As the world's largest research-based pharmaceutical company, Pfizer has had great success using modeling and simulation to predict and improve the results of strategic initiatives within research and development. The research organization has been using simulation, specifically Discrete Event Simulation (DES), for more than 20 years, and it has become ingrained in the R&D decision-making culture.
The adoption of DES within the manufacturing organization has been less centralized and less systematically applied to the decision-making process. There have been many successes applying simulation to manufacturing projects, but they have been isolated. Sharing these DES projects and what is learned with the whole technical organization is an important next step. [For Cancelarich's ideas on what modelers shouldn't do, see "Modelers Gone Wild: Six Practices to Prevent."]
Our Operational Excellence Group has done a great job of communicating, particularly through Manufacturing Lean and Six Sigma initiatives, how variation can impact all areas of the organization, Pfizer Global Engineering (PGE) has furthered this understanding through the use of simulation and modeling, so that Manufacturing understands how variation and decisions based on averages can often lead to uncertainty and surprises in operations (Figure 1).
We are moving in the right direction in establishing a modeling culture in Manufacturing at Pfizer, to help us improve our processes and organization. We are developing a critical mass of expertise within PGE such that teams can regularly use a variety of simulation tools to improve operations and address constraints.
Challenges to Adopting Simulation Modeling
As recently as a few years ago, we did not have the readily accessible hardware—i.e., desktops and laptops—and computing power now available on most engineers’ desks. The availability and variety of software tools has significantly improved over the last 15 years and model development requires less specialized programming experience.
The right mix of tools is important, but the challenge of developing a simulation/modeling culture is the biggest barrier to the application of simulation in many organizations. Many manufacturers may go about building this culture in the wrong ways.
Often, a good first step is understanding that, in Manufacturing, the combination of time and the variability of inputs leads to levels of complexity that cannot be easily explained in a spreadsheet. We are very comfortable developing complex spreadsheets today, but they have their blind spots. If they are not understood, a team will move forward based on insufficient data and understanding, which can lead to manufacturing inefficiencies or other issues. Spreadsheets are immensely valuable—however, it is important to understand when simulation will bring the much deeper insight that will be necessary to succeed in today’s competitive markets. If a team does not know that such tools or resources are available, and how to use them, that opportunity will be lost.
Also it is important to understand that the simulation effort is a tool to help improve decision-making and not the final deliverable. In many cases it is assumed that simulating the facility or operation will solve all of our issues. But simulation is like all the other process tools we apply—another deep-reaching application that can deliver unique and valuable “objective” insights, so we can continue to curb and balance our subjective viewpoints.
The Value of Simulation for Pharmaceutical Manufacturing
Simply going through the process of developing a model can deliver critical insights into an issue or problem. Applying simulation often makes the team better, as it helps bring new and improved ways of looking at the process or manufacturing problem. Potential constraints can be challenged and risks can become clearer. Modeling itself does not solve problems; the creativity of the team is required for this. However, a new perspective can significantly improve the likelihood of an innovative solution or confirm that a true and tested solution is correct. Simulation also helps instill an understanding that issues and systems are not static and will change and continue to change over time.
It is important to accept that systems are changing and will not remain static. It is also critical to understand where a system’s tipping point is, and modeling and simulation help with this. Tipping points can include: not meeting supply commitments; resource constraints; not meeting design specifications; running a variance on budgets; poor inventory management; an expired product; or regulatory inspection actions. Staying as far away from a tipping point while managing costs and resources is the real challenge we deal with every day.
During model development, sensitivity studies are done in order to better understand the process and what variables are important. Watching how variables interact as one particular input is changed provides insights that cannot be gained by a spreadsheet. This activity can also help the team focus on a critical number of issues and not the complexity of the process.
The model does not need to look exactly like reality. It simply needs to best represent interactions between people, equipment, materials, and time. Often looking outside normal ranges helps the team understand if a variable is critical or not. The model can help instill a focus on a data-driven approach and not an anecdotal one. The greatest ability of simulation is to use data—either actual or estimated—to see where the interactions diverge and become critical.