A Multi-Criteria Decision-Support Framework For ERP-Based Business Process Optimization in Mid-Sized Manufacturing Enterprises

Abstract

Enterprise resource planning systems provide integrated information for manufacturing operations, but their organizational value depends on selecting process improvements through reliable and transparent decision procedures. This study developed and evaluated a multi-criteria decision-support framework for ERP-based business process optimization in mid-sized manufacturing enterprises. A multisite longitudinal quasi-experimental design compared 20 intervention enterprises with 20 matched comparison enterprises across a 12-month pre-implementation period and a 12-month post-implementation period. The final dataset included 480 ERP users, 7,512 process-month observations, and 2,592,180 transaction records covering order processing, procurement, production planning, inventory, quality, maintenance, invoicing, and financial reconciliation. The framework combined the analytical hierarchy process with TOPSIS to weight optimization criteria and rank process alternatives. Data were analyzed using descriptive statistics, mixed-effects models, difference-in-differences estimation, interrupted time-series analysis, moderation, bootstrapped mediation, and structural equation modeling. ERP integration quality averaged 4.08, data quality averaged 4.12, and decision-support effectiveness averaged 4.04. Compared with matched enterprises, intervention enterprises achieved adjusted reductions of 6.50 hours in order-processing time, 1.33 days in procurement cycle time, 3.90 hours in production-release time, and $2.90 in administrative cost per transaction. Schedule adherence increased by 6.90 percentage points, forecast error declined by 3.90 percentage points, and inventory turnover increased by 0.74 annual turns. Transaction errors declined by 31%, stockouts by 27%, emergency purchases by 30%, late supplier deliveries by 24%, production defects by 20%, rework events by 23%, and compliance violations by 34%. Data quality significantly strengthened the integration–optimization relationship, while decision-support effectiveness mediated 48.6% of the total integration effect. The structural model explained 71% of the variance in business process optimization. AHP–TOPSIS priorities strongly corresponded with observed improvements, ρ = .93. The findings demonstrated that ERP-based optimization was most effective when integrated technology, reliable data, structured prioritization, organizational readiness, technological capability, and sustained process adoption operated as a coordinated manufacturing capability.