The manufactured products that rely on molten Acrylonitrile Butadiene Styrene (ABS) via Tederic machine (450t) suffer from some types of defects such as black dots, shrinkage, and bubbles, which weaken the competitiveness. Therefore, this work proposes the Jidoka recruit’s network system (JRNS) to set optimal operating parameters to reduce the processes’ output variations and cut down on total process wastage autonomously. The suggested JRNS consists of two sequential stages. The first stage in front propagation relies on response surface methodology (RSM) in classifying the significant operating parameters from 15 candidates experimentally (DOE) to identify the most crucial one. The second stage predicts the processes’ deviation by integrating two meta-heuristic methods called harmony search (HS) and weighted superposition attraction (WSPA) in the backpropagation to automatically reset the operating parameters to keep the product within the standard specification to avoid 11 defect chances. JRNS is innovative and has been used to upgrade autonomous control for enhancing the Tederic machine (450t) process to reduce 70.98% of molding defects. The JRNS (RSM, WSPA+HS) increases the operation efficiency from 94% to 100% and reduces the defects per million opportunity to the six sigma scale.