Fits when planners need exact optimization models for constrained network design across multiple scenarios.
Frontline Solvers
SMBOptimization and simulation tools for Excel-based network modeling.
Built-in scenario comparison harness that ties what-if sensitivity outputs to specific greenfield or brownfield network change sets.
Frontline Solvers turns distribution network inputs into optimization-ready models and produces facility siting and flow plans using a mixed-integer programming engine. It supports scenario comparison for greenfield vs brownfield facility changes, including capacity and service-level constraints and lane-based transportation costing.
- +Produces mixed-integer solutions for capacitated siting with lane cost fidelity
- +Scenario comparison supports greenfield and brownfield network change planning
- +CSV-based demand and network import reduces data wrangling overhead
- +Automation supports repeated what-if runs across planning horizons
- –Model setup requires disciplined configuration of constraints and capacities
- –API access for end-to-end ERP orchestration is narrower than model experts expect
- –Scenario management becomes harder as the number of what-if dimensions grows
- –Geospatial overlay and boundary constraint tooling is limited compared with GIS-first workflows
Best for: Fits when distribution planners need scenario-driven network modeling with repeatable what-if optimization runs.
Cosmo Tech
enterpriseSimulation and optimization platform for supply chain network planning.
Scenario comparison harness built around stored model configurations and repeatable run outputs for network design decision reviews.
Cosmo Tech supports distribution network design work where model configuration, data feeding, and optimization runs need to stay repeatable across scenario comparisons. The tool emphasizes workflow-driven network modeling with constraint setup, location and flow structure definition, and scenario outputs that can be audited through run histories.
- +Scenario comparison workflow keeps model runs and outputs consistently packaged
- +Configuration reuse reduces rework when switching between planning horizons
- +Integration paths support importing core demand and network input sets
- +Run history supports governance review across repeated what-if iterations
- –Advanced network constructs can require careful manual constraint modeling
- –Automation depth is limited when optimization needs custom pre and post processing
- –Large models may require tuning to keep optimization runtimes manageable
- –Requires setup discipline to keep inputs aligned across ERP and OMS feeds
Best for: Fits when teams need repeatable distribution network design runs with controlled scenario governance and frequent input refreshes.
Conclusion
After evaluating 10 distribution network design software, ToolsGroup Network Design stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right Distribution Network Design Software
Distribution network design software is used to build constrained models for DC footprint optimization, lane-based transportation costing, and capacity-aware facility siting so teams can compare strategic and tactical network scenarios. This guide covers ToolsGroup Network Design, AnyLogistix, Gurobi Optimizer, and the remaining tools in the short list, including o9 Solutions, Oracle SCM Network Design, SAP Integrated Business Planning, Optilogic, IBM Decision Optimization, Frontline Solvers, and Cosmo Tech.
The selection logic prioritizes scenario comparison repeatability, automation and API surface for connecting network design runs to planning workflows, and governance controls for keeping constraints and inputs aligned across what-if studies.
Distribution Network Design Software for scenario-driven network modeling and optimization runs
Distribution network design software turns distribution design inputs such as candidate centers, lanes, costs, capacities, and service constraints into optimization runs that produce facility siting and allocation decisions. ToolsGroup Network Design focuses on scenario management that keeps optimization inputs and constraint settings aligned for repeatable network comparisons, which helps analysts run controlled what-if studies.
AnyLogistix targets scenario-driven network configuration with GIS boundary overlay to connect demand and candidate sites to geographic constraints while using structured CSV and Excel import templates for fast input preparation. Where teams need code-driven control over mixed-integer programming solving, Gurobi Optimizer supports tunable MIP search parameters and callbacks that enable custom feasibility handling during solve.
Scenario governance, constraint alignment, and integration surfaces for network design runs
Network design software wins when scenario management keeps the same constraint logic, input mappings, and lane cost definitions consistent across repeatable what-if studies. ToolsGroup Network Design, Optilogic, and o9 Solutions focus on scenario comparison so teams can attribute changes to network structure rather than drift in model setup.
Scenario comparison that preserves constraints and inputs
ToolsGroup Network Design keeps optimization inputs and constraint settings aligned so repeated network comparisons stay controlled. Optilogic and o9 Solutions both provide scenario comparison harnesses that keep network structure, constraints, and outputs tied to the same what-if loop.
Constraint-driven modeling with lane and capacity logic
ToolsGroup Network Design uses constraint-driven modeling with lane and capacity logic to capture service requirements. Oracle SCM Network Design pairs capacity-aware facility siting with lane-level transportation cost handling for distribution footprint tradeoffs.
Geographic boundary constraints using GIS overlays
AnyLogistix uses a GIS boundary overlay to connect demand and candidate sites to geographic constraints during network configuration. This reduces manual rework when candidate locations must respect boundary rules that do not exist in tabular spreadsheets.
Code-driven optimization control via an API-first solver workflow
Gurobi Optimizer supports tunable MIP search parameters and callbacks so feasibility handling can be customized during solve. IBM Decision Optimization also supports mixed-integer programming formulations for multi-constraint network design across multiple scenarios.
Model-to-planning workflow chaining for downstream execution alignment
o9 Solutions links network design outputs to downstream planning steps inside one planning workflow through its scenario comparison harness. SAP Integrated Business Planning keeps scenario-based network design runs connected to SAP planning versions and downstream planning outputs for execution alignment.
Optimization modeling orchestration for repeatable scenario runs
IBM Decision Optimization integrates optimization modeling with automation-friendly run orchestration to support repeatable scenario comparisons. Frontline Solvers adds stored sensitivity-driven scenario comparison tied to greenfield and brownfield network change sets.
Stored run configurations and repeatable governance for network design reviews
Cosmo Tech packages scenario comparison using stored model configurations and repeatable run outputs for decision reviews. This configuration reuse reduces rework when switching between planning horizons but still requires careful manual constraint modeling for advanced constructs.
Choose by scenario repeatability depth, workflow integration path, and the amount of modeling control needed
The right distribution network design software depends on how scenario comparisons must stay repeatable. ToolsGroup Network Design focuses on disciplined scenario management that aligns inputs and constraint settings for controlled network comparisons.
Map the scenario comparison workflow to the tool’s governance mechanism
If scenario comparisons must keep optimization inputs and constraint settings aligned for repeatable network tradeoffs, prioritize ToolsGroup Network Design. If scenario execution needs to stay in step with planning workflows inside a single chain of steps, evaluate o9 Solutions for scenario-managed network design that feeds downstream planning.
Decide whether geographic constraints belong in GIS configuration or in data mapping
If candidate sites and demand must be constrained by geographic boundaries during network configuration, AnyLogistix fits because it provides GIS boundary overlay during scenario setup. If geographic constraints are already resolved upstream and the focus is on lane cost fidelity and capacity-aware siting, Oracle SCM Network Design can keep modeling aligned with lane-level transportation cost handling.
Pick the modeling control style based on how exact optimization needs to be customized
If code-driven control over mixed-integer programming solving is required, Gurobi Optimizer fits because it exposes tunable MIP search parameters and callbacks. If governance and scenario execution must remain inside a modeling workflow with exact optimization coverage, IBM Decision Optimization supports multi-constraint network design formulations with automation-friendly run orchestration.
Choose the integration entry point into the planning estate
If the organization standardizes on SAP planning versions and needs execution-aligned network outputs, SAP Integrated Business Planning supports scenario-based runs connected to SAP planning and downstream outputs. If the organization runs Oracle SCM planning staging and needs controlled scenario comparison with service constraints, Oracle SCM Network Design aligns scenario execution with Oracle data staging.
Confirm whether lane costing and master data prep can be operationalized
If lane rate feeds and lane-based transportation costing must align tightly with constraint-driven modeling, Optilogic focuses on lane-based transportation costing aligned with lane rate feed inputs. If master data preparation requires structured input templates, AnyLogistix provides CSV and Excel import templates but still requires structured preparation for constraints and costing.
Validate pre and post-processing needs for custom network constructs
If advanced constructs require custom pre and post processing around stored configurations, Cosmo Tech limits automation depth when optimization needs custom handling. If the goal is network design plus what-if sensitivity outputs tied to greenfield or brownfield change sets, Frontline Solvers provides built-in scenario comparison tied to those change planning scenarios.
Who should use these tools for distribution network design
Distribution network design software fits teams that run frequent what-if comparisons and need outputs that remain attributable to network design decisions. The strongest fit depends on whether the work is scenario governance inside a network-design environment or scenario execution inside an enterprise planning stack.
Distribution network analysts running frequent DC footprint and allocation studies
ToolsGroup Network Design is built for scenario-driven comparisons where optimization inputs and constraint settings must stay aligned across network alternatives. This matches repeatable network comparisons with tight constraints and service requirements.
Network planners that must enforce geographic boundary rules during scenario configuration
AnyLogistix supports GIS boundary overlay to connect demand and candidate sites to geographic constraints during network configuration. This reduces manual mapping work when boundaries drive candidate eligibility.
Data science teams building code-first mixed-integer network models with custom feasibility handling
Gurobi Optimizer supports tunable MIP search parameters and callbacks so feasibility handling can be custom-coded during solve. This suits teams that treat network design as a modeling and experimentation loop with repeatable optimization runs.
Enterprise planning teams standardizing on SAP or Oracle for governed scenario execution
SAP Integrated Business Planning keeps scenario-based network design runs connected to SAP planning versions and downstream outputs for execution alignment. Oracle SCM Network Design aligns scenario execution and comparison with Oracle SCM planning data staging to support controlled distribution footprint decisions.
Scenario review teams that need stored run configurations and repeatable decision packages
Cosmo Tech packages scenario comparison using stored model configurations and repeatable run outputs for network design decision reviews. Configuration reuse supports frequent input refreshes across planning horizons.
Common failure points in Distribution Network Design Software rollouts
Most rollout failures stem from scenario drift, inconsistent constraint parameterization, or integration assumptions that break the repeatability promise. The tools listed here each emphasize a different mechanism for scenario governance, so teams can misuse them by feeding inconsistent inputs or skipping mapping discipline.
Assuming scenario comparison will stay controlled without disciplined parameterization of costs, capacities, and constraints
ToolsGroup Network Design requires disciplined parameterization during model setup so cost, capacity, and constraint logic does not drift between scenarios. Teams should treat scenario configuration as governed inputs rather than ad hoc edits.
Choosing GIS-enabled configuration but skipping structured input preparation for constraints and costing
AnyLogistix provides CSV and Excel import templates, but structured input preparation is still required for constraints and costing. Teams should define lane and constraint templates before importing large demand and candidate datasets.
Expecting code-first solver APIs to automatically integrate into distribution network design UI workflows
Gurobi Optimizer exposes solver control through an API-first workflow, but requires custom modeling and UI integration for network design workflows. Teams should budget integration effort for the network design front end rather than assuming it is provided end-to-end.
Overestimating network modeling depth in enterprise planning suites
Oracle SCM Network Design and SAP Integrated Business Planning provide strong scenario alignment with their planning environments, but network modeling depth can lag specialist network optimization engines. Teams should validate the needed modeling constructs before committing to an enterprise-only approach.
Assuming automation depth is equal across scenario packages when custom pre and post-processing is needed
Cosmo Tech supports configuration reuse and stored run outputs, but automation depth is limited when optimization needs custom pre and post processing. Teams should identify custom data conditioning steps early and confirm they can be executed consistently.
How We Selected and Ranked These Tools
We evaluated each tool on scenario repeatability governance, constraint and input alignment across what-if runs, integration and automation surfaces, and how directly outputs connect to downstream planning workflows. Feature capability accounted for 40% of the score, ease of getting reliable runs accounted for 30%, and value for planning teams accounted for 30%.
ToolsGroup Network Design scored highest because scenario management kept optimization inputs and constraint settings aligned for repeatable network comparisons, which made repeated DC footprint and allocation studies easier to trust. Gurobi Optimizer scored strongly on code-driven mixed-integer solve control, but it relied more on custom modeling and UI integration for end-to-end network design workflows, which lowered ease.
Frequently Asked Questions About Distribution Network Design Software
How do scenario comparison workflows differ across ToolsGroup Network Design, o9 Solutions, and Optilogic?
Which tool supports code-driven mixed-integer programming models for distribution network design?
How does GIS mapping support network configuration in AnyLogistix compared with tools that rely on spreadsheets and ERP feeds?
When would greenfield versus brownfield modeling be a deciding requirement?
How do integration approaches affect how ERP, OMS, and WMS data become optimization inputs?
What breaks if a network model cannot enforce service-level constraints during optimization?
Which tools are designed to support automation-friendly batch scenario runs?
How does cloud versus on-prem deployment impact solver control when using Gurobi Optimizer with network design workflows?
What admin controls and governance mechanisms help keep scenario runs auditable in Cosmo Tech and Oracle SCM Network Design?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
Funding and competing interests
This study was co-funded by several of the vendors assessed in it. No single vendor funded it. Funders could propose the market studied.
They had no role in selecting vendors, setting or weighting criteria, analysing data or drawing conclusions, and they received the same factual-accuracy check as every other vendor assessed.
Market definition, inclusion rules and criteria weights were fixed by an editor before research began and did not change afterwards. Editorial process →
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