Z1.4 Inspection Requirements

Automotive inspection, TS 16949, IATF 16949

Question

Our customers require that we follow the ANSI Z1.4 standard for attribute sampling plans; however, it is not feasible to wait until lots are completed to perform inspections. Lots can be large and run for many days and waiting until lot completion to determine the sample size, based on the finished lot size, is too late because we will have missed our chance to correct any production issues that may result in defective parts. Another limitation is a lack of space to stage product while waiting for the final inspection of the completed production lots. Product is made as orders are received, and not typically stored as inventory, so our on-time delivery demands also hinder our ability to hold product for final inspections of completed production lots. Therefore, we are seeking guidance on a practical way to implement a in-process inspection during production that follows the ANSI Z1.4 standard.

Answer

Yes, you can sample as you produce to get to the sample size.  It is important that you keep track of your accept/reject items.  Since you know how long you are running the product, you can project the approximate lot size to get the sample size.  Work with your scheduler before the product starts so you can take samples early and continue on in the process.

In addition, if you have material changes as the product is running, I am sure that you are sampling then to make sure everything is set correctly, you can use those samples also.  As an example, let’s say your product is running 4 days and based on the projected lot size, you have a sample of 28 to take, you could take 7 samples each day spaced throughout the day or you take 10 samples the first day, 7 the second day, 6 the third day and 5 the last day of production.  You need to figure the right sequence that fits your history of the product.

Jim Bossert

Purchase a copy of Z1.4 here.

Defective Parts Per Million (DPPM) Calculation

Chart, graph, sampling, plan, calculation, z1.4

Question

Recently, there is a debate in my organization about Defective Parts Per Million (DPPM) computation.

Camp 1 - DPPM = (No of parts rejected / No of parts inspected) * 1,000,000
Camp 2 - DPPM = (No of parts rejected / No of parts received) * 1,000,000

We perform sampling inspection based on AQL.
Camp 1 insists they are correct and likewise for Camp 2.  Which is correct or more appropriate to reflect supplier quality?

Answer

This is not an uncommon question. If you look at the standard, they define the % nonconforming as the number of parts nonconforming/number of parts inspected x 100. If you are looking at DPPM, instead of multiplying by 100, you put in 1,000,000.

This means that by your definition, Camp 1 is correct. This is also what was intended by the creators of the sampling scheme.

Jim Bossert
Sr Performance Improvement Specialist
JPS Hospital
ASQ Fellow, CQE, CQA, CMQ/OE, CSSBB, CSSMBB
Fort Worth, TX

Acceptance Sampling Inspection

Automotive inspection, TS 16949, IATF 16949

Question

We have an Acceptance sampling inspection in place where we use the ANSI/ASQ Z1.4 – 2013 standard under Normal Inspection, using General Inspection Level II to drive our samples size and accept, reject criteria. We do not uses switching rules as we have always found them too difficult to manage. I have two questions.

If I have one lot that fails Acceptance sampling and I am trying to bound the issue is it suitable to bound it to the one affected lot if the lot before and after pass or do I need to carry out additional sampling.

My second question is if I have a batch that passes acceptance sampling but at a subsequent downstream process a defect being inspected for by the upstream acceptance sampling inspection is found how do I determine if the lot is acceptable? Do I trust the acceptance sampling inspection or react?

Answer

The first question is not an uncommon one and actually it is a good practice to isolate the lot and do 100% inspection of it.  That way you can estimate the % defective and if another failure occurs in the next 5 lots, then increase the sampling until you have some confidence that the supplier has fixed the problem.  Once that confidence is restored, then you go back to what you inspected originally.

The second question, is one that you have to understand how well do you follow the acceptance sampling process?  If your alpha level is at 95%, 5% of the time, you can accept a bad batch as good. That is the pure definition of the alpha risk.  If this failure falls within the 5%, your process is working and while you sort through the lot, and notify the supplier, it is not something that you over react to.

I hope this helps.

Jim

James Bossert, PhD, MBB, CQA, CQE, CqM/OE
Sr Performance Improvement Consultant

Switching Rules

Manufacturing, inspection, exclusions

Question 

We are planning to implement ANSI/ASQ Z1.4-2003(R2013) sampling inspection plan with our Finish products which are currently 100% inspected by QC Inspectors.  I read  about the importance of the switching rules  on a continuing stream of lots and have the following  questions:
1.Is it acceptable to select a specific plan (tightened, normal or reduced ) and use it without the switching rules?
2.Are there any exceptions which allow us to use a specific plan without applying  the switching rules?

Answer

  1. You can use any plan without using the switching rules but it does run the risk of not meeting the alpha risk in the end. These plans were developed to be used as documented. A normal plan is generally used and the switching rules come in when the clearance number has been obtained.  Some processes may never switch.  If you choose a plan that is tightened or reduced to start with, you potentially will either spend too much on inspection (tightened) or risk having bad product go to the customer (reduced).  It is a business decision for you to make if your customer is not demanding it.  The switching rules are there to protect the producer when the product is running very well or it has problems.
  2. If your customer is not requiring a particular plan, you can use what you want. It is a business decision, no reason for any exceptions.

I hope this helps.

Jim Bossert
Sr Performance Improvement Specialist
JPS Hospital
ASQ Fellow, CQE, CQA, CMQ/OE, CSSBB, CSSMBB
Fort Worth, TX

Inspection Sample Size

Analysis, Statistics, Control Charts, Statistical Methods, Audit, Auditing

Question

  1. The customer expects certain levels of inspection: pull 157 bottles for visual testing, but then they also want 20 pulled for dimensional testing. Can’t the 20 additional bottles be a subset of the original testing sample?
  1. When calculating the lot, do you pull the samples before or after your calculations? Do the samples get included in the produced quantity or not?  For example: If the customer orders 10,000 bottles and the level 2 inspection pulls 200 bottles that drops the total shipped to the customer to 9,800 pieces.  If 10,200 bottles are produced then the inspection level increases so that 315 bottles need to be pulled for testing.  What is the correct sample size and production number?

Answer

Hello,

Here are the responses to your questions:

  1. Yes since the first inspection is visual, you can use a subset for the additional testing.
  1. The lot size is 10,000. You should be putting the samples back into the lot if they are not destroyed by the testing. You send what is contracted for.  You are sampling with replacement.

Jim Bossert

SVP Process Design Manger, Process Optimization
Bank of America
ASQ Fellow, CQE, CQA, CMQ/OE, CSSBB, CSSMBB
Fort Worth, TX

Find more information about sampling here.

DMAIC Guidelines

Question

Are there general guidelines or target durations for each phase of DMAIC?

Knowing that we have fruit salad in our portfolio – apples, oranges, grapes, melons, plus 10 more – are there recommendations on how to generate meaningful guidelines for duration where trying to categorize a project may have a considerable number of characteristics and be quite different?

Answer

DMAIC process

Duration is a good metric but in the operational definition you need to add a complexity component.  You are seeing what happens when it is not added.  I have defined complexity as the number of groups/departments that need to be engaged in the project.  A simple example is when IT gets involved, there may be some additional time lags that need to be added so they can do their due diligence.  Likewise if you have a project that involves finance or legal, it will add time to the project.  Organization wide projects take more time than departmental ones so when scoping a project, consider how much longer it will take with more groups needed to be involved.  This may help in your estimates and tracking.

As for general guidelines for DMAIC, what I have told executives and belt candidates is that Define should take about 3 weeks, Measure about 8 -12 weeks (depending on how good your data is).  Analyze 3-4 weeks, depending on complexity and that you have data flowing consistently.  Improve 3-4 weeks depending how quickly you can get the improvement in place, training completed, and process stabilized. Control is generally 4 weeks, just to make sure that everything is running as expected and you can show the magnitude of the improvement.

These are mine but it all depends on the Measure phase and getting the baseline well defined and data flowing.  That is the most critical phase in DMAIC and shortcuts there will impact the project.

I hope this helps.

Jim Bossert

Sr Performance Improvement Specialist
JPS Hospital
Fort Worth, TX

Sampling Schemes

Inventory, Inspection, Review, Suppliers, Supplies

Question

Is there a sampling plan for determining the number of cases to pull in a batch from which you perform the ANSI/ASQ sampling of individual products?  For example: you receive 550 cases with 145 product vials/case.  Is it proper to sample a total of 500 vials from 25 cases (using square root of n+1) or would applying the ANSI/ASQ single level II be more appropriate?  We would then need to pull 500 vials from 80 cases.  Or is there a better statistical method?

Answer

There are two ways to answer this. One is to follow the standard and take samples from 80 cases until you get 500. It is assumed that the samples are random so that you do not always take the samples from the same location in the case.  That is following the standard.

The second is that you take a sample from 25 cases in a random manner.  That is fine also.  There are no standards for sampling from cases so either way will work.  Years ago, I developed a sampling scheme similar to what is proposed at the employer I was working with at the time.  Sometimes you have to be creative.

Jim Bossert

SVP Process Design Manger, Process Optimization
Bank of America
ASQ Fellow, CQE, CQA, CMQ/OE, CSSBB, CSSMBB
Fort Worth, TX

Six Sigma Black Belt

Chef, Six Sigma Black Belt

Question

I am currently an Executive Chef working that has been taking online classes for Green & Black Belt Six Sigma.  I am about halfway through my Black Belt classes and would like to pursue my certifications.  However, my company does not have a Six Sigma department and seem to be getting no where on working on a Six Sigma project so I could qualify for the Black Belt certification.  Do you have any advice or guidance that could help.

Answer

This is not an uncommon issue with a number of people.  What he should look into is to work as a volunteer at some non-profit organization on a Black Belt improvement project.  These organizations are always looking for help and this is a win-win for both him and the organization.  He will need to talk to them about what Six Sigma is and the type of project he is interested in doing.

Another possibility is to look at his place of work and if there is a part of the job that has to be done but no one likes doing it. If it is a process, then he could follow the DMAIC process and show improvement.  This could also serve as BB project if he can show the time savings was greater than 50%.

Jim

Jim Bossert
SVP Process Design Manger, Process Optimization
Bank of America
ASQ Fellow, CQE, CQA, CMQ/OE, CSSBB, CSSMBB
Fort Worth, TX

Statistical Methods and Control Charts

Analysis, Statistics, Control Charts, Statistical Methods

Question:
My question is regarding a threading process.  There is 100% inspection for go/no go check and about 5% rejection/rework.  The batch size is 5,000 nos and is completed in 3 days of production. Two such batches are produced in a month.

What type of control chart should be used to monitor the process? How should the process capability be calculated in this case?

Answer:
The type of control chart first depends on what type of data you are measuring.  If you are doing go/no go then you are limited to a “P” chart or a “C” chart.  A “P” chart looks at % good (or bad).  A “C” chart looks at the number of defects found.

If you are measuring thickness or strength, (something that can be measured), then you can use a X-bar/R chart or an X-bar/S chart depending on many samples are taken.

That is the simple answer; part of this depends on how you are taking samples and how often.  If samples are taken at the start and the finish, then I would probably recommend the “P” chart.

If you can measure throughout the manufacturing process, and you look at the type of defects, then I recommend a “C” chart.

Ideally, if you can get measurement data, you are better off with the X-bar/R or the X-bar/S charts.  These tend to be better predictors and it is easier to calculate capability.

With the capability for the go/no go data, you can get % defective, (or % good) and multiply that by 1,000,000 to get your capability estimate in defects per million.

Jim Bossert
SVP Process Design Manger, Process Optimization
Bank of America
ASQ Fellow, CQE, CQA, CMQ/OE, CSSBB, CSSMBB
Fort Worth, TX


Additional ASQ resources:

ASQ Learn About Quality- Control Charts

The Shewhart p Chart for Comparisons
by Marilyn K. Hart and Robert F. Hart

Control Chart to Analyze Customer Satisfaction Data

Control chart, data, analysis

Q: Let’s assume we have a process that is under control and we want to monitor a number of key quality characteristics expressed through small subjective scales, such as: excellent, very good, good, acceptable, poor and awful. This kind of data is typically available from customer satisfaction surveys, peer reviews, or similar sources.

In my situation, I have full historical data available and the process volume average is approximately 200 deliveries per month, giving me enough data and plenty of freedom to design the control chart I want.

What control chart would you recommend?

I don’t want to reduce my small scale data to pass/fail, since I would lose insight in the underlying data. Ideally, I’d like a chart that both provides control limits for process monitoring and gives insight on the repartition of scale items (i.e., “poor,” “good,” “excellent”).

A: You can handle this analysis a couple of ways.  The most obvious choice and probably the one that would give you the most information is a Q-chart. This chart is sometimes called a quality score chart.

The Q-chart assigns a weight to each category. Using the criteria presented, values would be:

  • excellent = 6
  • very good =5
  • good =4
  • acceptable =3
  • poor =2
  • awful=1.

You calculate the subgroup score by taking the weight of each score and multiply it by the count and then add all of the totals for the subgroup mean.

If 100 surveys were returned with results of 20 that were excellent, 25 very good, 25 good, 15  acceptable, 12 poor, and 3 awful, the calculation is:

6(20)+5(25)+4(25)+3(15)+2(12)+3(1)= 417

This is your score for this subgroup.   If you have more subgroups, you can calculate a grand mean by adding all the subgroup scores and dividing it by the number of subgroups.

If you had 10 subgroup scores of 417, 520, 395, 470, 250, 389, 530, 440, 420, and 405, the grand mean is simply:

((417+ 520+ 395+ 470+ 250+ 389+ 530+ 440+ 420+ 405)/10) = 4236/10 =423.6

The control limits would be the grand mean +/- 3 √grand mean.  Again, in this example, 423.6 +/-3√423.6 = 423.6 +/-3(20.58).   The lower limit is  361.86 and the upper limit is 485.34. This gives you a chance to see if things are stable or not.  If there is an out of control situation, you need to investigate further to find the cause.

The other choice is similar, but the weights have to total to 1. Using the criteria presented, the values would be:

  •  excellent = .3
  • very good = .28
  • good =.25
  • acceptable =.1
  • poor=.05
  • awful = .02.

You would calculate the numbers the same way for each subgroup:

.3(20)+.28(25)+.25(25)+.1(15)+.05(12)+.02(1)= 6+7+6.25+1.5+.6+.02=21.37

If you had 10 subgroup scores of 21.37, 19.3, 20.22, 25.7, 21.3, 17.2, 23.3, 22, 19.23, and 22.45, the grand mean is simply ((21.37+ 19.3+ 20.22+ 25.7+ 21.3+ 17.2+ 23.3+ 22+ 19.23+ 22.45)/10)= 212.07/10 =21.207.

The control limits would be the grand mean +/- 3 √grand mean.  Therefore, the limits would be 21.207+/-3 √21.207= 21.207+/-3(4.605).  The lower limit is  7.39 and the upper limit is 35.02.

The method is up to you.  The weights I used were simply arbitrary for this example. You would have to create your own weights for this analysis to be meaningful in your situation.  In the first example, I have it somewhat equally weighted. In the second example, it is biased to the high side.

I hope this helps.

Jim Bossert
SVP Process Design Manger, Process Optimization
Bank of America
ASQ Fellow, CQE, CQA, CMQ/OE, CSSBB, CSSMBB
Fort Worth, TX

Related Resources from the ASQ Knowledge Center:

Find more open access articles and resources about control charts in ASQ Knowledge Center search results:

Learn About Quality: Control Charts

The control chart is a graph used to study how a process changes over time. Data  are plotted in time order. A control chart always has a central line for the  average, an upper line for the upper control limit and a lower line for the lower control limit. These lines are determined from historical data. Read the full overview and download a free control template here.

Should Observations Be Grouped for Effective Process Monitoring? Journal of Quality Technology

During process monitoring, it is assumed that a special cause will result in a sustained shift in a process parameter that will continue until the shift is detected and the cause is removed.

In some cases, special causes may produce a transient shift that lasts only a short time. Control charts used to detect these shifts are usually based on samples taken at the end of the sampling interval d, but another option is to disperse the sample over the interval. For this purpose, combinations of two Shewhart or two cumulative sum (CUSUM) charts are considered. Results demonstrate that the statistical performance of the Shewhart chart combination is inferior compared with the CUSUM chart combination. Read more.

The Use of Control Charts in Health-Care and Public-Health Surveillance (With Discussion and Rejoinder), Journal of Quality Technology

Applications of control charts in healthcare monitoring and public health surveillance are introduced to industrial practitioners. Ideas that originate in this venue that may be applicable in industrial monitoring are discussed. Relevant contributions in the industrial statistical process control literature are considered. Read more.

Browse ASQ Knowledge Center search results for more open access articles about control charts.

Find featured open access articles from ASQ magazines and journals here.