Review of Lanner, Supply Chain Software Vendor

By Léon Levinas-Ménard

Last updated: April, 2025

Lanner is a venerable supply chain software vendor known for its decades‑long expertise in simulation and digital twin technology. The company delivers a predictive simulation environment that models entire supply chains via discrete event simulation, enabling decision‑makers to run “what‑if” scenarios that illuminate bottlenecks, reveal trade-offs, and guide resource allocation. Rooted in the legacy of its acclaimed WITNESS platform and continuously evolved through modern tools like the Java‑based L‑Sim BPMN Simulation Engine, Lanner’s solution produces quantifiable outputs—throughput, lead times, and capacity utilisation—that support expert human interpretation rather than offering full automation. This robust, expert‑driven approach provides clarity amidst supply chain complexity, ensuring that organizations make informed decisions based on detailed, simulation‑derived insights.

What the Solution Delivers

Practical Outcomes and Functionalities

Predictive Simulation and Scenario Analysis:
Lanner leverages predictive simulation to construct a digital twin of an organization’s supply chain. By executing “what‑if” scenarios—testing different production or sourcing strategies—it enables users to identify bottlenecks and evaluate trade-offs between cost, speed, and efficiency (¹).
Visualisation of Trade-offs and Compromises:
Its interface visually demonstrates how conflicting factors such as cost versus speed or global versus local sourcing impact performance, ensuring clear quantitative forecasts that underline the optimal allocation of resources (¹).

Deliverables in Non‑Ambiguous Terms

Forecasting and Bottleneck Identification:
The solution produces measurable outputs—including throughput metrics, lead times, and capacity utilisation—that inform users about the state of their operations under various simulated conditions.
Decision Support, Not Automation:
Rather than enforcing automated decisions, Lanner’s platform is designed as an advisory tool, providing simulation‑based insights that require expert interpretation to translate into actionable strategies (¹).

Underlying Technology and Methodology

Simulation Engines and Digital Twin Approach

Lanner builds on decades‑old expertise in simulation, evolving from the pioneering WITNESS tool of the 1980s to modern platforms that integrate discrete event simulation with a digital twin methodology. This approach creates a real‑time virtual representation of the physical supply chain, incorporating process dynamics, resource constraints, and variability to support iterative “what‑if” analyses for improved decision‑making (²³).

Technology Stack and Deployment Considerations

Evolving from legacy systems written in Fortran, Lanner now utilizes contemporary simulation engines—such as the Java‑based L‑Sim BPMN Simulation Engine—to combine robustness with modern software engineering practices. The solution is traditionally deployed as an on‑premise or integrated enterprise tool, working in concert with existing ERP and MES systems to provide comprehensive scenario analysis rather than a plug‑and‑play, fully automated decision engine (⁴).

Assessing State‑of‑the‑Art and Skeptical Perspectives

Maturity Versus Buzzwords

Although Lanner’s literature occasionally mentions “AI” and “ML,” the core technology remains based on deterministic, rule‑based discrete event simulation. The system’s accuracy and utility hinge on the quality of input data; even the most sophisticated simulation must be carefully calibrated by experts to reflect real‑world complexities (⁵).

Comparison With Modern Alternatives

Lanner’s long track record and proven simulation framework offer reliability and depth of analysis. However, its advisory and simulation‑centred approach contrasts with emerging platforms that integrate probabilistic forecasting and automated, machine learning‑driven decision optimization. In essence, while Lanner delivers detailed, scenario‑based insights for human decision‑making, newer models—exemplified by Lokad—aim to streamline and automate routine supply chain decisions.

Lanner vs Lokad

Lanner and Lokad represent two contrasting paradigms in supply chain software. Lanner is distinguished by its simulation‑based digital twin approach that accurately models supply chain dynamics through discrete event simulation. Its outputs—such as forecasts, bottleneck analyses, and performance metrics—serve as decision‑support tools that require expert interpretation. In contrast, Lokad capitalizes on advanced machine learning techniques and probabilistic forecasting within an end‑to‑end, cloud‑based SaaS platform. Lokad’s solution employs a domain‑specific programming language (Envision) to automate routine decisions and deliver actionable outputs directly to enterprise systems. Essentially, while Lanner relies on robust, expert‑driven simulation for insight, Lokad aspires to automate optimization through data‑driven, AI‑enabled predictive analytics.

Conclusion

Lanner delivers a robust predictive simulation environment that harnesses the power of digital twins and discrete event simulation to illuminate the complexities of modern supply chains. Its platform offers well‑quantified insights—ranging from throughput and lead times to detailed bottleneck identification—that empower supply chain experts to make informed decisions. However, its reliance on deterministic simulation and human oversight contrasts with the emerging trend of automated, AI‑driven decision optimization seen in platforms like Lokad. For organizations valuing depth, reliability, and expert‑driven analysis, Lanner remains a mature and dependable choice, even as the industry gradually shifts towards more automated, data‑centric approaches.