Open Source or COTS - What is the best simulation software for you?

Below is a structured analysis of the pros and cons of using Open Source versus Commercial Off-The-Shelf (COTS) simulation software for complex discrete event simulation (DES) models in logistics, supply chain, or manufacturing contexts. This assessment is designed for decision-makers and simulation practitioners evaluating tools for analytical modeling, scenario planning, and operational optimization. It is based on more than 25 years experience in developing simulation models.

Open source vs COTS software

Open source simulation software

Software with publicly available source code that can be freely used, modified, and redistributed (e.g., Salabim, SimPy, JaamSim, OMNeT++, BPSim frameworks).

COTS simulation software

Commercial, proprietary software licensed for use (e.g., AnyLogic, Enterprise Dynamics, FlexSim, Simio, Arena, Witness, ExtendSim, Plant Simulation).

Core comparison

Dimension	Open Source	COTS Simulation Software
Cost	No or low licensing cost	High licensing cost
Customization	High (with source code)	Moderate (configurable via API/plugins)
Support & Maintenance	Community	SLA & helpdesk
Ease of Use	Steeper learning curve	User-friendly GUI, knowledge transfer
Features & Libraries	Requires extension(s)	Extensive feature sets
Scalability & Performance	Depends on implementation	Optimized for performance & large models
Validation & Certification	User-driven validation	Build-in validation & verification
Vendor Risk	Low vendor lock-in	Medium vendor dependency
Integration	Requires custom development	Built-in connectors and ecosystem support

Detailed pros and cons

Open Source pros

1. Low upfront cost

   • No software license fees, enabling budget-constrained projects

   • Useful for research, academic environments, proof-of-concepts

2. Full transparency

   • Source code access ensures model logic and solver behaviors are visible for audit, debugging, and compliance

   • Reduces risks of “black box” decision logic

3. High flexibility

   • Ability to tailor algorithms, extend functionality, integrate with custom data pipelines or APIs

   • Ideal for unique domain requirements not supported out of the box elsewhere

4. Community contributions

   • Ecosystem momentum can produce plugins, modules, and shared best practices

   • Opportunity to leverage state-of-art research implementations

5. No vendor lock-in

   • No dependence on changes in vendor pricing, licensing terms, or discontinuation of products

Open Source cons

1. Support and maintenance burden

   • Typically lacks dedicated vendor support; relies on community forums or internal expertise

   • May require in-house capability to troubleshoot or extend

2. Steeper learning curve

   • Tools may require programming expertise (Python, Java, etc.) and deeper simulation knowledge

   • Less guided workflows compared to GUI-centric COTS tools

3. Feature gaps

   • May lack advanced analytics, optimization engines, visualization tools, or specific industry modules

   • Lack of verification & validation tools

   • Building such features from scratch can be time-intensive

4. Performance limitations

   • Scalability depends on user code quality, interpreter/compiler performance, and underlying algorithms

   • Large, complex models may require additional engineering

5. Documentation quality

   • Documentation varies widely in completeness and accuracy

   • Lack of standardized user manuals or structured training materials

COTS pros

1. Robust feature set

   • Pre-built components for entities, processes, resources, schedules, animations, KPIs

   • Integration with optimization engines, AI modules, and digital twin features.

2. User experience and productivity

   • Intuitive GUIs, drag-and-drop model building, scenario management, and reporting dashboards

   • Lower barrier to entry for domain experts without deep coding skills

   • Knowledge transfer during model handover

3. Commercial support and SLAs

   • Vendor support, training, and professional services available for rapid issue resolution

   • Reduces risk for mission-critical deployments

4. Validated and verified engines

   • Simulation cores often rigorously tested; adoption in regulated industries

   • Easier to defend results to external stakeholders, partners, or auditors

5. Ecosystem and Integration

   • Connectors to ERP/MES/WMS, data import/export utilities, and simulation data management

   • Standardized interoperability (e.g., REST APIs, database connectors)

6. Performance and optimization

   • Engines optimized for handling large event queues, complex logic, and enterprise data volumes

   • Built-in scenario comparators and optimization runs

COTS cons

1. License cost

   • Significant upfront and recurring fees

   • Costs often scale with users, cores, and modules

2. Vendor lock-in

   • Dependence on vendor roadmaps, pricing models, and release cycles

   • Models may be proprietary and hard to migrate to other platforms

3. Customization limitations

   • Extensions often constrained by vendor frameworks, scripting languages, or APIs

   • Deep customization may be costly or impossible

4. Feature complexity

   • Broad feature sets may overwhelm users or force unnecessary complexity

5. Upgrades and compatibility

   • Version changes may break models or require conversion efforts.

   • Legacy models might need refactoring on new releases

Typical use cases and when to choose which

Choose Open Source when:

• Budget constraints preclude expensive licensing

• You need full control of simulation logic and perfect transparency

• You have strong in-house technical expertise in programming and simulation theory

• Project scope is highly specialized and unlikely to benefit from standard commercial templates

Choose COTS when:

• You require rapid model development with intuitive tools

• Organizational stakeholders need vendor support and documented validation

• Integration with ERP/MES/WMS is essential with minimal custom engineering

• You prioritize reliability, performance, and established industry usage

Hybrid and strategic approaches

In practice, organizations sometimes adopt hybrid strategies:

• Prototyping in Open Source to explore feasibility and core logic before porting to a commercial tool

• Using Open Source for data pre-processing / analytics while the core simulation runs in COTS

• Co-development models with consultants or vendors to bridge capabilities between custom algorithms and commercial engines

• Cooperate with universities using PhD/Graduate students for model development

Conclusion

The decision between Open Source and COTS simulation tools is not binary. It should be based on a structured evaluation of requirements, resources, risk tolerance, integration needs, performance demands, and lifecycle considerations. A comprehensive tool evaluation matrix aligned with business objectives is recommended prior to procurement or adoption.

As extensive users of both Arena & Simio software, Systems Navigator has been developing commercial simulation models since 2003. In case you need our help in making the decision whether to use Open Source or COTS software for your simulation project, we are more than willing to share our experiences.