Decision support in water laboratories by data-driven Lean Six Sigma

Timely and accurate analytical data from water testing laboratories is critical for the operational management of water treatment plants. However, public laboratories operating under ISO/IEC 17025 often face systemic inefficiencies, resulting in prolonged Turnaround Times (TATs) and high analytical variation. These delays severely hinder utility managers’ ability to make proactive, evidence-based decisions about water treatment processes. This paper presents a data-driven decision-support framework based on the Lean Six Sigma (LSS) methodology to optimize information flow and operational efficiency. The empirical study was conducted at a central water testing laboratory affiliated with a public utility in Egypt. Utilizing the structured DMAIC (Define, Measure, Analyze, Improve, Control) approach, the research employed statistical process control and process capability indices (Cp, Cpk) to analyze operational data and identify root causes of delay and re-testing. Interventions, including 5 S workplace organization and standardized workflow redesign, were implemented to eliminate non-value-added activities. The results demonstrated a paradigm shift in laboratory performance; pre-analytical errors were reduced by 77.8%, and the Sigma Level for specific critical parameters (Lead (Pb), Turbidity, and Fluoride (F⁻)) improved significantly, reaching up to 5.9. By bridging ISO 17025 compliance with Lean Six Sigma, this study presents a highly scalable integration framework. Most importantly for decision-makers, Turnaround Time (TAT) was reduced by 75%, and a 10% chemical dosing optimization was targeted. This enhanced operational agility empowers managers to prevent water quality deviations and lays the groundwork for real-time monitoring applications in public utilities. The study provides a practical, scalable blueprint for public utilities to leverage statistical data analytics and LSS as robust decision-support mechanisms without the immediate need for complex IT infrastructure.