Reliability Engineering
Reduced to a complete field of engineering as a result of World War II, reliability engineering is the process of building things that are more reliable. There are many variations of this practice but there are a few that formalize methods through robust engineering discipline.
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Reliability equals 1 - the probability of failure. For multiple points of failure, the probability of failure is the probability that any critical component fails. This can be computationally expensive, especially in an optimization problem.
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Reliability optimization is often framed as a constrained optimization problem. For a given reliability limit, minimize cost. Reliability often requires massive Monte Carlo computations. Quante Carlo has proprietary technology enabling a thousand fold efficiency improvement.
Reliability

System
In structural, electrical and mechanical engineering, systems have many points of failure based on many interacting factors. Quante Carlo (QC) enables unpresented optimization of system reliability with respect to component failure.

Power Grid
Security Constrained Optimal Power Flow (SCOPF) is the challenging problem of finding the optimal way to distribute power while maintaining a contingency constraints in the event of line failures .

Site Reliability Engineering
In DevOps, compound service level objectives (SLOs) must be calculated repeatedly in real-time without adding additional overhead to the network itself. Quante Carlo enables these calculations at scale.

Network
Quality of Service (QoS) refers to a set of technologies that are designed to ensure that networks are efficient and reliable. QC is used to route network traffic to minimize resource cost without sacrificing reliability.

Supply Chain
On Time In Full (OTIF) is a metric used to measure the reliability of a supply chain. These metrics can be optimized at the warehouse and store level. Quante Carlo enables dynamic updating of optimal stocking targets that maximize profit with respect to established OTIF criteria.

Valuation Adjustment
As a result of the 2008 financial crisis, institutions have been relying on Monte Carlo to calculate highly dimensional risk such as Potential Future Exposure (PFE) for multiple counterparties. QC has been used to reduce these calculations from hours to seconds.