The profiles were smoothed somewhat before recording peak levels in an effort to account for the fact that many jobs can be
done slightly earlier or later than dictated by the schedule. Figure 6 shows the peak and average labor requirement for the
four labor pools for the high and low load scenarios. The disparity between peak and average levels indicates that a significant
benefit can be gained by cross-training.
Figure 6. Peak and average labor requirements. A: labor pool levels (high); B: labor pool levels (low).
We also investigated the effect of staffing levels on QC turnaround time. Additional resource studies were designed to quantify
the turnaround time by measuring how fast the laboratory processed different assays. The laboratory cycle time is measured
from when the sample is taken to when the test (or set of tests) is complete. This includes any time the sample spends waiting
to be processed plus the actual duration of the test, including any reprocessing required for failed tests. Completion of
the release testing is key in particular because it is the final step of the process at the site. Four staffing scenarios
were tested, with Case A being well-staffed and Case D being very lean.
Figure 7. Quality control (QC) turnaround time for different staffing level cases
Figure 7 illustrates the behavior of QC turnaround time for these four cases. As the results indicate, the turnaround time
initially decreases rapidly with increasing labor. With Case B, however, we have reached a point of diminishing returns, because
the further increase in labor supply for Case A yields little improvement. Figure 8 shows data for the 10 longest turnarounds
over any particular timeline, contrasted with the average turnaround time. As these data indicate, we see a marked improvement
as labor availability is increased. In Case D, the minimum labor case, the outlying turnaround times can be significantly
more than twice the average. In the other three cases, we see much better performance on average, with Cases A and B indicating
significantly faster turnaround times only on about 20% of the timelines. Still, these data indicate that substantial variability
in turnaround time may be expected on rare occasions.
Figure 8. Ten longest turnarounds contrasted with average turnaround by case
We have described the results of a mathematical programming-based approach to modeling a large-scale biologics facility for
design and analysis of the process. The method permits a Monte Carlo type treatment of stochastic parameters, even for very
large problem sizes. As the results have shown, including many aspects of daily plant operation in the analysis allows the
design to be fine tuned to increase capacity, anticipate and avoid operational difficulties, and provide insight into the
required level of many critical support services, such as CIP and labor. The results for many different cadences show that
because of process variability, notably in titers and cycle time, the optimum cadence must balance throughput with lots dropped
because of excessive hold time. Furthermore, this mathematical programming approach permitted the analysis of other biologics
products by industrial users, without any change to the customized algorithm.
Donald L. Miller is the co-founder and Derrick Schertz is a senior project engineer at Advanced Process Combinatorics, Inc., West Lafayette, IN. Christopher Stevens is an associate director of process support at Bristol-Myers Squibb, Inc., Devens, MA, 978.784.6413, firstname.lastname@example.org
Joseph F. Pekny is a professor of chemical engineering and interim head of industrial engineering at Purdue University, West Lafayette,
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