Assessment of the overall economic effect of implementing predictive maintenance systems on the life cycle cost of assets and the competitiveness of enterprises

Abstract

The article is devoted to the quantitative assessment of the overall economic effect of implementing predictive maintenance systems on the life cycle cost of industrial assets and the competitiveness of enterprises in the context of the transition to Industry 4.0. The aim of the study is to demonstrate, based on the integration of financial and technical indicators, how the transition from reactive and planned preventive maintenance to condition-based maintenance models transforms the cost structure, risk profile, and market positions of companies. The empirical base consists of a sample of 245 enterprises in mechanical engineering, the oil and gas sector, energy, and metallurgy for the period 2018–2023, for which more than 12 thousand primary documents on maintenance, downtime, and digitalization projects were analyzed; data from public reporting, confidential management information, and market sources were used. The methodology relies on a modified DCF approach and a life cycle cost (LCC) model, econometric analysis of panel data, clustering by the level of digital maturity, ABC costing, Monte Carlo simulation, and statistical tests to verify the significance of results. It is shown that the implementation of predictive maintenance reduces direct maintenance costs by about a quarter, unplanned downtime by more than half, revenue losses from downtime almost threefold, and is accompanied by an increase in OEE and operating profitability by tens of percent; over a 10-year horizon, the total life cycle cost of assets decreases by approximately 17–18% due to a sharp reduction in repair and disposal costs. The investment analysis of a sample of implemented projects demonstrates positive NPV values, an average IRR significantly higher than the WACC, and payback periods of about 2–3 years, with noticeable variability in results determined by data quality and the level of IT infrastructure. It was found that digitalization leaders enterprises with extensive PdM coverage show higher revenue growth rates, ROIC, asset turnover, and market share increase compared to conservatives, and the effect has a threshold nature: economic efficiency increases sharply when predictive monitoring covers at least 40–50% of critical equipment. It is concluded that predictive maintenance systems are a key element of value management strategy, transforming the maintenance and repair function from a cost center into a source of sustainable competitive advantage and requiring integration into the financial planning and asset management framework.