"background": "Manufacturing systems in developing economies often lack robust, field-validated methods for cost-effectiveness diagnostics. Existing evaluation frameworks are typically derived from high-income contexts and may not account for local operational constraints and resource availability. ", "purpose and objectives": "This study aimed to develop and empirically test a novel, randomised field trial methodology for evaluating the cost-effectiveness of diagnostic interventions in manufacturing plants. The primary objective was to determine the causal impact of a structured diagnostic protocol on operational cost reduction. ", "methodology": "A randomised controlled trial was conducted across a sample of manufacturing facilities. Plants were randomly assigned to a treatment group receiving the diagnostic intervention or a control group. Cost data were collected pre- and post-intervention. The primary analysis used a difference-in-differences model: C{it = \ + \1 (Treati \ Postt) + \2 Xit + \₈ₓ, where C is cost, with inference based on cluster-robust standard errors. ", "findings": "The diagnostic intervention significantly reduced average monthly operational costs by 18. 2% (95% CI: 12. 5% to 23. 9%, p<0. 01) in treatment plants relative to controls. The most substantial savings were identified in energy consumption and raw material waste streams. ", "conclusion": "The randomised field trial provides a rigorous methodological framework for cost-effectiveness evaluation in industrial settings. The applied diagnostic protocol demonstrated a statistically and economically significant positive impact. ", "recommendations": "Manufacturing enterprises should adopt structured diagnostic evaluations informed by field-trial evidence. Policymakers and engineering practitioners should prioritise interventions targeting energy and material efficiency. ", "key words": "randomised controlled trial, industrial diagnostics, cost-effectiveness, operational efficiency, manufacturing systems", "contribution statement": "This paper provides the first application of a randomised field trial methodology to evaluate engineering diagnostics in an industrial context, generating a novel dataset of causal evidence on cost-reduction interventions
Getachew et al. (Fri,) studied this question.