Production Control

ISO 9001 section 7.5.1 states:  “The organization shall plan and carry out production and service provision under controlled conditions.”  It goes on to state that controlled conditions means availability of product specifications, work instructions, equipment, and test equipment etc.

In an audit I was conducting recently, I came across a work instruction that instructed the technician to ‘use the custom jig.’  This raises the question:  Does ‘controlled conditions’ mean that jigs and fixtures should be identified and controlled?  It’s an important question from the auditor’s point of view.  If the fixtures are not controlled, should I write a corrective action?

To me, control means the same thing for a tool that it means for a document or  inspection equipment.   It should be identified, it should have a revision, changes to it should be approved prior to issue, and it should be inspected periodically to assure it is viable for use.

I raised the question of controlled tools with other quality professionals, and they all seemed to think the standard is moot on this point.  They all agreed that it makes prudent sense though.  Other standards such as AS9100 (the aerospace equivalent of ISO 9001) clearly state the requirement for tool and jig control.

Consider the potential problems:

·         A fixture used in production wears out over time and must be replaced.  Without a drawing, how do we make a new one that works the same as the last one?

·       The fabricator of the fixture leaves the company, or worse, we need to use the tribal knowledge of the person who made it to make another (assuming he or she remembers how).

·         A technician trainee doesn’t know what ‘use the custom jig’ means and doesn’t use the one we’ve fabricated for the purpose.

The standard leaves many things to interpretation by the external auditor.  Regardless of what the standard says, or how it is interpreted, be sure to document all production tooling with a tool number or other identifier, revision level, and a controlled drawing.  Inspect the tooling periodically to assure it has not worn to the point where it should be replaced.  It may make your ISO 9001 implementation a little easier.

For more information go to www.rosehillsystems.com

On Inspection

ISO 9001 section 8.2.4 states “The organization shall monitor and measure the characteristics of the product to verify that product requirements have been met.”   The standard goes on to say “Evidence of conformity with the acceptance criteria shall be maintained.”  It fails to point out though that quality cannot be inspected into the product.

Many organizations fail to achieve high quality, because they rely on inspection in the belief that they can inspect quality into the product.  Point 3 of Edward Deming’s 14 points states “Cease dependence on inspection to achieve quality.  Eliminate the need for inspection on a mass basis by building quality into the product in the first place.”  Phillip Crosby’s second absolute of quality says it differently: “The way to cause quality is through prevention not appraisal [inspection]”.

For a good example of what can happen when inspection is used as the only means of assuring quality read Austin, A L (2013) “Failure of Inspection The consequences of layering on quality checks” Quality Progress, January 2013.

In an audit I performed recently, the customer was inspecting welds using dye penetrant testing.  I issued a corrective action because no evidence that the tests had been performed was available.  The test reports didn’t include a place for the technician to indicate that the testing had been performed.  When inspection is performed assure that the results of inspection are recorded. 

Keep in mind that even 100% inspection is not 100% effective.  Inspectors make mistakes too.  Focus on mistake proofing production processes and designing quality into the product, not inspecting quality into the product.  Inspect when you must.  Select inspection points to occur just before high cost processes occur.  This assures that the product conforms to requirements before high cost is added to the product.  When you inspect assure that the results of inspection are recorded.

For more information go to www.rosehillsystems.com

The Case for Quality

ISO 9001 section 0.1 states: “The adoption of a quality management system should be a strategic decision of an organization.”  While ISO 9001 is the most widely adopted quality management system (QMS) standard in the world, many companies attach little or no importance to a well-designed QMS.

A recent article: Stauffer, R, Owens, D. “Lasting Impression: Quality management’s positive impact on the economy” Quality Progress, November 2012 makes the economic case for a QMS implementation, asking the question: “Does quality have a payoff?”

The authors reference the study “The Contribution of Quality Management to the UK Economy”, jointly commissioned by the Chartered Management Institute (CMI) and the Chartered Quality Institute (CQI). 

The study, researched and written by the Center for Economic and Business Research (CEBR), conducted a review of relevant business and economic literature, developed 18 case studies from various business and public service sectors, and surveyed 120 organizations.  From the literature research it concluded that an effective QMS resulted in:

·         Upward pressure on stock prices while unsuccessful quality management systems had the opposite effect.

·         Enhanced customer and employee satisfaction and retention.

·         Reduced costs.

·         Improvement in key financial performance metrics.

·         Greater focus on customer satisfaction.

From the organizations surveyed the CEBR found:

·         On average costs were reduced by 4.8%.

·         More than 93% agreed that the QMS was a significant contributor to success.

·         95% agreed that the QMS contributes to customer retention and satisfaction.

·         83% thought that the QMS provides consistent improvement and therefore higher productivity

·         83% agreed that without an effective QMS they could not justify pricing to customers.

The study examined key economic indicators in the UK for 2011.  The study determined that an effective QMS:

·         Contributed ₤86 billion ($135 billion) to GDP.

·         Contributed ₤8.4 billion ($13.1 billion) in corporate tax receipts.

·         Caused employment to be 1.43 million higher (4.94%) than it would otherwise have been.

The article makes a strong case for a well-designed QMS.  The results are likely transferrable to the US economy with similar results.

The benefits of an effective QMS take time to appear.  They do not happen overnight.  Commitment, involvement, and leadership from senior management are keys to success.

For assistance with ISO 9001 implementation see www.rosehillsystems.com

 

Acceptance Sampling

ISO 9001 section 8.2.4 says that the organization must monitor and measure the characteristics of the product in order to verify that the product meets requirements, but is silent on the methods to be applied to monitor and measure product characteristics.  The choice of inspection methods is left to the organization. 

Acceptance sampling is one such method.  Since it is not practical to inspect every item in a large batch, acceptance sampling allows you to infer batch quality by examining a random sample from the batch.  Statistically designed acceptance sampling plans, as a method of measuring product conformity, have been around since at least World War II.  Sampling plans like MIL-STD-105, authored by Harold F Dodge and others, have been in use for over 60 years.

Because not all items in the batch are examined, there are risks associated with statistical acceptance sampling.  Two risks are typically calculated:

·         The Acceptable Quality Level (AQL), also known as producer’s risk, is the percent defective that is likely to be accepted 95 % of the time.  There is a 5% chance that product of higher quality than the AQL will be rejected by the sampling plan.

·         The Lot Tolerance Percent Defective (LTPD), also known as consumer’s risk, is the percent defective that is likely to be accepted 10% of the time.  There is a 10% chance product as defective as the LTPD will be accepted by the sampling plan.

Each sampling plan has an associated operating characteristic curve (OC) which describes the probability of lot acceptance as a function of the lot’s percent defective.  The AQL and LTPD represent two points on the OC curve.  As the lot percent defective increases, the probability of accepting the lot based on the sampling plan that the OC curve represents decreases. 
In addition to the risks associated with acceptance sampling plans, there are some practical disadvantages:

·         While acceptance sampling greatly reduces the number of items inspected, other sampling methods such as statistical process control reduce inspection even further and provide process control feedback.

·         When a lot is rejected, we will know why the lot is rejected, but we will not know the root cause of the defect.  We will only know that the product is defective.  There is no control mechanism that will help us control the process the product comes from.

·         Acceptance sampling assumes random sampling, but in most cases the sample is stratified because the product is normally stored in boxes.  As such, it is possible that some product will never have a chance of being sampled and the sample will not be random.

·         Sampling plans based on AQL, LTPD, or AOQL (Average Outgoing Quality Limit) assume that a certain amount of defective material is acceptable.  This sends a message to employees and suppliers that some level of defectiveness is acceptable.  This is not the best message to send if the organization is trying to be a best in class producer.

Sampling plans can be designed by users, or selected from standards such as MIL-STD-105, now obsolete, or ANSI Z1.4 which implements the same plans.  A common pitfall suffered by users not familiar with sampling plan design is to design constant percentage sampling plans.  Avoid constant percentage sampling plans (a fixed percentage of the lot is sampled regardless of lot size).  For small lots, constant percentage sampling plans may not afford enough protection.  For large lots, an excessive amount of inspection will usually result, and the sampling plan will be over critical.

Consider a sampling plan that samples 10% of a lot.  For a 50 piece lot, a 5 piece sample will result in approximately a 1% AQL, but a 46% LTPD (a very weak sampling plan).  For a 5000 piece lot, a 500 piece sample will result in an AQL of .001% and the LTPD will be .46% - a plan unlikely to accept any lot.

While there are better ways to control production processes than acceptance sampling, acceptance sampling can be an effective method for a customer to protect itself from accepting defective purchased product.  Since the customer has no control over the manufacturing process, it is not important for it to understand what process variable caused defective material.  It need only know that the product is defective.  The customer is susceptible to stratified sampling, but accepts this disadvantage in favor of inspecting a small percentage of the entire batch.

For more information see www.rosehillsystems.com

 

Process Control using Shewhart Control Charts

ISO 9001 section 7.5.2 talks about controlling processes.  Some processes are controlled because the quality of the resulting product cannot be determined once the process is complete as 7.5.2 indicates, but there are many other good reasons to perform process control.  Collecting data on a process for the sake of collecting data is of little value.  It is best to collect and analyze process data. 

There are many tools for controlling processes.  The one I’ll talk about today is statistical process control using Shewhart Control Charts.  Walter Shewhart developed control charts around the turn of the last century.  They are tried and true, and I have used them to control a number of processes.

Control charts are based on the theory that the mean of a sample from a normal distribution is normally distributed, with the mean equal to the distribution mean and a standard deviation equal to the distribution standard deviation divided by the square root of the sample size.  In fact, for distributions that are non-normally distributed, the mean tends to be normally distributed.  So the technique is robust.

Shewhart control charts control the process mean by controlling the sample mean.  They control the process standard deviation by controlling the sample range.   Control is applied by calculating control limits for the sample mean and sample range and plotting the sample mean and range on mean (XBAR) and range (R) charts respectively. 

When a mean or range exceeds its control limit, we say that the process has gone out of control.  A data point that exceeds a control limit causes us to stop the process to understand the root cause of the out of control condition, or, more likely we make a process adjustment and note the adjustment on the chart.  For example, it is common for a machine tool to go out of control due to tool wear.  A simple machine adjustment brings the machine tool back into control.

One complaint about Shewhart Control Charts is that they are complicated to set up.  Some also complain that they require the machine operator to stop and make calculations.  In the old days, calculators and computers were not available, and it was more work for the machine operator to calculate the mean and range.  In the digital age though, these complaints are unfounded.  Software to create control charts is easy and inexpensive to come by, and some inspection tools actually do the work of calculating and plotting the control charts.

I put together a simple Excel spreadsheet that will do all the work for you.  You can download it here.  The spreadsheet has some fictitious data placed there so you can see the charts in action.  You can easily overwrite it with actual data.  Instructions for creating a control chart using the spreadsheet can be found here.  Instructions for using the control chart spreadsheet are also provided, and you can download them here.

For questions or help go to www.rosehillsystems.com

Process Control and the Process FMEA

ISO 9001 section 7.5.2 addresses the control of production processes where the resulting output cannot be verified by subsequent monitoring or measurement.   In these cases, defects only become apparent during the use of the product.  Examples of processes that fall within this section are:

• Welding
• Brazing
• Wave Soldering

There are many others.

Control of processes like these is accomplished by developing a well-defined process, validating that the process is effective, and controlling process variables using techniques such as statistical process control.

One tool for validating that the process is effective is the process FMEA (pFMEA).   Process FMEAs are interesting because they have wide applicability to a broad range of processes beyond those covered by 7.5.2.

FMEA stands for Failure Modes and Effects Analysis.  The technique has been around for a long time, having been first developed by the US Military (MIL-P-1629).  Many industries, such as automotive and aerospace have embraced the FMEA approach to both design and process validation.

In performing a process FMEA, the various steps in the process are presented in a spreadsheet.  Each process step has some likely failure mode(s).  Each failure mode has one or more root cause(s). 

For each root cause there is some effect on the product.  This is known as a failure effect.  To each failure effect is assigned a value for Severity (1-10).  The root causes of the failure effect are assigned frequency of occurrence (1-10), and a likelihood of detection (10-1). These three values are multiplied together to come up with a single Risk Priority Number (RPN).

The RPN is used to prioritize actions to improve the process.  As action is taken and process steps are modified, the RPN of the modified process is calculated to show the relative improvement.

There are 10 steps to conducting a pFMEA:

1. Review the process.  Create a process flow chart.  Liest each process step in a pFMEA spreadsheet.
2. Brainstorm the potential failure modes of each process step.
3. List the potential failure effects of each failure mode.
4. To each failure effect assign Severity (S) rankings (1 means not very severe, 10 means very severe).  Record the highest value among the failure effects identified as the severity ranking for the process step.
5. Identify the potential root causes of each failure mode.  To each root cause assign Occurence rankings (1 means occurence s are rare, 10 means the root cause occurs frequently).  When there is more than one root cause, reaord the highest value among the root causes.
6. To each root cause assign a Detection (D) ranking (10 means the root cause is unlikely to be dteected, 1 means it is very likely to be detected).  When there is more than one root cause, record the highest value among the root causes.
7. Calculate the RPN = S x O x D
8. Develop an action plan to improve the process, prioritizing on the highest RPNs.
9. Take the actions in the plan.
10. Recalculate the resulting RPNs after the actions are taken.

The FMEA method is not perfect.   The values assigned for Severity, Occurrence and Detection are somewhat arbitrary.  Moreover the values assigned are ordinal numbers or rankings, meaning a number assigned to a level.  They could as easily represent descriptions such as very low, low, medium, etc.  A higher value for a factor is more important than a lower value. 

Multiplication of ordinal numbers is not defined (We can’t come up with a numeric value for ”Low” x “High”).  A value of “4” is not necessarily four times as important as “1”.  The value “4” is just more important than “1”.  Still, the RPN has value, and when used properly the FMEA can direct a team towards process improvements.   In addition, the completed pFMEA provides evidence of how the process was developed.

A spreadsheet that can be used to perform a pFMEA can be downloaded here.  It is based on the process FMEA promoted by the Automotive Information Action Group (AIAG).  An example of a simple FMEA can be found here.   

For questions or assistance with FMEAs go to www.rosehillsystems.com

Customer Satisfaction

ISO 9001 section 8.2.1 states:
"As one of the measurements of the performance of the quality management system, the organization shall monitor information relating to customer perception as to whether the organization has met customer requirements.  The mehtods for obtaining and using this information shall be determined."

Measuring customer satisfaction means understanding what your customers think.  There are many ways to glean this information including:

• Customer data such as quality and on time delivery reports
• Customer compliments or complaints
• Warranty claims
• Customer satisfaction surveys

The problem with the first method is that most customers don’t provide them, so you’ll only hear from a few customers and at most monthly.  It’s hard to generalize from just a few customer reports.

Customer compliments are rarer still.  It’s more common to hear from a customer when something goes wrong than when you’ve done something right.  Complaints tell what’s going wrong, but do not reflect the good the organization is doing.

Warranty claims are a good way to tell what goes wrong and how much it’s costing.   With this type of data, preventive actions can be focused and feedback on the effectiveness of actions taken will show up in a short time.

Perhaps the best way to monitor customer satisfaction is to ask your customers what they think.  Toward this end, customer satisfaction surveys are a preferred method of listening to customers.  There are many ways to implement these:

• Bingo cards - A bingo card is a small post card on which you ask a few questions and allow the customer to rate each question on a scale like 1 – 5. The other side has your address.They are typically placed in packaging are perhaps the least effective survey method. Few customers complete and return them.The card usually goes to the wrong department.A receiving department will probably just throw them out. It’s unlikely that they will get to the end user, and when they do, the end user is unlikely to return them. I tried these for several years and got less than a 1% return.
•  On line surveys from web sites like SurveyMonkey.com can be helpful, but again, the customer must be motivated to go to the site and complete the survey. Some companies run contests and award prizes as an enticement. Some retailers even offer discounts on future purchases for completing the survey. They get the data they want, and possibly produce another sale.    
• Salesmen’s feedback – One approach is to require the sales force to contact their customers periodically and ask a few well designed questions. The salesman completes a survey form and turns it in for analysis. This can be a good source of data, but the customer will be reluctant to indicate that the salesman is not meeting their expectations.
• The best method I’ve seen is the telemarketing survey. Telemarketers, properly trained, can get to the end user, ask a short list of well-designed questions, and get good feedback which can be analyzed and studied. At the same time, the telemarketer will probe the customer for other potential business. While costly, a well designed and implemented telemarketing survey will reward the company with excellent data, and additional sales that may exceed the cost of the survey.

Whatever method you choose, keep in mind that you should be able to analyze  the data and draw conclusions.  For help with this topic go to www.rosehillsystems.com