Manufacturers face numerous challenges within their markets as the number of competitors grows and increasing material costs impact margins. Business methodologies such as Lean Six Sigma working in tandem with industry-specific approaches can increase the opportunity for success. An example of that is in the pharmaceutical industry, where an approach called process analytical technology (PAT) is used for manufacturing.
Four Components of PAT
Both the U.S. Food and Drug Administration (FDA) and the European Medicines Agency strongly encourage pharmaceutical companies to adhere to PAT Guidelines. PAT is a scientific program designed to reduce risk and is, essentially, about improving processes for effectiveness and efficiency in the pharmaceutical industry. PAT consists of four basic components:
- Process understanding
- Risk-reduction-based approach
- Regulatory strategy to accommodate innovation
- Real-time release
Because PAT is a system for measuring, analyzing and controlling manufacturing, it supports using any techniques that also lower risk. By implementing the tools and techniques used in Lean Six Sigma, many PAT principles are automatically satisfied.
Why Lean Six Sigma?
Continuous improvement systems are often used to boost operational performance. Areas that are commonly targeted include labor utilization, inventory levels, quality, asset utilization and cost of goods. In 2005, ARC Advisory Group, a research and advisory firm in manufacturing and supply chain solutions, reported that 80 percent of manufacturers are applying one or more continuous improvement methodologies to their plants. The most popular programs include Lean Six Sigma, Six Sigma, total quality management and theory of constraints.
Of these methodologies, Lean Six Sigma is the strongest candidate to partner with PAT. Using a standard DMAIC (Define, Measure, Analyze, Improve, Control) model, Lean Six Sigma controls process and product variation by defining problems, and then measuring and analyzing the data. This optimizes quality control in the manufacturing process and improves production rates, enhances innovation and reduces cycle times.
In fact, tools used in Lean Six Sigma are often simpler than PAT for an employee to grasp. Examples of these tools include a SIPOC (suppliers, inputs, process, outputs, customers) diagram, and failure mode and effects analysis.
One of the reasons that Lean Six Sigma works well with PAT is the use of statistical process control and design of experiments (DOE). Statistical process control is an effective method of monitoring a process through the use of control charts. Its purpose is to isolate the natural variation in the process from other sources of variation that can be traced or whose causes may be identified. DOE includes the design of all information-gathering exercises where variation is present, whether under the full control of the experimenter or not. It is used to study processes and products. In a purely scientific procedure, DOE does not go after optimization, but rather simply the relationship between variables. In Lean Six Sigma, as well as PAT, the goal is to understand the complex relationships between several factors.
Achieving Common Goals
Both PAT and Lean Six Sigma promote reducing production cycle times, preventing rejects, increasing automation and facilitating continuous process improvement. Lean Six Sigma can play an important role in the application of PAT by providing a simplified template and a bevy of tools.
About the Author: Terra Vanzant-Stern, Ph.D., project management professional (PMP), is a Master Black Belt and lead facilitator at SSD Global Inc. She specializes in consulting and teaching for Lean thinking, Six Sigma and Lean Six Sigma. She is the chair-elect for the Denver Section of the American Society for Quality. Vanzant-Stern may be reached at [email protected].
