A Summary of MSA Approaches for Attribute Measurement Systems

Attribute measurement systems (abbreviated as AMS in the rest of this article) are systems which differentiate the measured parts into some categories, such as OK and NG, instead of characterizing them with variable data. For the convenience of discussion, this article will further divide AMS into two types:

Type 1: AMS for characteristics which can also be evaluated with variable measurement systems. An example of such AMS is the system with a pin gauge. It differentiates the measured parts into two categories: OK or NG, but the diameter of the hole can also be measured with a variable measurement system such as CMM.

Type 2: AMS for true attribute characteristics which cannot be evaluated with variable measurement systems (abbreviated as VMS in the rest of this article). An example of such AMS is the manual visual inspection system on products appearance. The spec of appearance is often not defined with variable data.

In AIAG’s MSA manual (4^th edition), four approaches are introduced for the MSA of AMS, as listed below:

1.       Gauge performing curve

2.       Signal detection theory

3.       Attribute control charts

4.       Hypothesis test analysis

None of the above approaches can assess all the five types of variability of a measurement system, i.e. bias, linearity, stability, repeatability and reproducibility. They all have certain limitation as explained below:

The 1^st approached is an analytical method which gives good quantitative assessment on the measurement system. But it is only good to study the bias and repeatability. Also, it is only good for Type 1 AMS, as the reference value of the samples used in this study must be available in quantitative data. Readers may refer to AIAG’s MSA manual (Chapter III, Section C) for how to establish the gauge performing curve a measurement system. It is quite clearly illustrated there.

Same as the 1^st approach, the 2^nd approach can only be used for Type 1 AMS too. It can be used to assess the GRR and GRR only of the measurement system. It also gives a quantitative result, but it is an estimated one. The estimation becomes better with the increase of sample size. As this approach gives only an estimation of GRR, it should be approved by the customers before it is used.  

         The 3^rd approach is only briefly mentioned in the MSA manual (P.145, 4^th edition). This approach is only good for stability study. Same as the stability analysis of a VMS, one needs to follow the below steps: pick a sample, establish the control charts and then study the control charts (please refer to this article for stability study of VMS). Picking sample for stability study of AMS is much more critical than the stability study of VMS. It is probably the key for a good stability study of AMS. In AIAG’s manual (please refer to the footnote in P.145, 4^th edition), it’s noted that for the chosen sample, np must be greater than 4, i.e. if the sample is measured ten times, there are at least 4 times this sample is categorized as NG. So the sample should not be an OK sample which is far away from the spec limit, otherwise there is a low chance of getting an NG result from measurement. It should not be an NG sample which is far away from the spec limit either, otherwise, UCL and LCL are same and the control charts makes no sense (please refer to formula of UCL and LCL of p charts and np charts in the SPC manual). So the sample must be close to the spec limit in the gray area of the below image, with a chance of having both OK and NG results.  

In principle, this approach can be used for both Type 1 and Type 2 AMS, but if AMS involves human judgement, this approach may not work well, because the appraisers can remember the past judgements and it can influence the upcoming judgements. So this approach is good if the equipment makes the judgement, but not suggested if the appraisers make the judgement.  

The 4^th approach can be used for both Type 1 and Type 2 AMS. But unfortunately, the kappa value obtained with this approach can only assess the agreement between different appraisers and does not yield any quantitative assessment of any type of variation of the measurement systems. One cannot really tell how big the variation of the measurement system is with this approach. Therefore it should approved by the customers if an organization plans to use it. 

As a summary, below is the table which lists up the possible MSA approaches for AMS. Please be noted that the agreement analysis in the last column does not belong to the five types of MSA defined in the MSA manual.

Measuring Equipment Calibration or Verification

    In 7.1.5.2 of ISO9001:2015, it requires that measuring tools shall be calibrated or verified or both:

So in which cases shall the measuring equipment be verified, in which cases shall they be calibrated, and in which cases shall they be done both?

    To answer this, one first needs to understand the difference between verification and calibration. The measuring process is a process to assign a value to the measured characteristic. There are two values which should be noted here: one is the true value of the characteristic ut, and the other is the assigned value of the characteristic ua, i.e. the result obtained from the measurement. Verification is the serial of activities done to prove that the difference between ut and ua is within the acceptable range (which can be defined internally, according to customer requirements, according to national standard, etc.). So the result from verification is a judgement of “Acceptable” or “Not Acceptable”. Calibration, however, is the serial of activities which are done to establish the proper relationship between ut and ua, so that acceptable ua can be obtained from the measuring process. Below are two examples of calibration:
Example 1: a digital caliper gives the reading of a dimension. It first transfers the measured dimension ut into an electrical signal, and then transfers the electrical signal into a digital reading ua. The manufacturer of the digital caliper must study how the signal transferring from the measured dimension into the digital reading is done and establish their relationship properly. Otherwise, the reading ua may depart significantly from the actual dimension ut.
Example 2: an electronic scale is used to weigh the products. An operator measured 5 samples with known value and obtained following results:

From the above measurements, the operator found that the reading is always 0.1kg greater than the true value. So for future measurements, when he records the weight of products (please be noted that here ua is the recorded value, not the reading from the scale), he’ll subtract 0.1kg from the reading.

    Now let’s go back to the question: when shall the measuring equipment be calibrated, when shall they be verified, and when shall they be done both? The answer is given as below:

• If the measuring equipment is purchased, the suppliers have already done calibration, so the equipment shall be first verified before use. If it does not pass the verification (i.e. the judgement is “Not Acceptable), then it should be further calibrated.
• If the measuring equipment is homemade, the equipment shall be calibrated first. Once the calibration is done, for future use, it should be further verified, and calibrated if necessary, just as the purchased equipment.

How to Choose the Right Control Chart for Statistical Process Control

    For characteristics which are evaluated with variable data, AIAG’s SPC manual recommended 4 types of control charts:

1. Average and Range Chart (X ̅-R Chart)
2. Average and Standard Deviation Chart (X ̅-s Chart)
3. Median and Rang Chart (X ̃-R Chart)
4. Individuals and Moving Range Chart (X-MR Chart)

    Arising from the 4 options, a question that many SPC beginners may have is: which control chart should I use for my process? The answer can be found in P.177 of the 4th edition of the SPC manual, but it is very brief. Below is a more detailed explanation about the selection procedure of the right control chart:

    First, let’s take a look at the X ̅-R Chart, which is probably the most popular chart. For this chart, the control limits of X ̅ and R are calculated with the following equations:

where R ̅ is the average of ranges of individual subgroup. From the above equations, it can be seen that when R ̅ is 0,and. The control limits make no sense in such case. That means if one needs to use X ̅-R chart, there must exist detectable within-subgroup variation. The above equations also show that the width between UCL and LCL of X ̅  is. In order not to see too many undesired out of control signals, the X ̅-R Chart should only be used when the between-subgroup variation is insignificant compared with within-subgroup variation. Otherwise, the future data point of X ̅ may easily go out the control limits due to big between-subgroup variation (Note: generally, big between-subgroup variation, compared with within-subgroup variation, is not accepted as it indicates the existence of special causes. But if both Cpk and Ppk are significantly larger than the requirement, e.g. Cpk=8, Ppk=5, it may be accepted). As a summary, to use X ̅-R chart, the within-subgroup variation must not be 0 and the between-subgroup variation must be insignificant compared with within-subgroup variation.

    Here is an example when the X ̅-R chart should not be used: The process is to produce a chemical solution by adding solvent into the distilled water. It is produced one bath a time, and one bath is one lot. The monitored characteristic is the composition of the solution. As the solution is homogenous, taking several samples from one bath as one subgroup gives no within-subgroup variation. On the other hand, the difference between different lots may be more significant, as the amount of solvent added to each bath may differ. So using X ̅-R chart is inappropriate for this process.

  In the above case, one should use the X-MR chart. The X-MR chart requires no subgroups (or you may consider that the subgroup size is 1). Individual sample is taken, and the difference between consecutive samples is calculated to estimate the process variation and the control limits, as in the following equations:

where (MR) ̅ is the average of the differences between all consecutive samples.

    X ̅-s chart is close to X ̅-R chart. The key difference is that the process variation in this chart is estimated with the average standard deviations, instead of average ranges, of the subgroups, and the control limits are calculated with the following equations:

where s ̅ is the average of the standard deviations of individual subgroup. Similar to X ̅-R Chart, X ̅-s chart should only be used when within-subgroup variation is detectable, and the between-subgroup variation is insignificant compared with within-subgroup variation.

Since X ̅-s chart is very close to X ̅-R chart, when should they be used respectively? In fact, X ̅-s is always preferred over X ̅-R chart, as the process variation σ ̂_X ̅  is better estimated with average standard deviation of subgroups than average range of subgroups. One may consider using X ̅-R chart only if the SPC cannot be done with computer and hence calculation of standard deviation is inconvenient. But if the subgroup size is 9 or more, the estimation of σ ̂_X ̅  with average range of subgroups becomes too poor, and X ̅-s chart should be used in this case, even the calculation of standard deviation is difficult.

    X ̃-R chart is also close to X ̅-R chart. But in this chart, median, instead of average, of each subgroup is monitored. Comparing X ̃-R chart with X ̅-R chart, the latter is preferred, since for process control, one cares about the location of the process average, which is better estimated with X ̅ than X ̃. X ̃-R chart should be used only if the calculation of subgroup average is not convenient, e.g. when doing SPC on a paper.

    As a summary, when choosing the right control charts, one needs to follow the below priority:

1. Average and Standard Deviation Chart (X ̅-s Chart)
2. Average and Range Chart (X ̅-R Chart)
3. Median and Rang Chart (X ̃-R Chart)
4. Individuals and Moving Range Chart (X-MR Chart)

i.e. X ̅-s chart should be always considered first, but if the subgroup size is less than 9 and the calculation of standard deviation is not convenient, X ̅-R chart can be used as an alternative. And if the calculation of subgroup average is also not convenient, then X ̃-R chart can be used instead of X ̅-R chart. All the above control charts require that subgroups with detectable within-subgroup variation can be obtained, and the between-subgroup variation is insignificant compared with within-subgroup variation. If it’s not possible to obtain such subgroups, one can then use X-MR chart.

How to Determine Whether Recommended Actions are Needed after the Initial Risk Assessment with FMEA

    AIAG’s FMEA manual (P.57 and P.103 in 4th edition) requires that once the initial risk assessment is done in FMEA, it should be decided whether further efforts are needed to reduce the risk. In the 3rd revision of the manual,  a threshold of RPN=100 was set as the criteria, above which actions are needed, and vice versa. In the 4th edition, however, it is recommended NOT to use such a threshold to determine the need for actions, as it is against the concept of continual improvement (If a threshold is defined, no further actions will be taken if the RPNs in an FMEA are all below it). The 4th revision of FMEA manual now requires that the FMEA team should do review on their FMEAs and continually take new actions for improvement.

    Now let’s see how FMEA review should be done and in which cases new actions should be proposed.

    First, review should be done shortly after the initial establishment of the FMEA to identify all failure modes with S=9 or 10, as required in the 4th edition of the FMEA manual (P.103).

For these failure modes, actions should be proposed to reduce the risk. For example, the design team may change the design to reduce the score of severity until S≤8. If change of the design is not possible, the design team should then propose actions to reduce the score of occurrence and detection. How much should they be reduced? It depends on the customer requirements. For example, I have seen customer which requires that occurrence must be reduced until O=1 for any failure modes with S=9 or 10. In such cases, the FMEA team needs to think of some error proofing methods to eliminate the occurrence of the failure modes. If no customer requirements exist, the organization can determine internally their requirement for occurrence and detection when S=9 or 10. A reasonable requirement is to reduce the possibility of occurrence until O≤3, as most automotive customers require Cpk≧1.33 for special characteristics (Failure modes with S=9 or 10 should be regarded as special characteristics due to safety concern. Cpk≧1.33 is equivalent of a PPM≤63, O=3 is equivalent to PPM≤10 and O=4 is equivalent to PPM≤100. To meet the customer requirement of Cpk≧1.33, O should at least be 3). The above review and actions should be ideally taken before the release of product or process.

    Once the actions have been proposed and taken for all failure modes with S=9 and 10, and have effectively reduced O and D to the desired level, the FMEA team should now do review on failure modes with S≤8 with a regular interval, e.g. once a year, after the release of the product and process (such review is not mandatory before product or process release). Since there are so many failure modes in the FMEA, it’s not possible to propose and take actions for all of them, the organization should establish criteria to pick the failure modes for which actions will be proposed and taken. The 4th edition of FMEA manual requires that those failure modes with the highest O or D should be picked (P.103):

Other than that, no further requirement is specified in the manual. The organization has its own freedom to set up more specified criteria (but of course customer requirements should be followed if there’s any). To limit the work load within a reasonable scale, the organization may use the criteria below to pick out the failure modes with S≤8 for further improvement:

1. The team should first pick out the failure modes with highest O or D (no priority between O and D). 
2. If there exist more than 3 failure modes which meet the above criteria, the team should pick out, among them, the ones with highest RPN.
3. If there still exist more than 3 failure modes which meet the above criteria, the team should pick out, among them, the ones with highest SxO or SxD (no priority between SxO and SxD).  
4. If there still exist more than 3 failure modes which meet the above criteria, the team should pick out, among them, the first three as appeared in the control plan.

    Let’s take a look at one example with 8 failure modes as shown below. In this example, the FMEA team first picks out No. 3, 4, 5, 6 and 7, as they have the highest O or D=8 (there’s no priority between O and D). Among these five failure modes, the team then looks RPN, and picks out those with highest RPN=240, which are No. 3, 4, 6 and 7. Among these four, the team then looks at the ones with highest SxD or SxO (there’s no priority between SxO and SxD), and picks out No. 3, 4 and 6. At this point, there’re only three failure modes left, then the team should propose and take actions for them for improvement.

    As summary, when there’s no customer requirement exists, the organization may establish such criteria to review FMEA and take actions for continual improvement:

1. Review the FMEA shortly after its establishment and before the product and process release and take actions for all failure modes with S=9 and 10, to reduce the occurrence until O≤3;
2. Review the FMEA once a year after product and process release, and follow the criteria below to pick out no more than 3 failure modes and take actions for improvement:

• Pick out the failure modes with highest O or D (no priority between O and D). 
• If there exist more than 3 failure modes which meet the above criteria, among them pick out the ones with highest RPN.
• If there still exist more than 3 failure modes which meet the above criteria, among them pick out the ones with highest SxO or SxD (no priority between SxO and SxD).  
• If there still exist more than 3 failure modes which meet the above criteria, among them pick out the first three as appeared in the control plan.

How to Do Stability Study on a Measurement System

    Measurement system analysis includes five types of studies: bias, linearity, stability, repeatability and reproducibility. Among all of them, stability study is relevant easy and requires not too much concept in statistics. One can do it with some basic knowledge in SPC.

    Now let’s take a look at the detailed steps for stability study:

1. Pick a sample

    The first step is to pick a sample which will be measured and monitored. The sample is not necessary to be a calibrated standard with known value. It can just be any sample, and its value can also be unknown. But of course, same as any other analysis, this sample must have little within sample variation.

2. Collect the measurement data and establish the control chart with control limits

    Control charts, with established control limits, are needed for stability study. This can be done same as the conventional SPC (You may refer to this article about how to establish the control limits). The difference is, for process control, multiple samples are collected and one datum is read from each sample to form one subgroup, while for stability study, multiple readings are obtained from the same sample, as picked in Step 1, to form one subgroup.

    So how many readings should be obtained for each subgroup and how many subgroups should be collected before the control limits can be established? In P.86 of the 4th edition of AIAG’s MSA manual, 5 measurements are done in each shift, and totally 100 measurements are done to establish the control limits, i.e. 20 subgroups with a subgroup size of 5. But the 2nd edition of AIAG’s SPC manual suggests collecting at least 25 subgroups with at least 100 data points before establishing the control limits (P.57). My personal opinion is to stick with the rules in the SPC manual.

    An example of the control charts established is shown in the graph below. AIAG's MSA manual suggests using Xbar-R or Xbar-s control charts.

Figure 1 Control charts with control limits for stability study, cited from 4th edition of AIAG's MSA manual.

3. Analyze the control charts and determine the stability of the measurement system

    After establishing the control charts and control limits, one can then analyze the control charts and see whether any data point indicates a special cause. Same criteria for identifying the special cause in SPC, as shown below, can be used here.

Figure 2 Criteria for Identifying Special Cause, Cited from 2nd Edition of AIAG's SPC manual

    If no data point indicates a special cause in the control charts, then one can concludes that the stability of the measurement system is good. Otherwise, special cause exists in the measurement system and one needs to identify it and take actions to remove it to improve the stability.

4. Keep on monitoring 

    The stability study of the measurement system is completed up to Step 3. But if you’re careful, you don’t have to stop here. You can just keep collecting readings from the picked sample, with the defined subgroup size and sampling frequency, and plot the measured results in the control charts. By doing so, you are continuously monitoring the stability of the measurement system and actions can be taken whenever a special cause is detected. A continuous monitoring on the measurement system is certainly better than once a year study.

How to Write D3 Containment Actions of an 8D Report

When customers issue claims to suppliers and require 8D reports, many of them require preliminary reports up to D3 within 24 hours. In such preliminary reports, the most important part, undoubtedly, is D3 Containment Actions.

The purpose of containment actions is to quarantine the detected problem, and prevent it from occurring to the same and other customers again. To make the containment actions effective, one must identify and analyze all the products with the potential risk same as the detected one. These products include those on the customer side, those on the way to the customers, those stored in the supplier’s warehouse, WIP in the production line, and even the raw materials if the problem is caused by the raw material. The risk associated with all these products should be analyzed based on the historical production and inspection data. Re-inspection should also be conducted if necessary. Based on the analysis and re-inspection results, one then determines how to deal with these products. If risk exists, they should be recalled or quarantined.

The following template may be used to summarize the containment actions in the 8D report

In the above column of “Attachment”, evidences can be attached to support the analysis and inspection result. In the column of “Actions Taken”, the actions taken based on the analysis and inspection results can be described, such as recall or quarantine if the risk exists.

It should be noted that the containment actions should not be limited to the customer who issued the claim, and should also not be limited to the same products as the claimed ones. One must analyze whether same risk exists to the other customers and the other products. If yes, actions should be taken to quarantine such risk as well. 

Some Skills and Experience Sharing in Doing 5 Whys Analysis

                Problem: Superman was late for his work this morning
                Why 1:  Why was Superman later for his work this morning?
                              Because he woke up late.
                Why 2: Why did he wake up late?
                             Because the alarm clock did not ring.
                Why 3: Why didn’t the alarm clock ring?
                            Because the battery was dead?
                Why 4: Why was the battery dead?
                 Because it was not replaced promptly before the end of its lifetime.
                Why 5: Why wasn’t the battery been replaced promptly before the end of its lifetime?
                 Because there was no established plan to replace the battery regularly.   

     Above is an example of 5 whys analysis (please see this article for introduction of 5 why analysis). It is a powerful tool for root cause analysis, but if it's not properly used, it may not lead to the right root cause. Below are suggestions that the users of 5 whys analysis need to keep in mind in order to effectively use this tool.

Always Try to Find the Systematic Cause
    One of the widely mistakes in 5 whys analysis is that the users stop too soon before the real root cause is identified. One can use the following criteria to determine whether the cause he has identified is the real root cause or not:
    If an action taken to resolve the cause can effectively prevent the recurrence of the problem, the cause is the root cause.
    Generally the causes of any problem occurred in a company can be classified as technical causes and systematic causes. Technical causes are the technical issues with 5M1E (direct issues with machine, material, method, men, measurement and environment, e.g. malfunction of the machine, defect materials and incorrect processing parameters), which result in the occurrence of the problem. While systematic causes are issues in the management procedures (e.g. ineffective procedure to prevent the machines from malfunction) and the management team (e.g. insufficient allocation of resources) which result in the technical causes of the problem. Normally, when doing 5 whys analysis, one should not stop at the technical cause and should continue until the systematic cause is identified. It is important to realize that resolving the technical cause can only restore the situation to normal. It is just a corrective action and cannot prevent the recurrence of the same problem. In the “Superman is late for his work” example, the technical issue is the battery was dead. If Superman thinks this was the root cause and stops asking himself further "why"s, he’ll just replace the battery and move on with his life. Inevitably, however, the battery will die again and he’ll probably wake up late again, and the problem recurs. Therefore he should not stop at the technical issue. He should do further analysis until he finds out the systematic cause: he did not have a plan to regularly replace the batter before it reaches its lifetime.

Do NOT Attribute the Root Cause to People
    Another common mistake of using the 5 whys analysis is that people, but not the system, is attributed as the root cause of the problems. For example, operators being careless is often regarded as the root cause for defective products that the operators produce in the production line. Such analysis is meaningless, as no corresponding action exists to ensure that operators will be careful all the time in the future to prevent the recurrence of the problem. If human error is the technical cause of the problem, the person doing the 5 why analysis may further ask himself: Is the training provided to the operators sufficient? Are the operators properly evaluated after the training to ensure they’re capable of performing their tasks? Is there error proofing method or reminding system existed to prevent the human error? Accordingly, the root cause can be attributed to insufficient training, insufficient evaluation, no error-proofing method or no reminding system.

Keep Clear Cause-and-Effect Relation Between Each Why and Its Answer
    To lead to the right root cause with the 5 whys analysis, it must be kept in mind to remain the tight cause-and-effect relationship between the question and the answer of each "why", so that there in turn exists a cause-and-effect relationship between the root cause and the occurred problem. It is not rare to see that cause-and-effect relation is not clearly observed in the analysis. Particularly, I have personally seen many cases, in which the root cause of the problem were just re-statement of the problem in different words. 

Ask the Right Question 
    In 5 whys analysis, the answer to the current "why" is the question for the next "why". One may lose the cause-and-effect relationship between the root cause and the occurred problem if incorrect question is asked. Such a mistake is quite common when the answer to the current "why" is too long or multiple reasons are identified, and hence the user cannot find the proper question for the next “why”. It is suggested to make the answer short in each “why”, and the question of each “why” should be exactly same as the answer to the previous "why", so that it does not cause confusion and the cause-and-effect logic can be well maintained (Note: In cases that multiple reasons exist for a “why”, it is probably not a good idea to use a single 5 why analysis to find the root cause. Instead, one may use other methodologies, such as Fault Tree Analysis).

Avoid the Leap of Logic
    Another common mistake in 5 whys analysis is the leap of logic, i.e. the answer to a "why" is not the direct reason. In the “Superman is late for his work” example, the answer to the 1st "why" is “Because he woke up late”. Some people may answer “Because the alarm clock did not ring”. Alarm clock did not ring, however, was not the direct reason of being late. Waking up late was.
    When identifying the root cause of real issues occurred in a company, the leap of logic may occur when the user of 5 why analysis overlooks some steps in a process, e.g. the communication between owners of the different process steps. Let’s take a look at following example:
                Problem: a defective product is produced
                Why 1:  Why was the defective product produced?
                              Because the machine suddenly broke down.
                Why 2:  Why did the machine suddenly break down?
                              Because the machine is low of cooling oil.
                Why 3:  Why is the machine low of the cooling oil?
                              Because the operator did not refill the machine with the cooling oil.
                Why 4:  Why didn’t the operator refill the machine with the cooling oil?
                              Because the operator was not aware that the machine needs to be refilled with
                              cooling oil regularly.
                Why 5:  Why was the operator not aware that the machine needs to be refilled with
                              cooling oil regularly?
                              Because the operator did not receive such a plan.
               Why 6:  Why didn’t the operator receive such a plan?
                              Because the equipment engineer did not establish the plan.
When answering the 4th “why”, some people may write the answer of the 6th “why” instead. If so, there's a leap of logic. Therefore, the user of 5 why analysis should not overlook any step in a process which should be conducted in order to effectively prevent the occurrence of a problem.

Change Control

    IATF16949:2016 requires control on product or process changes, which are specified in 8.3.6 and 8.5.6. To fulfill these requirements, one may follow the change control procedure as outlined below:

Step 1: Identification of Needs for Changes
 
    The needs for changes may come internally or externally. Internal needs arise from process improvement, capacity expansion, cost reduction, risk management, etc. External needs arise from customer request, customer claims, governmental and legal requirements, etc. It should be noted that the scope of product and process changes is really broad. It’s not limited to the changes in product specification or process parameters which directly impact the product quality. It also includes changes such as new suppliers, new machines, new production line, new control limits of a process, and even new frequency for preventive maintenance. In general, any change in machine, material, measurement, method and environment should be included in the scope of the change control procedure. Regarding to the changes in man, as IATF16949 already have separate requirements in employees qualification, it can be excluded from change control procedure.

Step 2: Change Application

    When the need for change is identified, an owner for the change should be assigned and the owner should submit an application of the change proposal to a Change Review Board. The Change Review Board should be a cross-functional team consisting of people from different departments.
When the owner submits the change application, he should identify the content of the change, the purpose of the change, the change level (if a company has a pre-established classification of change levels) and the proposed date of implementation. He should also indicates whether it is a temporary change, and the proposed end date of the change if the answer is “yes”. In automotive industry, customers usually require that suppliers should notify them in advance of product or process changes. In this case, the owner should indicate as well, according to customer requirements, whether the proposed change needs customer notification. Shown below is the suggested information which can be included in a change application.

Step 3: Internal Review on the Change Application

    After the Change Review Board receives the application, the members should review the application. If the application is not accepted, the board can terminate the flow. If the application is accepted, it must go to Step 4 if customer notification is needed before further action.

    After accepting the application and completing Step 4 if necessary, the board needs to determine whether verification is needed for the changes. If no, then the owner can just implement the change as proposed, and the process stops here. If yes, it goes to Step 5 for change verification.

Step 4: Customer Notification of the Change Proposal

    If customer notification is needed according to customer requirements, the customer contact window in the company should send a change notification to the impacted customers, in the format required by the customers. Customer feedback must be received before further action. If customers approve the application, it goes back to Step 3 for internal review on the need of change verification. If customers decline the application, the process stops here.

Step 5: Change Verification

    If the change needs verification before implementation, the owner should identify evaluation items and the acceptance criteria, and conduct verification accordingly to determine whether the change can achieve the desired result and cause no negative impact. The owner can consider use the following table to summarize the verification results.

After completing the verification activities, the owner submits the verification reports to the Change Review Board.

    Regarding the verification needed for the change, the company or the customers may have a pre-defined list of evaluation items for each type of change. A good example is the Delta Qualification Matrix suggested by ZVEI (German Electrical and Electronic Manufacturers' Association). If such list exists, the owner can also include the evaluation items and acceptance criteria in the change application in Step 2.

Step 6: Internal Review on the Verification Report

    After receiving the verification report, the Change Review Board should review the verification activities and results and decide whether the change is acceptable. If not, the change should be rejected and the process stops here. If yes and customer notification is needed, it should go to Step 7 before further action. Otherwise, the change is approved and the owner can start the change implementation (Step 8).

Step 7: Customer Notification of the Verification Report

    If customer notification is needed, the verification report should be submitted to the customers after internal approval. If the customers approve the change based on the verification report, it can then be implemented internally as described in Step 8. If the customers reject the change, the process should be terminated here.

Step 8: Implementation of Changes

    After the change is approved by the Change Review Board and customers if necessary, the owner can then start the change implementation. The owner should identify what documents should be updated based on the change and notify the document owners to update them accordingly. The owner should also verify the change is implemented according to the planned date. If not, it should be updated with the customers for the proposed new date. Also, the owner should further follow up the product and process after change, and see whether the change achieve the desired objective and whether any negative impact is resulted from the change.

    Above, the full process of a change control is described. As some final words of this article, if customer notification is needed, it is suggested that an application should always be submitted before any verification activities. Some companies tend not to notify their customers before they have the verification results. However, if the verification is done and even the results is good, the customers still may reject the change for some other concern. In such case, the verification done is just a waste of resource.