Validation of cleaning of pharmaceutical manufacturing equipment

The systematic approach developed to assess the amount of residues left on manufacturing equipment surfaces from product carryover is known as cleaning validation. Current trends have seen increasing demand for rapid sample analysis time along with low detection limits for verification of cleaning validation samples. A total organic carbon (TOC) method is sensitive to the ppb range and is less time consuming than high performance liquid chromatography (HPLC). The purpose of this study is to demonstrate how to develop and validate a TOC method for cleaning applications. Validation of the cleaning procedures for manufacturing or processing equipment has been presented in this paper. A sensitive and reproducible method was developed and validated for the determination of cephradine in swab samples. The method for determining residues of cephradine on manufacturing equipment surfaces was validated for precision, linearity, accuracy, limit of quantification and % recovery of a potential contaminant. The sampling procedure using cotton swabs was also validated. A mean recovery from stainless steel plate close to 78% was obtained. The assay was linear over the concentration range of 30 to 600 ng ml⁻¹ concentration (R 0.9987). The calculated limit of contamination value was less than 315 g cm⁻², during three consecutive cleaning trials.

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1. Introduction

Pharmaceutical products are very much susceptible to contamination from shared manufacturing equipment. In many cases, the same equipment may be used for processing different products.¹ Cleaning validation is the process of assuring that cleaning procedures effectively remove residue from manufacturing equipment below a predetermined level. This is necessary to assure the quality of upcoming product using the same equipment, to prevent cross-contamination. Good manufacturing practice in pharmaceutical manufacturing plants states that the equipment must be maintained in a clean and orderly manner.^2–8 Mostly, cleaning validation samples have been measured using high performance liquid chromatography (HPLC) methods, which are often time consuming and subject to a number of interferences. Total organic carbon (TOC) analysis is a new method which has previously been applied to only measurement of carbon residues on production surfaces for pharmaceutical equipment and for water quality checking. We have applied the TOC analysis method to examine the cephradine residue. This developed and validated method offers extremely low detection capability in parts per billion (ppb), rapid sample analysis time and therefore quick turn-around of production, equipment and facilities. The method allows the measurement of extraneous materials such as process intermediates and cleaning agents, which are not possibly detected by other non specific or specific methods. TOC for cleaning validation has several advantages over specific methods. Only one method is needed for all cleaning validation analysis, the method is simpler to implement and easier to validate than chromatographic techniques and less time consuming. The method always produces a worst-case result, assuming that all residues are the active substance. TOC analysis demonstrated the better correlation to cleaning validation compounds in comparison to traditional analytical methods. Some qualities that make TOC a viable part of a cleaning validation include: high sensitivity, high recovery of samples, non-specific measurement and ease of use, minimal interferences and cost effectiveness. Cost savings could be attained by using cleaning validation studies. For example, by reducing a 12 h cleaning turnaround time to 6 h, per day savings on all batches could be achieved. Cephradine was chosen for cleaning, it is a cephalosporin product manufactured by most of the pharmaceutical industries. This product can cross-contaminate the other running products, which are being manufactured using the same equipment pieces like cone blender, grall mixer, encapsulation machine, blistering machine and packaging machine etc. As far as the cleaning process is concerned, cephradine has been selected due to its low water solubility and high toxicity value. This method depends on various parameters like surface type (stainless steel, glass, vinyl),^9–13 and it was necessary to establish the way of addition of the drug on different surfaces and procedures to collect the sample.^14,15 TOC analysis involves the oxidation of carbon and the detection of the resulting carbon dioxide. A number of different oxidation techniques exist, including photocatalytic oxidation, chemical oxidation, and high-temperature combustion.

2. Experimental

2.1 Chemical and reagents

Cephradine reference standard was provided by Bristol Myers Squibb Pharmaceuticals, TOC grade water was prepared by Multi column distillation plant (Spirax ultra Pure System, USA). Phosphoric acid and sodium persulafte were purchased from Scharlau (Barcelona, Spain). Absorband TX762 absorbent cotton swabs were from Texwipe (Upper Saddle River, NJ).

2.2 Instrumentation and methodology

The development of this method and validation were performed on a Anatel A-2000 wide range TOC Analyzer. It measures TOC directly by adding phosphoric acid to the sample to reduce pH to approximately 2 to 3. At this low pH, any inorganic carbon that is present is liberated as CO₂ into a nitrogen carrier gas and is directly measured by a non-dispersive infrared (NDIR) detector. Any remaining carbon in the sample is assumed to be TOC. A sodium persulfate oxidant is then added to the sample, and in the presence of UV radiation, the remaining carbon is oxidized to CO₂. The amount of CO₂ generated is then measured by NDIR to determine the amount of TOC originally present in the water.

2.3 Sample preparation

The TOC swabbing performs the swabbing procedure as follows. An aliquot of 20 ml TOC grade water into 20 ml TOC vial. Desorb a polyster tipped swab in TOC vial containing 20 ml TOC grade water. Using one side of the moistened swab, swab 100 cm², moving from left to right and pour into TOC vial containing 20 ml TOC grade water. Using dry swab and perform additional swabbing on the same sampling area without desorbing into the water. The sealed TOC vial is vortexed for 10 s and analyzed for TOC.

3. Method validation

Linearity was tested using standard calibration curve at a concentration range of 30 to 600 ng ml⁻¹. These standards were tested six times in agreement to ICH guidelines.¹⁶ A calibration curve was constructed and the proposed method was evaluated by its correlation coefficient and intercept value, calculated in the corresponding statistical study (ANOVA) (p < 0.05).¹⁷ The accuracy was evaluated by the recovery of cephradine (300 ng ml⁻¹) at three different levels (150 ng ml⁻¹, 300 ng ml⁻¹, and 450 ng ml⁻¹), each level tested three times. The swabbing recovery study, which involved spiking cephradine on 100 cm² 316 L stainless steel coupons, allowing the coupons to dry, recovering the cephradine with swabs, and desorbing the swabs into TOC grade water. These swabbing samples were then analyzed for TOC. Swabbing recovery included the following steps: Swabbing blank determination on ten 100 cm² 316 L stainless steel coupons was preformed. First TOC grade water was spread on all SS 316 L coupons, subsequently obtain the water sample with swab and analyzed on TOC for blank determination. Then impregnated 150 ng ml⁻¹, 300 ng ml⁻¹ and 350 ng ml⁻¹ on swabs and poured into 20 ml TOC grade vials containing the same water which was used in blank preparation and vortexed for 10 s and analyzed for TOC for standard readings. For recovery studies we spread (150 ng ml⁻¹, 300 ng ml⁻¹ and 350 ng ml⁻¹) of standard solution in an area of 10 × 10 cm on nine coupons. We desorbed a polyester tipped swab in TOC vial containing 20 ml TOC grade water. Using one side of the moistened swab, swab 100 cm², moving from left to right and pour into TOC vial containing 20 ml TOC grade water. Using dry swab perform additional swabbing on the same sampling area without desorbing into the water. The sealed TOC vial is vortexed for 10 s and analyzed for TOC. According to the ICH recommendations,¹⁶ precision was considered at two levels, repeatability and intermediate precision. On this account, six-sample replicates were consecutively tested in the same equipment at a concentration of 100% of the regular analytical working value.

4. Evaluation of maximum allowable carry over

The maximum allowable carry over limit of cephradine as potential cross-contaminant was calculated through several methods.¹⁸ The total surface area of the equipment chain in direct contact with the product was accounted for in the calculations. This accounts also for the maximum daily intake of a following product and for its batch size that will be manufactured next with the same equipment. 0.1% approach was calculated by

10 ppm approach was calculated by

MAC = 10 x S (1/A) (g/cm2)

Where, MAC is the maximum allowable carry over residue of API permitted after cleaning, allowed into the next product; it is assumed that the total amount of residue is distributed homogenously into the following product; D the lowest daily therapeutic dose of the contaminant; S the lowest batch size of the product to follow; I the maximum daily intake of the product to follow; F the safety factor (can vary from 100 to 100 000 depending on the product nature, e.g., topical, oral or injectable preparations); A the total surface area of equipment in direct contact with the products, calculated on the basis of the assumption that all the products come into contact with all the equipment pieces of the chain.

5. Results and discussion

5.1 Method validation

5.1.1. Accuracy. The recovery value for each concentration was calculated by comparing the blank corrected recovery mean TOC value to the blank corrected impregnated mean TOC value. The mean recovery data (mean ± R.S.D.) for each level were (115.47 ± 2.25%, 104.30 ± 1.53% and 98.10 ± 2.70% respectively, (Table 1).
Table 1 Accuracy

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S. No	Swabbing Blank (ppb)/A	Impregnated Sample 150 ng ml−1 (X)	Impregnated Sample 300 ng ml−1 (Y)	Impregnated Sample 450 ng ml−1 (Z)	Impregnated Sample 150 ng ml−1 - Swabbing Blank (ppb) (X-A)	Impregnated Sample 300 ng ml−1 - Swabbing Blank (ppb) (Y-A)	Impregnated Sample 450 ng ml−1 - Swabbing Blank (ppb) (Z-A)
1	125	375	632	886	250	507	761
2	128	364	641	881	236	513	753
3	121	361	620	861	240	499	740
4	119	384	618	867	265	499	748
5	124	367	647	874	243	523	750
6	129	387	635	876	258	506	747
7	130	374	637	869	244	507	739
8	124	370	645	874	246	521	750
9	118	369	623	894	251	505	776
10	125	381	619	861	256	494	736
Mean	124	373	631	874	249	507	750
SD	4.0	8.6	11.0	10.5	8.9	9.3	11.7
%RSD	3.2	2.3	1.74	1.21	3.57	1.87	1.56

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S. No	Cephradine Recovery 150 ng ml−1 (B)	Cephradine Recovery 300 ng ml−1 (C)	Cephradine Recovery 450 ng ml−1 (D)	Cephradine Recovery 150 ng ml−1 (B) – Blank mean	Cephradine Recovery 300 ng ml−1 (C) – Blank mean	Cephradine Recovery 450 ng ml−1 (D) – Blank mean
1	413	653	837	289	529	713
2	405	645	867	281	521	743
3	428	661	875	294	537	751
Mean	415	653	859	288	529	735
%RSD	2.81	1.23	2.33	2.27	1.51	2.72
%Recovery
S. No	(Swabbing blank corrected recovery / Blank corrected impregnated sample Mean) X 100
	% Recovery 150 ng ml−1			% Recovery 300 ng ml−1		% Recovery 450 ng ml−1
1	115.6			104.3		95.1
2	112.8			102.7		99.1
3	118.0			105.9		100.1
Mean	115.47			104.30		98.10
%RSD	2.25			1.53		2.70

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5.1.2 Linearity. Linearity was determined at ten levels representing from 30 to 600 ng ml⁻¹ (10.0% to 200%) A calibration curve was constructed and the proposed method was evaluated by its correlation coefficient, slope and intercept values, which were 0.99986, 56324.18 and −5.6952 respectively. The limits of detection and quantification were 10 and 30 ng ml⁻¹ respectively. 5.1.3 Precision. 5.1.3.1 Precision repeatability. Repeatability precision was determined by performing swabbing, which involved spiking cephradine on 316 L stainless steel coupons, recovering the cephradine with swabs, and desorbing the swabs into TOC grade water. These swabbing samples were then analyzed for TOC. Swabbing was performed with six replicates using the following cephradine concentrations: 150 ng ml⁻¹, 300 ng ml⁻¹ and 450 ng ml⁻¹. The precision repeatability was performed in the same manner as in the accuracy study. The data of Table 2 shows that the average results of precision repeatability within 100 ± 10.0% of test concentrations of 150 ng ml⁻¹, 300 ng ml⁻¹ and 450 ng ml⁻¹ and R.S.D. was less than 5.0% (Table 2).
Table 2 Precision repeatability

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Swabbing Blank Mean (ppb)
115
Impregnated Sample 150 ng ml−1	Impregnated Sample 150 ng ml−1 – swabbing blank mean	Impregnated Sample 300 ng ml−1	Impregnated Sample 300 ng ml−1 – swabbing blank mean	Impregnated Sample 450 ng ml−1	Impregnated Sample 450 ng ml−1 – swabbing blank mean
415	300	649	534	890	775
401	286	615	500	902	787
398	283	641	526	867	752
411	296	648	533	856	741
387	272	610	495	892	777
408	293	621	506	859	744
	Mean = 288.33		Mean = 515.67		Mean = 762.67
	%RSD = 3.53		%RSD = 3.37		%RSD = 2.54
Cephradine Recovery 150 ng ml−1	Cephradine Recovery 150 ng ml−1 – swabbing blank mean	Cephradine Recovery 300 ng ml−1	Cephradine Recovery 300 ng ml−1 – swabbing blank mean	Cephradine Recovery 450 ng ml−1	Cephradine Recovery 450 ng ml−1 – swabbing blank mean
435	320	655	540	905	790
425	310	625	510	889	774
405	290	612	497	914	799
425	310	634	519	911	796
401	286	627	512	896	781
409	294	631	516	890	775
	Mean = 301.67		Mean = 515.67		Mean = 785.83
	%RSD = 4.49		%RSD = 2.74		%RSD = 1.36
S.No	Precision result (150 ng ml−1)	Precision result (300 ng ml−1)	Precision result (450 ng ml−1)
1	106.7%	100.9%	101.9%
2	108.3%	101.6%	98.3%
3	102.5%	95.4%	106.3%
4	104.7%	97.8%	107.4%
5	105.1%	102.7%	100.5%
6	100.3	101.6%	104.2%
Mean	104.6%	100.04%	103.1%
%RSD	2.75	2.79	3.37
Lower control limit (LCL)	101.6%	97.1%	99.5%
Upper control limit (UCL)	107.6%	103.0%	106.8%

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5.1.3.2 Precision intermediate. The second analyst carried out intermediate precision on a different day. The swabbing recoveries were performed in the same manner as in the accuracy study. The average results of precision intermediate were within ± 10.0% of test concentrations of 150 ng ml⁻¹, 300 ng ml⁻¹ and 450 ng ml⁻¹ and with 92.3 – 106.8% confidence interval, which indicate a good precision. 5.1.4 Robustness. Robustness tests examine the effect that operational parameters have on the analysis results. For the determination of a method's robustness, a number of method parameters, for example, solution stability, pH, flow rate, injection volume, detection wavelength or diluent composition, are varied within a realistic range, and the quantitative influence of the variables is determined. If the influence of the parameter is within a previously specified tolerance, the parameter is said to be within the method's robustness range. In this study, only one factor was evaluated which was solution stability. The stability of swab sample taken from SS coupon was evaluated at room temperature, at intervals of 1, 24, and 48 h.¹⁹ The results obtained (mean = 107.50%, 110.25%, 91.25% respectively) revealed that samples retained a potency of 100 ± 10% as tested against freshly prepared impregnated standard solution.

5.2. %Recovery from stainless steel, vinyl and glass surfaces

Each plate (S.S. plate, Vinyl and Glass plate) was spread with variable aliquots (150 ng ml⁻¹, 300 ng ml⁻¹ and 350 ng/ ml) of standard solution in an area of 10 × 10 cm. Similar procedure, as used in method validation was applied to lift the residues from the surfaces. The % recoveries from each surface showed that the recovery was influenced by the type and the size of surface and not by the level of the drug spiked.

5.3. Establishing limits of cross-contamination on clean equipment

Swab sampling of areas hardest to clean was done from the equipment train used in the manufacturing and residual was found in g/swab (Table 3). The lowest obtained values were selected as limit of maximum allowable carry over (MAC) for this study.

Table 3 Sample analysis from hard to clean areas from the equipment train

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Equipment name	Sampling point	Cephradine (g/ cm2)
		Batch 01	Batch 02	Batch 03
Cone blender	Dispensing end	52.01	41.6	21.54
	Side wall	21.21	17.63	14.72
	Outlet mouth wall	12.12	13.09	12.15
Grall mixer	Outlet	6.9	9.80	11.6
	Near gasket	61.68	69.6	38.2
	Blades (chopper)	8.9	68.4	55.7
Fitz-mill	Inside grooves	221.5	324	142
	Sieve bottom	247	296.2	315.4
Encapsulation machine	Inside punch assembly	258.6	149.8	146.8
	Inside dye assembly	44.37	35.6	69.3
	Inside dye	22.29	6.80	60.3
Blister machine	Brush	9.6	2.1	3.8
	Belt	8.4	6.7	5.9

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A lowest calculated value of 315 g cephradine/cm² was obtained when the 0.1% dose limit criterion was used for the total equipment chain which was justified by the principle that an active pharmaceutical ingredient (API) at a concentration of 1/1000 of its lowest therapeutic dose will not produce any adverse effects.¹⁸

The lowest calculated value was obtained when 10 ppm acceptance criterion was applied. When less than 10 ppm of cephradine was allowed into the next manufactured product, a limit of 587 g cephradine/cm² was determined as MAC.

5.4. Assay of swab samples collected from the equipment train

Swab samples collected from different locations of the manufacturing equipment train were analyzed with the new method. For the current study it was observed that all data obtained lie within 2s of the sample mean and well below the MAC (Table 3). This gave the confidence that the manual cleaning procedures tested do provide sufficient removal of the residues from the equipment train.

6. Conclusion

A rapid and reliable TOC method for determination of residues of cephradine on pharmaceutical manufacturing plant equipment has been developed and validated. This assay technique fulfilled all the requirements to be identified as a reliable and feasible method, including accuracy, linearity, recovery and precision data. It is a non-specific and precise analytical procedure and its quick and rapid analysis allows the analysis of a large number of samples in a short period of time. Therefore, this TOC method can be used for a routine residual analysis. The level of contamination found after equipment cleaning was monitored during several consecutive runs. The results obtained confirm that the cleaning procedures used are able to remove residues from equipment surfaces well below the calculated limit of contamination.

References

1		PIC/S PI 006, Recommendations on Validation Master Plan, Installation and Operational Qualification, Non-Sterile Process Validation, Cleaning Validation, 2007.
2		PDA Technical Report No. 29: Points to Consider for Cleaning Validation, PDA J. Pharm. Sci. Technol., 52, 1–23 (1998).
3		R. C. Hwang, How to Establish an Effective Maintenance Program for Cleaning Validation, Pharm. Technol., 2000, 24, 62–67.
4		D. A. LeBlanc, Establishing Scientifically Justified Acceptance Criteria for Cleaning Validation of Finished Drug Products, Pharm. Technol., 1998, 23, 136–148.
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6		F. Laban, S.T.P. Pharma Pract., 1997, 7, 87–127.
7		Guide to Inspections Validation of Cleaning Processes, Food and Drug Administration, Office of Regulatory Affairs, Washington DC, 1–6 (1993).
8		PIC/S, Recommendations on Validation Master Plan, Installation and Operational Qualification, Non-Sterile Process Validation, Cleaning Validation, PI 006–2, 2004.
9		O. W. Reif, P. Solkner and J. Rupp, J. Liq. Chrom., 1996, 50, 399–410.
10		K. D. Altria, E. Creasey and J. S. Howels, Routine capillary electrophoresis trace level determinations of pharmaceutical and detergent residues on pharmaceutical manufacturing equipment, J. Liq. Chromatogr. Relat. Technol., 1998, 21, 1093–1105 [Links].
11		J. Lambropoulos, G. A. Spanos and N. V. Lazaridis, Development and validation of an HPLC assay for fentanyl, alfentanil, and sufentanil in swab samples, J. Pharm. Biomed. Anal., 2000, 23, 421–428 [Links].
12		T. Mirza, M. Lunn, F. Keeley, R. George and J. Bodenmiller, J. Pharm. Biomed. Anal., 1999, 19, 747–756 [Links].
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16		ICH, Q2B Validation of analytical procedure: methodology, International Conference on Harmonisation, London, 1995.
17		J. A. M. Pulgarín, A. Molina and M. T. Pardo, Direct determination of naftopidil by non-protected fluid room temperature phosphorescence, Analyst, 2001, 126, 234–238 [Links].
18		G. L. Fourman and M. V. Müllen, Determining cleaning validation acceptance limits for pharmaceutical manufacturing operations, Pharm. Technol., 1993, 17, 54–60.
19		Validation of Chromatographic Methods, Center of Drug and Research Reviewers guidance Evaluation (1994).

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Cleaning Validation

Cleaning validation ensures that specific cleaning processes consistently clean to pre-defined limits to prevent contaminants (product or cleaning process related) from leaving residues that will adulterate and adversely affect the safety and quality of the next product manufactured. Cleaning validation is documented proof of consistently and effectively cleansing a system or equipment item.
We can deliver a complete package from development and execution of engineering studies and protocols for pharmaceuticals and biologics to creation of standard cleaning procedures, logs and training documentation.

Capabilities

PharmaSys has extensive experience in developing defensible acceptance criteria and ensuring the execution is conducted in an orderly manner. Our highly skilled individuals can ensure that equipment-cleaning procedures are removing residues to pre-defined levels of acceptability. Our approach involves:

• A complete understanding of regulatory FDA requirements, guidelines and expectations.
• Selection and evaluation of effective cleaning methods and processes.
• Establishment of residue limits and calculation sample acceptance criteria.
• Selection of sampling and analytical methods.
• Simplification of the cleaning validation program.

Created SOPs and methods (analysis and recovery) for cleaning validation studies for:

• bioburden and pyroburden (analysis and sample recovery).
• detergent residuals (analysis and sample recovery).
• drug substance/product residuals (sample recovery).

Drafted, executed sampling and testing cleaning validation engineering studies protocols for:

• Solid Dose: encapsulating Equipment and Tablet Compression Equipment
• LCO: Blending Tanks/ Kettles, & Fillers.
• Parenterals: Portable Tanks, Vial and Ampoule Fillers, Lypholizers.
• Components: Stopper, vial and ampoule Washers.

If you are interested in finding out more about PharmaSys' world-class offerings, complete and submit the form provided below. You will be contacted to discuss your needs and provided with information about how PharmaSys can help you achieve your compliance and validation goals.

The Research Process - Validation

The following people reviewed the Standards during the developmental phase and provided feedback to the Consortium to validate and/or improve the content and organization of the final Standards. The Standards represent a framework for all of these organizations to work together to "Accelerate Entrepreneurship in America." We are indebted to all who took the time to provide feedback.

National Organizations

Priscilla McCalla
Director, Professional & Program Development, DECA Inc., Reston, VA
Darrell A. Luzzo
Senior Vice President-Education, Junior Achievement Inc., Colorado Springs, CO
Del Daniels
Manager, Affiliate Projects, Office of Program Partnership, The National Foundation for Teaching Entrepreneurship - NFTE, New York, NY
Joyce Macek
Program Manager, "NFTE University" The National Foundation for Teaching Entrepreneurship - NFTE. , New York, NY
Juan Casimiro
CEO/Founder, Ascend YEI / Casimiro Foundation, Miami, FL
Alice Marshall Darnell
Educational Consultant, Kauffman Foundation, Kansas City, MO
James R. Gleason
President, MarkED/Career Paths, Columbus, OH
Joan Gillman
Executive Director, U S Association for Small Business & Entrepreneurship (USASBE), University of Wisconsin, Madison, WI
Aaron Siegel
President and CEO, Freedoms Foundation at Valley Forge, Valley Forge, PA
Frank Kennedy
CEO & Founder, MagicBiz, Inc., Haverford, PA
Sheena Lindahl
President & Co-Founder, Extreme Entrepreneurship Education Corporation, New York NY
Mathew Georghiou
CEO and Lead Software Designer, GoVenture by MediaSpark Inc., Sydney, NS, Canada
Carolyn King-Richmond
President/CEO, MicroSociety Philadelphia, PA

Miscellaneous and International

John Vallon
Analyst, Swedish Office of Science & Technology, Los Angeles, CA
Elisabeth Maria Bittner
Ministry of Education Youth and Women, Mainz, Germany
Andrea Haus
Instructor, University of Koblenz-Landau, Landau, Germany
Caryl Pierre Chase
Senior Trainer, RBTT ROYTEC, Port of Spain, Trinidad and Tobago
Rosario M Hocog
Business Consultant, SBDC, Northern Marianas College, Saipan, MP
Dave Ellis
Toucan Europe, Tanzaro House - Ardwick Green North, Manchester, UK
Jorge Olmos Arrayales
Entrepreneurship Coordinator, ITESM High School, Mexico City Campus Del. Tlalpan, Mexico
Eva Palacios Salazar
Secretariat of Public Education , San Miguel Ajusco Tlalpan, Mexico
Eji Yamane
Professor, School of Education, Mie University, Tsu, Mie Prefecture, Japan

State or Regional Organizations

Jennifer Williamson
Operations Manager, NE AL Entrepreneurial System, Anniston, AL
Ronni Cohen
Director of Delaware Financial Literacy Institute/ K-12 and Adult programs, Claymont, DE
Hazel King
Executive Director, Illinois Institute for Entrepreneurship Education, Chicago, IL
Zira Smith
Director of Education, Illinois Institute for Entrepreneurship Education, Chicago, IL
Tom Welch
Director, Seeding Innovation, Office for the New Economy, Frankfort, KY
Joanne Lang
Executive Vice President, Kentucky Science & Technology Corporation, Lexington, KY
Melba Kennedy
Assistant Administrator, Career & Technical Education, Louisiana Department of Education, Baton Rouge, LA
Karen Pohja
Education Consultant, Michigan Department of Career Development, Lansing, MI
Jori Martinez-Woods
Executive Director, Financial Innovations for Young People, Clayton, MO
Trey Michael
Marketing Education Consultant, NC Department of Public Instruction, Raleigh, NC
Donna Fricke
North Dakota Tech Prep, Bismarck State College, Marketplace for Kids, Bismarck, ND
Marilyn K Kipp
Executive Director, Marketplace for Kids, Mandan, ND
Gregg Christensen
Director, Marketing & Entrepreneurship Education, Nebraska Department of Education, Lincoln, NE
Bonnie Sibert
Business Education, Nebraska Department of Education, Lincoln, NE
Constance Manchester-Bonenfant
Education Consultant, NH Department of Education, Concord, NH
Marjorie Gillespie
Tech Prep Coordinator, SWREC #10, Truth or Consequences, NM
Abbejean Kehler
Director, Ohio Council on Economic Education, Columbus OH
Russ Combs
ACEnet, Inc., Athens, OH
Lettie Dilbeck
Program Administrator, Marketing Education Division, Oklahoma Dept of Career and Technology Education, Stillwate OK
Michele Govora
Business & Marketing Education Advisor, Pennsylvania Department of Education, Harrisburg, PA
Ken Proudfoot
President, Enterprise Institute of Rhode Island, Inc. Providence, RI
Earlene England
Marketing/DECA Consultant, Tennessee Department of Education, Nashville TN
Julie Felshaw
Education Specialist, Utah State Office of Education, Salt Lake City, UT
Kristin Crowe
Executive Director, Washington DECA, Kirkland, WA
Cindi Blansfield
Program Supervisor, Business & Marketing Pathway, Department of Secondary Public Instruction, Olympia, WA
Marie J Burbach
Education Consultant, WI Department of Public Instruction, Madison, WI
Linda Scott
Program Education Consultant, Wyoming Department of Education, Cheyenne, WY

Adult Education

Teresa Peralta
Entrepreneur Training Coordinator, California Indian Manpower Consortium, Inc. Sacramento, CA
Brandy Bertram
Microbusiness Development Corporation, Denver CO
Jamaluddin Husain
Director, Entrepreneurship Center, Purdue University Calumet, Hammond IN
Lynelle Thomas Dixon
Program Coordinator, Urban Restoration Enhancement Corporation, Baton Rouge, LA
Karen Southall Watts
Consultant and Coach, Watts Consulting, Hillsborough, NC
Wialillian Howard
President/Owner, Wialillian & Company, Charlotte, NC
Amini Kajunju
Executive Director, Workshop in Business Opportunities, New York, NY
Pat Carter
President, Carter Business Consultants, Dayton, OH
Ann Marie Marshall
Director, MicroEnterprise Association, Providence RI
Sally Carlson
Microenterprise Coordinator, Willamette Education Service District, Salem OR
Charles J. Loomis
Bellevue Community College, Kirkland WA

College Level

Cuyler V Reid
Associate, Leadership for Educational Entrepreneurs, Arizona State University Glendale, AZ

Program Coordinator/Instructor, Miami-Dade College Entrepreneurial Education Center Miami, FL
Dawn Bowlus
Student Activities and Outreach Coordinator, John Pappajohn Entrepreneurial Center, University of Iowa, Iowa City, IA
>Richard Leake
Associate Professor, Luther College, Decorah, IA
Barbara J. Phipps, Ph.D.
Director, Center for Economic Education, University of Kansas, School of Education, Lawrence, KS
Carol Carter
Assistant Director, Entrepreneurship Institute, Louisiana State University Management Dept., Baton Rouge, LA
Keramat Poorsoltan
Frostburg State University, Frostburg, Maryland
Diane Sabato
Director of Special Projects, Entrepreneurial Institute, NACCE, Springfield Technical Community College, Springfield, MA
Robert L Wyatt
Dean, Breech School of Business Administration, Sam Walton Fellow of Free Enterprise Drury University, Springfield, MO
Beth S Eckstein
Director, Economic Education, East Carolina University, College of Business, Greenville NC
Ken L Dillo
Small Business Center Director, Wake Technical Community College, Raleigh, NC
Sallie Neville Merritt
Director, Small Business Center, Catawba Valley Community College, Hickory, NC
Peter H. Hackbert
Siebens Endowed Chair in Entrepreneurship, Sierra Nevada College, Incline Village NV
Michael Gordon
Professor, State University of New York (SUNY), Canton, NY
Carl Hemmeler
Administrator, Transitional Workforce Department, Columbus State Community College, Columbus, OH
Roger Sylvester
Director, Center for Economic Education, Wright State University, Dayton, OH
James M. Kohut
Associate Professor and Chair, Department of Marketing, Williamson College of Business Administration, Youngstown State University, Youngstown, OH
William G. Vendemia
Associate Professor, Youngstown State University, Youngstown, OH
Arlene Floyd
Director of Associate Degree and Tech Prep Programs, Youngstown State University Youngstown, OH
Dr. Anthony C. Warren
Director of the Farrell Center for Corporate Innovation & Entrepreneurship, Director of the Garber Venture Capital Center, Farrell Clinical Professor of Entrepreneurship, Smeal College of Business, The Pennsylvania State University, University Park, PA
Richard Shane
Head, Department of Economics, South Dakota State University, Brookings, SD
H B Rajendra
Associate Professor, Business Administration, Lemoyne-Owen College, Memphis TN
Robert Wall
Director - SBDC, NE Texas Community College, Mount Pleasant, TX
Ron Mitchell
University of VA, Charlottesville, VA
Rama Asundi
Professor, University of Puerto Rico (Mayagüez Campus) College of Business Administration, Mayagüez, PR
David Munoz
Professor, University of Puerto Rico (Mayagüez Campus), College of Business Administration, Mayaguez, PR
Vance Gough
Entrepreneurship Instructor, Mount Royal College, Calgary, Alberta, Canada

High School

Kristie Howell
teacher, Hale County Schools, Greensboro AL
Nancy Compton
teacher, Hale County Technology Center, Greensboro AL
Michael A Vialpando
Department Chair, La Joya Community High School, Avondale, AZ
DeAnne McLemore
Consultant, Marketing & Creative Arts, Los Angeles County Office of Education, Regional Occupational Program, Downey, CA
Eugene Selivanov
CFO, Ivy Academia, Woodland Hills, CA
Howard A Sanders
Marketing Teacher , Mesa Ridge High School, Colorado Springs, CO
Dianne Lauramoore
Teacher, F W Buchholz High School Gainesville, FL
Janet Clark
Marketing Teacher, Auburndale High School, Auburndale, FL
Toni Elliott
Teacher, Polk County School Board, Winter Haven, FL
Eileen Mercuris
Business Education Instructor, PCM High School, Prairie City IA
Rich Gaard
Marketing Education Teacher/Coordinator, Decorah High School, Decorah, IA
Dale Wambold
Business Teacher, Gladbrook-Reinbeck High School, Reinbeck, IA
William J Ratzburg
Director, Northern Kane County Regional Vocational System, Elgin, IL
Mercedes F Feliz
Teacher Coordinator, W.E.C.E.P, Kelvyn Park High School, Chicago, IL
Toya Pannell
Secondary Business Education Teacher, Harper High School, Chicago, IL
Sharon Garcia
Program Coordinator, Marketing/Entrepreneurship, Wells Community Academy High School, Chicago, IL
David Nickoley Marketing Education Instructor, Streamwood High School, Streamwood, IL
Drew Boettcher
Counselor, Chicago Public Schools, Chicago, IL
James E Vahey
Business/ Technology Teacher, Dunbar Vocational Career Academy, Chicago, IL
Ruth Ann Falls
Business Computer Department Chair, Olathe Falls High School, Olathe, KS
Matt Silverthorne
Business Education Teacher, Wichita North High School, Wichita, KS
Willadean Carter
Coordinator for School to Careers, Tech Prep, and High Schools That Work, Monroe County High School, Tompkinsville, KY
Mitzi Holland
Teacher, Monroe County High School, Tompkinsville, KY
Cynthia Sanders
Computer Lab Assistant, Lincoln County High School, Stanford, KY
Jerry Morgan
Vocational Coordinator, Livingston Parish School Board, Livingston, LA
Rosalind Denning
Entrepreneurship Coordinator, Detroit Public Schools, MI
Regina Cosey
Entrepreneurship 2000, Printing Teacher, Murray Wright High School, Detroit, MI
Joyce Gray
Entrepreneurship 2000, Child Care Teacher, Murray Wright High School, Detroit, MI
Christine Harris
Entrepreneurship 2000 and Foods/Nutrition, Clothing Management Teacher, Murray Wright High School, Detroit, MI
Jane Turner
Entrepreneurship 2000, Office Technology Teacher, Western International High School, Detroit, MI
Rosa Williams
Teacher, Entrepreneurship Club and Manicuring, Crockett Career/Technical Center, Detroit, MI
Kristen McClure
Marketing Teacher, Comstock High School , Kalamazoo, MI
Beverly Babcock
Family and Consumer Science Teacher, Humboldt High School, St. Paul Public Schools, St. Paul, MN
Kim Eickelberg
Business Teacher, Humboldt Senior High School, St. Paul, MN
Shelli Ray
Marketing Teacher, Blue Springs South High School, Blue Springs, MO
Ray Anderson
Marketing Instructor, Rockbridge Senior High School, Columbia, MO
Michelle Totra
Marketing Teacher, Lindbergh High School, St. Louis, MO
Colby Schmid
Marketing Teacher, Lindbergh High School, St. Louis, MO
De Ann Holland
Special Education Teacher, Gulfport High School, Gulfport, MS
Deborah Thompson
Director, Special Education, Moss Point School District, Moss Point, MS
David Friedli
Principal, Umonhon Nation Public School, P O Box 280 - 100 Main Street, Macy, NE
Charles E Easton
Cherokee High School, Cherokee Central Schools, Cherokee, NC
Lisa Carder
Business Educator, Boone Central High School, Albion NE
Kathleen Gladem
Business Educator, Boone Central High School, Albion NE
Pat Browning
Secondary Counselor, Walthill Public School, Walthill, NE
Joannie Hegge
Teacher, Winnebago Public School, Winnebago, NE
Jean Knapp
Family and Consumer Science Teacher, Winnebago Public School, Winnebago, NE
Joanne C Melanson
Vocational Department Chairperson, Woodsville High School, Woodsville, NH
Liliana Taylor
Business Education Teacher, Business Education Teacher, Hatch Valley High School Hatch, NM
Jerry Lenny Coca
Work Study Coordinator/ Transition Specialist, Onate High School, Las Cruces Public Schools, Las Cruces, NM
Candice McDonald
Career Education Coordinator, Las Cruces Public Schools, Las Cruces, NM
Sherry Hulsey
Career Specialist, Las Cruces Public Schools, Las Cruces, NM
Patti Harrelson
21st Community Leaning Center Coordinator, Southwest Regional Educational Center Truth or Consequences, NM
Kathleen Frosini
Director, Career & Tech Ed, Clark County School District, Las Vegas, NV
Kenneth A Michnal
Curriculum Specialist, Clark County School District, Las Vegas, NV
Gertrude M Vinci
Teacher, Regional Technical Institute, Reno, NV
John Morris
Principal, Entrepreneur High School, Cincinnati, OH
Douglas Bahnsen
Instructor, Ripley-Union-Lewis-Huntington Jr./Sr. High School, Ripley, OH
Robinetta West
Small Business Entrepreneurship Teacher, Bowsher High School, Toledo, OH
Lanette Duncan
Marketing Teacher, Tulsa Public Schools, Tulsa, OK
Kim Smith
Instructor, Tulsa Technology Center, Tulsa, OK
Pat Almeida
Bel Air High School, El Paso, TX
Kathy Kubinski
Business Teacher, Bel Air High School, El Paso, TX
Diane Baray
Marketing Education/ DECA Advisor, Ysleta High School, El Paso, TX
Jim Cooke
Marketing Education Teacher, Midland Senior High School, Midland, TX
Joanne Priest
Career & Technology Teacher, Bel Air High School, El Paso, TX
Rochelle Rosas
Marketing Teacher, J M Hanks High School , El Paso, TX
David Pendergrast
Marketing Teacher, Huguenot High School, Richmond, VA
Michael Artson
President & CEO, Granville Academy Northern Virginia, Woodbridge, VA
Cari Bruley
Business & Technology Teacher, Burlington High School, Burlington VT
Eric Christianson
Business Education Teacher, Cle Elum High School, Cle Elum WA
Chris Sande
Technology Education Teacher, John R Rogers High School, Spokane, WA
Jamie Walton-Hughes
Business Education Instructor , Sammamish High School, Bellevue School District, Bellevue, WA
Debora Koenig
Marketing Teacher, Monroe High School, Monroe WA
Carol Wiseley
Marketing Teacher, Mercer Island High School, Mercer Island, WA
Ann Smith
Instructor, Sno-Isle Tech Skills Center, Everett, WA
Lora Lee Brabant
Marketing Instructor, Port Angeles High School, Port Angeles, WA
Lori Jacobs
Teacher, Kent-Meridian High School, Kent, WA
Susan Dinger
Family & Consumer Science Teacher, Logan Middle School, LaCrosse WI
Shawn Bradbury
Enterprise Education Teacher, Stephensville High School, Stephenville, NL, Canada

Elementary/Middle School

Laurel Miller
3/4th Grade Teacher, Team Leader, EduPreneurship Student Center, Scottsdale, AZ.
Troy A Brown
Principal, Butler Elementary School/ Savannah-Chatham Schools, Savannah, GA
Tara Greenway
S.E.A.R.C.H. Teacher, Butler Elementary School/ Savannah-Chatham Schools Savannah, GA
Wendy Wilson
Teacher , Butler Elementary School/ Savannah-Chatham Schools, Savannah, GA
Marilyn Hrymak
Family and Consumer Science Teacher, Kimball Middle School - District U-46, Elgin, IL
Rose Jenkins
Librarian K-8, Frost School, Livingston, LA
Daphne Lucas
8th grade history teacher, Albany Middle School, Albany, LA
Tara Matthews
8th grade language arts teacher, Denham Springs Jr. High School, Denham Springs, LA
Beth Thompson
8th grade language arts teacher, Southside Jr. High School, Denham Springs, LA
Tiara Harper
8th grade teacher , West Side Jr. High School, Walker, LA
Coralee Davis
7th grade teacher, Live Oak Middle School, Watson, LA
Nancy Baughman
Teacher, Springfield Jr. High School, Springfield. LA
Rebecca Morgan
Teacher, Levi Milton Elementary School, Walker, LA
Kirk Arnold
6th grade life preparation/careers teacher, Independence Public Schools, Independence, MO
Marcus C. Miller
7th Grade Careers-World of Work, Independence Public Schools, Independence MO
Mildred D. Harder
World of Work Teacher-8th grade, Independence Schools, Independence, MO
Claudia Freeman
teacher-grades 4-5, Pass Road Elementary School, Gulfport School District, Gulfport, MS
Dusti Shoemaker Smith
Special Education Teacher, Bayou View Middle School, Gulfport, MS
Connie Smith
CoCoordinator for Region 6 and Statewide Team, Marketplace for Kids, Jamestown, ND
Paula Bantom Waters
Rush Middle School, Camden, NJ
Danica Kelly
Teacher, Fidalgo Elementary, Anacortes, WA

Community-Based Programs

Brenda Gray
Education Consultant, Native American High Performance Learning Community, Tucson, AZ
Susan Burns
Director, Digital Kids Initiative, Northeast Indiana Innovation Center, Fort Wayne, IN
Jihad T Muhammad
Board Chairman/Founder, ECLUB International Foundation, Gary, IN
Almetris Stanley
Executive Director/ Instructor, Westside Youth Technical Entrepreneur Center, Chicago, IL
Mike Tomas
Coordinator, Entrepreneurship Development, YMCA Alliance of Metropolitan Chicago, Chicago, IL
Phoebia Williams
President and Founder, ABW Youth Training Center, Chicago, IL
Randy Treichel
Enterprise Development Youth Express, St. Paul, MN
Judith H Johnson
Education Consultant, Native American High Performing Learning Community Lincoln, NE
Lavera Floyd
Executive Director & CEO, An Entrepreneur Conference for Youth, Dayton, OH
John Hartman
Assistant Director, Michael King Smith Kid's Fund, Inc., McMinnville, OR
Velvet Stainbrook
Fund Administrator, Michael King Smith Kid's Fund, Inc., McMinnville, OR
Cora Mae Haskell
Business & Finance Trainer , Four Bands Community Fund, Inc, Eagle Butte, SD
Terri Chapman
President, CEO Academy, Inc, Nashville, TN
Frank Azeke
Founder/Director, Academy of Future Entrepreneurs, Virginia Beach, VA
Carol Byrd
Extension Agent 4-H Youth Development, Patrick County, Virginia Cooperative Extension Stuart, VA
Gary Larrowe
Coordinator, Carroll County Public Schools in partnership with Virginia Tech, Hillsville, VA
Janet Collier
Washington REAL Institute Directors & Trainers, Glenoma, WA

Six Sigma Validation Process

Six Sigma Validation Process

• IQ - Installation Qualification
• OQ - Operational Qualification
• PQ - Performance Qualification

Methods and Tools for Process Validation

Dr. Wayne A. Taylor

ABSTRACT

There are many statistical tools that can be used as part of validation. Control charts, capability studies, designed experiments, tolerance analysis, robust design methods, failure modes and effects analysis, sampling plans, and mistake proofing are but a few. Each of these tools will be summarized and their application in validation described.

1.  INTRODUCTION

Validation requires documented evidence that a process consistently conforms to requirements. It requires that you first obtain a process that can consistently conform to requirements and then that you run studies demonstrating that this is the case. Statistical tools can aid in both tasks.

2.  USES OF THE TOOLS

This section describes the many contributions that statistical tools can make to validation. Each tool appearing in bold is further described in Section 4.

One tool that is particularly useful in organizing the overall validation effort is a failure modes and effects analysis (FMEA) or a closely related fault tree analysis (FTA). An FMEA involves listing out the potential problems or failure modes and evaluating their risk in terms of their severity, likelihood of occurring and ease of detection. Where potential risks exists, the FMEA can be used to document which failure modes have been addressed and which still need to be addressed. As each failure mode is addressed, the controls established are documented. The end result is a control plan. Addressing the individual failure modes will require the use of many different statistical tools.

Failures or nonconformities occur because of errors made and because of excessive variation. Obtaining a process that consistently conforms to requirements requires a balanced approach using both mistake proofing and variation reduction tools. When a nonconformance occurs because of an error, mistake proofing methods should be used. Mistake proofing attempts to make it impossible for the error to occur or at least to go undetected.

However, many nonconformities are not the result of errors, instead they are the result of excessive variation and off-target processes. Reducing variation and proper targeting of a process requires identifying the key input variables and establishing controls on these inputs to ensure that the outputs conform to requirements. Strategies and tools for reducing variation and optimizing the process average are described in Section 3.

The end result is a control plan. The final phase of validation requires demonstrating that this control plan works, i.e., that it results in a process that can consistently conform to requirements. One key tool here is a capability study. A capability study measures the ability of the process to consistently meet the specifications. It is appropriate for measurable characteristics where nonconformities are due to variation and off-target conditions. Testing should be performed not only at nominal, but also under worst-case conditions. When pass/fail data is involved, acceptance sampling plans can be used to demonstrate conformance to specifications. Finally, in the event of potential errors, challenge tests should be performed to demonstrate that mistake proofing methods designed to detect or prevent such errors are working.

Depending of circumstances, not all tools need be used, other tools could be used instead and the application of the tools can vary.

3.  STRATEGIES AND TOOLS FOR REDUCING VARIATION AND OPTIMIZATION

Each unit of product differs to some small degree from all other units of product. These differences, no matter how small, are referred to as variation. Variation can be characterized by measuring a sample of the product and drawing a histogram. For example, one operation involves cutting wire into 100 cm lengths. The tolerance is
100 ± 5 cm. A sample of 12 wires is selected at random and the following results obtained:

      98.7    99.3     100.4      97.6    101.4     102.0

    100.2    96.4    103.4     102.0      98.0    100.5

A histogram of this data follows. The width of the histogram represents the variation.

Of special interest is whether the histogram is properly centered and whether the histogram is narrow enough to easily fit within the specification limits. The center of the histogram is estimated by calculating the average of the 12 readings. The average is 99.99. The width of the histogram is estimated by calculating either the range or standard deviation. The range of the above readings is 7.0 cm. The standard deviation is 2.06 cm. The standard deviation represents the typical distance a unit is from the average. Approximately half of the units are within ± 1 standard deviation of the average and about half of the units are more than one standard deviation away from the average. On the other hand, the range represents an interval containing all the units. The range is typically 3 to 6 times the standard deviation, depending on the sample size.

Frequently, histograms take on a bell-shaped appearance that is referred to as the normal curve as shown below. For the normal curve, 99.73% of the units fall within ± 3 standards deviation of the average.

For measurable characteristics like wire length, fill volume, and seal strength, the goal is to optimize the average and reduce the variation. Optimization of the average may mean to center the process as in the case of fill volumes, to maximize the average as is the case with seal strengths, or to minimize the average as is the case with harmful emissions. In all cases, variation reduction is also required to ensure all units are within specifications. Reducing variation requires the achievement of stable and capable processes. The figure below shows an unstable process. The process is constantly changing. The average shifts up and down. The variation increases and decreases. The total variation increases due to the shifting.

Instead, stable processes are desired as shown below. Stable processes produce a consistent level of performance. The total variation is reduced. The process is more predictable.

However, stability is not the only thing required. Once a consistent performance has been achieved, the remaining variation must be made to safely fit within the specification limits. Such a process is said to be stable and capable. Such a process can be relied on to consistently produce good product.

A capability study is used to determine whether a process is stable and capable. It involves collecting samples over a period of time. The average and standard deviation of each time period is estimated and these estimates plotted in the form of a control chart. These control charts are used to determine if the process is stable. If it is, the data can be combined into a single histogram to determine its capability. To help determine if the process is capable, several capability indices are used to measure how well the histogram fits within the specification limits. One index, called C_p, is used to evaluate the variation. Another index, C_pk, is used to also evaluate the centering of the process. Together these two indices are used to decide whether the process passes. The values required to pass depend on the severity of the defect (major, minor, critical).

While capability studies evaluate the ability of a process to consistently produce good product, it does little to help achieve such processes. Reducing variation and the achievement of stable processes requires the use of numerous variation reduction tools. Variation of the output is caused by variation of the inputs. Consider a pump. An output is flow rate. Suppose the pump uses a piston to draw solution into a chamber through one opening and then pushes it back out another opening. Valves are used to keep the solution moving in the right direction. Flow rate will be affected by piston radius, stroke length, motor speed and valve backflow to name a few. Flow rate varies because piston radius, stroke length, etc. varies. Variation of the inputs is transmitted to the output as shown below.

Reducing variation requires identifying the key input variables affecting the outputs and then establishing controls on these inputs to ensure that the outputs conform to their established specifications. In general, one must identify the key input variables, understand the effect of these inputs on the output, understand how the inputs behave and finally, use this information to establish targets (nominals) and tolerances (windows) for the inputs. One type of designed experiment called a screening experiment can be used to identify the key inputs. Another type of designed experiment called a response surface study can be used to obtain a detailed understanding of the effects of the key inputs on the outputs. Capability studies can be used to understand the behavior of the key inputs. Armed with this knowledge, robust design methods can be used to identify optimal targets for the inputs and tolerance analysis can be used to establish operating windows or control schemes that ensure the output consistently conforms to requirements.

The obvious approach to reducing variation is to tighten tolerances on the inputs. This improves quality but generally drives up costs. The robust design methods provide an alternative. Robust design works by selecting targets for the inputs that make the outputs less sensitive (more robust) to the variation of the inputs as shown below. The result is less variation and higher quality but without the added costs. Several approaches to robust design exist including Taguchi methods, dual response approach and robust tolerance analysis.

Another important tool is a control chart. A control chart can be used to help determine whether any key input has been missed and if so to help identify them. Many other tools also exist for identifying key inputs and sources of variation including component swapping studies, multi-vari charts, analysis of means (ANOM), variance components analysis, and analysis of variance (ANOVA).

When studying variation, good measurements are required. Many times an evaluation of the measurement system should be performed using a gage R&R or similar study.

4.  DESCRIPTIONS OF THE TOOLS

A brief description of each of the cited tools follows:

1. Acceptance Sampling Plan – An acceptance sampling plan takes a sample of product and uses this sample to make an accept or reject decision. Acceptance sampling plans are commonly used in manufacturing to decide whether to accept (release) or to reject (hold) lots of product. However, they can also be used during validation to accept (pass) or to reject (fail) the process. Following the acceptance by a sampling plan, one can make a confidence statement such as: "With 95% confidence, the defect rate is below 1% defective."
   
   
2. Analysis of Means (ANOM) – Statistical study for determining if significant differences exist between cavities, instruments, etc. It has many uses including determining if a measurement device is reproducible with respect to operators and determining if differences exists between fill heads, etc. Simpler and more graphical alternative to Analysis of Variance (ANOVA)
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3. Analysis of Variance (ANOVA) – Statistical study for determining if significant differences exist between cavities, instruments, etc. Alternative to Analysis of Means (ANOM).
   
   
4. Capability Study – Capability studies are performed to evaluate the ability of a process to consistently meet a specification. A capability study is performed by selecting a small number of units periodically over time. Each period of time is called a subgroup. For each subgroup, the average and range is calculated. The averages and ranges are plotted over time using a control chart to determine if the process is stable or consistent over time. If so, the samples are then combined to determine whether the process is adequately centered and the variation is sufficiently small. This is accomplished by calculating capability indexes. The most commonly used capability indices are C_p and C_pk. If acceptable values are obtained, the process consistently produces product that meets the specification limits. Capability studies are frequently towards the end of the validation to demonstrate that the outputs consistently meet the specifications. However, they can also be used to study the behavior of the inputs in order to perform a tolerance analysis.
   
   
5. Challenge Test – A challenge test is a test or check performed to demonstrate that a feature or function is working. For example, to demonstrate that the power backup is functioning, power could be cut to the process. To demonstrate that a sensor designed to detect bubbles in a line works, bubbles could be purposely introduced.
   
   
6. Component Swapping Study – Study to isolate the cause of a difference between two units of product or two pieces of equipment. Requires the ability to disassemble units and swap components in order to determine if the difference remains with original units or goes with the swapped components.
   
   
7. Control Chart – Control charts are used to detect changes in the process. A sample, typically consisting of 5 units, is selected periodically. The average and range of each sample is calculated and plot. The plot of the averages is used to determine if the process average changes. The plot of the ranges is used to determine if the process variation changes. To aid in determining if a change has occurred, control limits are calculated and added to the plots. The control limits represent the maximum amount that the average or range should vary if the process does not change. A point outside the control limits indicates that the process has changed. When a change is identified by the control chart, an investigation should be made as to the cause of the change. Control charts help to identify key input variables causing the process to shift and aid in the reduction of the variation. Control charts are also used as part of a capability study to demonstrate that the process is stable or consistent.
   
   
8. Designed Experiment – The term designed experiment is a general term that encompasses screening experiments, response surface studies, and analysis of variance. In general, a designed experiment involves purposely changing one or more inputs and measuring the resulting effect on one or more outputs.
   
   
9. Dual Response Approach to Robust Design – One of three approaches to robust design. Involves running response surface studies to model the average and variation of the outputs separately. The results are then used to select targets for the inputs that minimize the variation while centering the average on the target. Requires that the variation during the study be representative of long term manufacturing. Alternatives are Taguchi methods and robust tolerance analysis.
   
   
10. Failure Modes and Effects Analysis (FMEA) – An FMEA is systematic analysis of the potential failure modes. It includes the identification of possible failure modes, determination of the potential causes and consequences and an analysis of the associated risk. It also includes a record of corrective actions or controls implemented resulting in a detailed control plan. FMEAs can be performed on both the product and the process. Typically an FMEA is performed at the component level, starting with potential failures and then tracing up to the consequences. This is a bottom up approach. A variation is a Fault Tree Analysis, which starts with possible consequences and traces down to the potential causes. This is the top down approach. An FMEA tends to be more detailed and better at identifying potential problems. However, a fault tree analysis can be performed earlier in the design process before the design has been resolved down to individual components.
    
    
11. Fault Tree Analysis (FTA) – A variation of a FMEA. See FMEA for a comparison.
    
    
12. Gauge R&R Study – Study for evaluating the precision and accuracy of a measurement device and the reproducibility of the device with respect to operators. Alternatives are to perform capability studies and analysis of means on measurement device.
    
    
13. Mistake Proofing Methods – Mistake proofing refers to the broad array of methods used to either make the occurrence of a defect impossible or to ensure that the defect does not pass undetected. The Japanese refer to mistake proofing as Poka-Yoke. The general strategy is to first attempt to make it impossible for the defect to occur. For example, to make it impossible for a part to be assembled backwards, make the ends of the part different sizes or shapes so that the part only fits one way. If this is not possible, attempt to ensure the defect is detected. This might involve mounting a bar above a chute that will stop any parts that are too high from continuing down the line. Other possibilities include mitigating the effect of a defect (seat belts in cars) and to lessen the chance of human errors by implementing self-checks.
    
    
14. Multi-Vari Chart – Graphical procedure for isolating the largest source of variation so that further efforts concentrate on that source.
    
    
15. Response Surface Study – A response surface study is a special type of designed experiment whose purpose is to model the relationship between the key input variables and the outputs. Performing a response surface study involves running the process at different settings for the inputs, called trials, and measuring the resulting outputs. An equation can then be fit to the data to model the affects of the inputs on the outputs. This equation can then be used to find optimal targets using robust design methods and to establish targets or operating windows using a tolerance analysis. The number of trials required by a response surface study increases exponentially with the number of inputs. It is desirable to keep the number of inputs studied to a minimum. However, failure to include a key input can compromise the results. To ensure that only the key input variables are included in the study, a screening experiment is frequently performed first.
    
    
16. Robust Design Methods – Robust design methods refers collectively to the different methods of selecting optimal targets for the inputs. Generally, when one thinks of reducing variation, tightening tolerances comes to mind. However, as demonstrated by Taguchi, variation can also be reduced by the careful selection of targets. When nonlinear relationships between the inputs and the outputs, one can select targets for the inputs that make the outputs less sensitive to the inputs. The result is that while the inputs continue to vary, less of this variation is transmitted to the output causing the output to vary less. Reducing variation by adjusting targets is called robust design. In robust design, the objective is to select targets for the inputs that result in on-target performance with minimum variation. Several methods of obtaining robust designs exist including robust tolerance analysis, dual response approach and Taguchi methods.
    
    
17. Robust Tolerance Analysis – One of three approaches to robust design. Involves running a designed experiment to model the output’s average and then using the statistical approach to tolerance analysis to predict the output’s variation. Requires estimates of the amounts that the inputs will vary during long-term manufacturing. Alternatives are Taguchi methods and the dual response approach.
    
    
18. Screening Experiment – A screening experiment is a special type of designed experiment whose primary purpose is to identify the key input variables. Screening experiments are also referred to as fractional factorial experiments or Taguchi L-arrays. Performing a screening experiment involves running the process at different settings for the inputs, called trials, and measuring the resulting outputs. From this, it can be determined which inputs affect the outputs. Screening experiments typically require twice as many trials as input variables. For example, 8 variables can be studied in 16 trials. This makes it possible to study a large number of inputs in a reasonable amount of time. Starting with a larger number of variables reduces the chances of missing an important variable. Frequently a response surface study is performed following a screening experiment to gain further understanding of the affects of the key input variables on the outputs.
    
    
19. Taguchi Methods – One of three approaches to robust design. Involves running a designed experiment to get a rough understanding of the effects of the input targets on the average and variation. The results are then used to select targets for the inputs that minimize the variation while centering the average on the target. Similar to the dual response approach except that while the study is being performed, the inputs are purposely adjusted by small amounts to mimic long-term manufacturing variation. Alternatives are the dual response approach and robust tolerance analysis.
    
    
20. Tolerance Analysis – Using tolerance analysis, operating windows can be set for the inputs that ensure the outputs will conform to requirements. Performing a tolerance analysis requires an equation describing the effects of the inputs on the output. If such an equation is not available, a response surface study can be performed to obtain one. To help ensure manufacturability, tolerances for the inputs should initially be based on the plants and suppliers ability to control them. Capability studies can be used to estimate the ranges that the inputs currently vary over. If this does not result in an acceptable range for the output, the tolerance of at least one input must be tightened. However, tightening a tolerance beyond the current capability of the plant or supplier requires that improvements be made or that a new plant or supplier selected. Before tightening any tolerances, robust design methods should be considered.
    
    
21. Variance Components Analysis – Statistical study used to estimate the relative contributions of several sources of variation. For example, variation can on a multi-head filler could be the result of shifting of the process average over time, filling head differences and short-term variation within a fill head. A variance components analysis can be used to estimate the amount of variation contributed by each source.