Exclusive On-Line Article
PHARMACEUTICAL ENGINEERING®
Achieving Six Sigma
The Official Magazine of ISPE March/April 2007, Vol. 27 No. 2
This article highlights how a manufacturer of medical devices obtained Six Sigma quality in production by the use of Design of Experiments (DoE) and Statistical Process Control (SPC) and discusses how these tools can be an important step toward the Future Desired State.
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Table A. Typical Six Sigma training.
Achieving Six Sigma Quality in Medical Device Manufacturing by Use of Design of Experiments and Statistical Process Control by Per Vase
A
Introduction
major healthcare company wanted to introduce an ultrasonic welding technique for making a critical component for one of their new medical devices. A failure in a welding would have serious consequences for the customer. The Acceptable Quality Level (AQL) was a sub-ppm error rate since millions of weldings have to be made each year. Such a low AQL can not be ensured by a traditional offline QC sampling inspection. Instead, a lean production layout was needed. All welded components should be monitored for welding quality in-line at production speed. Bad parts should be sorted out automatically by the welding equipment. To ensure on-target quality and high yield, the monitoring of welding quality should be used to control the process from Statistical Process Control (SPC) charts. Prior to the implementation of SPC, Design of Experiments (DoE) was used to correlate Critical To Quality (CTQ) attributes to parameters that can be measured quickly and non-destructively on all samples to obtain timely measurements. In addition, DoE has been used to establish the correlation between % of organization
process result and process settings, the so called transfer function. By using the transfer function, it is possible not only to monitor, but also adjust the process and control manufacturing to ensure final product quality. Finally, DoE has been used to establish the Design Space. Data is quickly, conveniently, and visually displayed using SPC charts on monitors as immediate operator information and stored in a database for trend analysis over a longer period of time. By using the SPC system, Six Sigma quality has been obtained. A general description of the Six Sigma tools used and methodology employed is presented, including how they can be of value for the pharmaceutical industry.
Background The FDA defines in their Guidance for Industry1 Process Analytical Technology (PAT) as: “The Agency considers PAT to be a system for designing, analyzing, and controlling manufacturing through timely measurements (i.e., during processing) of critical quality and performance attributes of raw and in-process materials and processes with the goal of ensuring final
Training Subjects
Training Duration
Roles
100
How to read a control chart and a capability index
1 day
Act on control charts
10
How to perform a DoE, create a control chart, and select the right capability index
2 weeks
Green Belts. Project participant in DoE and SPC projects with supervision
1
How to manage projects using DoE and SPC
4 weeks + project
Black Belts. Project Manager. Supervisor.
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MARCH/APRIL 2007 PHARMACEUTICAL ENGINEERING On-Line Exclusive
1
Achieving Six Sigma product quality.” Tools for controlling manufacturing from measurements of CTQ parameters have been available for more than 80 years since W.A. Shewhart in 1924 introduced the control chart concept in Bell Laboratories. Although frequently used in some industries (e.g., the automotive industry), control charts have never obtained as widespread use as they deserve, and especially within the pharmaceutical industry they are rarely used. There are several reasons for this. Three of the main reasons why control charts have never previously made the breakthrough within the pharmaceutical industry are:
pharmaceutical manufacturing typically has a process sigma level of 2.5 in productions, corresponding to a Cp of 0.83 or 150000 ppm defects. In comparison, pharmaceutical release has a quality sigma level of 5 corresponding to a Cp of 1.67 or 200 ppm defects. No other industry has this three orders of magnitude defect difference between produced quality and released quality. It is the result of an incredible effort in QA and QC, especially in end-product testing and sorting, leading to Quality by Inspection. This is done to absolute perfection and there is not more to gain following this route. However, there are two drawbacks to this working practice:
1. no urgent need for change
1. It drives the prices up, due to high Costs of Poor Quality (CoPQ).
2. lack of operational process understanding before implementing SPC 3. implementation attempt by statisticians instead of end users
No Urgent Need for Change The pharmaceutical industry has for many years been in a special environment with strong regulation and patent protection. Production efficiency and yields have not, as in many other industries, been the major competition parameter. As a result of this, pharmaceutical manufacturing has a low manufacturing performance compared to other industries.2,3 A famous article in The Wall Street Journal expressed it this way: “pharmaceutical manufacturing techniques lag far behind those of potato-chip and laundry-soap makers.”2 In order to avoid defective products reaching the market, heavy Quality Assurance (QA) and Quality Control (QC) strategies have been established. A recent study by IBM3 shows that
2. It makes it impossible to improve the released quality even further. As it is said in the FDA PAT Guidance, “The health of our citizens depends on the availability of safe, effective, and affordable medicines.” The pharmaceutical industry has to find a more efficient way of controlling manufacturing processes to make medicines affordable for a larger group of customers. In addition, the quality needs to be improved further; 200 ppm is not good enough for critical characteristics. The industry can not continue to increase the QC efforts by even larger sample sizes in end product testing; the limit is reached! This general industry trend also can be seen in the latest ISO sampling standard,4 which moves away from traditional AQL sampling methods and recommends screening (continuous monitoring) and process control instead for critical characteristics. This issue also is highlighted in a recent publica-
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Figure 1. Illustration of Capability index Cp and Cpk.
2
PHARMACEUTICAL ENGINEERING On-Line Exclusive MARCH/APRIL 2007
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Achieving Six Sigma Sigma Level
Yield %
Cp before Sorting
System Downtime each year (days)
CoPQ % of Sales (8)
CoPQ % of Sales (9)
1
30
0.33
255
>40
>70
Non competitive
2
69
0.67
112
30-40
>40
Non competitive
3
93
1.00
24
20-30
25-40
Average Pharma Sigma = 5 after sorting (3)
4
99.4
1.33
2,27
15-20
15-25
Average Other Industries
5
99.98
1.67
0.085
10-15
5-15
6
100
2.00
0.0012
