Simulation Modeling Helps Bring Pain Reliever to Market
ProcessModel
Pharmacia’s Searle plant
in Caguas, Puerto Rico, produces more than 20 brands of
pharmaceuticals. Faced with the responsibility of undertaking
a launch of the new prescription drug Celebrex, Searle Caguas
had the daunting task of producing launch requirements in
three months, plus packaging and shipping the capsules in
a timely manner. Searle assembled an ad hoc task force—the
Celebrex Readiness Team—to manage the introduction
and commercial production of the new product.
The team searched for a simulation- modeling solution
that was affordable, user-friendly and had animation capabilities.
The company had never used simulation technology before;
buying a tool that does what had previously been done in
Excel spreadsheets would be difficult to justify if the
tool were expensive. It also had to be easy to understand
and use because of the short amount of time available to
complete the project. Additionally, it had to be powerful
enough to handle different process details without losing
model credibility and validity. The animation feature was
necessary in order to depict the process operation for the
team members to verify model logic in operation.
Searle Caguas turned to ProcessModel for all these reasons.
“There are a few tools within this niche for simulation
modeling at a macro process level.” Says Santos Sanabria,
system improvement engineer for the Searle Caguas plant.
“However, the best-in-class is ProcessModel.”
Sanabria presented ProcessModel to the Celebrex Readiness
Team.
ProcessModel Inc. is a Utah-based company and inventor
of the ProcessModel tool that creates a computerized version
of a business process using a flowchart interface. ProcessModel
provides a detailed statistical report, which allows the
user to compare existing processes with proposed fixes.
The model then uses data to determine how the process will
perform under various scenarios. It allows the user to examine
the effect of two important factors present in most businesses:
randomness (or fluctuation) and interdependence (dependent
events). Searle’s manufacturing process for the drug
launch needed to be evaluated using these factors.
The manufacturing process includes six basic steps: receiving
raw materials; packaging components and testing samples
that ensure their suitability and purity for the manufacturing
process; granulation; filling the capsules; testing samples
of the drug at the analytical laboratory; and bottling,
packaging and shipping worldwide.
The entire manufacturing process was input to ProcessModel,
and because of the short timeline, Sanabria suggested a
top-down approach by developing a macro-level simulation
model of the whole process using existing data. Submodels
were developed adding more information as needed. This approach
could facilitate handling the simulation effort by breaking
the process in manageable segments and obtaining quick results
at each stage of the process. The receiving operation at
the warehouse was the first area to be modeled.
The existing warehouse was reasonably utilized, but with
production volumes doubling, a new warehouse would need
to be built. Management wanted to avoid such an expensive
expansion, so inventory models were developed and tested
with ProcessModel to determine their adequacy to guarantee
the material supply for the new product. The inventory models
assumed a kanban replenishment mode, which dramatically
reduced the inventory levels at the site. Procurement practices
for the existing products represented a reduction of more
than $1 million in the site’s inventory-carrying costs
and avoided the expansion of the warehouse.
ProcessModel was then used to determine the resources
needed for a full receiving process of the different components
and raw materials. Detailed receiving schedules were prepared
and validated with the vendors. The warehouse simulation
scene was ready to integrate the whole Celebrex process
in a single simulation model.
The model included all stages, starting at the receiving
warehouse and ending at the shipping warehouse, all located
in Caguas. When the full production schedule was input into
the model, the capsule samples taken from the encapsulated
lots started to accumulate in a long queue at the laboratory,
waiting for approval. Laboratory calculations had been made
in Excel, and no unexpected delays were factored into the
calculations. Excel assumed a fixed daily average arrival
rate of samples to the laboratory. ProcessModel considered
random variability in the arrival pattern and concluded
that three shifts of chemists working seven days a week
were needed in order to cope with the demand of the production
schedule.
Based on the successful results obtained from the application
of simulation modeling at Searle Caguas, the management
team is supporting and requiring process simulation analyses
as part of its regular decision-making process. New products
being introduced at Searle Caguas are subjected to the scrutiny
of simulation modeling in order to “test drive”
process capabilities ahead of time and take preventive actions
early on.
ProcessModel
- Helps analyze critical processes
- Allows users to experiment
- Improves actual operations
www.processmodel.com
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