Confronting Inflation and Improving Business sustainability in the Mechanical Industry

Abstract

The study was conducted to examine how mechanical manufacturing firms can strengthen business sus tainability under rising inflation by integrating project management practices, lean operational methods, and value-based pricing models. The objective of the work was to develop a unified framework that helps firms confront inflation-driven increases in raw material, labor, and energy costs while maintaining compet itiveness and product quality. The research was implemented by analyzing Project Management Metrics (PMM), Lean Management Metrics (LMM), and the Hedonic Pricing Model (HPM) as complementary sys tems capable of mitigating inflationary pressures. A document analysis and quantitative modelling approach were used, including hedonic regression to iso late quality-adjusted prices from pure inflation effects, project control metrics to assess cost and schedule performance, and lean flow metrics to evaluate operational efficiency. The results indicated that hedonic pricing enabled firms to identify and prioritize high-value features that justify price adjustments in inflation ary markets. Project management metrics improved financial control by highlighting budget overruns at tributable to inflation, while lean metrics minimized waste and improved production flow, reducing the op erational impact of rising input costs. The combined use of PMM, LMM, and HPM created a structured method for forecasting budget needs, optimizing resource allocation, and reducing inflation-driven ineffi ciencies. The conclusions demonstrated that an integrated value-based approach offers a resilient strategy for me chanical manufacturers operating in inflationary conditions. The system improved the alignment between market value, project execution, and operational performance, enabling firms to maintain profitability de spite inflation. Future development of the approach may include applying advanced regression models, ma chine learning algorithms, and time-series forecasting to enhance predictive accuracy for inflation trends, cost fluctuations, and long-term pricing