FMEA & Control Plan

FMEA is an integral part of any QS 9000 compliant compliant quality system. Figure 1 illustrates the role of FMEA in a typical quality system.

Figure 1. The role of FMEA in a quality system. This portion of the site is intended to provide the visitor with useful and pertinent information regarding the FMEA process. It can serve as a reference document for individuals or teams familiar with FMEAs. It is not a comprehensive instruction guide for performing an FMEA, and should not be used as such.

Definitions: Cause Critical Characteristi Criticality  Current Controls Customer Detection Effect Failure Mode FMEA Element

• Function • Occurrence • Risk Priority Numbe • Severity  • Significant Characte • Special Process Char • Special Product Cha

Cause

 A Cause is the means by which a particular element of the design or process results in a Failure Mode.

Critical Characteristics: Critical Characteristics are Special Characteristics defined by Ford Motor Company that affect customer safety  and/or could result in non-compliance  with government regulations and thus require special controls to ensure 100% compliance.

Criticality: The Criticality rating is the mathematical product of the Severity and Occurrence ratings. Criticality = (S) × (O). This number is used to place priority on items that require additional quality planning.

Current Controls: Current Controls (design and process) are the mechanisms that prevent the Cause of  the Failure Mode from occurring, or  which detect the failure before it reaches the Customer.

Customer:

Customers are internal and external departments, people, and processes that  will be adversely affected by product failure.

Detection:

Detection is an assessment of the likelihood that the Current Controls (design and process) will detect the Cause of the Failure Mode or the Failure Mode itself, thus preventing it from reaching the Customer.

Effect:

 An Effect is an adverse consequence that the Customer might experience. The Customer could be the next operation, subsequent operations, or the end user.

Failure Mode: Failure Modes are sometimes described as categories of failure. A potential Failure Mode describes the way in which a product or process could fail to perform its desired function (design intent or performance requirements) as described  by the needs, wants, and expectations of  the internal and external Customers.

FMEA Element: FMEA elements are identified or analyzed in the FMEA process. Common examples are Functions, Failure Modes, Causes, Effects, Controls, and Actions. FMEA  elements appear as column headings in the output form.

Function:  A Function could be any intended purpose of a product or process. FMEA  functions are best described in verb-noun format with engineering specifications.

Occurrence: Occurrence is an assessment of the likelihood that a particular Cause will happen and result in the Failure Mode during the intended life and use of the product.

Risk Priority Number: The Risk Priority Number is a mathematical product of the numerical Severity, Occurrence, and Detection ratings. RPN = (S) × (O) × (D). This number is used to place priority on items than require additional quality planning.

Severity: Severity is an assessment of how serious the Effect of the potential Failure Mode is on the Customer.

Significant Characteristics: Significant Characteristics are Special Characteristics defined by Ford Motor Company as characteristics that significantly affect customer satisfaction and require quality planning to ensure acceptable levels of capability.

Special Process Characteristics: Special Process Characteristics are process characteristics for which variation must be controlled to some target value to ensure that variation in a Special Product Characteristic is maintained to its target  value during manufacturing and assembly.

Special Product Characteristics: Special Product Characteristics are product characteristics for which reasonably anticipated variation could significantly affect a product’s safety or compliance with governmental standards or regulations, or is likely to significantly  affect customer satisfaction with a product.

 Acronyms: 8-D

Eight Disciplines of Problem Solving

ISO

 AIAG

 Automotive Industry Action Group

 APQP

 Advanced  Advanced Product Quality Planning

QFD

 ASQC

 American Society for Quality Control

QOS

DOE FMEA  FTA 

Design of Experiments Experiments

RFTA 

International Organization for Standardization Quality Function Deployment Quality Operating System Reverse Fault Tree Analysis

Potential Failure Mode and Effects  Analysis

RPN

Risk Priority Number

Fault Tree Analysis

SPC

Statistical Process Control

The FMEA discipline was developed in the United States Military. Military Procedure MIL-P-1629, titled Procedures for Performing a Failure Mode, Effects and Criticality Analysis, is dated November 9, 1949. It was used as a reliability evaluation technique to determine the effect of system and equipment failures. Failures were classified according to their impact on mission success and personnel/equipment safety. The term "personnel/equipment", taken directly from an abstract of Military Standard MIL-STD1629, is notable. The concept that personnel and equipment are interchangeable does not apply in the modern manufacturing context of producing consumer goods. The manufacturers of  consumer products established a new set of priorities, including customer satisfaction and safety. As a result, the risk assessment tools of the FMEA became partially outdated. They have not been adequately updated since.

In 1988, the International Organization for Standardization issued the ISO 9000 series of business management standards. The requirements of ISO 9000 pushed organizations to develop formalized Quality Management Systems that ideally are focused on the needs, wants, and expectations of customers. QS 9000 is the automotive analogy to ISO 9000. A Task Force representing Chrysler Corporation, Ford Motor Company, and General Motors Corporation developed QS 9000 in an effort to standardize supplier quality systems. In accordance with QS 9000 standards, compliant automotive suppliers shall utilize Advanced Product Quality  Planning (APQP), including design and process FMEAs, and develop a Control Plan.

 Advanced Product Quality Planning standards provide a structured method of defining and establishing the steps necessary to assure that a product satisfies the customer’s requirements. Control Plans aid in manufacturing quality products according to customer requirements in conjunction with QS 9000. An emphasis is placed on minimizing process and product  variation. A Control Plan provides "a structured approach for the design, selection, and implementation of value-added control methods for the total system." QS 9000 compliant automotive suppliers must utilize Failure Mode and Effects Analysis (FMEA) in the  Advanced Quality Planning process and in the development of their Control Plans.

The Automotive The Automotive Industry Action Group (AIAG) and the  American Society for Quality Control (ASQC) copyrighted industry wide FMEA standards in February of 1993, the technical equivalent of the Society of Automotive Engineers procedure SAE J-1739. J-1739. The standards are presented in an FMEA Manual approved and supported by all three automakers. It provides general guidelines for preparing an FMEA. This site is dedicated to overcoming some deficiencies of the Potential Failure Mode and Effects Analysis (FMEA), as it is currently being deployed in the U.S. automotive industry. An FMEA is commonly defined as "a systematic process for identifying potential design and process failures before they occur, with the intent to eliminate them or minimize the risk associated with them."

In the progression of time, a Failure Mode comes between a Cause and an Effect. One of the most confusing issues for new practitioners of FMEA is that any  Cause that itself has a Cause might be a Failure Mode. Any Effect that itself has an Effect might also be a Failure Mode. In different contexts, a single event may be a Cause, an Effect, and a Failure Mode. Consider for example Figure 2, a series of  events that could occur during the life of  a disposable penlight.

In an analysis of the exterior casing of a penlight, " Allows excess moisture" would be a Failure Mode. One of the intended functions of the penlight case is to protect the internal components from excess moisture during normal operation. A failure to prevent moisture during normal operation is a Failure Mode since protective casings and other design features are intended to intended to prevent moisture. Causes appear above the Failure Mode in Figure 2. Effects appear below. In the analysis of the penlight bulb penlight  bulb,, a different Function and Failure Mode(s) must be considered. The penlight bulb is intended to provide light of specific intensity when the device is activated during its expected lifetime. This is one of its Functions, or intended purposes. A dim bulb is a failure to provide the specified intensity of light and is therefore a Failure Fa ilure Mode of the penlight bulb. This example illustrates that Causes, Effects, and Failure Modes can change depending on the Function being analyzed. Functions change depending on the object of the analysis, either product or process. Therefore, an early, important step in an FMEA is to clearly  define the scope: the component, system, or process that is to be analyzed.

Most real-world systems do not follow the simple CauseEffect model. A single Cause may have multiple Effects. A  combination of Causes may lead to an Effect, or they may  lead to multiple Effects. Causes can themselves have Causes, and Effects can have subsequent downstream Effects. The Failure Mode must also be considered in all of these models. Figure 3 illustrates the relationship between a Function, a Failure Mode, Potential Causes, and Effects. In the FMEA  model presented in this document, Causes do not automatically result in the Failure Mode. The term ‘Potential’ is used to describe Causes, to indicate this uncertainty. The model also assumes that all Effects will result given that the Failure Mode has occurred. Therefore, ‘Potential’ is not used not used to describe Effects.

Figure 3. FMEA Relationships In Figure 3, A pentagon is used to represent the Failure Mode for two reasons. First, Failure Modes can be grouped into one of five categories of failure. Secondly, the asymmetrical asymmetrical pentagon is mirrored to indicate that Failure Modes can also be described as anti-Functions.

FMEA Elements are the building blocks of related information that comprise an analysis. The team approach is almost essential in identifying FMEA  elements. Although actual document preparation is often the responsibility of an individual, FMEA input should come from a multi-disciplinary team. The team should consist of knowledgeable individuals with expertise in design, manufacturing, assembly, service, quality, and reliability. The responsible engineer typically leads the FMEA team. Members and leadership may vary as the system, product, and process designs mature.

Once the object of the analysis has been established, the next step in the FMEA process is to identify Functions. A Function is an intended purpose of the product or process being analyzed. If a system is being considered, Functions of individual subsystems should also be identified. Potential Failure Modes, or categories of failure, can then be identified by describing the way in which the object fails. Failure Modes fall into 1 of 5 possible failure categories: complete failure. partial failure. intermittent failure. failure over time. over-performance of Function.

In the penlight example, suppose "Provide Light at 3 ± .5 candela" is defined as a Function. The following Failure Modes could be identified: no light. dim light. erratic blinking light. gradual dimming of light. too bright.

The purpose of these five Failure Mode groupings is to assist the FMEA team identify all possible Failure Modes. Looking for Failure Modes in these groupings may reveal some unusual failure possibilities that otherwise would not have been considered. Poorly  defined Functions may also be revealed. In this example, a light that does not turn off (over-performs its Function) is a product failure, even though the Function " Provide light at 3 ± .5 candela" does not fail. This implies the need for an additional Function, such as " Default to off when not in use ", which may have been overlooked when Functions were originally identified. The original Function is rephrased as: " Provides light at 3 ± .5 candela when on ." A partial, intermittent, gradual or overperformance type failure of one Function may be a complete failure of  another unidentified Function. Use of the Failure Mode categories can help reveal these Functions.

 After Functions and Failure Modes have been established, the next step in the FMEA process is to identify potential downstream consequences when the Failure Mode occurs. This should be a team  brainstorming activity. After consequences have been identified, they must be fit into the FMEA model as Effects. In the FMEA  model presented by the Haviland Consulting Group, it is assumed that Failure Mode Effects always occur when the Failure Mode occurs; there is no representation for the likelihood that a Failure Mode will result in an Effect. The Procedure for Potential Consequences is applied to account for unlikely or remote consequences. The Procedure explicitly associates Effects with the circumstances under which they occur through the identification of  additional Failure Modes.

The Procedure for Potential Consequences: Begin with a Failure Mode (referred to as FM-1), and a list of all its potential consequences. •

•

Separate the consequences that can be assumed to result whenever the Failure Mode FM-1 occurs. Identify these as Effects of FM-1. •

 Write additional Failure Modes for the remaining consequences (consequences which could result when FM-1 occurs, depending on the circumstances under which FM-1 occurred). The new Failure Modes imply that unlikely consequences will result will result by  including the circumstances under which they occur. Separate the consequences that can be assumed to result whenever the • additional Failure Modes and their special circumstances occur. Identify these consequences as Effects of the additional Failure Modes.

Consider this example, which illustrates the Procedure for Potential Consequences. During the Effects brainstorm, the team may tend to identify very severe consequences and the unlikely circumstances under  which they occur. When analyzing the penlight bulb, a team member may observe that the bulb could prematurely burn out when being used as a flashlight, the resulting darkness causing the user to trip, fall, and  be injured. Another member may observe that atmospheric pressure  variation could cause the bulb to explode while being used for an eye examination, resulting in injury. Such extraneous predicaments are typical of a brainstorm and can be expected. But rather than write a new Failure Mode for every bizarre situation recorded by the team, the t he events should be grouped into a broad Effect category, such as " injury or death". Ultimately, Effects are categorized into one of ten groups, according to Severity. It is advantageous advantageous to write Failure Modes that encompass all the Effects in a Severity grouping, such as " Fails to provide 3 ± .5 candela of light under critical conditions". All product failures that lead to injury or death are

automatically included; there is no need to attempt to identify all the circumstances under which injury or death could result. result. The tradeoff  for this convenience is that the likelihood of failure under any critical

The first step in analyzing risk is to quantify the Severity of the Effects. Effects are rated on a scale of 1 to 10, 10 being the most severe. The team should agree on consistent evaluation criteria and a sensible ranking system. The design and process ranking systems presented in Table 1 and Table 2 are based on AIAG standards. Effects are evaluated as a group when assessing risk, even though they are assigned Severity values individually. It is assumed that all Effects will result if the Failure Mode occurs. Therefore, the most serious Effect takes precedence when evaluating risk potential. This model accounts for Causes that have multiple Effects. Issuing design and process changes can reduce Severity ratings. Notes of interest:

The scales do not discriminate between failures that results in catastrophic death, minor injury, or government regulation violation.  A defect noticed by most customers is less than halfway  up the Severity scale. "No effect" has a ranking of 1. There is no zero. Note: These tables tables differ slightly from those published published  by the AIAG. Specific references to motor vehicles have  been removed. The tables are similar to AIAG tables in that they are suggested ranking suggested ranking systems. Because these tables are suggested, the actual criteria used to prioritize risk should be documented with the FMEA.

 Effect 

Criteria: Severity of Effect for   DFMEA

Hazardous –no  warning

Failure affects safe product operation or involves noncompliance with government regulation without warning.

Hazardous –  with warning

Failure affects safe product operation or involves noncompliance with government regulation with warning.

 Very High

Product is inoperable with loss of primary p rimary Function.

High

Product is operable, but at reduced level of performance.

Moderate

Product is operable, but comfort or convenience item(s) are inoperable.

Low 

Product is operable, but comfort or convenience item(s) operate at a reduced level of performance.

 Very Low 

Fit & finish or squeak & rattle item does not conform. Most customers notice defect.

Minor

Fit & finish or squeak & rattle item does not conform. Average customers notice defect.

 Very Minor

Fit & finish or squeak & rattle item does not conform. Discriminating customers notice defect.

None

No effect

Table 1.

Suggested evaluation criteria and ranking system for the Se verity of  Effects for a design FMEA 

 Effect 

Criteria: Severity of Effect for   PFMEA

 R ank

Hazardous – no warning

May endanger machine operator or assembly operator. Failure affects safe product operation or noncompliance with government regulation. Failure will occur without warning. w arning.

10

Hazardous –  with warning

May endanger machine operator or assembly operator. Failure affects safe product operation or noncompliance with government regulation. regulation. Failure will occur with warning.

9

 Very High

Major disruption to production line. 100% of product may have t o be scrapped. The product is inoperable with loss of primary Function.

8

High

Minor disruption to production line. Product may have to be s orted and a portion scrapped. The product p roduct is operable, but at a reduced level of performance.

7

Moderate

Minor disruption to production line. A portion of the product may  have to be scrapped (no sorting). Product is operable, but some comfort / convenience item(s) are inoperable

6

Low 

Minor disruption to production line. 100% of the product may have to  be reworked. Product is operable, but some comfort / convenience items operate at a reduced level of performance.

5

 Very Low 

Minor disruption to production line. Product may have to be s orted and a portion reworked. Fit & finish or squeak & rattle item does not conform. Most Customers notice the defect.

4

Minor

Minor disruption to production line. A portion of the product may  have to be reworked on-line but out-of-station. Fit & finish or squeak  & rattle item does not conform. Average customers notice the defect.

3

 Very Minor

Minor disruption to production line. A portion of the product may  have to be reworked on-line but in-station. Fit & finish or squeak & rattle item does not conform. Discriminating customers notice the defect.

2

None

The Failure Mode has no Effect.

1

Table 2.

Suggested evaluation criteria and ranking system for the Severity of Effects in a process FMEA 

 After Effects and Severity have been addressed, the next step is to identify Causes of Failure Modes. This is another team activity. Identification should start with Failure Modes that have the most severe Effects. In a design FMEA, design deficiencies that result in a Failure Mode are Causes of failure. Design deficiencies that induce a manufacturing or assembly error are also included in design FMEAs as Causes. The design FMEA assumes that manufacturing and assembly  specifications are met, and only seeks to identify failures resulting from product design. In a process FMEA, Causes are specific errors described in terms of  something that can be corrected or controlled. The process FMEA  assumes that the product is adequately engineered, and will not fail  because of a design deficiency. This does not imply that all inputs to the process meet engineering specifications. Variation in purchased parts and material used in the process should be considered in the process FMEA.

Causes are rated in terms of Occurrence. Occurrence is the likelihood that that a particular Cause will occur and result and result in the Failure Mode during the intended life and use of the product. This definition is distinctly different different from the definition published by the AIAG. In the AIAG FMEA model, Occurrence is simply the likelihood that a Cause or mechanism of failure will occur. It is assumed that the failure itself  could occur, could occur, but not necessarily. Since Occurrence Occurrence is defined only as the likelihood the Cause will occur, there is no way of quantifying the likelihood that the Failure Mode and subsequent Effects will result. The Occurrence definition preferred by The Haviland Consulting Consulting Group, used in combination with the Procedure for Potential Consequences, Consequences, accounts for the fact that Causes do not always lead to Failure Modes and subsequent Effects. When applicable, the Procedure for Potential Consequences isolates severe Effects from the group by definition of a new  Failure Mode with special circumstances. The Causes of the new Failure Mode are assigned Occurrence values. The new Occurrence values represent the remote likelihood that the customer will experience the Effect. In the AIAG model, there is no risk-prioritizing advantage to writing additional Failure Modes because Causes and Failure Modes are not necessarily linked by Occurrence.

The Ford Motor Company has added a Cause-Failure Mode condition to the AIAG model, stating that if the Cause occurs, the Failure Mode always results. This condition effectively links Occurrence Occurrence with Failure Modes, but Failure Mode-Effect causality in the Ford model is not mentioned in their FMEA handbook. If the Ford model assumes that Effects do not always occur when the Failure Mode occurs, then Occurrence has no real bearing on whether the customer will experience the Effect. If  the Ford model assumes that Effects always occur when the Failure Mode occurs, then the FMEA team is forced to assume that a Cause will automatically lead to every  possible Effect. Effect. This is generally untrue, leading leading to overestimated overestimated risk. In the FMEA  model presented in this thesis, the Procedure for Potential Consequences and a new  Occurrence definition are used to handle this Cause-Failure Mode-Effect causality  problem. The Ford and AIAG models do not include the Procedure for Potential Consequences, Consequences, or a similar solution. Unlike Effects, Causes are not evaluated as a group when assessing risk. Separate  values are assigned to each Cause of the Failure Mode. Current Controls sometimes prevent the Cause of failure, the Failure Mode itself, or its Effects. Such Controls, designated Type (1), are most desirable and can reduce initial Occurrence ratings.

Table 3 and Table 4 are based on AIAG standards. Most failure rates will fall between two numbers on the scale. The standard practice is to round to the higher of the two Occurrence values. For example, a failure that occurs every 14,000 parts would be assigned an Occurrence value of 4, even though 1/14000 is closer to 1 /15000 (Occurrence = 3) than it is to 1/2000 (Occurrence = 4). For cases where the failure rates are completely  unknown, assume that Occurrence = 10. Occurrence ratings can be based information from similar products and process when available.

Notes of Interest: •The probabilities represent the likelihood that the Cause will occur and result in the Failure Mode, not just the chances that it  will occur. •The scales are not linear. • An Occurrence of 10 does not discriminate between failures that occur over half the time and failures that occur every time. • An Occurrence of 1 does not discriminate between remote and zero chance of failure. Note: These tables differ differ slightly from those published by the  AIAG. Specific references to motor vehicles have been removed. The tables are similar to AIAG tables in that they are suggested  ranking systems. Because these tables are suggested, the actual criteria used to prioritize risk should be documented with the FMEA.

 Probability of Failure  Very High: Failure is almost inevitable

High: Repeated failures

Moderate: Occasional failures

Low: Relatively few failures Remote: Failure is unlikely  Table 3.

 Failure Rates 1 in 2

 R ank 10

1 in 3

9

1 in 8

8

1 in 20

7

1 in 80

6

1 in 400

5

1 in 2000

4

1 in 15,000

3

1 in 150,000

2

1 in 1,500,000

Suggested evaluation criteria and ranking system for the Occurrence of Failure in a design FMEA 

1

 

Probability of Failure  Very High: Failure is almost inevitable High: Generally associated with processes similar to previous processes that have often failed Moderate: Generally associated with processes similar to pervious processes which have experienced occasional failures, but not in major proportions Low: Isolated failures associated with similar processes  Very Low: Only isolated failures associated with almost identical processes Remote: Failure is unlikely. No failures ever associated with almost identical processes Table 4.

C  pk

Failure Rates ≥

1 in 2

< 0.33

 R ank 10

1 in 3

≥

0.3 3

9

1 in 8

≥

0.51

8

1 in 20

≥

0.67

7

1 in 80

≥

0.83

6

1 in 400

≥

1.00

5

1 in 2000

≥

1.17

4

1 in 15,000

≥

1.33

3

1 in 150,000

≥

1.50

2

≥

1.67

1

≤

1 in 1,500,000

Suggested evaluation criteria and ranking system for the Occurrence of  Failure in a Process FMEA 

Current Control Design and Process controls are grouped according to their purpose. preventt the Cause or Failure Mode from occurring, or Type (1): These controls preven reduce their rate of occurrence. These controls detect the Cause of the Failure Mode and lead to Type corrective action. (2): These Controls detect the Failure Mode before the product reaches the Type customer. The customer could be the next operation, subsequent (3): operations, or the end user. The distinction between controls that prevent  that  prevent failure failure (Type 1) and controls that detect  failure (Types 2 and 3) is important. Type 1 controls reduce the likelihood that a Cause or Failure Mode will occur, and therefore affect Occurrence ratings. Type 2 and Type 3 Controls detect Causes and Failure Modes respectively, and therefore affect Detection ratings.

Detection values are associated with Current Controls. Detection is a measurement of the ability of Type of Type (2) Controls to detect Causes or mechanisms of failure, or the ability of  Type (3) Controls to detect subsequent Failure Modes. One Detection value is assigned to the system of Current Controls, which represents a collective ability to detect Causes or Failure Modes. Controls can be grouped and treated as a system when they operate independently, as each individual individ ual Control increases overall detection capabilities. capabilitie s. The design and process ranking systems presented in Table 5 and Table 6 are based on AIAG standards. Notes of Interest: •High values indicate a lack of detection ability. •The tables are not quantitative; relative terms are used. •The adjectives used to describe the likelihood of Detection indicate a generally linear relationship. • A Detection value of 1 does not imply 100% detection. Note: These tables differ slightly from those published by the AIAG. Specific references to motor vehicles have been removed. The tables are similar to AIAG tables in that they are

 Det  D etec ecti tion on  Absolute Uncertaint  y   Very  Remote Remote  Very Low  Low  Moderate Moderatel  y High High  Very High  Almost Certain Table 5.

Crit Cr iter eria: ia: Li Like keli liho hood od of of Det Detec ecti tion on by Des Desig ign n Con Contr trol  ol  Design Control does not detect a potential Cause of failure or subsequent Failure Mode; or there is no Design Control  Very remote chance the Design Control will detect a potential Cause of  failure or subsequent Failure Mode Remote chance the Design Control will detect a potential Cause of failure or subsequent Failure Mode  Very low chance the Design Control will detect a potential Cause of failure or subsequent Failure Mode Low chance the Design Control will detect a potential Cause of failure or subsequent Failure Mode Moderate chance the Design Control will detect a potential Cause of  failure or subsequent Failure Mode Moderately high chance the Design Control will detect a potential Cause of  failure or subsequent Failure Mode High chance the Design Control will detect a potential Cause of failure or subsequent Failure Mode  Very high chance the Design Control will detect a potential p otential Cause of  failure or subsequent Failure Mode Design Control will almost certainly detect a potential Cause of failure or subsequent Failure Mode

 R ank 10 9 8 7 6 5 4 3 2 1

Suggested evaluation criteria and ranking system for the Detection of a Cause of failure or Failure Mode in a design FMEA.

R  ank 

Dete tect ctio ion n

Cri ritteri ria: a: Li Lik kel elih ihoo ood d of Dete tect ctio ion n by Pr Proc oce ess Co Cont ntro roll

 Almost Impossible

No known Controls available to detect Failure Mode or Cause

10

 Very Remote

 Very remote likelihood current Controls with detect Failure Mode or Cause

9

Remote

Remote likelihoo hood current Control rols with detect Failure Mode or Cause

8

 Very Low 

Very lo low li likelihood cu current Co Contro trols wi with de detec tect Fa Failure Mo Mode or or Ca Cause

7

Low 

Low likelihood current Controls with detect Failure Mode or Cause

6

Moderate

Modera derate te like likeli liho hoo od cu curren rrentt Con Contr tro ols with ith det deteect Failu ilure Mode ode or or Ca Cause use

5

Moderately  High

Moderately high likelihood current Controls with detect Failure Mode or Cause

4

High

High likelihood current Co Controls with detect Failure Mode or Cause

3

 Very High

 Ver  Very y hi high like likeli liho hoo od cu curren rrentt Con Contr tro ols with ith det deteect Failu ilure Mode ode or or Ca Cause use

2

 Almost Certain

Current Controls almost certain to Failure Mode or Cause. Reliable detection controls are known with similar processes.

1

Table 6.

Suggested evaluation criteria and ranking system for the D etection of a Cause of failure or Failure Mode in a process FMEA 

 A tabular FMEA documentation form has been standardized by the AIAG. All input data must be organized on the output form in the spaces and columns provided. Some companies compile FMEA data on  worksheets, and then transfer the information to the form. Other companies with electronic versions of the form can fill in the table as FMEA FMEA elements elements are identified. FMEA  Facilitator collects the input data through an organized and intuitive interface and places it on the form automatically.

The fundamental purpose of the FMEA is to recommend and take actions that reduce risk .  Actions taken often result in a lower Severity, Occurrence, or Detection rating. Adding validation or verification controls can reduce Detection. Design or process revision may result in lower Severity and Occurrence ratings. The revised ratings are documented with the originals on the tabular FMEA form. If no action is i s recommended, the decision not to act should also be noted. Effective follow-up programs are also necessary, as the purpose of o f the FMEA is defeated if any recommended actions are left undressed.

The Risk Priority Number (RPN) is a mathematical product of the seriousness of a group of Effects (Severity), the likelihood that a Cause  will create the failure associated with those Effects (Occurrence), and an ability to detect the failure before it gets to the customer (Detection). In equation form, RPN = S • O • D. This number is used to help identify the most serious risks, leading to corrective action. Inspection of the equation reveals that the RPN method for assessing risk is an oversimplification. Severity, Occurrence, and Detection are not equally   weighted with respect to one another in terms of risk. The distortion is compounded by the non-linear nature of the individual ranking scales.  As a result, some S-O-D scenarios produce RPNs that are lower than other combinations, but more risky. Furthermore, the RPN scale itself has some non-intuitive statistical properties. The initial and correct observation that the scale starts at 1 and ends at 1000 often leads to incorrect assumptions about the middle

Incorrect Assumption The average of all RPN  values is roughly 500.

 Actual Statistical Data The Average RPN value is 166.

Roughly 50% of RPN values 6% of all RPN values are above are above 500. (The 500. (The median is 105.) median is near 500.) There are 1000 possible RPN values.

There are 120 unique RPN values.

Table 7. RPN Scale Statistical Data

The 1000 RPN numbers generated from all possible combinations of  Severity, Occurrence, and Detection are plotted on this histogram histogram.. High Severity values merit special attention, particularly when coupled  with high Occurrence values. The term Criticality was developed to call attention to these combinations. Criticality is defined as the mathematical product of Severity and Occurrence. This definition does not fully correct the problem. Severity and Occurrence are still unequal in terms of risk, and their ranking scales are still non-linear. The contour plot of Figure of  Figure 4 compares Criticality with an expert risk assessment of the actual S-O combination, with 5 chosen as an arbitrary Detection value. Deep blue signifies a strong correlation between Criticality and risk, while red indicates a large discrepancy. The discrepancy between RPN and expert risk assessment of the 1000 SO-D combinations is even more prominent. Despite the shortcomings of  RPN and Criticality, they are used and misused regularly for risk  assessment.

Figure 4. Criticality and Risk Discrepancy Dis crepancy Contour Plot This contour plot represents the estimated discrepancy   between Criticality and actual risk for a system with average failure detecting controls (Detection = 5). Notice the dramatic discrepancy at [Severity = 10, Occurrence = 2]. In this situation, a failure with potentially catastrophic consequences is expected to occur (eventually), and can therefore also pose great risk. Based on the the Criticality scale, this situation is assigned a value of 20 (out of a possible po ssible maximum of 100), and may consequently be overlooked.

The AIAG defines a Special Product Characteristic as a product characteristic for which reasonably anticipated variation could significantly affect a product’s safety or compliance with governmental standards or regulations, or is likely to significantly affect customer satisfaction with a product. Ford Motor Company divides Special Characteristics into two categories: Critical Characteristics and Significant Characteristics Critical Characteristics are defined by Ford as product or process requirements that affect compliance with government regulation or safe product function, and which require special actions or controls. In a design FMEA, they are considered  Potential  Critical Characteristics. A Potential Critical Characteristic exists for any Severity rating greater than or equal to 9. In the process FMEA, they are referred to as  Actual  Critical Characteristics. Any characteristic with a Severity of 9 or 10 which requires a special control to ensure detection is a Critical Characteristic. Examples of product or process requirements that could be Critical Characteristics include dimensions, specifications, tests, assembly sequences, tooling, joints, torques, welds, attachments, and component usages. Special actions or controls necessary to meet these requirements may involve manufacturing, assembly, a supplier, shipping, monitoring, or inspection. Significant Characteristics require special controls because they are important to customer satisfaction. Severity ratings between 5 and 8 coupled with an Occurrence rating greater than 3 indicate Significant Characteristics. In a design FMEA, they are  Potential  Significant Characteristics. In the process FMEA, if a special control is required to ensure detection then an  Actual  Significant Characteristic exists. Companies have not standardized a method for grouping and denoting Special Product Characteristics.  Nomenclature and notation will vary.

Contents  What

is Control Plan?  Why Control Plan?  Process without Control plan…. example  Linkage between flow charts, process FMEA, control plans, Proc. Doc. and other documents  Description and usage of Columns of Control Plan  Changing Control Plan  Expectations  Practice session

What is Control Plan ? APQP Manual definition … 

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The Control Plan describes the actions that are required at each phase of the process to ensure that all process outputs will be in state of control Control plan is a living document, reflecting the current methods of control, and measurement systems used Accessible at work station

Why Control Plan ? Why do we need Control Plan?  Communicates

what do we have to check and control throughout the process  Helps new people to understand the process and its control steps  Control plan is needed to document and communicate the  process & Product parameters, process controls, Check  methods & Reaction plans.  It standardize the manufacturing process.  Helps to produce consistent and predictable Quality.  Enhance the Customer confidence.  It is a requirement of the TS 16949 standard

Without Control Plan ? What will happen……… Don’t know what to check, how to check and when to check to control process Product quality depends on individual’s discretion Don’t know what to do when some thing goes wrong. Result in inconsistent and unpredictable product quality. Low confidence level of customer.

Control Plan - Example  Example : Polish Crankshaft  Process / Operation number 

090-

Process / operation description

Product / Special Process Characteristic characteristic specification / Pro Product Proces esss class tolerance

Surface Polish journa ls finish

Proc. Doc. 3800 -090

 How important it is …? What to check ?

Control Plan - Example Example : Polish Crankshaft  Evaluation valua tion / measurement technique

Sample Si z e Fre q.

Control Method

Reaction Reaction Plan Pla n

100 % inspection of  SPC (X bar-R every previous pre vious 5 chart), chart), operator  opera tor  Surf tester  5 5th crankshaft,replace verification of  (Tylor hobson)  journals cranks belts/readjust belt and haft machine, notify line machine setup leader 

 How many and when to check ?  How to check ?

What to do when some thing is wrong ?

Linkage with Key Docs. Linkage

FLOW CHART

• Graphic description of  the process flow • Each operation listed

PFMEA

• Describes what can go wrong at each operation • Pro-active tool

CONTROL PLAN

• How to control each factor affecting quality • What to do if something is wrong

PROC DOC

• Detailed process steps • Gauges, tools, methods • Specifications, limits, acceptance criteria

Other Docs. Or Activities Gauging

Mistake proofing

Training

SPC Control chart

CONTROL PLAN

Proc. docs.

Non conformance reports

Control Plan - Columns Description Control Plan Columns’ description and usage 1. 2.

3. 4. 5. 6. 7. 8. 9. 10. 11. 12.

Category : Prototype,Pre launch or production Control Plan number : Control plan number should be linked with Flow chart and Process documentation for each station / operation Part Part name name / latest latest change change level level : verify verify the part part name name,, lates latestt chang changee level indicates the number of times document is changed Operat Operation ion desc descrip ription tion : Indicat Indicates es the the name name of parti particul cular ar opera operation tion.. Supplie Supplierr Plant Plant : Name Name of the plan plantt prepar preparing ing and and usin using g contro controll Plan. Plan. Key Key cont contact act : Per Perso son n resp respon onsi sible ble for for con contr trol ol plan plan Core Core team : indicat indicates es the the name name of indiv individu iduals als of of cross cross func function tional al team team responsible for latest revision. Supplier approval: Check for the sign and date of of approving authority. Date (Orig.) : is the date of compilation of original control Plan Date Date (Rev) (Rev) : is the the latest latest revis revision ion date date Customer / other approvals : approvals approvals from customers customers Process / operation number : this item number is referenced from the Process Flow chart and Process FMEA.

Control Plan - Columns Description Control Plan Columns’ description and usage 13. Process name/ operation description : this column indicates all important operational steps 14. Machine, device,Jig,Tools : For each operation the processing equipment e.g. machine, jig or other tools are identified 15. Product characteristics characteristics : are the features or the properties of a part that are described on on drawing or engineering engineering specifications e.g. journal diameter, deck height, surface finish e.t.c. 16. Process characteristics : are the process process variables that have a cause and effect relation with identified product characteristic e.g. concentration of chemical, washer temperature and cycle time are process characteristics in washing operation and they have cause and effect relation with cleanliness (product characteristic). 17. Special characteristic class : referenced from customer drawings, specifications, non conformance and warranty data, and FMEA. COER uses “Critical/safety” and “major” class

Control Plan – Columns Description Control Plan columns’ description and usage 18. Specification / tolerances :Product and process characteristics are required to be controlled, check the specifications of these characteristics in Spec./ tolerance column. If specs. are referred to Proc doc.,check the related proc. doc. 19. Evaluation/ measurement technique :Identifies the measurement system e.g. gages, fixtures or test equipment. Make sure that the indicated gage is available at your station, also check for valid calibration 20. Sample size / frequency :Follow the sample size and frequency indicated in the control plan for inspection. 21. Control method column gives the brief description of how the operation will be controlled. controlled. For example : Operations may be controlled by SPCcontrol charts, 100% inspection, sampling inspections or mistake proofing in tooling etc. Use the mentioned control. 22. Reaction plan specifies the corrective actions/ Steps to be initiated when nonconformance is observed. Reaction may be Scrap, Hold, Rework,  Notify supervisor, reset machine e.t.c.

Updating Control Plan Changing Control Plan :  Control

Plan is a living document and should be updated to reflect the changes in product / process characteristics, control methods and characteristic measurements  If something is wrong in your Control Plan, it MUST be changed  Take a copy of the Control Plan and note the changes  Give the copy with changes to your Team Leader, Supervisor or Quality Assurance  Changes will be reviewed and incorporated .New document will be issued in 1 or 2 weeks.

Control Plan Expectations Expectations  We

need to review existing control plans and create new Control Plan where ever necessary by the end of  July’2003  1 or 2 volunteers are required per team to help  Team volunteers will have to help Quality Engineer to review measurement techniques, gauges, frequency of  checking, control methods and reaction plans.  Team volunteers will work in Cross functional team to formalize the Control Plan

Control Plan

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Control Plan – Practice Session Practice Session :

Make groups and create control plan for any  process or operation. Must indicate the Process/ operation name, product /  process characteristic, Evaluation/ measurement techniques, frequency of checking, control method and reaction plan.