Project Cost Performance Prediction via Monte Carlo Simulation
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Solution Overview
Problem
Engineering design projects face challenges in quantifying the risk to cost performance due to volatility in design requirements and aggressive schedules, which have not been effectively addressed by existing technologies.
Innovation Solution
A computer-implemented method and apparatus that uses historical data and Monte Carlo simulations to predict the cost performance index (CPI) by analyzing lifecycle overlap and requirements volatility, allowing for the quantification of risk and preparation of mitigation strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If design requirements are released early to enable aggressive project schedules, then project productivity is improved, but cost performance deteriorates due to requirements volatility
Solution Approach 1:
The patent applies preliminary action by performing Monte Carlo simulations and risk analysis before project execution to predict cost performance outcomes. The system calculates expected CPI values based on lifecycle overlap and requirements volatility metrics, allowing project managers to identify potential cost issues before they materialize, thereby preparing mitigation strategies in advance while maintaining aggressive schedules
Solution Approach 2:
The patent implements feedback by continuously monitoring lifecycle overlap and requirements volatility metrics during project execution, comparing actual cost performance against predicted CPI values from simulations. This feedback loop enables dynamic adjustment of project parameters to maintain cost performance while preserving schedule agility
2Duration of action of moving object
If hardware and software design begin before design requirements are complete, then project duration is reduced, but manufacturing precision deteriorates due to incomplete specifications
Solution Approach 1:
The system performs preliminary risk assessment and cost performance prediction by calculating lifecycle overlap metrics that quantify the extent to which design work begins before requirements are complete. Monte Carlo simulations are run in advance to predict the impact of early design initiation on final cost performance, allowing teams to understand the precision risks before committing to compressed schedules
Solution Approach 2:
The patent applies parameter changes by using the lifecycle overlap metric as a controllable variable that can be adjusted to balance project duration against design precision. The system allows dynamic modification of schedule parameters while monitoring their impact on predicted cost performance, enabling optimization of the trade-off between early delivery and specification accuracy
3Measurement precision
If Monte Carlo simulations are performed to predict cost performance, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts the essential risk factors from complex project environments by focusing on two key metrics: lifecycle overlap and requirements volatility. The Monte Carlo simulations are configured to model only these critical parameters rather than attempting to simulate every possible project variable, thereby achieving meaningful prediction accuracy while keeping the model manageable and interpretable
Data Source
AI summary
A computer implemented method and model to perform project scenario simulations to facilitate understanding the risks associated with various factors such as, for example, project schedules and volatility in the design requirements/specifications. In one example, the model includes a statistical definition of a relationship between the cost performance index for the model and project parameters, such as requirements volatility and lifecycle overlap.