NOVEMBER 20239TECH OUTLOOKIn the SCA, the minimum and maximum cumulative variation is calculated for a CQA. Each unit operation may have an intermediate specification in which the maximum and minimum cumulative variation can be compared. Ultimately, by continuing to accumulate the variation for all unit operations, these overall cumulative maximum and minimum variation values are compared to the finished product specification range (Figure 2). Further analysis of potency on stability would be conducted by stacking any degradant generation to the finished product equation. Figure 2: SCA Equation for Finished Product PotencyFinished Product Potency as % of Target = (Laminate Potency * Patch Area as % of Target)Where, Laminate Potency = Blend Potency * Coat Weight as % of TargetThe difference between the potential stacked maximum and minimum values and the respective finished product upper and lower specification limits (USL and LSL respectively) can be calculated. This difference represents the potential for the product to not comply with specifications at any intermediate point and for the finished product. This analysis is displayed in Table 1.Table 1: SCA Maximum and Minimum Variation Comparison to USL and LSLOnce all the data is input for the maximum and minimum variation ranges, a histogram depicting the largest contributors to potential variation via control ranges can be created to determine what sources of variation are the largest contributors (Figure 3):Figure 3: Histogram for Sources of VariationIn parallel to the variation analysis using specification ranges, a historical maximum and minimum variation should be accumulated. This technique is beneficial as it allows visualization of the potential to reduce raw material specification ranges. The histogram, as shown in Figure 3, would provide the largest sources of variation first, which when coupled with the raw material historical performance to specification data, provides an effective mode to enhance the overall control strategy without increasing the chance of failing upstream raw material specifications. This assumes adequate historical data has been captured to accurately represent the true variation of that control. This is depicted in Table 2. Table 2: SCA Maximum and Minimum Variation Comparison to USL and LSL with Historical VariationTable 3 displays the final revised variation potentials after enhancing the control strategy using historical variation to tighten specification ranges. In this case coat weight and patch area controls were reduced to align closer with historical ranges.Table 3: SCA Revised Variation Potential with Enhanced Control StrategyIn conclusion, SCA provides a low-risk approach for enhancement of control strategy that allows for greater ability to meet product specifications. The SCA approach defines the largest potential sources of variation and represents a measure of process ability to meet specifications for each process step
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