Reducing “Faltantes Facturados No Enviados” (FFNE) from 1.10%–4.01% to ≤1% in a Beauty Distribution Operation

Abstract

This project applies Lean Six Sigma methodology to reduce “Faltantes Facturados No Enviados” (FFNE) billed but undelivered or misdelivered orders, within a large-scale order fulfillment operation. Contractually, the FFNE rate must remain below 1%; however, data show that since the beginning of warehouse operations, this indicator has consistently exceeded the contractual requirement, fluctuating between 1.10% and 4.01%. Statistical control analysis confirms that the process is currently out of statistical control, showing high variability and recurring nonconformities across campaigns. From the analysis of operational data and customer claims, makeup products were identified as the category with the highest incidence of errors, particularly lipsticks and eyeliners. Between the second quarter of 2023 and the third quarter of 2025, 2,253 claims were generated from 114,771 orders, accumulating $14,317 in associated costs. These findings demonstrate the magnitude and financial impact of the problem on the organization’s performance and customer satisfaction. A comprehensive root cause analysis revealed multiple underlying factors, including manual handling errors, product similarity, and verification and traceability gaps. Additionally, operational wastes were classified according to Lean Six Sigma principles into categories of high, medium, and moderate impact to prioritize improvement actions. The project aims not only to reduce the FFNE rate below 1% to meet contractual requirements but also to enhance traceability and process control throughout the fulfillment and dispatch stages, reducing non–value-added activities and increasing operational efficiency. These efforts directly contribute to Sustainable Development Goals 8 (Decent Work and Economic Growth) and 9 (Industry, Innovation and Infrastructure). Keywords: Lean Six Sigma, FFNE, Order Fulfillment, Process Improvement, Operational Waste, Traceability, Root Cause Analysis