An Innovative Biotech Manufacturer


Leaders of a biotech manufacturer sought to reduce the high production costs resulting from small batch sizes and manual processing. They could not rigorously evaluate different scenarios related to process improvement, plant scheduling strategy, and capacity planning due to a lack of data and insufficient software tools and other resources.


The firm retained Princeton Consultants to build a manufacturing plant simulation and develop a custom scheduling optimization solution. For the baseline, the team used both the current manufacturing state and elements of the planned future state when the treatment has emerged from clinical trials and production has become commercialized. One goal of the simulation was to test the scalability of existing processes. The simulation was designed to identify resource bottlenecks and best use of capital improvements. It enabled evaluation of the impact of changes in process, resources, and sequence on throughput, timeline reliability, turnaround time, product quality and cost.

Other features included sensitivity analyses and efficient frontier tradeoffs, and the assessment of tradeoffs between different objectives such as cost vs. throughput and turnaround time vs. quality. Planned and unplanned maintenance was incorporated. Scenario comparisons and visualizations were created to support analysis. The team developed a production scheduling optimization solution to maximize throughput, minimize cycle time, minimize operator overtime, and balance resource utilization. The solution was embedded and used within the simulation, where the problem was highly complex. The optimizer was designed to be very fast because of its frequent use during the simulation.


Stochastic - manufacturing process is highly variable, making scheduling difficult

Static/Dynamic - due to the high variability of the manufacturing process and potential for product failures, the schedule is constantly updated to incorporate the latest estimates of production completion and recover from disruptions and failures

Real Time - the scheduling optimization model is used “real-time” in the simulation so it must provide sufficient performance to not limit the usability of the simulation


The manufacturing plant simulation provides a high-value decision support tool with a rigorous analytic process. The production scheduling optimization solution has demonstrably improved the quality of the simulation. The firm’s leaders can make better, data-driven manufacturing decisions, using simulated data when actual data is not available.

They can answer strategic questions such as:

  • How scalable is my existing process?
  • How does adjusting the manufacturing process affect throughput, timeline reliability, turnaround time, product quality and overall cost?
  • What are my resource bottlenecks and how can I eliminate them?
  • What is the impact of planned and unplanned maintenance?
  • How do I prioritize different product lines that utilize the same resources?
  • How do I manage conflicting goals like minimizing turnaround time and maximizing quality?
  • How do I optimize operator shifts to maximize throughput and minimize overtime?

The firm is well positioned in its market, which has recently seen drug approvals, intense investor activity and major acquisitions.