Quality Function Deployment

     

Quality Function Deployment 

QFD is a structured process that provides a means for identifying and carrying the customer’s voice through each stage of product development and implementation. QFD is achieved by cross-functional teams that collect, interpret, document, and prioritize customer requirements to identify bottlenecks and breakthrough opportunities.

QFD is a market-driven design and development process resulting in products and services that meet or exceed customer needs and expectations. It is achieved by hearing the voice of the customer, directly

stated in their own words, as well as analyzing the competitive position of the company’s products and services. Usually, a QFD team is formed, consisting of marketing, design, and manufacturing engineers,

to help in designing new products, using customer inputs and current product capabilities as well as  competitive analysis of the marketplace. QFD can be used alternately for new product design as well as focusing the efforts of the QFD team on improving existing products and processes. QFD combines tools from many traditional disciplines, including engineering, management, and marketing.

Measurement system Analysis

Measurement System Analysis

Measurement data is used to take a decision “to adjust a manufacturing process or not ” or  “ to accept a component or not”.

• Gauges are used on the shop floor to take such a decision.

• To ensure that the decision taken is correct, gauge being used should be both  accurate and consistent.

Bias 

Bias is the difference between the true values (reference value) and the observed average of measurements on

the same characteristics on the same part. Bias is the measure of the systematic error of the measurement system. It is the combined effects of all sources of variation known or unknown.

Probable causes for excessive bias are

Instruments needs calibration,Worn instrument, fixture, Improper calibration,Poor quality instrument, Linearity error,Wrong gauge for application,Environment (temperature, humidity, vibration, cleanliness 

Repeatability 

Repeatability is known as “within appraiser’ variation, Repeatability is the variation in measurements obtained with one measurement instrument when used several  times by one appraiser while measuring the identical characteristic on the same part .This is the inherent variation or capability of the equipment itself

Repeatability is “within system” variation when the condition of measurements are fixed and defined.  (fixed part, instrument, standard, method, operator, environment etc. )

Probable causes for poor Repeatability:

Within part            : Form, position, surface finish, taper

Within instrument  : Repair, wear, equipment or fixture failure, poor quality maintenance

Within standard    : Quality, wear, class

Within method       : Variation in set up, technique, zeroing, holding, clamping

Within appraiser    : Technique, position, lack of experience, feel, fatigue

Within environment: Temperature, humidity, vibration, lighting

Reproducibility

Reproducibility is known as “between appraisers” variation, Reproducibility is the variation in the average of measurements made by  different appraisers using the same measuring instrument when   measuring identical characteristic on the same part.

Probable causes for poor Reproducibility

Between appraisers

Average difference between appraisers A, B, C caused by training,technique, skill and experience.Instrument design, or method lacks robustness operator training effectiveness.

Gauge R & R or GRR

It is an estimate of combined variation of Repeatability and Reproducibility.GRR is the variance equal to the sum of within system andbetween system variances.

The Foundation of everything in Quality is measurement. so we measure for two primary reasons one is to make a decision. second is for  process improvement. As known generally there are two data attribute and variables  

• Attribute Data:  Categorical, named only, arbitrary scales, also known as Discrete Data ( kappa statistic is relevant ) 

May be required 50 parts two to three appraisers each three times inspection , the outcome of result in the form of  NG/P

P - Positive outcome by operator ; NG - Negative outcome by operator 

compare between the appraisers % of good and bad parts.

K= (po- pe)/(1-pe)

Kappa cross tab method between operators if the 

Values < 0.40 indicate poor agreement between appraisers
Values > 0.75 indicate good to excellent agreement ( max = 1 )

• Continuous Data:  Allows for infinitely finer sub-divisions,also known as Variables Data ( Kandall's statistic is relevant )

MSA factors Impacting Variation

• Gage

• Appraiser

• Method

• Product 

• Environment

%GRR = stdev(gauge)/stdev(total) -- In terms of round numbers, the %GRR guidelines are generally the same as the PTR guidelines. BTW, a %GRR of 30% is the same as saying that the measurement system variance is 9% of the total variance (in other words, less than 10%).

Note that if the part-to-part variation increases, %GRR goes down. This does not mean you should ask your friends in the fab to increase part-to-part variability. Ratios are just that – ratios. If your part-to-part variability is extremely low than your %GRR doesn’t compare directly with someone else’s %GRR where there is considerable part-to-part variability. If you're going to do a gauge r&r study, don't just pick two or three parts. You're either going to underestimate part variability or over estimate it, neither of which is helpful.

Also note that if you use 6 as your sigma multiplier for PTR, then %GRR divided by PTR (approximately) equals Cp.

Again, use your data and experience to determine how the %GRR metric can help you decide whether your measurement system is capable.

NDC = square-root[2*variance(process)/variance(gauge)] -- The number of distinct categories derives from another gauge metric, the discrimination ratio. Technically, the ndc can be interpreted as the number of non-overlapping confidence intervals that cover the range of the product variation. (Less technically, ndc can be interpreted as “never don’t concentrate” if you’re a Simpson’s fan.)

More practically, you can view the ndc as the number of distinct categories that the measurement system “sees” within a given parameter. Relatively large amounts of measurement error mean that two parts that are truly quite different from each other may look very similar to each other when measured. Relatively small amounts of measurement error mean that the measurement system can differentiate between two parts that are similar but not identical to each other.

The usual ndc guidelines state that ndc should be 5 or more, and that values less than 2 suggest a non-capable measurement system. An ndc of 5 is actually equivalent to a %GRR of around 27.1%, so the ndc and %GRR guidelines are not consistent with each other. See Some Relationships Between Gage R&R Criteria by William H. Woodall and Connie M. Borror in Quality and Reliability Engineering International (2008; 24:99-106) for more information.

Use your data and experience to determine if the ndc metric can help you measure and improve your measurement system.

Remember that dataConductor’s gauge r&r results can be easily filtered and sorted, and in combination with other statistics you can quickly spot unusual results. It's easy to drop in a line plot or build a scatterplot to compare appraisers. Sorting the min/mean/max plot from low to high in the default gauge r&r output is a great way to spot whether variability changes as the absolute measurement changes.

Remember too that gauge metrics are there to help you improve your measurement system, but the focus should be on the substance of the metrics and not just the repetition of their use.

APQP

TS 16949 CORE TOOLS

Core tools 

It is noting but  understand the basic requirements of the ISO/TS 16949 Quality Management System Requirements, and the 5 Core Tools.

1. Advanced Product Quality Planning (APQP)
2. Failure Mode and Effects Analysis (FMEA)
3. Production Part Approval Process (PPAP)
4. Fundamental Statistical Process Control (SPC)
5. Measurement System Analysis (MSA)

Advanced product quality planning (or APQP) is a framework of procedures and techniques used to develop products in industry, particularly the automotive industry. 

It is a defined process for a product development system for General Motors, Ford, Chrysler and their suppliers. According to the Automotive Industry Action Group (AIAG), the purpose of APQP is "to produce a product quality plan which will support development of a product or service that will satisfy the customer." The process is described in the AIAG manual, 

History 

Advanced product quality planning is a process developed in the late 1980s by a commission of experts gathered from the 'Big Three' US automobile manufacturers: Ford, GM and Chrysler. This commission invested five years to analyze the then-current automotive development and production status in the US, Europe and especially in Japan. At the time, the success of the Japanese automotive companies was starting to be remarkable in the US market.

APQP is utilized today by these three companies and some affiliates. Tier I suppliers are typically required to follow APQP procedures and techniques and are also typically required to be audited and registered to ISO/TS 16949. This methodology is now being used in other manufacturing sectors as well.

The APQP process is defined in the AIAG's APQP manual, which is part of a series of interrelated documents that the AIAG controls and publishes. The basis for the make-up of a process control plan is included in the APQP manual.These manuals include:

• The failure mode and effects analysis (FMEA) manual
• The statistical process control (SPC) manual
• The measurement systems analysis (MSA) manual
• The production part approval process (PPAP) manual

The Automotive Industry Action Group (AIAG) is a non-profit association of automotive companies founded in 1982.

[EDIT]MAIN CONTENT OF APQP

APQP serves as a guide in the development process and also a standard way to share results between suppliers and automotive companies. APQP specify three phases: Development, Industrialization and Product Launch. Through these phases 23 main topics will be monitored. These 23 topics will be all completed before the production is started. They cover aspects as: design robustness, design testing and specification compliance, production process design, quality inspection standards, process capability, production capacity, product packaging, product testing and operators training plan between other items.

APQP consists of five phases:

• Plan and Define Program
• Product Design and Development Verification
• Process Design and Development Verification
• Product and Process Validation
• Launch, Feedback, Assessment & Corrective Action

The Advanced Product Quality Planning process consists of four phases and five major activities along with ongoing feedback assessment and corrective action as shown below.

A further indication of the APQP process is to examine the process outputs by phase. This is shown in the table below:

The APQP process involves these major elements:

• Understand customer needs . This is done using voice of the customer techniques to determine customer needs and using quality function deployment to organize those needs and translate them into product characteristics/requirements.
• Proactive feedback & corrective action. The advance quality planning process provides feedback from other similar projects with the objective of developing counter-measures on the current project. Other mechanisms with verification and validation, design reviews, analysis of customer feedback and warranty data also satisfy this objective.
• Design within process capabilities. This objective assumes that the company has brought processes under statistical control, has determined its process capability and has communicated it process capability to its development personnel. Once this is done, development personnel need to formally determine that critical or special characteristics are within the enterprise's process capability or initiate action to improve the process or acquire more capable equipment.
• Analyze & mitigate failure modes. This is done using techniques such as failure modes and effects analysis or anticipatory failure determination.
• Verification & validation. Design verification is testing to assure that the design outputs meet design input requirements. Design verification may include activities such as: design reviews, performing alternate calculations, understanding tests and demonstrations, and review of design documents before release. Validation is the process of ensuring that the product conforms to defined user needs, requirements, and/or specifications under defined operating conditions. Design validation is performed on the final product design with parts that meet design intent. Production validation is performed on the final product design with parts that meet design intent produced production processes intended for normal production.
• Design reviews . Design reviews are formal reviews conducted during the development of a product to assure that the requirements, concept, product or process satisfies the requirements of that stage of development, the issues are understood, the risks are being managed, and there is a good business case for development. Typical design reviews include: requirements review, concept/preliminary design review, final design review, and a production readiness/launch review.
• Control special/critical characteristics. Special/critical characteristics are identified through quality function deployment or other similar structured method. Once these characteristics are understood, and there is an assessment that the process is capable of meeting these characteristics (and their tolerances), the process must be controlled. A control plan is prepared to indicate how this will be achieved. Control Plans provide a written description of systems used in minimizing product and process variation including equipment, equipment set-up, processing, tooling, fixtures, material, preventative maintenance and methods.