How Process Simulation Saved My Job

I was responsible for the process improvement of a product comprising the biggest pharmaceutical launch in the history of the world. The product, Celebrex, was slated to be the largest money maker the industry had ever seen. As you might imagine, we had planned the launch in great detail from production to marketing. Production was confident they had the process and manpower in place to match the high demand to be generated from intense, coordinated marketing. You know how you get those uneasy feelings that something is just not right. Well, I had that feeling about production.

How the Production Process Worked

We acquired accurate processing times for every aspect of production and inspection, this allowed us a better understanding of the entire production process. This was achieved by conducting a detailed analysis of each stage of the production process and recording the time it took to complete each task.

We then used this data to calculate the throughput using spreadsheets that took into account the time it took to produce a batch of the product, the time it took to test the batch, and the time it took to release the batch for shipment. The spreadsheet also accounted for the time needed to clean and prepare the equipment for the next batch.

The basic production process worked as follows: raw materials were received, processed, and turned into finished products. A sample was then taken from the batch and sent to the central lab for testing. The lab would test the sample for quality and safety. If the sample passed the lab’s tests, the corresponding batch would be released for shipment. However, if the sample failed the lab’s tests, the entire batch would be rejected, and the production process would start again.

The spreadsheet illustrated that one shift of testing could handle 24 hours of production. This meant that we could produce and test products around the clock, without any delay.

However, after rechecking all the work, it still didn’t feel right. That uneasy ‘feeling’ finally caused me to request a process simulation of production line by creating a digital twin of what we do. After much research, I found ProcessModel to be the right tool for this.

Creating a Process Map

I started by creating a simple process map in ProcessModel. I did that by identifying the various objects involved in the process. These objects included products, workers, machines, materials, and anything else that was relevant to the process. Next, I added entities for arrivals to represent when these entities (raw materials for the medicine) entered the process. For example, products might arrive at the production line. This helped to identify the starting point of each entity within the process. Then came the time to add activities to the process map that showed how the entities would flow through the process. Activities represented the steps involved in producing the product or delivering the service. For example, a product might be mixed, inspected, and packaged before being shipped to customers. Each activity was connected to other activities by routes that showed the flow of entities through the process. The I added storages to the process map where entities would wait for further processing or for the next activity to be available. For example, a warehouse could be used to store medicine until the lab report comes back, and then they are ready to be shipped. Lastly, the resources were added to the process map that were located near activities where they would be used. Resources included workers, and machines.

Using this process map, I was able to get a clear picture of how the actual process flow looked like. This became the base model.

Getting the Model Simulation Ready

To get the base model ready for simulation, the first step was to update the simulation settings. This involved setting the number of hours the simulation would run for, which would determine the length of time the model would be simulated. Next, the arrivals information was updated to show when the arrivals (raw materials for the medicine) would happen during the simulation. This involved specifying the frequency and the number of entities that would arrive during each period. After that, the activities in the model were updated to reflect the actual capacity of each activity in the real system. This involved setting how much time an entity would spend at each activity, as well as how many entities could be processed by each activity at a given time.

Some activities might also have input or output queues, which needed to be specified in the model. The next step was to update the resources in the model. This involved updating the resource properties to reflect the number of resources they represented in the real system, as well as the cost of each resource. Some resources might also require multiple shifts, which would need to be created in the model. Once all of the updates were complete, the model was simulation ready.

Results of Process Simulation

Once the model was ready for simulation, the next step was to simulate the model using ProcessModel simulation software. During the simulation, the software would generate a virtual environment that would mimic the real-world process based on the model inputs I provided, and allowed me to observe how the system behaved. During the simulation, I carefully reviewed the process flow to ensure that it was going exactly as expected. In this case, the arrival information that was calculated using the spreadsheet was used to simulate the process flow, so the expected output was known beforehand. I used this expected output to verify that the simulation results would be accurate. After the simulation was completed, I was able to review and verify the results based on real data.

The simulation showed that production was going to be short two shifts in the testing area, which was a significant problem that needed to be addressed. To understand why the simulation showed such a large discrepancy, a detailed review of the simulation was conducted. It became apparent that the original calculations were based on average production rates, which did not account for variability in the real-world process. In reality, machines can jam or break down, causing batches to be released at the same time and then none for a period of time. This loss of production time cannot be magically recovered, and it results in delays in the overall production schedule. As a result, it was clear that a new approach was needed to accurately calculate production rates and avoid delays in the production schedule.

Improving the Process

After finding the disastrous results, I used the simulation software to test different scenarios and identify areas for improvement. I tested various changes and improvements to the system in a safe and controlled environment, without the need for costly and time-consuming real-world experiments.

The simulation became a valuable tool that me to be agile and adapt to changing demands. It allowed us to hire and train as needed to meet the demands of our product release. What would have happened to my job, if I was responsible for the process improvement on a process that produced 1/3 of it’s planned capacity? It is hard to say, because that train wreck never happened, but it wouldn’t have been pretty.

These are excerpts from an interview with Searle

The process improvement project described above is a perfect example of why simulation provides accurate prediction of production processes. Any complex process, that has time (when things happen) as one of the input elements, is nearly impossible to accurately predict with traditional methods of analysis (spreadsheets, flowcharts or value stream maps). So, the moral of the story is “use process simulation, accurately predict the the outcome of your process improvement project and save your job.”