Models of investment payback in digitalization and automation of production capacities under demand uncertainty, resource price volatility, and logistics risks

Abstract

The article is devoted to the development of models for evaluating the payback of investments in the digitalization and automation of production capacities under conditions of high demand uncertainty, resource price volatility, and logistics risks. The aim of the study is to compare traditional deterministic approaches to investment analysis with adaptive probabilistic models that integrate the real options method and scenario modeling, as well as to quantitatively assess how digitalization affects the risk–return profile of industrial projects. The empirical base consists of financial and management reporting data from 145 industrial enterprises in mechanical engineering, metallurgy, and the chemical industry, covering more than 300 large-scale automation and digital transformation projects implemented between 2018 and 2023. The research methodology includes a modified DCF approach with Monte Carlo simulation (10,000 iterations for typical project models), evaluation of real options for expansion, abandonment, and technology switching using the Cox–Ross–Rubinstein binomial model, calculation of cash flow volatility based on GARCH models, correlation-regression analysis of the influence of macroeconomic and industry-specific risk factors, as well as stress testing of perfect storm scenarios involving simultaneous shocks in demand, resource prices, and logistics. It is shown that digital projects demonstrate higher NPV and shorter payback periods compared to traditional modernization, particularly under pessimistic scenarios, with a reduction in the probability of losses by almost 40%; the effect of damping logistics and price shocks is identified due to reduced cost elasticity relative to resource prices and a gentler EBITDA decline trajectory amid supply chain degradation. The analysis of the NPV distribution structure revealed a decrease in tail risks and an increase in the Sharpe ratio of the portfolio of digital projects, indicating an improved risk–return relationship. The study concludes that neglecting the option value of managerial flexibility leads to systematic underestimation of the efficiency of digital investments, whereas comprehensive automation combined with deep digitalization of supply chain planning and management serves not only as a driver of productivity growth but also as a tool for hedging operational risks, requiring a revision of corporate and banking approaches to investment project evaluation.