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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• demand schedule -- monthly orders from each customer;
• product supply schedule -- lot numbers, dates and quantities for product supplied into inventory;
• re-evaluation duration -- number of months from manufacture date until product needs to be re-evaluated; and
• site inventory hold times.
• Outputs from the model included:
• demand filled table -- specifics on which product lots and quantities were used to fill each order;
• lots past re-evaluation dates -- lot numbers, quantities and timing on when lots went past re-evaluation dates; and
• final inventory -- attribute data for lots still in inventory at the end of the simulation.

Results/output: We performed multiple simulations, looking at different demand and supply scenarios as well as the impact of potentially extending the re-evaluation duration. Output results, such as number of lots going past re-evaluation dates per year, were captured for each scenario and compared. We then calculated the re-evaluation testing loads and costs for the analytical labs for the different scenarios. Figure 2 shows the impact on "cushion time" (the days remaining from when an order was filled until that lot goes past re-evaluation timing) for two re-evaluation timing scenarios.

Background: A plant was planning to install a new purified water system, including a surge tank and distribution system, to upgrade the quality of water used in one area of the site. The primary option was to supply from an existing purified water tank and distribution system. However, there were concerns about whether the distribution pumps and heat exchangers on that system could handle the increased load. The sizing of the surge tank, distribution and point-of-use loads for the new system along with target supply flow rate into the new surge tank also needed to be specified.

Actions: We constructed a discrete event model of the new purified water system as well as its interaction and supply from the existing system (Figure 3). The model contained the new surge tank, distribution and points of use along with the existing surge tank, supply and distribution. It allowed us to assess the new surge tank and loads while also seeing how the additional supply of water to the new purified water distribution system from the existing system impacted the latter's loads and supply capabilities.

Input parameters to the model included:
• draws for both water systems -- flows, durations and frequencies (using distributions to incorporate variability in frequency);
• water supply flow rates into both surge tanks; and
• surge tanks parameters -- maximum volume, volume that would resume filling tank and volume that would start supplying draws again if the tanks went empty.

Results/output: We performed multiple simulations to evaluate the impact of different surge tank sizes and supply flow rates for the new purified water surge tank and distribution system. The surge tank sizes ranged from 10,000 L to 20,000 L with supply flow rates from 125 L/min to 250 L/min.

The modeling showed that a 15,000-L tank with a 150-L/min water-supply flow rate maintained volume but decreasing the supply flow rate to 125 L/min, even with 20,000-L tank, allowed the surge tank to go empty.

It also revealed that if the new surge tank received 200 L/min the existing surge tank would have trouble maintaining volume -- it would become empty if its supply flow rate was 300 L/min but would easily maintain volume at a supply flow rate of 400 L/min.

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