Optimizing Operational Productivity and Process Reliability in Agro-Industrial Canned Young Green Jackfruit Processing: An Integrated DMAIC and FMEA Framework

This study provides a practical and replicable improvement model for productivity and inspection reliability improvement in resource-constrained food logistics environments. This study presents an engineering-based optimization of productivity and process reliability in an agro-industrial post-harvest processing system for canned young green jackfruit using an integrated Define–Measure–Analyze–Improve–Control (DMAIC) and Failure Mode and Effects Analysis (FMEA) framework. The case-study production system experienced high raw-material loss, prolonged blanching cycles, and low inter-operator inspection agreement, which reduced process yield and logistics throughput. Root causes were identified through process mapping and fishbone analysis and prioritized using FMEA Risk Priority Number (RPN) scoring. Key improvement actions included optimizing blanching time, standardizing supplier grading to reduce material variability, and strengthening inspection decisions through Attribute Gage Repeatability and Reproducibility (Gage R&R)-based training and criteria alignment. After implementation, productivity increased by 2.31%, raw-material loss decreased by 1.90%, and inter-operator inspection agreement improved by 16%, exceeding the benchmark. Blanching time was reduced from 3 to 1 min at ≥90 °C, shortening cycle time by 67% and generating an estimated annual cost saving of USD 7200 without major capital investment. The results demonstrate that structured, risk-based improvement combined with validated measurement systems can enhance workforce consistency, process stability, and logistics flow efficiency in agro-industrial food processing environments, providing a replicable improvement model for agro-industrial processing small and medium-sized enterprises (SMEs).