Hierarchical AHP and networked ANP models in one application
LPS Software · Decision Analytics
LPS ANP Analyzer
In preparationTransparent AHP/ANP decision models with ratings, sensitivity analysis and professional Excel export.
The current product build 1.6.0 is being prepared for public release.
Version 1.6.0Windows 10/11DE / ENfrom €69
Decision AnalyticsFreely definable clusters, nodes and influences are evaluated using relative ANP comparisons or absolute rating scales and documented transparently.
Purpose
The product in its working context.
LPS ANP Analyzer models complex decisions as hierarchies or networks and combines pairwise comparisons, ratings, supermatrices and result analysis in one workflow.
Core value
Freely definable clusters, nodes and influences are evaluated using relative ANP comparisons or absolute rating scales and documented transparently.
Key benefits
Concrete value rather than a feature list.
Rating mode for many or continuously added objects
Sensitivity, benefit/cost, SVG and Excel analysis
Key capabilities
From the model to a usable result.
- Define clusters, nodes and influence relationships freely
- Perform pairwise comparisons using the Saaty scale
- Evaluate consistency ratios of local matrices
- Calculate unweighted, weighted and limit supermatrices
- Use relative ANP and absolute rating scales
- Generate a hierarchy tree or network graph automatically
- Run sensitivity analyses for criteria and feedback blocks
- Calculate benefit/cost ratios
- Save and reload models as JSON
- Create a professional Excel report with matrices and results
Applications and target groups
For professional analysis and design tasks.
Strategic Decisions
Weight alternatives, criteria and feedback relationships transparently.
HR & Evaluation
Assess employees, suppliers, projects or products using calibrated rating scales.
Consulting & Teaching
Document decision logic, consistency and sensitivity transparently.
Application views
Visual product views are being prepared.
Real application screenshots will be added after visual approval.
Network structure and pairwise comparisonsLPS ANP Analyzer
Supermatrix, results and sensitivity analysisLPS ANP Analyzer
System requirements
Local and controlled use.
- Windows 10 or Windows 11
- Local desktop application
- German and English interface
- JSON model, SVG graph and Excel report (.xlsx)
Trial and licensing model
One-off payment with no automatic renewal.
14-day local trial · one named user · up to two personal devices · no automatic renewal.
Launch prices through 31 December 2026
Pricing options
Before release: The live payment integration is prepared. Public checkout will only be enabled after the final release test.
Before purchase: Purchase & Licence Terms · Refunds & Withdrawal · Privacy. Payment processing is handled by Paddle as Merchant of Record.
1 year
69 €
Time-limited one-off licence with no automatic renewal.
2 years
119 €
Longer usage period with no automatic renewal.
Perpetual
179 €
For the current major version line; updates and standard support included for 12 months.
Live payment integration prepared; public release is still pending.
Purchase buttons remain disabled until final approval. After a successful purchase, the customer portal, licence and protected installer download are provided for the relevant product entitlement.
Frequently asked questions
Important questions before trial and purchase.
Does the application support both AHP and ANP?
Yes. Acyclic models can be evaluated as AHP hierarchies; feedback and interdependencies are modelled as ANP networks.
What is rating mode?
Instead of pairwise comparisons between all objects, calibrated scales are defined for each criterion. This is especially suitable for many or frequently added objects.
Can results be exported?
Yes. Models can be saved as JSON; graphs can be exported as SVG and complete analyses as Excel workbooks.
Next step
Live checkout, customer portal and licence fulfilment are prepared.
The purchase and licence chain is prepared for live operation. Exactly one controlled live smoke test will follow before public release.