Advanced Manufacturing Technology Investments Under Uncertainty: An Integrated Calibration, Implementation Conditioning, and Downside-Risk Framework

Surveys report that only 6.7% of manufacturing firms apply formal risk analysis to Advanced Manufacturing Technology (AMT) investment decisions, yet AMT implementation failure rates of 50–75% persist. This gap between evaluation practice and investment reality produces systematic mispricing. This paper develops the Calibration-Adjusted Implementation-Conditional (CAIC) framework, which integrates five analytical stages into a single pipeline: a discounted cash flow (DCF) baseline with three-stream cash flow decomposition; stream-differentiated calibration of expert parameter intervals; Monte Carlo simulation with Cholesky-decomposed correlation; implementation conditioning through a critical success factor (CSF) logistic probability model; and downside-risk quantification via Value at Risk (VaR) and Conditional Value at Risk (CVaR). Applied to a $25 million Flexible Manufacturing System (FMS) investment representative of automotive Tier 1 suppliers, CAIC produces a decision reversal from the DCF baseline recommendation of accept (net present value, NPV=+6.97M) to the conditioned recommendation of reject (E[NPV]=−2.79M, P(NPV<0)=58.4%, CVaR95=21.1M). The $9.8 M swing in expected value is driven by implementation conditioning rather than calibration, identifying organizational readiness as the decisive parameter. Sensitivity analysis confirms decision robustness. The framework uses inputs already generated in AMT investment review processes and is implementable with standard simulation software.