"background": "Evaluating the cost-effectiveness of interventions in manufacturing systems within developing economies presents significant methodological challenges. Conventional experimental designs are often infeasible in operational industrial settings, creating a need for robust quasi-experimental approaches tailored to engineering management contexts. ", "purpose and objectives": "This paper develops and presents a structured quasi-experimental framework for conducting cost-effectiveness diagnostics in manufacturing plants. The primary objective is to provide a methodological tool for engineers and managers to rigorously assess the impact of technical and operational interventions on production costs and system performance. ", "methodology": "The proposed framework employs a difference-in-differences design, utilising a treatment group of plants implementing a specified intervention and a matched control group. The core statistical model is a fixed-effects panel regression: Y{it = \ + \ (Treati \ Postt) + \ Xit + \ + \ + \₈ₓ, where robust standard errors are clustered at the plant level to account for serial correlation. Diagnostic tests for parallel trends and placebo interventions are integral to the framework. ", "findings": "As a working paper, this article presents the methodological framework and its theoretical underpinnings rather than final empirical results. A pilot application of the diagnostic tests indicated that a key assumption of parallel pre-intervention trends held for over 80% of the matched plant pairs, supporting the framework's validity for the intended context. ", "conclusion": "The developed framework provides a viable and rigorous alternative to randomised controlled trials for engineering economic evaluations in real-world manufacturing environments. It formalises a structured process for causal inference where experimental randomisation is impractical. ", "recommendations": "Practitioners should apply the full suite of diagnostic tests, particularly the parallel trends assessment, before relying on model estimates. Further research should validate the framework with empirical case studies across different manufacturing sub-sectors. ", "key words": "quasi-experimental design, cost-effectiveness, manufacturing systems, difference-in-differences, engineering management, impact evaluation",
Natasha Rowe-Pugh (Sun,) studied this question.