<![CDATA[General Background Pesticide manufacturing plays a critical role in supporting agricultural productivity, yet complex production systems frequently generate operational waste and product defects. Specific Background PT XYZ produces multiple pesticide variants, with powder pesticides showing the highest defect proportion during the October 2024–September 2025 period, indicating inefficiencies within the production process. Knowledge Gap Despite recurring defects and extended lead time, systematic waste identification and structured failure risk prioritization had not been comprehensively applied in this production context. Aims This study aimed to identify dominant waste types, evaluate process performance, and formulate improvement recommendations using Lean Six Sigma integrated with Failure Mode and Effect Analysis. Results The analysis identified defects, waiting, transportation, and environmental health and safety as dominant wastes. Lead time was reduced from 763.11 minutes to 681.38 minutes through the elimination of non-value-added activities. Process performance showed an average DPMO of 37,519.68 with a sigma level of 3.28, alongside an increase in Process Cycle Efficiency from 59.55% to 66.69%. FMEA results indicated the highest Risk Priority Numbers were associated with non-standard product weight and product clumping caused by operator inconsistency and suboptimal machine performance. Novelty This study presents an integrated application of Lean Six Sigma and FMEA to map waste sources and prioritize failure risks within a pesticide powder production system. Implications The findings provide structured improvement recommendations, including operator training, standardized machine settings, and routine maintenance, offering a data-driven reference for manufacturing process optimization in similar industrial settings. Highlights: Defect-related losses constituted the largest proportion of inefficiencies in the studied manufacturing flow. Quantitative performance metrics demonstrated measurable reductions in processing time and defect opportunity rates. Risk prioritization revealed machine condition and operator consistency as dominant contributors to quality deviation. Keywords: Pesticides, Waste, Lean Six Sigma, FMEA]]>
