Multi-factorial Lean Six Sigma product optimization for quality, leanness and safety

Abstract

Purpose – The purpose of this study is to provide a method for Lean Six Sigma (LSS) improvement projects that may aid LSS practitioners to plan and conduct robust and lean product/process optimization studies for complex and constrained products, such as those encountered in food industry operations. Design/methodology/approach – The technique is to be used for replicated LSS product experimentation on multiple effects elicited on several product traits. The authors compress replicated information reducing each response to simpler lean and robust median and range response components. Then, the desirability method is utilized to optimize concurrently location and dispersion contributions. Findings – The suggested method is demonstrated with a case study drawn from the area of food development where cocoa-cream filling for a large-scale croissant production operation undergoes a robust screening on two crucial characteristics – viscosity and water activity – that influence product and process performance as well as product safety. Originality/value – The proposed method amalgamates concepts of fractional factorial designs for expedient experimentation along with robust multi-factorial inference methods easily integrated to the desirability function for determining significant process and product effects in a synchronous multi-characteristic improvement effort. The authors show that the technique is not hampered by ordinary limitations expected with mainstream solvers, such as MANOVA. The case study is unique because it brings in jointly lean, quality and safety aspects of an edible product. The showcased responses are unique because they influence both process and product behavior. Lean response optimization is demonstrated through the paradigm.

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