Novonesis presents a comprehensive overview of measurement system analysis methodologies tailored to complex industrial processes.
Lene Bjørg Cesar, representing the application research team in household care, shares practical insights into how data-driven approaches transform product development and quality assurance.
The company, formed through the 2024 merger of Christen Hansen and Novo Times, operates across more than 30 industries with over 10,000 employees, making standardized yet flexible MSA approaches essential.
Cesar's background in agricultural science and applied statistics from DTU informs her focus on three core pillars: structured data capture using FAIR principles, data analysis, and design of experiment methodology.
The presentation illustrates MSA applications across three distinct scenarios. Quality control MSA examines pre-production and production products, measuring activity levels through validated assays while identifying measurement system sources of variation.
The baking process application reveals how industrial bread production involves approximately 50 ingredients and multiple process stages—mixing, bench time, proofing, and baking—each introducing potential variation.
By systematically analyzing these stages, researchers identify which factors significantly impact enzyme performance and which can be controlled through process standardization.
The laundry performance testing demonstrates how MSA evaluates enzyme efficacy across different soil types—grass, tomato, chocolate, and oil—revealing that variation patterns differ significantly by soil type and production round.
A crucial theme throughout is the necessity of involving laboratory personnel and production teams in the MSA process. Rather than imposing statistical conclusions, Cesar advocates for collaborative investigation, direct observation, and transparent communication about why process changes matter.
This human-centered approach to statistical analysis ensures sustainable adoption of improved methodologies and builds organizational understanding of variation sources and their implications for product quality and performance.
From the meeting "Certain when Uncertain".
