“I was responsible for the process improvement for 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. We acquired accurate processing times for every aspect of production and inspection. We had calculated throughput using detailed spreadsheets. The basic production process worked as follows: the product was manufactured; a sample taken from the batch and tested at the central lab; when the sample was approved by the lab, the corresponding batch would be released for shipment. The spreadsheet illustrated that one shift of testing could handle 24 hours of production. After rechecking all the work it still didn’t feel right.
That uneasy ‘feeling’ finally caused me to request a process simulation of production. The simulation showed that production was going to be short two shifts in the testing area! How was it possible that we could have been off by 300 percent? After a detailed review of the simulation it became apparent why we would miss our production shipment schedule. We planned on batches finishing on a set schedule. All the spreadsheet calculations were based on these average production rates. In reality, machines jam, or break down, which causes batches to be released at the same time and then sometimes none for a period of time. Once production time is lost , it can’t be magically recovered. It is lost forever.
The simulation 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.” (Exerpts 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.”