{ "background": "Evaluating the cost-effectiveness of interventions in complex manufacturing systems presents significant methodological challenges, particularly in resource-constrained industrial settings. Existing frameworks often lack the rigour to isolate causal effects from confounding operational variables, leading to unreliable diagnostics for capital investment and process re-engineering decisions.", "purpose and objectives": "This article presents a novel quasi-experimental framework designed to diagnose cost-effectiveness in manufacturing systems. The primary objective is to provide a structured methodology for engineering practitioners to robustly measure the impact of technical interventions on production costs and output, controlling for external market and supply chain fluctuations.", "methodology": "The proposed framework employs a difference-in-differences design, leveraging panel data from treatment and control units within a plant or across comparable facilities. The core econometric model is specified as $C{it} = \\alpha + \\beta1 (Treati \\times Postt) + \\beta2 X{it} + \\mui + \\lambdat + \\epsilon{it}$, where $C{it}$ is unit cost, $Treati$ and $Postt$ are binary indicators, $X{it}$ are time-varying controls, and $\\mui$ and $\\lambda_t$ are unit and time fixed effects. Inference is based on cluster-robust standard errors at the production line level.", "findings": "Application of the framework to a pilot study demonstrated its operational feasibility, revealing that the methodological approach successfully isolated intervention effects from seasonal demand variations. A key diagnostic output indicated a central tendency where approximately 70% of the observed cost variance was attributable to the engineered intervention, with other factors accounting for the remainder.", "conclusion": "The developed framework provides a technically robust and practicable methodology for cost-effectiveness analysis in industrial engineering contexts. It addresses a critical gap in applied engineering economics by offering a structured, quasi-experimental approach suitable for the dynamic conditions of manufacturing systems.", "recommendations": "Practitioners should adopt this framework during the planning phase of any process intervention to establish