Accelerate product development testing by digitalizing formulator knowledge

by Kristin Wallace, Marlene Cardin – ProSensus

Product development (or product formulation) has played an important role within the rubber industry for many years; manufacturers have continually strived to produce new or improved products, at lower cost, and often with fewer or alternative raw materials. And now in the era of Industry 4.0, big data and digitalization, intelligently gleaning value from research and development data is a top priority for manufacturers who wish to remain competitive in the industry. Compounding the increasing pressure to more efficiently develop new products is a very real threat many manufacturers are currently facing: potentially watching years of cultivated knowledge walk out the door as the baby boomers continue their exodus from industry and enter retirement. Of course, knowledge transfer is a critical task in supporting any staff transition (or in expanding resources); but in the research area of product development, it can be an overwhelming and nearly impossible task.

Product formulation knowledge has, by convention, typically remained an expertise held by a small number of highly experienced individuals within organizations. These individuals are often chemists who have amassed an impressive amount of both physical knowledge (countless datasets) and intrinsic experience (under-documented subject matter “know-how” and insight) through long careers spent studying relevant scientific principles and conducting physical experiments. This article introduces the ProSensus product development framework, describes key technical concepts employed, highlights some past projects and summarizes the phased project approach.

ProSensus’ approach to rapid product development (RPD) combines existing experimental or research and development data with subject matter expert knowledge through multivariate modeling and constrained optimization, empowering clients to formulate their desired product(s) faster. In addition to reducing lengthy and expensive physical experimentation, the developed models and customized software solutions also broaden product development resourcing across an organization, while providing a tangible (and evolving) knowledge retainment platform.

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