Integrated Decision Support Systems (iDSS)
Living models that link RAM to LCC and EBITDA
Definition
An iDSS is a computational framework that integrates reliability physics, maintenance economics, and financial outcomes into a unified model—enabling executives to ask "what if" questions that span technical and business domains.
What Makes It Different
Traditional business intelligence systems display outcomes. An iDSS reveals causal mechanisms.
Typical Dashboard
- Shows: "Equipment availability = 87%"
- Cannot explain why
- Cannot simulate alternatives
- Disconnected from financial impact
Knar iDSS
- Shows: Which failure modes drive downtime
- Simulates: Impact of inspection intervals
- Quantifies: EBITDA sensitivity to availability
- Optimizes: Maintenance spend vs. production risk
Core Components
RAM Model Layer
Probabilistic failure modeling using Weibull, Markov, or Monte Carlo methods. Accounts for degradation mechanisms, operational stress factors, and maintenance effectiveness.
LCC Integration Layer
Links reliability outcomes to cost streams: maintenance labor, spare parts, lost production, emergency response. Enables NPV optimization across maintenance strategies.
Business Impact Layer
Connects operational performance to EBITDA, cash flow, and strategic KPIs. Allows executives to evaluate technical decisions in business terms.
Scenario Engine
Enables "what if" exploration across thousands of combinations: feed variability, maintenance schedules, market conditions, capital investments.
Case Study: Chemical Processing Facility - Sulfuric Acid Regeneration Unit
Challenge
A chemical processing facility operates a sulfuric acid regeneration unit where catalyst degradation, feed variability, and heat exchanger fouling create uncertain production capacity over a 5-year planning horizon. Financial models assumed constant throughput; operational reality was far more complex.
Solution
Built an iDSS using AspenTech Fidelis that:
- Modeled catalyst decay kinetics as a function of feed sulfur content and operating temperature
- Integrated heat exchanger fouling rates into capacity forecasts
- Linked production variability to sulfuric acid pricing and contract delivery obligations
- Optimized turnaround timing against NPV of lost production vs. maintenance cost
Outcome
Executive team could evaluate strategic decisions—turnaround schedules, catalyst replacement strategies, contract negotiations—with full visibility to technical risk and financial consequence. The model is now maintained internally and updated quarterly.