Integrated Decision Support Systems (iDSS)
We don't advise — we build computational engines that simulate thousands of scenarios to identify optimal strategies under uncertainty.
What Is iDSS?
An iDSS is not a dashboard. It's a computational decision engine that integrates reliability physics, lifecycle economics, and business risk into a unified framework for evaluating complex industrial decisions.
Instead of providing recommendations based on static analysis, we build systems that allow you to explore thousands of "what-if" scenarios, quantify trade-offs, and select strategies with measurable confidence levels.
Core Capabilities
RAM-LCC-Risk Integration
Seamlessly link reliability models (MTBF, failure distributions) with lifecycle cost models (capex, opex, downtime penalties) and business risk metrics (NPV sensitivity, market volatility).
Probabilistic Forecasting
Generate probability distributions for outcomes over 5-, 10-, or 20-year horizons, not single-point estimates that hide uncertainty.
Scenario Exploration
Evaluate investment options, maintenance strategies, and operational policies under varied market conditions, regulatory environments, and technical constraints.
Full Traceability
Every prediction is traceable from component failure modes through system availability to EBITDA impact, with documented assumptions and validation protocols.
Real-World Example
Chemicals Manufacturer: 12 Expansion Pathways Under Uncertainty
Challenge: A major chemicals producer needed to evaluate capital expansion options across three sites under volatile feedstock prices, changing environmental regulations, and uncertain product demand.
Solution: We built an iDSS integrating:
- Reliability models for 6 critical equipment families
- Monte Carlo simulation of feedstock price trajectories
- Regulatory compliance cost models
- NPV analysis with probabilistic cash flow forecasts
Result: The client identified the optimal pathway with 98% confidence of superior ROI, avoiding a $40M investment in a site with hidden reliability risks.
Behind the Scenes: Tools & Methods
Software Platforms
- • AspenTech Fidelis Reliability
- • Wolfram Mathematica
- • IBM CPLEX Optimization
- • Custom Python/R analytics modules
Methodologies
- • Stochastic simulation (Monte Carlo)
- • Hybrid decomposition analysis
- • Sensitivity and uncertainty quantification
- • Multi-objective optimization
Quality Assurance
Every iDSS deliverable undergoes protocol-driven validation with client sign-off on assumptions, data sources, calculation logic, and output interpretation guidelines.