Discrete Event Simulation Offers Real Value

Pharmaceutical plants gain design, troubleshooting and other insights from models.

By Bernard McGarvey, Richard Dargatz and David Dzirbik, Eli Lilly and Company

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Chromatography operations. The plant has chromatography steps where cycle times vary within a given column pack and from pack to pack. The models incorporated equations to predict column cycle times as a function of run number. Simulations assessed potential capacity increase options such as raising column resin loading and whether a particular chromatography step needing an additional column also would require another mainstream tank.

Results weren't always as expected.

For instance, column loading analysis showed that raising the column resin loading wouldn't achieve the anticipated capacity increase -- due to charge preparation and charge tank limitations. We identified and simulated potential changes to charge preparation and charging operations to gain additional capacity.

Mainstream tank analysis revealed that the existing tank configuration could achieve target capacities if an additional column were added, and that changing elution logic constraints instead of adding an additional mainstream tank could provide more capacity if needed.

Support systems. Thorough analysis and modeling are necessary to ensure such systems won't limit product operations. DES has proven very useful for assessing shared support systems such as CIP, buffer makeup and high-quality water.

DES also has helped increase the effectiveness of scheduled maintenance by illustrating how planned downtime windows move through the plant.

Now, let's look at two examples of how DES was used for a product supply chain analysis and an equipment sizing and load analysis.
Background: Inventory was being built for potential launch of a new final product. The bulk material has re-evaluation timing -- the product must be re-sampled and tested to verify it's still acceptable for use. The tests can be costly and put a substantial resource load on the analytical lab. With the inventory build, concerns arose that a large quantity of lots could go past their re-evaluation dates. Extending the re-evaluation timing was an option but the impact of the potential extended timing on reducing lot re-evaluation was unknown.

Actions: We constructed a discrete event model to simulate the steps associated with product generation, inventory management and order fulfillment. At a high level, the model feeds items along two paths: those representing demands/product orders travel one path while those representing product flow travel the other. The paths converge, matching specific lots to specific demands based on first-in-first-out along with meeting the particular demand requirements. Product enters the model in two ways -- via initial inventory and a product supply schedule, both of which are read into the model at initiation. Thirteen sites order the product; one requests full product lots while the others want specific increments smaller than a full lot. So, the model must track residual lots. Before orders are filled, a check is made to verify that each lot involved is within dating requirement. Lots that go past dating requirements pass through a retesting loop before being returned to inventory. Checks again are made on dating requirements after hold times at the sites to simulate site inventories. Figure 1 shows a simplified flow schematic of the process.

Inputs to the model included:
• initial product inventory -- lot numbers, dating information and quantities for each specific lot in inventory;

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