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New products and processes don't have the benefit of relevant historical data that can be used as a guide. Researchers therefore need to supplement their subject matter knowledge with data analytics to drive faster, better-informed decisions from the existing data and to solve problems correctly the first time.
JMP provides the ability to manage project dependencies and timelines. Missed milestones add to work and stress levels and may lead to late product launches and high product costs (or low margins) once on the market.
Scientists investigate manufacturing processes with an emphasis on quality and regulatory compliance while managing trade-offs in speed and cost.
All along the R&D and process development path, there are measurements to determine product quality and yield along with process capability. Scientists collect and assess data from laboratory methods, including highly sophisticated instruments that output large amounts of data.
Actively manipulate factors according to a pre-specified design to quickly and easily gain useful new understanding.
Separate common and special causes to assist process analysis efforts, including problem investigations, out-of-control conditions and ongoing monitoring for stability.
Assess batch poolability, establish expiration dating and easily calculate confidence limits and crossing times – all in adherence to ICH Q1 guidelines.
Identify and evaluate all sources of variability with respect to a finished product's quality parameters.
Analyze precision, accuracy, linearity, bias and reproducibility. Fit curves and compare models for a wide range of sigmoidal responses (4p, 5p). Efficiently assess parallelism for relative potency and apply streamlined methods for cut point determination.
Find the sweet spot in a design where performance is minimally sensitive to variation for all critical quality attribute (CQA) goals in your process, following ICH Q11 guidelines.
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