Financial evalutaion of advanced technologies in manufacturing.

This dissertation formulates a comprehensive financial assessment framework for the investment in advanced manufacturing technologies namely AI/ML, Manufacturing Execution Systems (MES), and Generative AI amidst conditions of uncertainty. It mitigates the shortcomings inherent in deterministic capital budgeting by incorporating models that account for benefit uncertainty, the temporal lag in value realization, and the path-dependent nature of implementation outcomes. Risk-adjusted valuation is actualized through metrics based on discounted cash flow (DCF) analysis (NPV/IRR), supplemented by Monte Carlo simulation to produce distributions of outcomes and measures of downside risk. The analysis distinctly differentiates economic desirability from financial viability by evaluating projects against specific affordability and capacity constraints pertinent to the firm. Managerial flexibility is integrated through Real Options reasoning (including deferral, phased scaling, and abandonment), permitting the treatment of adoption as a series of contingent decisions rather than a singular commitment. Empirical calibration is conducted utilizing financial statement data from FY2020–FY2024 for three diverse firms (Recordati, DiaSorin, Philogen) to establish comparable financial profiles. The framework yields joint outputs: risk-adjusted viability assessments, feasibility categorizations, and financially prudent implementation roadmaps categorized by firm and technology. The findings elucidate how identical categories of technology may necessitate divergent optimal sequencing strategies contingent upon capital limitations and financial robustness. By interlinking valuation, feasibility, and phased roadmapping within a cohesive decision framework, the dissertation addresses a recognized gap between financial evaluation and implementation strategy in the realm of digital manufacturing. The key contribution is a decision-making protocol that is appropriate for investment committees, facilitating a transparent comparison of projects that are attractive yet infeasible, feasible yet low in value, or best approached through incremental adoption.