Improving Industrial Production Quality Assurance: An Analysis of

MCDM and FMEA Methodologies

SAFIYE TURGAYa

DAMLA KARAa

SULTAN ÇİMENa

ESMA SEDEF KARAb

aDepartment of Industrial Engineering

Sakarya University

54187, Esentepe Campus Serdivan-Sakarya

TURKEY

b Rüstempaşa Mahallesi, İpekyolu Caddesi, No:120,

Sapanca (54600) Sakarya,

TURKEY

Abstract: - The modern business context is so cut-throat, therefore, organizations should place emphasis on

process leadership in the quest to provide the best quality products to their clients. Quality management

practices that incorporate FMEA are a significant measure that can help in finding and solving issues with high

impacts. This study deals the technique called (FMEA) and that its character is forward-looking, which means

that it could identify, prioritize and eliminate slots leading to different sort of failures, that result in optimal

performance and customer satisfaction. Study makes use of FMEA as an important component of the quality

management system by interconnecting it with other approaches like Six Sigma, TQM and ISO 9001, which

could bring these paradigms to even higher level, if implemented properly. From this case studies and good

practices from real organizations, we will discuss strategic benefits of applying FMEA into management

practices of quality as well as affecting versatility for different scenarious. A FMEA method is an engineering

methodology designed to detect and eliminate problems in systems, designs, processes and solution that may

happen and thus prevent loss of resources due to mistakes made by users. The study researches the application

of FMEA tool in the area of quality improvement. Indeed, with FMEA aiming to improve efficiency through

the prioritization of these types of errors and the focus on the errors of highest risk priority. It is also provided

with the high tech machinery required for industrial grade cables producing for automotive and electronic

industries. Via FMEA methodology, the study reviewed error situations, which had a chance of happening after

the product has been used by the customer. The study, additionally, used MCDM (Multi-Criteria Decision-

Making) techniques to upgraded decision-making available at the FMEA analysis at the same time. What could

be pointed out as its main feature is the key role of FMEA as a strategic tool. It could allow organization to

reach world-class level in different areas by simply grasping its theoretical and practical fundamentals.

Key-Words: - : FMEA (Failure Modes and Effects Analysis), Quality Management, Risk Assessment,

Process Optimization, Maintenance, Corrective Actions, Process Efficiency, MABAC,

MULTIMOORA, fuzzy Grey Relational Analysis (GRA)

Received: July 25, 2023. Revised: April 12, 2023. Accepted: June 4, 2024. Published: July 4, 2024.

1 Introduction

The continuous battle of the modern business is to

uphold the same quality and standards and improve

them if possible for their organizations to remain

competitive against their peers by consumers who

strive for top-notch service. In this regard, quality

management is a crucial component that entails the

use of the multiple quality control tools and

techniques for the progress over time. A FMEA is

equally an indispensable tool along with Failure

Modes and Effects Analysis (FMEA). For the last 5

years, the quality has not only been one of the most

critical types of the competitive advantage but also

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E-ISSN: 2732-9984

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has proved to be an essential one. In the modern-day

market, being a step ahead in sustain the high

standards of quality is crucial for long time well

being. As a result, businesses of various kinds

introduce different measures to make sure and

increment quality of a product and/or service. It

translates into continuous making efforts to identify

and discard any factors that may have an adverse

effect on the quality of the products or services and,

on the other hand, gives the customers satisfaction

level a corresponding turn. Abundant kinds of

methods to be applied for quality and process

improvement, the failure mode and effects analysis

method has the considerable position. Through the

use of FMEA, organizational reliability and quality

are systemically enhanced by means of a systematic

elimination of errors, a minimum fail-safe

measurement, and a reduction of the vulnerability of

making mistakes.

This section will specifically address FMEA’s

important role in quality management as a

significant element of process improvement (where

a few strategies will be mentioned). With time

organizations face more challenging and demanding

markets. The active detection and prevention of

risks stands out as one of the most significant in this

case. FMEA is a systematic and preemptive tool that

helps threat assessment. If evaluates potential flaws

in processes, systems, and products. Failure modes

of these are identified, prioritized and subsequently

rectified. The intention of this application is to look

into the main concepts of FMEA and the approach

to involving this method into the quality

management systems. Through awareness of rapid

FMEA feature, companies can use it to attain

perfection, imagine their processes, avoid mistakes,

and result in better results. The forthcoming

discussions are going to be accompanied by case

studies and examples of good practice, which are

singling out FMEA implementation as a useful

improvement contributor for many processes and

products quality.

Moreover, the talk identified the competitive

advantages of linking FMEA with the always used

quality management techniques such as Six Sigma

and TQM. FMEA and ISO 9001 synergies

underlined in this regard which shows that these

frameworks effectively work together committing to

a continuous improvement and adherence to

industry standards.

Simply speaking, the above introductory part gives a

general overview of how FMEA is utilized in

quality management. Over time, when organizations

come across a challenge or are competing with their

peers, FMEA gets to be more and more important as

it not only stops or reduces risks in the production

process but also continues to improve the

organization to last in the sector.

2 Literature Survey

The whole FMEA literature is about importance of

the topic and strategic application in order to raise

current processes. This section starts with reviewing

the existent literature as a basis on which to build a

deeper understanding of FMEA in the light of

improving the quality of business processes.

Many academic papers give extraordinary emphasis

to FMEA’s proactive character since it, among

others, identifies and mitigates risks and problems in

various fields of practice [1], [2], [3], [4],

[5]. Crunching down on the problem, some

researchers again highlight that, through FMEA, one

would be able to not only identify faults, but also

reliability and risk minimization, before the

customers are affected. The literature agrees with

the idea that FMEA’s thorough approach helps

organizations remain at the top of quality [6], [7],

[8]. Real-world examples, mostly learned from the

studies and literature depict successful FMEA's

implementations. These specific cases illustrate that

companies in different industries such as the

manufacturing, healthcare, cars and the service

sector are already reaping the benefits of the FMEA

tool which is used to implement process

improvements as well as constantly improving the

quality of the product. The write up in our survey

discloses also the fact that FMEA is overlapping

certain quality managing techniques like Six Sigma

and TQM [9], [10], [11], [12]. Practitioners in this

area have extensively considered how these

techniques go hand in hand with FMEA. It therefore

offers organizations with the best approach which

combines both continuous improvement and

recognition of industry standards [13], [14], [15].

Besides, this literature suggests the FMEA

application in conjunction with the ISO9001 to

exemplify the contribution it makes to quality

system standard around the world. The conversation

further deals with the different scenarios of how the

FMEA can be used, including its principles within

lean manufacturing processes as well as its

significance in a multiplicity of industries/sectors

[16].

Conclusively, this literature review furnishes

available data that gives a comprehensive

insinuation of the fundamental concept of the

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FMEA within quality management [17], [18], [19],

[20], [21], [22]. Through combining findings from

scholarly contributions and what is done in reality,

the survey will be our starting point that will

facilitate for subsequent sections to expound

FMEA’s strategies for process enhancement in this

research.

3 Methodology

In this part of FMEA basic structure is described,

and process of analysis is presented by using

MCDM methods also in an organized form.

3.1 Failure Modes and Effects Analysis

(FMEA) Method

To delve into the strategies for process enhancement

using Failure Modes and Effects Analysis (FMEA)

as a key component of quality management, it is

essential to understand the methodological approach

and the mathematical model employed in the

application of FMEA [23], [24]. This section

outlines the steps involved and the mathematical

foundation that underpins the systematic risk

assessment and improvement strategies offered by

FMEA (in Fig.1).

Figure 1 The proposed framework steps

The concepts related to FMEA methodology are as

follows [25]:

Customer: Individuals or entities impact by a

specific type of error.

Error Type: Failure to meet customer needs and

expectations; the product or process fails to perform

the desires function adequately or at all.

Reason for Error: The factor responsible for causing

a specific type of error.

Error Effect: Situations that may lead to customer

dissatisfaction.

Existing Controls: Activities conducted during the

FMEA study to prevent errors from occurring and

reaching the customer.

FMEA Element: Topics examined within the FMEA

study.

Emergence: The likelihood of a specific error

occurring during the product's known lifespan.

Detection: The effectiveness of existing controls in

preventing errors from reaching the customer.

Weight: The assessment of the impact of the error's

effect on the customer.

Risk Priority Number: A value obtained by

multiplying the severity, occurrence, and detection

ratings assigned to each failure mode.

     (1)

Criticality: The multiplication of the occurrence of

the error by the likelihood that the error can be

detected [26] , [27].

3.1.1 Objectives of FMEA

The primary objective of the FMEA technique is to

analyze potential types of errors in both the product

and the process, examining their impact on the

customer and assessing their associated level of risk.

The aims also include the prevention of potential

errors by identifying them before they manifest in

the product or process. Additionally, FMEA aims to

scrutinize the design characteristics of the product

concerning its manufacturing and assembly

processes, ensuring alignment with customer

expectations. Once potential error types are

identified, corrective measures are implemented to

prevent their occurrence, thereby reducing the

likelihood of errors and contributing to the overall

product development [28], [29].

3.1.2 Process steps of FMEA

The main purpose of FMEA is to identify possible

failure modes of a system or process, analyze the

effects and identify measures to reduce the risks

associated with failure modes. When performing an

FMEA, risks are prioritized according to defined

criteria and actions are taken starting with higher

priority failure modes. The process of performing an

FMEA consists of 10 steps, which are summarized

in Figure 2 [30], [31], [32].

3.2 Application of Muti-Criteria Decision -

Making techniques to FMEA

Multi-Criteria Decision-Making (MCDM)

techniques can be applied to FMEA (Failure Mode

and Effects Analysis) analysis to enhance decision-

making by considering multiple criteria

simultaneously [33], [34], [35], [36].

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Here's a step-by-step guide on how to apply MCDM

techniques to FMEA:

Step 1: Identify Decision Criteria

Identify the criteria that are important for evaluating

failure modes in your FMEA. These criteria could

include severity, occurrence, detectability, cost of

prevention, impact on safety, impact on production,

etc.

Step 2: Assign Weights to Criteria

Assign weights to each criterion based on their

relative importance. The weights reflect the

significance of each criterion in the decision-making

process.

Step 3: Evaluate Failure Modes

For each failure mode identified in the FMEA,

assess its performance on each criterion. You can

use a scoring system or qualitative assessment to

evaluate severity, occurrence, detectability, etc., for

each failure mode.

Step 4: Normalize Scores

Normalize the scores for each criterion to ensure

they are comparable. This is particularly important

if the scales of the criteria are different.

Normalization ensures that each criterion

contributes equally to the decision-making process.

Step 5: Apply MCDM Technique

Choose an appropriate MCDM technique to

integrate the normalized scores across criteria and

rank the failure modes. Some commonly used

MCDM techniques include:

 MULTIMOORA method)

 Fuzzy GRA

 MABAC with FMEA,

Step 6: Rank Failure Modes

Rank the failure modes based on the aggregated

scores obtained from the MCDM technique. The

higher the score, the higher the priority for

addressing that failure mode. This is a great help in

terms of specifying the mitigating activities and

ensuring an adequate use of resources.

Figure 2 The FMEA process steps

Step 7: Decision-Making

Use the ranked list of failures as a basis to identify

areas requiring risk avoidance measures. Attention

should be centered on major SMFs that include

potential consequences and frequency of

occurrence.

With the application of MCDM method to FMEA

analysis, you will be able to make multi-criteria

evaluation and prioritize failure modes, which

would result in the more informed decisions and

more effectiveness in risk management. Each

MCDM technique will apparently have its own

advantages and disadvantages, so you will need to

apply the most suitable technique based on the

incident as well as your FMEA analysis.

3.2.1 MULTIMOORA

MULTIMOORA method includes, in its sequence,

several stages, and there is no strict math formula

during that, however, the mathematical expressions

that are involved in the whole process are described.

Define or normalize the matrix X of n×m

dimensions, where m is the number of alternatives,

and n is the number of criteria. Normalize the as

each one is in a comparable scale. This can be

obtained by different normalization methods like the

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min-max normalization, z-score normalization, or

decimal scaling.

Assign weights to the candidates. Let  

󰇛󰇜 be the vector that describes the

relative significance of each criterion. Generally, the

weights employed although can be determined

subjectively, through expert opinion or analytically

by methods such as the Analytical Hierarchy

Process (AHP) and the Analytical Network Process.

Compute the performance ratio for each alternative

by ratio analysis. Undefined







  







 (2)

this is where  stands for the normalized value of

alternative i on criterion j and  is the weight

assigned to criterion j. Prolong the ratio analysis to

the string multiplicative form.





 (3)

here  composed of the proportion between

alternative i and criterion j computed in prior

stage. The alternatives being ranked will be the ones

with the best scores S. The highest rated alternative

is viewed as the best choice. These expressions are

indeed the essential mathematical formulas in the

MULTIMOORA approach. Nonetheless, the

particular execution could be diversely affected

using the particular subject and the decision-maker’s

preferences.

3.2.2 Fuzzy GRA

The Fuzzy GRA (Fuzzy Grey Relational Analysis)

approach consisting of fuzzy logic is one of the

extensions of Grey Relational Analysis (GRA) that

deals with imprecision and vagueness in decision

making. GRA is a tool for determining the relative

proximity of different alternatives as to their criteria

measure results. Normalize the matrix X, with m

alternatives and n criteria, into a base matrix. It can

be done via using different fuzzy normalization

approaches, for example, max-min and centroid

method, in order to ensure the equivalence and

compatibility of all the criteria. Develop the fuzzy

grey relational coefficients between the reference

alternative (which may be the best or worst

situation) and each alternative i in relation to all of

the criteria to be considered. Consider X as the

reference option with asterisk. Let x* denote the

reference alternative. The fuzzy grey relational

coefficient ) between alternative i and x* for

criterion j is typically calculated using a formula

like:



󰇡

󰇢󰇡

󰇢

󰇡

󰇢󰇡

󰇢 (4)

where 

and  represent level of fuzzy

membership of an alternative with respect to x* and

alternative i with respect to criterion j, and  is the

distinguishing coefficient. Compute the fuzzy grey

relational grade GRi for each alternative by

aggregating the fuzzy grey relational coefficients

across all criteria. This can be done using

aggregation methods such as the arithmetic mean,

geometric mean, or ordered weighted averaging

(OWA).









 (5)

Rank the alternatives based on their fuzzy grey

relational grades . Higher values indicate

higher similarity to the reference alternative and

thus better performance.

In the above formulas,  represents the value of

alternative i on criterion 

represents the

membership grade of the reference alternative on

criterion j, and  represents the membership

grade of alternative i on criterion j. The

distinguishing coefficient  adjusts the sensitivity of

the grey relational coefficients to differences

between alternatives.

3.2.3 MABAC

The MABAC (Multi-Attributive Border

Approximation area Comparison) method is a multi-

criteria decision-making (MCDM) approach that

aims to rank alternatives based on their performance

across multiple criteria. Normalize the decision

matrix X of size mn, where m is the number of

alternatives and n is the number of criteria. For the

sake of the comparison, this stage of process makes

all the parameters uniform. Z-score normalization

and min-max normalization are examples of

nornalization techniques. Create the criteria scaling

to indicate which criteria are more

significant. Consider the vector of weights  

󰇛󰇜. In which  represents the weight

of criterion j possibly provided by expert judgment,

analytical methods( for example, Analytic Hierarchy

Process), or stakeholder preferences. Provide a

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definition preference function for all criterion

j and alternatives i. The preference function shows

how much satisfaction or preference for specific

criterion value. It may appear in different shapes, for

instance, as a linear, triangle, Gaussian, or

trapezoidal function, which  is determined by the

type of the criteria and the way in which the

decision-maker prefers to solve the problem.

 󰇡󰇢

󰇡󰇢 (6)

where  is defined as the ideal value of

criterion j(usually the maximum value or minimum

value, depending on the type of the criterion -

maximization or minimization). Establish the ideal

solution and anti-ideal solution for each index by

weighting them. The value of, the best possible

value of criterion j, is the positive ideal solution of

the criterion, and the value of , the worst possible

value, is the negative one. Such a result can be

achieved by taking the best and the worst of all

criteria and for every alternative. Get the MABAC

score of alternative i, if, by summing the

borderline approximations produced on each

criterion. Different aggregation functions like

weighted sum as well as weighted product can be

utilized in order to achieve this.





 (7)

Rearrange the choices from less effective ( MABAC

score = v) to most effective( MABAC score = 

). The higher score show that alternative has

superior performance, thus the alternative with the

highest score will be ranked first while the second

highest score will be ranked after that. These

mathematical formulations unveil for the user

MABAC main moves during multi-criteria decision-

making. It is an organized tool that helps in the

examination and the ranking of alternatives based

on their capacity to perform very well considering a

number of criteria.

3.3 Improving decision making by combining

FMEA with MULTIMOORE, Fuzzy GRA

and MABAC

FMEA (Failure Mode and Effects Analysis) stands

as the systematic approach in recognizing the

probable failure modes that a system can have, the

effects if they occur, and the likelihood of their

happening now with our preventive controls. This is

one of the most critical methods that industries like

the food and beverage, automotive, aerospace,

health care, etc utilize to improve manufacturing

processes, products and systems. Taking into

account FMEA and MCDM together enables

making clear-cut decisions possibly in situations

consisting various failure modes or risks

assessments and at the very same time where

numerous factors or criteria should be taken into

consideration. Here's given the combined MCDM

within FMEA analysis in below:Here's given the

combined MCDM within FMEA analysis in below:

1. Identify Attributes or Criteria: Specify those key

characteristics that are actually important in

assessing failure modes. These factors can be

represented by at least occurrence, severity,

detectability, prevention costs, impact on safety,

impact on productivity, etc.

2. Assign Weightings: Assign weights to each

attribute based on their relative importance. This

weighting reflects the significance of each criterion

in the decision-making process.

3. Evaluate Failure Modes: For each failure mode

identified in the FMEA, assess its performance on

each criterion. Scoring system or qualitative

assessment evaluates severity, occurrence,

detectability, etc., for each failure mode.

4. Calculate Scores: Multiply the scores of each

failure mode by the assigned weights for each

criterion. This gives a weighted score for each

failure mode on each attribute.

5. Aggregate Scores: Aggregate the weighted scores

across all attributes to obtain a total score for each

failure mode. This represents the overall evaluation

of the failure mode considering all criteria.

6. Ranking and Prioritization: Rank the failure

modes based on their total scores. The higher the

score, the higher the priority for addressing that

failure mode. This helps in prioritizing mitigation

efforts and allocating resources effectively.

7. Decision-Making: Utilize the ranked list of failure

modes to inform decision-making regarding risk

mitigation strategies. Focus on addressing high-

priority failure modes first, considering their

potential impact and likelihood of occurrence.

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By integrating MCDM with FMEA, you can

systematically assess and prioritize failure modes

based on multiple criteria, enabling more informed

decision-making and proactive risk management.

4 Case Study

The company strives to enhance its competitiveness

by focusing on the quality and production efficiency

of its products. Cables, being crucial components

influencing the performance of military vehicles,

demand durability and world-class production

standards. Customer companies subject their

suppliers to rigorous inspections, monitoring their

endeavors to achieve the desired quality.

Consequently, the company is dedicated to attaining

production quality and ensuring the effective

operation of its established quality management

system. This study aims to identify, systematize, and

illustrate the advantages of the cables manufactured

by the company. An examination is conducted on

the cables returned by customers for correction, with

a focus on understanding the reasons for the returns.

Some of the customer companies cables are subject

to returns, and the company consistently faces

challenges with these cables, resulting in decreased

factory efficiency. By pinpointing the reasons for

returns, the identified problems have been

addressed, and a systematic solution has been

devised and documented.

4.1 Determination of Emergence, Severity

and Detection Values

While calculating the emergence, severity and

detection values, all customer returns were

examined. The emergence rating table was used for

the selected product types while the average value

was determined, the Severity rating table was used

while determining the determination value. The

scoring system is made with a rating of 1-10 (in

Table 1-6).

Table 1: Statement rating table

Table 2: Occurrence probability rating table

Table 3: Severity rating score table

Table 4: Severity rating score table

Table 5: Severity rating score table

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Table 6: Detectability Evaluation Chart

4.2 Analyzing with MULTIMOORA Method

In this method, the best and worst alternatives are

determined for each criterion and then a score is

made based on the distance of the performance of

each alternative to these best and worst alternatives.

First, let's review the activities in the dataset and

their criteria (in Fig.3):

Figure 3 FMEAS’s criteria

Activities:

1. Documentation of approval of connector strength

before shipment

2. Pin control for each cable

3. Updating on a common server for everyone to

notice when the picture is updated

4. Training of people who control and standardize

the crimping process

5. No other material is used without customer

approval

6. Pin control for each cable

7. Adding to the ones to be checked in the

intermediate control

8. Making a connector check at every stage of the

quality control

9. The person who makes the marking is competent

to understand the technical picture

10. Adding the terminal to the technical drawing

according to the branches

11. Using the desired size value tubing

12. Detecting short circuits while testing the cable

13. While measuring the length of the cable, it is

done with a quality controller.

14. Using quality macaron and glue while

retouching

The MULTIMOORA method steps are:

Step 1: Standardization of Data

Let's standardize the data so that the performance of

each alternative for each criterion is comparable

Step 1: Standardization of Data

Let's standardize the data so that the performance of

each alternative for each criterion is comparable

Step 2: Determining the Weights

Weights are determined for the criteria. However,

since there is no information here about the

importance of each criterion, we will assume the

weights are equal.

Step 3: Calculating Performance Scores

We will calculate performance scores for each

alternative.

Step 4: Sorting

We ranked the alternatives according to their

performance scores and listed the applied steps:

Step 1: Standardization of Data

Let's calculate the standardized values of the data

for each criterion.

Step 2: Determining the Weights

We considered the weights equal: 

 

Step 3: Calculating Performance Scores

The performance scores calculated for each

alternative (in Table 7).

Table 7: Calculating the performance scores for

each alternatives

Step 4: Ranking

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Let's list the alternatives according to their

performance scores.

Ranking (from highest score to lowest score):

1. (125243); 2. (113580); 3. (127584); 4. (117529);

5. (W18); 6. (802147); 7. (W58); 8. (801374); 9.

(125026); 10. (W42); 11. (125026); 12. (802147);

13. (113580); 14. (W42)

This ranking is based on the performance of each

alternative evaluated according to the

MULTIMOORA method.

4.3 Calculation and Evaluation of the Risk

Priority Number (RPN)

The variables of RPN are calculated with FMEA

ratings for severity, detection, and Probability

tables, where numbers one and ten represent the

lowest and most significant risk factors,

respectively. The RPN values range from 1 to 1000.

The absolute best to absolute worst RPN value is

ranges from 1 to 1000. A FM with such a greater

RPN is more important and has a greater priority.

The risk priority coefficient (RPC) obtained with

multiplying the emergence value, severity value and

detection values that was calculated for each

selected error type (in Table 8).

Table 8: RPN Evaluation Chart

The distribution of RPN values defined with error

types such as cable construction error, adapter error,

marking error, common errors, pinning error,

connector error, socket error, panel error, retouching

error and terminal error (in Figure 4). For each type

of RPN error, we can analyze the most frequently

encountered risk situations and the least frequently

occurring risk situations from the distribution chart.

Figure 3: Distribution of RPN Values by Error

Types

Probabilities of specific activities, risk severities,

detectability levels and recommended activities are

given for each cable code in the data set. First of all,

RPN for each cable code calculated using Fuzzy

GRA and MABAC methods.

1. Fuzzy GRA (Grey Relational Analysis): We

determined RPN values for each activity by

calculating the similarity levels of the data.

2. MABAC (Multi-Attribute Border Approximation

area Comparison): We considered RPN values

by evaluating activities in terms of probability,

risk severity and detectability.

4.3.1 Calculating RPN with using Fuzzy GRA

Method

For Fuzzy GRA analysis, the steps of normalizing

the data obtained and calculated the similarity

degrees. The degree of similarity of each activity to

other activities done.

Step 1: Normalization of Data

Let's normalize the data by dividing each criterion

by the maximum value. For example, the maximum

value for the probability criterion is 8. Let's

normalize the data according to this value )in Table

9).

Step 2: Fuzzy GRA Calculation

To calculate Fuzzy GRA, we calculated the degree

of similarity between each activity and other

activities. For this, we took the absolute value of the

difference between the values of other activities for

each criterion for each activity and the same

criterion. We calculated the degrees of similarity by

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multiplying these differences respectively by a

certain factor (usually 0.5) in Table 10.

Table 9: Normalization of Data

Next, we will calculate the degrees of similarity by

multiplying each difference by a certain factor.

Table 10: Differences between probability criterion

of other activities

Step 3: Interpreting the Results

Degrees of similarity are interpreted and

relationships between activities are determined. A

higher degree of similarity means more similarity.

By completing these steps, we can obtain Fuzzy

GRA analysis results. However, in order to perform

the full analysis, to calculate the differences and

similarity levels of each activity and other activities

between all criteria.

4.3.2 4.4.2 Calculating RPN with using

MABAC Method

Let's analyze each activity in your data set with the

MCDM method. First, we will evaluate each

activity in terms of probability, risk severity and

detectability using the MABAC (Multi-Attribute

Border Approximation area Comparison) method.

Next, we prioritized each activity.

Step 1: Calculating MABAC Scores

Let's consider a simplified example of applying

MABAC to FMEA analysis for a manufacturing

process. We focused on three criteria: severity,

occurrence, and detectability. We used a scale of 1

to 10 for each criterion, with 10 being the highest

importance or severity.

Identify Attributes and Assign Weightings

 Severity weight = 0.4

 Occurrence weight = 0.3

 Detectability weight = 0.3

Step 2: Evaluate Failure Modes

Let's consider two failure modes: "Machine

Breakdown" and "Material Shortage."

Machine Breakdown:

 Severity: 9

 Occurrence: 7

 Detectability: 6

Material Shortage:

 Severity: 7

 Occurrence: 8

 Detectability: 5

Step 3: Calculate Scores

For Machine Breakdown:

  󰇛󰇜  (8)

  󰇛󰇜 (9)

  󰇛 󰇜 

(10)

  󰇛󰇜 

(11)

For Material Shortage:

  󰇛  󰇜 

(12)

  󰇛 󰇜 

(13)

  󰇛 󰇜 

(14)

  󰇛󰇜 

(15)

Step 4: Ranking and Prioritization

 Machine Breakdown total score = 7.5

 Material Shortage total score = 6.7

So, based on this analysis, "Machine Breakdown" is

prioritized over "Material Shortage" for mitigation

efforts. With this prioritized list, the team can now

focus on addressing the causes and consequences of

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machine breakdowns, such as implementing

preventive maintenance schedules, upgrading

equipment, or improving monitoring systems.

This numerical example demonstrates how MABAC

can be applied to FMEA to systematically evaluate

and prioritize failure modes based on multiple

criteria, facilitating more informed decision-making

in risk management (in Table 11).

Table 11: Activity’s MABAC Score

Step 2: Determining the Order of Priority

Let's prioritize activities according to MABAC

scores:

1. 117529 (155.5) ; 2. 127584 (136.5); 3. 801374

(110.0); 4. W18 (104.5); 5. 125243 (101.5); 6.

802147 (87.5); 7. W58 (70.5); 8. 113580 (55.5); 9.

125026 (39.0); 10. W42 (37.5)

In this way, the priority order of each activity was

determined. Starting with the highest priority

activity, the order is as above.

Variable types of data included in the proposed

model are given as cable code, possibility, severity,

detectability, the first RPN, recommended activities

and post-activity RPN (in Table 12).

In RPN value calculations made in the previous

stages, error types with high risk levels determined.

The preventive activities to be taken discussed and

the FMEA form is formed and the RPN values again

examined. It can be understood from the decrease in

the RPN values where the risk reduced with the

preventive activities taken in the Appendix-2.

5 Conclusion

The initiation of FMEA application involved a

comprehensive examination of the target process.

This encompassed scrutinizing customer complaints

and quality control records to pinpoint existing

issues. Current and potential problems were

systematically identified, and an evaluation of

existing controls was conducted. The causes and

effects of errors were elucidated, followed by the

determination of Risk Priority Number (RPN)

values on the FMEA form. Subsequent to these

assessments, system improvements were outlined.

Post-improvements, a reevaluation of risk analysis

took place, gauging the efficacy of the implemented

enhancements.

Table 12: Table of the first and Post Activity RPN

for each Cable Code

The processes facilitated in their utilization in future

projects, to establishing a corporate memory for the

company. This initiative elevated the reliability of

the company's products and services, so it enhanced

the competitive process, and bolstered customer

satisfactions. The company under- went a positive

transformation in image and experienced of their

reduction in warranty costs. Furthermore, this

approach significantly contributed to the

accumulation of engineering knowledge. It is

imperative to underscore the continuous need for

controls and evaluations to sustain the achieved

improvements over time.

Acknowledgement:

It is an optional section where the authors may write

a short text on what should be acknowledged

regarding their manuscript.

References:

[1] Ervural, B., Halil Ibrahim Ayaz,H.İ., A fully

data-driven FMEA framework for risk

assessment on manufacturing processes using a

DESIGN, CONSTRUCTION, MAINTENANCE

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hybrid approach, Engineering Failure Analysis,

Volume 152, October 2023, 107525.

[2] Akhtar, M.J., Naseem, A., Ahsan, F., A novel

hybrid approach to explore the interaction

among faults in production process with

extended FMEA model using DEMATEL and

cloud model theory, Engineering Failure

Analysis, Volume 157, March 2024, 107876

[3] Filz, M.A., Langner, J.E.B., Herrmann, C.,

Thiede, S., Data-driven failure mode and effect

analysis (FMEA) to enhance maintenance

planning, Computers in Industry, Volume

129, August 2021, 103451

[4] Ramere, M.D., Laseinde, O.T., Optimization of

condition-based maintenance strategy

prediction for aging automotive industrial

equipment using FMEA, Procedia Computer

Science, Volume 180, 2021, Pages 229-238

[5] Ghiaci, A.M., Ghoushchi, S.J., Assessment of

barriers to IoT-enabled circular economy using

an extended decision- making-based FMEA

model under uncertain environment, Internet

of Things, Volume 22, July 2023, 100719

[6] Hassan, F., Nguyen, T., Le, T., Le, C.,

Automated prioritization of construction

project requirements using machine learning

and fuzzy Failure Mode and Effects Analysis

(FMEA), Automation in Construction, Volume

154, October 2023, 105013.

[7] Ribas , J.R., Severo, J.C.R., Guimarães, L.F.,

Perpetuo, K.P.C., A fuzzy FMEA assessment

of hydroelectric earth dam failure modes: A

case study in Central Brazil, Energy Reports,

Volume 7, November 2021, Pages 4412-4424.

[8] Kumru, M., Kumru, P.Y., Fuzzy FMEA

application to improve purchasing process in a

public hospital, Applied Soft Computing,

Volume 13, Issue 1, January 2013, Pages 721-

733.

[9] Boral, S., Chakraborty, S., Failure analysis of

CNC machines due to human errors: An

integrated IT2F-MCDM-based FMEA

approach, Engineering Failure Analysis,

Volume 130, December 2021, 105768.

[10] Balaraju, J., Raj, M.G., Murthy, C.S., Fuzzy-

FMEA risk evaluation approach for LHD

machine – A case study, Journal of Sustainable

Mining, Volume 18, Issue 4, November 2019,

Pages 257-268

[11] Mzougui, I., Felsoufi, Z.E., Proposition of a

modified FMEA to improve reliability of

product, Procedia CIRP, Volume 84, 2019,

Pages 1003-1009

[12] Hassan, A., Siadat, A., Dantan, J.Y., Martin, P.,

Conceptual process planning – an improvement

approach using QFD, FMEA, and ABC

methods, Robotics and Computer-Integrated

Manufacturing, Volume 26, Issue 4, August

2010, Pages 392-401

[13] Lo, H.W., Liou, J.J.H., A novel multiple-

criteria decision-making-based FMEA model

for risk assessment, Applied Soft Computing,

Volume 73, December 2018, Pages 684-696.

[14] Mutlu, N.G., Altuntas, S., Risk analysis for

occupational safety and health in the textile

industry: Integration of FMEA, FTA, and

BIFPET methods, International Journal of

Industrial Ergonomics, Volume 72, July 2019,

Pages 222-240

[15] Takao ITO, T., Wang, H., Hwang, S.H., Wang,

B., Wang, L., Somasundaram, G. , Risk

assessment for biopharmaceutical single-use

manufacturing: A case study of upstream

continuous processing, Biologicals, Volume

84, November 2023, 101713.

[16] Ru-xin, N., Zhang-peng, T., Xiao-kang, W.,

Jian-qiang, W., Tie-li, W., Risk evaluation by

FMEA of supercritical water gasification

system using multi-granular linguistic

distribution assessment, Knowledge-Based

Systems, Volume 162, 15 December 2018,

Pages 185-201

[17] Liu, Y., Yang, Z., He, J., Li, G., Zhong, Y., A

new approach to failure mode and effect

analysis under linguistic Z-number: A case

study of CNC tool holders, Engineering

Failure Analysis, Volume 154, December

2023, 107688.

[18] Li, H., Hunag, C.G., Soares, C.G., A real-time

inspection and opportunistic maintenance

strategies for floating offshore wind turbines,

Ocean Engineering, Volume 256, 15 July

2022, 111433.

[19] Liu, Z., Bi, Y., Liu, P., A conflict elimination-

based model for failure mode and effect

analysis: A case application in medical waste

management system, Computers & Industrial

Engineering, Volume 178, April 2023, 109145

[20] Wan, C., Yan, X., Zhang, D., Qu, Z., Yang, Z.,

An advanced fuzzy Bayesian-based FMEA

approach for assessing maritime supply chain

risks, Transportation Research Part E:

Logistics and Transportation Review, Volume

125, May 2019, Pages 222-240

[21] Chen, Z.S., Jun-Yang Chen, J.Y., Chen, Y.H.,

Yang, Y., Jin, L., Herrera-Viedma, E., Pedrycz,

W., Large-group failure mode and effects

analysis for risk management of angle grinders

in the construction industry, Information

Fusion, Volume 97, September 2023, 101803.

DESIGN, CONSTRUCTION, MAINTENANCE

DOI: 10.37394/232022.2024.4.4

Safiye Turgay, Damla Kara,

Sultan Çi

men, Esma Sedef Kara

E-ISSN: 2732-9984

Volume 4, 2024

[22] Turgay, S., Aydın, A., Risk Mitigation for

SMEs: A Step-by-Step Guide to Implementing

an Effective Framework, Financial Engineering

and Risk Management (2023), Vol. 4 Num. 1,

DOI: 10.23977/ferm.2023.060808, ISSN 2616-

3349

[23] Jin, G., Sperandio, S., Girard, P., Selecting risk

response strategies to minimize human errors in

a design project for factories of the future,

Expert Systems with Applications, Volume

225, 1 September 2023, 120120.

[24] Soares, E., ⁎, Isabel da Silva Lopes, I.S.,

Pinheiro, J., Methodology to Support

Maintenance Management for the Identification

and Analysis of the Degradation of Equipment

Reliability, IFAC-PapersOnLine, Volume 54,

Issue 1, 2021, Pages 1272-1277.

[25] Kropatschek, S., Steuer, T., Kiesling, E.,

Meixner, K., Ayatollahi , I., Sommer, P.,

Stefan Biffli S., Analysis of Quality Issues in

Production With Multi-view Coordination

Assets, IFAC-PapersOnLine, Volume 55, Issue

10, 2022, Pages 2938-2943.

[26] Schulte, J., Knuts, S., Sustainability impact and

effects analysis - A risk management tool for

sustainable product development, Sustainable

Production and Consumption, Volume

30, March 2022, Pages 737-751.

[27] Lu, Y.J., Lee, W.C.,, Wang, C.H., Using data

mining technology to explore causes of

inaccurate reliability data and suggestions for

maintenance management, Journal of Loss

Prevention in the Process Industries, Volume

83, July 2023, 105063.

[28] Shahri, M.M., Jahromi, A.E., Houshmand, M.,

Failure Mode and Effect Analysis using an

integrated approach of clustering and MCDM

under pythagorean fuzzy environment, Journal

of Loss Prevention in the Process Industries,

Volume 72, September 2021, 104591.

[29] Dhalmahapatra, K., Garg, A., Singh, K.,

Xavier, N.F., Maiti, J., An integrated

RFUCOM – RTOPSIS approach for failure

modes and effects analysis: A case of

manufacturing industry, Reliability

Engineering & System Safety, Volume

221, May 2022, 108333.

[30] Dora, M., Kumar, M., Goubergen, D.V.,

Molnar, A., Gellynck, X., Food quality

management system: Reviewing assessment

strategies and a feasibility study for European

food small and medium-sized enterprises, Food

Control, Volume 31, Issue 2, June 2013, Pages

607-616.

[31] Guillén, A.J., Crespo, J., Gómez, A., Sanz, J.F.,

A framework for effective management of

condition based maintenance programs in the

context of industrial development of E-

Maintenance strategies, Computers in Industry,

Volume 82, October 2016, Pages 170-185.

[32] Zhen Hua, Z., Jing, X., Martínez, L., An

ELICIT information-based ORESTE method

for failure mode and effect analysis considering

risk correlation with GRA-DEMATEL,

Information Fusion, Volume 93, May 2023,

Pages 396-411

[33] Nand Gopal, N., a, Dilbagh Panchal , D.,

Fuzzy decision support system for sustainable

operational performance optimization for boiler

unit in milk process industry, Applied Soft

Computing, Volume 135, March 2023, 109983.

[34] Turgay, S. ,Ayma, S.B., Determined by Tolerances

with Rough Set Based MCDM, Industrial

Engineering and Innovation Management (2021) 4:

34-47 Clausius Scientific Press, Canada,

[35] Turgay, S., Dinçer, E.G., Kazancı, S., Navigating

Uncertainty: A Comprehensive Approach to Risk

Management in R&D Projects with the Gravity

Search Algorithm Based MCDM. Industrial

Engineering and Innovation Management (2023)

Vol. 6: 95-103. DOI:

http://dx.doi.org/10.23977/ieim.2023.061013.

[36] Chang, C. L., Wei, C. C., Lee, Y. H., (1999).

Failure mode and effects analysis using fuzzy

method and grey theory. Kybernetes, 28(9),

1072-1080

APPENDICES

APPENDIX-1

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APPENDIX-2

Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

S.Turgay, D.Kara and S.Çimen – investigation,

S.Turgay- validation and

S.Turgay, E.S.Kara- writing & editing.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

No funding was received for conducting this study.

Conflict of Interest

The author has no conflicts of interest to declare.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.en

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