Addressing Uncertainty in Digital Risk Evaluation Using a Fuzzy-FMEA Methodology

Addressing Uncertainty in Digital Risk Evaluation Using a Fuzzy-FMEA Methodology | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Case Report Addressing Uncertainty in Digital Risk Evaluation Using a Fuzzy-FMEA Methodology Lina Naciri, Safae Merzouk, Maryam Gallab, Mario Nardo, Aziz Soulhi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6423322/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Alongside with the four past revolutions that crowned the industrial field, and the innovative technologies that grows with the digitalization trend, manufacturing systems are becoming more complex especially in the automotive industry. Even though this evolution plays an important role in enhancing companies’ performance, they also present many risks to manage, which leads stakeholders to the need of implementing a strong strategy to assess risks such as Failure Modes and Effects Analysis (FMEA). Nowadays, manufacturing flows confronts different types of variables continuously and dynamically interacting (social, environmental, financial, political, educational, cultural, etc.), leading to uncertainty in the decision-making approach, specifically during the ranking of a failure severity. Accordingly, this article explores how the latest version of FMEA (AIAG/VDA) approach can be strengthened through Fuzzy-logic to overcome uncertainty in judgments. It also presents a use case based on an RFID system implemented within an automotive company. FMEA AIAG/VDA Decision-making Smart factory Fuzzy-logic Literature review automotive industry Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Article Highlights Literature analysis to identify and compare the most used and efficient method for risk assessment. Identify potential use of decision-making methods to support FMEA and resolve uncertainty. Use case based on a Radio Frequency Identification (RFID) system implemented by an automotive manufacturer. 1 Introduction Failure Modes and Effects Analysis is an approach frequently relied one to evaluate risks that first appeared in the 1950s, after its formalization in military standards by the United States Armed Forces for assessment of the impact of failures on the successful completion of the mission and on the safety of equipment [ 1 ]. It was then adopted on 1960’s by the NASA as a way to enhance and authentify the effectiveness of the Apollo space program hardware. In the 1970's, Ford Motor manufacturer reintroduced FMEA for safety consideration after the disastrous “Pinto affair”, and now uses FMEA effectively for production and design improvement. Nowadays, FMEA usage was extended and became popular in many other industries as an effective tool to analyze and improve quality, safety and reliability of systems: Aeronautics and automotive industries, ship navigation, sustainable manufacturing, pharmaceutical industry, healthcare, information systems, liquefied natural gas storage facility, offshore wind turbine, foodgrains supply chain, and so on [ 2 ]. In 1982 was founded the Automotive Industry Action Group (AIAG) in Michigan that developed a set of recommendations and a guide to improve quality in the North American automotive field, by representatives of the three largest automotive manufacturers: Ford, General Motors and Chrysler, to then extend to Japanese manufacturers such as Toyota, Honda and Nissan. In 2019, AIAG, in collaboration with the German Association of the Automotive Industry (VDA) published the first international framework on FMEA. AIAG and VDA combined their respective regional FMEA manual to create a unified and international guideline. The first edition highlights the importance of a process-oriented approach to guide suppliers into meeting the product performance required by global automakers. The AIAG & VDA PFMEA approach relies on Seven Steps: Planning & Preparation; Structure Analysis; Function Analysis; Failure Analysis; Risk Analysis; Optimization; Results Documentation [ 3 ]. Since its biggening, FMEA approach was based on evaluating the risk of occurrence (O), severity (S) and detection (D) of a failure using ranking standards with a scale from 1 to 10, thus relying on experts’ judgment. However, with the development of manufacturing processes and the introduction of new technologies, the complexity and uncertainty involved makes it hard to proceed with risk assessment. In addition to that, FMEA has some limitations such as weights of factors and results sensibility to the perspectives of the experts leading the risk assessment, which makes uncertainty and subjectivity common issues while adopting FMEA approach [ 4 ]. In today's highly competitive world driven by customers increased expectations, decreased life cycle of equipment and product, and innovative economic models, manufacturing systems’ reliability has become a key driver of production especially in the automotive industry. Indeed, considering that vehicles became more compact and with the introduction of new materials and complex components, automotive manufacturing processes became as well more complex and more critical, and manufacturers found themselves in a situation where they have to cope with these changes by reinforcing their systems. In addition to that, defects in vehicle can lead to very critical consequences and is more likely to be affecting human being’s safety (explosion, car crash, etc.), which makes the risk assessment performance a mandatory task at early design stage of the project, to exhaustively explore potential failure modes and anticipate their effects not only on the operability aspect, but also on workers/user safety. Another challenge to which were confronted the automotive industrials was therefore to provide good quality services, products and processes in a risk-free environment, prevent excessive costs, eliminate wastes and respect deliveries specifications (Time, quantity, variety, etc.), which results in the obligation of identifying potential failure modes with enough time-space to scheme and implement corrective actions before failures occurrence by adopting FMEA approach [ 5 ]. Whether it is in everyday life or in industrial fields, when it comes to decision-making or to criteria evaluation, there is always an oscillation from 0 to 100 between not being sure at all and being completely sure. In addition to that, judgments are relative to each one’s perception and lessons learned from past experience. Uncertainty has therefore a major and direct impact on defining the risk factors (O, S, D) of a failure mode. Accordingly, to reduce unplanned events, ensure operations reliability and increase products quality in a risk-free environment, FMEA approach may be completed by Multiple Criteria Decision-Making (MCDM) approaches. Many of them were introduced over the years to overcome uncertainty in complex systems like Analytic Hierarchy Process (AHP), Data Envelopment Analysis (DEA), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), etc. But most frequently adopted one was proven to be fuzzy logic, which can be applied as well in combination with other MCDM methods [ 6 ]. Accordingly, the objective of this study is to spot potential use of decision-making methods to support FMEA and resolve uncertainty, thus through a literature review which purpose is to go through previous studies and researches, to explore how they overcame fuzziness during risk assessment and which decision-making tools were used especially in the automotive industry. This review was completed by a use case based on a Radio Frequency Identification (RFID) system implemented by an automotive manufacturer and previously studied by the authors [ 7 ]. First section highlights, through an introduction, the scope and purpose of the study, while the second section is dedicated to summarizing the particularity of the study, explaining the approach adopted, and explore the previous works related to our research area to identify the gap. The third section was dedicated to explaining the proposed models that will be used in the use case presented in the fourth section. Finally, this paper ends with a conclusion that emphasize the research scope, methodology and results. It also exposes researchers’ perspectives and complementary future works. 2 Contribution and research methodology To identify the best method for risk assessment, research was conducted on previous literature reviews and risk assessments, in different areas and for different purposes. A comparison of the different solutions was also performed to evaluate their pros and cons and decide which one matches our purpose. Findings show that, for example, Hazard and Operability (HAZOP) method is not suitable when we need a high credibility risks measurement [ 8 ]. It is a brainstorming based qualitative approach mostly used for “One failure at the time” which makes it not efficient for complex processes with multiple failures or domino effects [ 9 ] such as automotive industry process. On the other side, MOSAR (Method Organized for a Systematic Analysis of Risk) was used to identify and prevent risks based on the qualitative and quantitative dataset of a defined system, thus through 10 steps, each one of them corresponding to interacting subcomponents. None of these steps can be skipped, which makes MOSAR not a flexible method [ 10 ]. Fault tree analysis (FTA) is an analytical method that relies on deductive reasoning to determine the occurrence of an undesired event. It is time-consuming, requires experts’ analysis, does not detect all failure modes, especially those with common cause, is not accurate, and in many real-world applications, does not allow to easily determine the exact values to the probabilities of occurrence [ 11 ]. Preliminary hazard analysis is a comparative method effective to identify and evaluate hazards in a system. However, its qualitative assessment leads to results that may not be detailed and reasonable enough as they lack precision due to fuzziness and randomness of information. It also may conduct to subjective assessment [ 12 ]. When it comes to FMEA, it was proven to be the most effective and easy to use analytical method to assess and eradicate potential failures of a product, process or service. Thanks to its proactivity, FMEA is the technique that perfectly matches safety and reliability requirements in industrial manufacturing sector [ 2 ], which makes it the most suitable method to use for our study, especially that it is focused on production flows. In addition to that, FMEA approach is an IATF (International Automotive Task Force) requirement and one of the Quality Core Tools. Its usage is therefore mandatory in the automotive industry [ 13 ]. However, the traditional FMEA was much criticized due to weaknesses in measurement scale, computation of risk priority numbers (RPN), absence of risk factors’ weight, weak mathematical formulation and more [ 14 ], which generates a high level of uncertainty. Nevertheless, these weaknesses can be treated by the fusion of FMEA with decision-making approaches such as Fuzzy logic. Choosing to use Fuzzy Logic in this research comes from results of a previous comparison study performed by the authors, and its effectiveness observed during its usage for the risk assessment of maintenance activities also studied by the authors [ 6 , 15 ]. Many other methods exist for risk analysis such as SAM (System-Action-Management) [ 16 ], Cognitive Reliability and Error Analysis Method (CREAM) [ 17 ], TRIPOD [ 18 ], etc., but are poorly considered as their main purpose is to represent the organizational aspect during the risk assessment process. In addition to that, these methods do not support complexity of processes as well as the different interactions between the elements of a given system. Across the literature review, many decision-making methods were used to judge RPN score of failure modes according to traditional FMEA method. The contribution of our study is that, on the contrary of the other studies in risk assessment area, it considers the new version of FMEA (VDA + AIAG) in accordance with automotive requirements, and presents a model for a better decision-making in the context of assessing risks. Additionally, the use case was chosen in a way to emphasize an industry 4.0 technology that was not adopted previously in the same context: the RFID system. To accomplish this research, the 40 papers selected for the literature review were extracted from the most reliable multidisciplinary databases like Scopus and Web of Science, alongside with popular and reliable publishers such as ScienceDirect, springer and IEEE, using specific key words (Risk assessment methods, Risk analysis, Risk prediction, Actions prioritization, FMEA, AIAG/VDA, Decision-making methods, Fuzzy-Logic, etc.). After the analysis of the selected papers, results allowed the authors to identify research gap and therefore constitute the purpose of our study described previously. When it comes to the challenges and difficulties faced during this research, it mainly concerns resource and information availability and accessibility for analysis, complexity of studied processes, diversity of decision-making and risk assessment methods and the lack of case studies within the automotive industry. An intense literature review was also conducted to evaluate FMEA usage evolution over the years, especially with the appearance of MCDM methods and innovative technologies that has seen the light through the fourth industrial revolution. FMEA traditional approach was widely used in different industries since its appearance, but recently, due to its limitations (factors’ weight equality, reliability on experts’ judgment, etc.), researchers adapted this approach to their need by bringing some modifications such as additional determinants (quality costs, system capabilities, economic aspect, etc.) [ 19 , 20 ] or sub-factors [ 21 ], or the combination with MCDM methods. Indeed, researches showed that using MCDM methods to define FMEA criteria, such as AHP and TOPSIS, ends up with more precise results, and more accurate priorities attribution compared to the traditional methods [ 22 ]. Nevertheless, in decision-making discipline, Fuzzy Logic appears as most adopted method for risk assessment. Considering the ranking method adopted in FMEA approach, decision-making process is confronted to a lot of uncertainties [ 23 ]. In this context, Fuzzy TOPSIS, applied to a nuclear reheat valve installation, was adopted for failures ranking considering subjectivity and objectivity while affecting weights, which prevents the under/overestimation of these failures [ 24 ]. Fuzzy FMEA was applied to a Liquefied Petrol Gas supply chain system to evaluate risk of failures in maintenance [ 14 ], to enhance safety of an aircraft landing project in combination with AHP and DEA methods [ 25 ], to analyze risks in health, safety and environment [ 26 ], to determine failure modes of an internet banking service quality [ 27 ], and so on. Which shows that the fuzzy FMEA combination is used in many different disciplines and industries thanks to its efficiency and reliability. In the automotive field, which is the context of our study, FMEA approach was also frequently adopted to proceed with risk assessment of different systems. For example, it was applied to predict maintenance strategies for equipment aging in the automotive industry for a company of motors production [ 5 ], the latest being one of the most critical and risky departments in every industry. FMEA was also applied to Brake Oil Filling Machines assembly lines to evaluate and prevent risks that may occur during leakage tests performance and the processes that goes with it (pressure/vacuum, fill/charge, leveling of various fluids, etc.) [ 28 ]. In another researches, the adoption of FMEA was effective to support decision-making while performing Vehicle Flexible Component risk assessment to identify and prioritize quality issues [ 29 ], but also to generate needed prediction in a vehicle braking system [ 30 ]. In terms of safety, even if it is not much aborded by the literature, Health, Safety and Environment (HSE) management is also concerned by risks which can be evaluated and prioritized using FMEA. Indeed, HSE risk score was evaluated in an enterprise specialized in production of automotive spare parts using a DEA-FMEA approach [ 31 ]. 3 Description of the proposed model Following this literature review, this section develops the methods that were identified as the most suitable for an improved risks assessing and decision-making process: FMEA and Fuzzy-logic. 3.1 Failure mode and effects analysis (FMEA) FMEA, following its latest version (AIAG and VDA fusion), is a 7 steps risk assessment approach [ 13 , 32 ] used to identify and prioritize root causes based on 3 factors: Severity, occurrence and detection. As an input (data base), it relies on lessons learned from previous projects, customer claims and several meetings with all departments (Quality, logistics, production, process, etc.) to evaluate the potential defects. The FMEA database is updated upon each design/process change, and is revised yearly. Ranking standards as defined by AIAG [ 3 , 32 ]: Severity [S]: AIAG evaluates severity by taking into consideration the effect on the product from two perspectives. The first one is customer effect, which represents the impact that directly effects the final product (the sailable car) whether when it comes to its primary/secondary functions or to its safety. The second perspective is the effect from manufacturer side, and it concerns the impact on product disruption during the assembly step and on operators’ safety. Ranking values are presented in Fig. 1 . Due to the lack of expertise and experimentation possibilities, impact cannot be clearly evaluated especially from customer side. In this case, fuzzy logic can be applied to evaluate severity ranking; Occurrence [O]: The occurrence of a defect is judge by its probability to happen, but also on the process capability (Cpk) that measures how close a process is running to its specification limits. Historical data and similar process data can also be taken into consideration while evaluating occurrence. The ranking values by intervals are presented by Fig. 2 . Nevertheless, the experts’ judgment can still be confronted to uncertainty. Without a reliable data, the intervals can only be an assumption and therefore lead to wrong ranking. To overcome this, the authors suggest the usage of sensors linked to FMEA database in a way to automatically update occurrence ranking; Detection [D]: When it comes to detection, it represents at which point the defect can be detected during the process, and is judged according to the inspection type adopted in the process. The ranking values are presented in Fig. 3 . Once rankings are defined and agreed on, next step is to set the Action Priority (AP). This method is introduced to prioritize severity, then occurrence and finally detection. Thus, according to the failure prevention intent. The extraction of AP scores (Table 1 ) presents AIAG/VDA judgement using High/Medium/Low priority for action, and is based on the score obtained by concatenating S, O and D rankings. Table 1. Extraction of Action Priority ranking standard [3]. S O D S&O&D AP 10 10 10 101010 H 10 10 9 10109 H 10 10 8 10108 H 10 10 7 10107 H 10 10 6 10106 H 10 10 5 10105 H 10 10 4 10104 H 10 10 3 10103 H 10 10 2 10102 H 10 10 1 10101 H 10 9 10 10910 H 10 9 9 1099 H 10 9 8 1098 H 10 9 7 1097 H 10 9 6 1096 H 10 9 5 1095 H 10 9 4 1094 H 10 9 3 1093 H 10 9 2 1092 H 10 9 1 1091 H 10 8 10 10810 H 10 8 9 1089 H 10 8 8 1088 H 10 8 7 1087 H 10 8 6 1086 H 10 8 5 1085 H 10 8 4 1084 H 10 8 3 1083 H 10 8 2 1082 H 10 8 1 1081 H 10 7 10 10710 H 10 7 9 1079 H 10 7 8 1078 H 10 7 7 1077 H 10 7 6 1076 H 10 7 5 1075 H 10 7 4 1074 H 10 7 3 1073 H 10 7 2 1072 H 10 7 1 1071 H 10 6 10 10610 H 10 6 9 1069 H 10 6 8 1068 H 10 6 7 1067 H 10 6 6 1066 H 10 6 5 1065 H 10 6 4 1064 H 10 6 3 1063 H 10 6 2 1062 H 10 6 1 1061 H 10 5 10 10510 H 10 5 9 1059 H 10 5 8 1058 H 10 5 7 1057 H 10 5 6 1056 H 10 5 5 1055 H 10 5 4 1054 H 10 5 3 1053 H 10 5 2 1052 H 10 5 1 1051 M 10 4 10 10410 H 10 4 9 1049 H 10 4 8 1048 H 10 4 7 1047 H 10 4 6 1046 H 10 4 5 1045 H 10 4 4 1044 H 10 4 3 1043 H 10 4 2 1042 H 10 4 1 1041 M 10 3 10 10310 H 10 3 9 1039 H 10 3 8 1038 H 10 3 7 1037 H 10 3 6 1036 M 10 3 5 1035 M 10 3 4 1034 L 10 3 3 1033 L 10 3 2 1032 L 10 3 1 1031 L 10 2 10 10210 H 10 2 9 1029 H 10 2 8 1028 H 10 2 7 1027 H 10 2 6 1026 M 10 2 5 1025 M 10 2 4 1024 L 10 2 3 1023 L 10 2 2 1022 L 10 2 1 1021 L 10 1 10 10110 L 10 1 9 1019 L 10 1 8 1018 L 10 1 7 1017 L 10 1 6 1016 L 10 1 5 1015 L 10 1 4 1014 L 10 1 3 1013 L 10 1 2 1012 L 10 1 1 1011 L 9 10 10 91010 H 9 10 9 9109 H 9 10 8 9108 H 9 10 7 9107 H 9 10 6 9106 H 9 10 5 9105 H 9 10 4 9104 H 9 10 3 9103 H 9 10 2 9102 H 9 10 1 9101 H 9 9 10 9910 H 9 9 9 999 H 9 9 8 998 H 9 9 7 997 H 9 9 6 996 H 9 9 5 995 H 9 9 4 994 H 9 9 3 993 H 9 9 2 992 H 9 9 1 991 H 9 8 10 9810 H 9 8 9 989 H 9 8 8 988 H 9 8 7 987 H 9 8 6 986 H 9 8 5 985 H 9 8 4 984 H 9 8 3 983 H 9 8 2 982 H 9 8 1 981 H 9 7 10 9710 H 9 7 9 979 H 9 7 8 978 H 9 7 7 977 H 9 7 6 976 H 9 7 5 975 H 9 7 4 974 H 9 7 3 973 H 9 7 2 972 H 9 7 1 971 H 9 6 10 9610 H 9 6 9 969 H 9 6 8 968 H 9 6 7 967 H 9 6 6 966 H 9 6 5 965 H 9 6 4 964 H 9 6 3 963 H 9 6 2 962 H 9 6 1 961 H 9 5 10 9510 H 9 5 9 959 H 9 5 8 958 H 9 5 7 957 H Priority High (H): need for reviews and actions. Appropriate actions must be identified to enhance prevention and/or detection means or explain then record the reason that makes used controls proper ones; Priority Medium (M): should for reviews and actions. Appropriate actions should be identified to enhance prevention and/or detection means or explain then record the reason that makes used controls proper ones; Priority Low (L): must for reviews and actions. Appropriate actions may or may not be identified to improve prevention or detection means [ 3 , 32 ]. 3.2 Fuzzy-logic Based on a comparative study held on 2024 [ 6 ], Fuzzy logic was identified as the most efficient approach to solve uncertainty issues. Indeed, Fuzzy logic, through favorizing the condition of being partially true and partially false at once, is suitable to resolve ambiguity and uncertainty [ 33 ], thus by using linguistic terms represented by membership functions [ 34 ]. Diverse fuzzy number shapes are available, but triangular fuzzy one is mostly adopted [ 35 ]. A triangular fuzzy set (TFS) is shaped using a triplet [c, a, b] limited by a [0–1] range, where c and b respectively correspond to the left and right vertex of the TFS as shown in Fig. 4 [ 36 ]. A value of zero (0) is out of range, a value of one (1) on the other hand is fully representative of the set, while a value higher than zero and lower than one is not completely among the range [ 33 ]. The fuzzy sets are associated with their corresponding class and represented by a triangular membership function [ 14 ]. Two definitions can be used for calculations: Definition 1 [ 37 – 39 ] Considering Y a triangular fuzzy number, A = (c 1 , a 1 , b 1 ), and B = (c 2 , a 2 , b 2 ), (a i , b i and c i : positive real numbers): Y = A⊗ B = (c 1 c 2 , a 1 a 2 , b 1 b 2 ) (1) Definition 2 [ 40 , 35 ] The TFS Y = (c, a, b) is a specific instance of a generalized trapezoidal fuzzy number. The representation of the graded mean integration for Y is expressed as: P(Y) = \(\:\frac{c\:+\:4a\:+\:b}{6}\) (2) 3.3 Fuzzy-FMEA model As highlighted previously, the purpose of the research is combining Fuzzy Logic and FMEA methods to overcome uncertainty and reinforce the risk assessment process. This combination, as described by the model in Fig. 5 , consists on following the AIAG/VDA FMEA 7 steps for risk identification and then exploit Fuzzy logic to evaluate severity parameter, the latest being the one exposed to uncertainty. 4 Use case: Risk evaluation using Fuzzy-FMEA for an RFID system implementation In order to concretize our risk assessment model, an RFID system implemented within an automotive company in Morocco was chosen as an example to identify how and at which step the fuzzy-logic can be used in combination with the FMEA approach. In this study, we will use the new version of PFMEA (Process Failure Modes and Effects Analysis) according to AIAG/VDA strategy (7 steps analysis). 4.1 PFMEA approach according to AIAG/VDA standard First Step: Planning & Preparation As the basis of the analysis, this step consists on describing the process to be reviewed at first, which will represent the scope of the study, to then set a timing plan for the execution of PFMEA strategy. It is during this step that the header of PFMEA document is filled according to the template presented in Fig. 6 . For confidentiality matters, the filled header will not be presented in this paper. This example is based on an RFID system already studied by the authors in a previous work in the context of improving boxes feeding and control flow within an automotive company [ 7 ]. Feeding process is ensured by a dedicated operator and starts when a box gets empty. The operator (distributor) picks the empty boxes from the assembly line and puts them in the RFID structure so that the concerned reference (mentioned in each box) gets scanned. The reference then appears on the warehouse system supervised by a warehouse agent in charge of preparing a trolley with replacement of the empty boxes. Before putting the replacement box in the trolley, warehouse agent scans it to be removed from the picklist. Every 2 hours, the distributor gets the emptied boxes trolley to the warehouse, and brings back the one with replacement boxes back to the assembly line. In the warehouse picking area, the gate does not open unless all needed components mentioned in the picklist are scanned. In case of missing components in the plant, to not block replacement of the remaining components, warehouse operator fills and alert in the system and the trolley can be released. The risk assessment previously performed on this system showed some weaknesses and threats of the RFID system such as dependance to the network, Reading range/perturbation, cyber-attacks and scan of wrong label. These elements, alongside with other risks detected during the study, will serve as an input and will therefore be reflected on the PFMEA analysis. The study will focus only on the failure modes that may be directly related to the RFID system and will not include those related to the feeding flow. Second Step: Structure Analysis Through the process structure analysis, a breakdown is performed to identify process items, steps/sub-steps and work elements. This will serve as a basis to the function analysis step. It is mandatory to be completed before moving forward. As shown in Table 2 , risk assessment of our study was focused on the “Components order preparation” and “Forwarding of components to assembly area” processes, with “Empty boxes scan in assembly line”, “Trolley feeding” and “Feeding of components in assembly stations” as process steps. AIAG/VDA strategy necessitate the consideration of the 4M’s (Man, Method, Machine, Material) during the review of activity taking place within the process, which was not considered previously in the traditional PFMEA. Table 2 Structure analysis (step 2). 1. Process Item System, Subsystem, Part Element or Name of Process 2. Process Step Station No. and Name of Focus Element 3. Process Work Element 4M Type Components order preparation Empty boxes scan in assembly line Machine Machine Machine Man Trolley feeding Method Man Method Man Man Method Man Forwarding of components to assembly area Feeding of components in assembly stations Man Man Method Man Many tools can be used in this step to perform the breakdown such as process diagram or structure tree. In this use case, we will rely on the process flow chart already available and developed by the company. Due to confidentiality matters, the process flow chart will not be presented in this paper. Third Step: Function Analysis A function defines what the process item or step was designed for. One single process item or step can have many functions. Each function must be clearly described. When it comes to the characteristics, it is defined as a distinguishing feature directly related to a function and can be shown by the work instructions, manufacturing drawings, etc. The function analysis of the RFID system is presented in Table 3 . Concerned function is to “Ensure conform feeding of components. Table 3 Function analysis (step 3). 1. Function of the Process Item Function of System, Subsystem, Part Element or Process 2. Function of the Process Step and Product Characteristic (Quantitative value is optional) 3. Function of the Process Work Element and Process Characteristic Ensure conform feeding of components Ensure empty boxes trolley preparation and generate picking list Generate the pick list Check the preparation of the needed reference for each assembly line Ensure feeding of the empty trolley with the correct component and according to the needed quantity Ensure empty boxes trolley preparation and generate picking list Ensure empty boxes trolley preparation and generate picking list Ensure empty boxes trolley preparation and generate picking list Ensure correct feeding method of components in assembly lines Respect & follow the Work instruction Correct identification Respect & follow the Work instruction Once again, completing the function analysis step will push the analysis to the next step which is the failure analysis. Fourth Step: Failure Analysis At this step, we enter the core of the risk analysis approach in which we define the failures causes, modes and effects, alongside with their relationship to deduce the risk assessment. A failure can either be a non-conformity, an incomplete task or an unintentional/unnecessary activity. Based on the failure effect, and as presented in Table 4 , severity rank can already be defined. Table 4 Failure analysis (step 4). 1. Failure Effects (FE) Severity (S) of FE 2. Failure Mode (FM) of the Process Step 3. Failure Cause (FC) of the Work Element Assembly line stoppage 4 Connection to the system failure Network issue 4 Loss of power supply 4 Cyber-attack Stock inaccuracy 4 Wrong box detection RFID structure location in assembly line Assembly line stoppage 2 Incorrect qty transfer Unknown qty needed 2 Warehouse operator inattention Stock inaccuracy Assembly line stoppage 4 Mixing between dedicated assembly lines Locations mixture Assembly line stoppage 5 Collecting the wrong components Manual operation Untrained operator Unclear/missing work instruction 5 Mixing similar components 5 Missing / Wrong identification 2 Not collecting all components needed Missing box Assembly line stoppage 4 Feeding the component in the wrong location Manual operation Non respect of WI 4 Mixing similar components 4 Incorrect / Missing cells identification in the side of feeding operator Component Information tracking loss 2 Lost identification Wrong feeding by operator At this step, we can already determine the severity score as the effects are now identified, and this is where authors are facing uncertainty. Each stakeholder, based on its experience, background and knowledge, has a different vision and attributes a different severity rank. In addition to that, sometimes the situation does not match with the ranking table of AIAG/VDA standard as the ranking from customer effects is lower or higher than the one from manufacturing effect. In this case, the higher rank is used, which may lead to a misjudgment and therefore reflect an incorrect action priority score. For example, with a wrong severity rank, we can result in a Low action priority when the risk must be ranked as Medium or High. In this situation, the risk evaluated will not be prioritized and no preventive or corrective actions will be considered. The risk will then not be eliminated and will probably lead to a customer claim and therefore impact the customer satisfaction. At this step, fuzzy logic will be used to re-evaluate the severity ranks attributed to each failure effect. A comparison and observation will then be performed to evaluate the impact of uncertainty in severity judgment without. Fifth Step: Risk Analysis To complete the risk analysis, occurrence and detection needs to be rated. To do so, prevention and detection controls currently established must be evaluated. Once it is done, the action priority number is automatically calculated enabling the classification of risks into: High, Medium and Low. The result of the risk analysis step is presented in Table 5 . Table 5 Risk analysis (step 5). Current Prevention Control (PC) of FC Occurrence (O) of FC Current Detection Controls (DC) of FC or FM Detection (D) of FC or FM PFMEA AP Special Characteristics Back-up servers Regular system updates 2 Frequent network maintenance 6 L Power supply back-up (Generator) 2 Frequent electrical maintenance 6 L Cyber-security system (Firewall) Limited and controlled access Operator sensitization 2 Effective design and code reviews Report unusual and mysterious pop-ups/emails 6 L Feeding operators training Add counter for boxes scanning Add isolation 3 Self-control Display last scanned reference Process audit Semestrial physical inventory 4 L Add box capacity in the RFID tag Warehouse operators training 1 Check by warehouse operators Check by feeding operators 7 L Add box capacity in the RFID tag Warehouse operators training 2 Check by warehouse operators Check by feeding operators 7 L Add project name to the RFID tag Separation of picking area by project Identification of Racks in picking area Warehouse operators training Feeding operators training Work Instruction update 2 Self-control Visual check by feeding operators Storage process audit 4 L Scan RFID tag before putting it in the trolley Feeding Operators training Locations identification 4 Self-control Detection in subsequent operations 4 L Add visual aids Feeding Operators training Locations identification 3 Self-control Detection in subsequent operations 4 L Pre-identified boxes Add visual aids Team training 2 Self-control Quality Audit Process audit 4 L WH gate opens only if all needed components in the picklist are scanned Team training 2 Self-control Process audit Detection in subsequent operations 4 L Operators training Locations identification Distribution list 2 Self-control Detection in subsequent operations 4 L Feeding Operators training Distribution list Locations identification Separation between similar components in line 4 Self-control Detection in subsequent operations 4 L Procedure Team training Quality audit 2 Self-control Detection in subsequent operations 4 L Feeding Operators training Work instruction 3 Self-Control Visual check by assembly operator 7 L The occurrence rank can easily be determined by calculating the failure rate and the Cpk based on the database gathered by keeping trace of each failure that occurs. In the context of industry 4.0, to make it more reliable and to eliminate the risk of human mistake due to manual work, the authors suggest the usage of smart sensors that can be directly connected to the ERP system and will automatically be updated after each occurrence by calculating the failure rate and the Cpk. These values can then be reflected on the ranking table presented by AIAG/VDA standard to determine the correct occurrence rank. Sixth Step: Optimization The interest of this step is defining actions that will potentially eliminate the failure causes, reduce its rate of occurrence, or enhance detection ability, and then, once implemented, asses their effectiveness by recalculating the AP score by updating S, O and D rankings in a different column as shown in Table 6 , without bringing any modifications to the previous rankings (except in case of PFMEA revision). Defined actions must be assigned to concerned responsible with a clear closure date. The status can be set on open (No action defined yet), decision pending (Actions were defined but are still waiting approval), implementation pending (Actions were defined and approved but are still waiting implementation), completed (Actions were defined, approved and implemented, and the final assessment was done) or not implemented (Actions defined but implementation was not approved due to practical or technical limitations). Table 6 Optimization (step 6). Recommended Actions Responsible Person's Name Target Completion Date Status Action Taken with Pointer to Evidence Completion Date Severity (S) Occurrence (O) Detection (D) SpProd Char PFMEA AP Remarks None None As we can observe in the Table 8 , all AP scores are judged as Low. Therefore, no recommended actions were determined. Seventh Step: Results Documentation Finally, documentation step consists on emphasizing, through a report, results of the PFMEA analysis performed. According to AIAG/VDA, this report includes below statements: The final status compared to original defined goals; The analysis scope and identification of what is new; How functions were improved; The high-risk determined failures; Actions taken or planned for addressing the high-risk failures including their status; Plan for ongoing PFMEA improvement actions. 4.2 Fuzzy-PFMEA approach according to AIAG/VDA standard This step consists on repeating the fourth, fifth and sixth step of the PFMEA approach using Fuzzy logic to re-evaluate the severity ranking, recalculate the AP score, and analyse the results. Fourth Step: Failure Analysis As described previously, defining the severity rank of a failure effect is constrained by uncertainty. It is therefore very important to overcome it in order not to misjudge the risk. For that, this step will be re-evaluated using fuzzy logic approach to define a proper severity rank. In our case, and according to AIAG/VDA severity ranking table, our parameters will be defined as Customer effect (C) and Manufacturing/Assembly Effect (M). Table 7 and Table 8 describes the meaning and linguistic terms used for each rank of C and M respectively. Table 7 Customer effect (C). Rank 1 2 3 4 5 6 7 8 9 10 Effect No effect Annoyance > 20% Annoyance > 50% Annoyance > 75% Degradation of secondary function Loss of secondary function Degradation of primary function Loss of primary function Predictable safety risk Unpredictable safety risk Linguistic term L1 L2 L3 L4 M1 M2 H1 H2 H3 H4 Table 8 Manufacturing/Assembly Effect (M). Rank 1 2 3 4 5 6 7 8 9 10 Effect No effect Minor Disruption Moderate Disruption Moderate Disruption Moderate Disruption Moderate Disruption Significant Disruption Major Disruption Predictable safety risk Unpredictable safety risk Linguistic term L1 L2 L3 L4 L5 L6 M1 M2 H1 H2 Accordingly, the fuzzy sets will be defined as described in Table 9 . Table 9 Fuzzy sets. (1.0, 1.0, 2.0) (1.0, 2.0, 3.0) (2.0, 3.0, 4.0) (3.0, 4.0, 5.0) (4.0, 5.0, 6.0) (5.0, 6.0, 7.0) (6.0, 7.0, 8.0) (7.0, 8.0, 9.0) (8.0, 9.0, 10.0) (9.5, 10.0, 10.0) C L1 L2 L3 L4 M1 M2 H1 H2 H3 H4 M L1 L2 L3 L4 L5 L6 M1 M2 H1 H2 The graphical representation of these fuzzy sets, proper to the parameter C for example, is presented in Fig. 7 . By applying the equations 1 and 2, we can calculate the new severity rank. For example, if a failure effect is judged as C = M1 and M = L6, the numeric application will be as below: Y = C x M = (4.0, 5.0, 6.0) x (5.0, 6.0, 7.0) = (20.0, 30.0, 42.0) Severity (S) = \(\:\frac{1}{6}\) (20.0 + 120.0 + 42.0) = 30,5 Accordingly, and following the same approach, Severity ranking for all the failure effects were re-calculated. Results are presented in Table 10 . Table 10 Failure analysis (step 4) using fuzzy-logic. 1. Failure Effects (FE) Severity (S) of FE 2. Failure Mode (FM) of the Process Step 3. Failure Cause (FC) of the Work Element Assembly line stoppage 16.33 Connection to the system failure Network issue 16.33 Loss of power supply 16.33 Cyber-attack Stock inaccuracy 12.33 Wrong box detection RFID structure location in assembly line Assembly line stoppage 2.5 Incorrect qty transfer Unknown qty needed 2.5 Warehouse operator inattention Stock inaccuracy Assembly line stoppage 12.33 Mixing between dedicated assembly lines Locations mixture Assembly line stoppage 30.33 Collecting the wrong components Manual operation Untrained operator Unclear/missing work instruction 30.33 Mixing similar components 30.33 Missing / Wrong identification 2.5 Not collecting all components needed Missing box Assembly line stoppage 12.33 Feeding the component in the wrong location Manual operation Non respect of WI 12.33 Mixing similar components 12.33 Incorrect / Missing cells identification in the side of feeding operator Component Information tracking loss 2.5 Lost identification Wrong feeding by operator Results analysis Once the new severity ranks are defined and freed from fuzziness, we now get to determine the new AP scores. For that, to align with AIAG/VDA AP score determination, severity ranks must be converted into an interval of [ 1 – 10 ], thus by calculation all possible combinations. The max value obtained (98.375) is the equivalent of the highest severity rank in AIAG/VDA table (10). Accordingly, 16.33 will be equivalent to 2, 12.33 to 1, 2.5 to 1, and 30.33 to 3. After reflecting the converted severity ranks on the PFMEA table, hopefully, there was no impact on the AP score. All of them remained “Low” as the severity ranks decreased compared to the initial ones. Next step is to analyze and evaluate the impact of using fuzzy logic, thus based on the comparison presented in Table 11 . Table 11 Comparison of severity ranking with and without using Fuzzy logic. Standard severity Fuzzy severity 2. Failure Mode (FM) of the Process Step 3. Failure Cause (FC) of the Work Element 4 2 Connection to the system failure Network issue 4 2 Loss of power supply 4 2 Cyber-attack 4 1 Wrong box detection RFID structure location in assembly line 2 1 Incorrect qty transfer Unknown qty needed 2 1 Warehouse operator inattention 4 1 Mixing between dedicated assembly lines Locations mixture 5 3 Collecting the wrong components Manual operation Untrained operator Unclear/missing work instruction 5 3 Mixing similar components 5 3 Missing / Wrong identification 2 1 Not collecting all components needed Missing box 4 1 Feeding the component in the wrong location Manual operation Non respect of WI 4 1 Mixing similar components 4 1 Incorrect / Missing cells identification in the side of feeding operator 2 1 Lost identification Wrong feeding by operator As we can observe from the comparison, all severity ranks decreased. This is explained by the fact that all these failure effects have more impact on the manufacturing site than the customer. During the FMEA meetings, the team had to choose the highest rank since AIAG/VDA standard does not separate rankings, when in reality, considering that customer effect has more importance than the manufacturing effect, the severity was overestimated as proven by Fuzzy logic approach. For example, if we consider the failure modes “Wrong box detection” and “Mixing between dedicated assembly lines“, severity was initially ranked at 4. After using fuzzy PFMEA, the new rank decreased to 1. Indeed, putting a box in the wrong assembly line can impact production lines as this will generate a line stoppage and may also lead to a rework of all parts produced. But on the other hand, as we are here talking about components that are not similar, the probability of their assembly on the product is very low as they will not fit. If they do, the process is equipped by several Poka-Yokes that will detect the usage of a wrong component. It is then very difficult for such product to be sent to the customer and there’s therefore no impact from customer side, which explains that the new rank is 1. If we consider the failure mode “Collecting the wrong components”, here we are talking about similar components, which makes the probability to reach the customer higher as the Poka-yokes, if not frequently checked and controlled, cannot be a 100% reliable. Sending a product with wrong components may lead to some annoyance at the customer side, which justifies the new severity rank of 3. 5 Conclusion Lately, the industrial world, especially the automotive sector, has grown strong enough to generate a competitive environment with high diversity and more complex systems. With this complexity comes doubts and contradictory opinions monitored by each ones’ experience, expertise and point of view, which makes the decision process more challenging and complex. Many solutions were developed over the years to overcome this challenge, but depending on the factors involved in the decision-making process, and following a comparison study previously held by the authors, Fuzzy Logic is most suitable to use for this problematic. Another necessity to consider nowadays is the risk assessment process. A safe production environment first starts with a proper management of risks. This paper, following a constructive literature review, identified and studied the most used risk assessment methods based on their effectiveness and were they fit best. FMEA approach was without any doubts the most suitable one to this study. Withing this scope, this paper introduces a new concept which is Fuzzy-FMEA approach considering the latest updates from AIAG/VDA edition. A tool to properly overcome uncertainty and accurately define the AP score during risk assessment process. For this, and from a digitalization aspect, the study focused on the analysis of an RFID system were PFMEA approach was applied. During the ranking process, uncertainty was faced during the severity judgment step as many rankings were suggested, each based on a different opinion. Sometimes, the decision-makers face a situation where the effect from customer and manufacturing sides are not at the same level, and therefore choose the highest rank. At this step, the Fuzzy logic approach can help the stakeholders to align on a single ranking for each risk and therefore overcome the uncertainty that occurred. Indeed, by applying the suggested Fuzzy model to the use case of the RFID system, results showed that the severity ranks were overestimated as the effect on manufacturing was higher than the effect on customer, which pushed the team to choose the highest rank. By analyzing the new ranks obtained after applying Fuzzy logic approach, results seemed more logical. In terms of perspectives, the model can be developed further by combining it with artificial intelligence algorithms for prioritization and classification of actions. The model will then be as presented in Fig. 8 . Declarations Author contributions L.N. and M.G.; methodology, L.N.; validation, M.G., A.S., S.M. and M.D.N.; formal analysis, L.N., M.G., S.M. and M.D.N.; investigation, L.N. and M.G.; resources, L.N., M.G. and M.D.N.; data cura-tion, L.N. and M.G.; writing—original draft preparation, L.N.; writing—review and editing, M.G., S.M. and M.D.N.; visualization, M.G., A.S., S.M. and M.D.N.; supervision, M.G. and M.D.N. All authors have read and agreed to the published version of the manuscript. Funding This research received no external funding. Data availability No datasets were generated or analyzed during the current study. Competing interests The authors declare no competing interests. Ethics, Consent to Participate, and Consent to Publish declarations Not applicable. 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[3].\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6423322/v1/8a0737c0fcbadf11a85a6d08.png"},{"id":82580897,"identity":"ae27a666-adb2-43e2-a7d8-504362e94ba7","added_by":"auto","created_at":"2025-05-13 06:36:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":116347,"visible":true,"origin":"","legend":"\u003cp\u003eDetection ranking [3].\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6423322/v1/ad55afe0badcd3374d7ce513.png"},{"id":82583874,"identity":"a8957f4a-f5ad-4d3d-8488-69e476e7aafa","added_by":"auto","created_at":"2025-05-13 06:52:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5986,"visible":true,"origin":"","legend":"\u003cp\u003eTriangular fuzzy set representation.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6423322/v1/784d79ddf3ecef1c582481ea.png"},{"id":82580898,"identity":"e1693bd2-68c6-4fea-a5ab-cd60f1c67172","added_by":"auto","created_at":"2025-05-13 06:36:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":25466,"visible":true,"origin":"","legend":"\u003cp\u003eFuzzy-FMEA model representation.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6423322/v1/56bf9b4a1ba87f37bf6d8683.png"},{"id":82580905,"identity":"ce8958af-89a1-4f20-a663-9f4a5d8b76ae","added_by":"auto","created_at":"2025-05-13 06:36:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":83010,"visible":true,"origin":"","legend":"\u003cp\u003ePlanning \u0026amp; Preparation (Step 1).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6423322/v1/36e58b4ba3cd46a0cdcde502.png"},{"id":82582366,"identity":"f36a72bb-7d34-4d5b-9e1f-e7a05ce96a4b","added_by":"auto","created_at":"2025-05-13 06:44:53","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":37907,"visible":true,"origin":"","legend":"\u003cp\u003eFuzzy sets graphical representation for C parameter.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6423322/v1/cad65dd8795b5d7fd146acf7.png"},{"id":82580913,"identity":"e73f2c43-87b0-4687-b287-c1497eb9874b","added_by":"auto","created_at":"2025-05-13 06:36:53","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":32137,"visible":true,"origin":"","legend":"\u003cp\u003eActions prioritization model representation\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6423322/v1/c8adfb95ca76b50449860f26.png"},{"id":87414790,"identity":"d3acffef-6bec-45a6-a1a2-c6af1533c4b2","added_by":"auto","created_at":"2025-07-23 14:24:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2615285,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6423322/v1/ff28e927-eeff-4762-b103-109bb15f8da2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Addressing Uncertainty in Digital Risk Evaluation Using a Fuzzy-FMEA Methodology","fulltext":[{"header":"Article Highlights","content":"\u003cul\u003e\n \u003cli\u003eLiterature\u0026nbsp;analysis\u0026nbsp;to\u0026nbsp;identify\u0026nbsp;and compare the most used and efficient method for risk assessment.\u003c/li\u003e\n \u003cli\u003eIdentify potential use of decision-making methods to support FMEA and resolve uncertainty.\u003c/li\u003e\n \u003cli\u003eUse case based on a Radio Frequency Identification (RFID) system implemented by an automotive manufacturer.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1 Introduction","content":"\u003cp\u003eFailure Modes and Effects Analysis is an approach frequently relied one to evaluate risks that first appeared in the 1950s, after its formalization in military standards by the United States Armed Forces for assessment of the impact of failures on the successful completion of the mission and on the safety of equipment [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It was then adopted on 1960\u0026rsquo;s by the NASA as a way to enhance and authentify the effectiveness of the Apollo space program hardware. In the 1970's, Ford Motor manufacturer reintroduced FMEA for safety consideration after the disastrous \u0026ldquo;Pinto affair\u0026rdquo;, and now uses FMEA effectively for production and design improvement. Nowadays, FMEA usage was extended and became popular in many other industries as an effective tool to analyze and improve quality, safety and reliability of systems: Aeronautics and automotive industries, ship navigation, sustainable manufacturing, pharmaceutical industry, healthcare, information systems, liquefied natural gas storage facility, offshore wind turbine, foodgrains supply chain, and so on [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn 1982 was founded the Automotive Industry Action Group (AIAG) in Michigan that developed a set of recommendations and a guide to improve quality in the North American automotive field, by representatives of the three largest automotive manufacturers: Ford, General Motors and Chrysler, to then extend to Japanese manufacturers such as Toyota, Honda and Nissan.\u003c/p\u003e \u003cp\u003eIn 2019, AIAG, in collaboration with the German Association of the Automotive Industry (VDA) published the first international framework on FMEA. AIAG and VDA combined their respective regional FMEA manual to create a unified and international guideline. The first edition highlights the importance of a process-oriented approach to guide suppliers into meeting the product performance required by global automakers.\u003c/p\u003e \u003cp\u003eThe AIAG \u0026amp; VDA PFMEA approach relies on Seven Steps:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ePlanning \u0026amp; Preparation;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eStructure Analysis;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFunction Analysis;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFailure Analysis;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRisk Analysis;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eOptimization;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eResults Documentation [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eSince its biggening, FMEA approach was based on evaluating the risk of occurrence (O), severity (S) and detection (D) of a failure using ranking standards with a scale from 1 to 10, thus relying on experts\u0026rsquo; judgment. However, with the development of manufacturing processes and the introduction of new technologies, the complexity and uncertainty involved makes it hard to proceed with risk assessment. In addition to that, FMEA has some limitations such as weights of factors and results sensibility to the perspectives of the experts leading the risk assessment, which makes uncertainty and subjectivity common issues while adopting FMEA approach [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn today's highly competitive world driven by customers increased expectations, decreased life cycle of equipment and product, and innovative economic models, manufacturing systems\u0026rsquo; reliability has become a key driver of production especially in the automotive industry. Indeed, considering that vehicles became more compact and with the introduction of new materials and complex components, automotive manufacturing processes became as well more complex and more critical, and manufacturers found themselves in a situation where they have to cope with these changes by reinforcing their systems. In addition to that, defects in vehicle can lead to very critical consequences and is more likely to be affecting human being\u0026rsquo;s safety (explosion, car crash, etc.), which makes the risk assessment performance a mandatory task at early design stage of the project, to exhaustively explore potential failure modes and anticipate their effects not only on the operability aspect, but also on workers/user safety. Another challenge to which were confronted the automotive industrials was therefore to provide good quality services, products and processes in a risk-free environment, prevent excessive costs, eliminate wastes and respect deliveries specifications (Time, quantity, variety, etc.), which results in the obligation of identifying potential failure modes with enough time-space to scheme and implement corrective actions before failures occurrence by adopting FMEA approach [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhether it is in everyday life or in industrial fields, when it comes to decision-making or to criteria evaluation, there is always an oscillation from 0 to 100 between not being sure at all and being completely sure. In addition to that, judgments are relative to each one\u0026rsquo;s perception and lessons learned from past experience. Uncertainty has therefore a major and direct impact on defining the risk factors (O, S, D) of a failure mode. Accordingly, to reduce unplanned events, ensure operations reliability and increase products quality in a risk-free environment, FMEA approach may be completed by Multiple Criteria Decision-Making (MCDM) approaches. Many of them were introduced over the years to overcome uncertainty in complex systems like Analytic Hierarchy Process (AHP), Data Envelopment Analysis (DEA), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), etc. But most frequently adopted one was proven to be fuzzy logic, which can be applied as well in combination with other MCDM methods [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccordingly, the objective of this study is to spot potential use of decision-making methods to support FMEA and resolve uncertainty, thus through a literature review which purpose is to go through previous studies and researches, to explore how they overcame fuzziness during risk assessment and which decision-making tools were used especially in the automotive industry. This review was completed by a use case based on a Radio Frequency Identification (RFID) system implemented by an automotive manufacturer and previously studied by the authors [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFirst section highlights, through an introduction, the scope and purpose of the study, while the second section is dedicated to summarizing the particularity of the study, explaining the approach adopted, and explore the previous works related to our research area to identify the gap. The third section was dedicated to explaining the proposed models that will be used in the use case presented in the fourth section. Finally, this paper ends with a conclusion that emphasize the research scope, methodology and results. It also exposes researchers\u0026rsquo; perspectives and complementary future works.\u003c/p\u003e"},{"header":"2 Contribution and research methodology","content":"\u003cp\u003eTo identify the best method for risk assessment, research was conducted on previous literature reviews and risk assessments, in different areas and for different purposes. A comparison of the different solutions was also performed to evaluate their pros and cons and decide which one matches our purpose. Findings show that, for example, Hazard and Operability (HAZOP) method is not suitable when we need a high credibility risks measurement [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. It is a brainstorming based qualitative approach mostly used for \u0026ldquo;One failure at the time\u0026rdquo; which makes it not efficient for complex processes with multiple failures or domino effects [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] such as automotive industry process. On the other side, MOSAR (Method Organized for a Systematic Analysis of Risk) was used to identify and prevent risks based on the qualitative and quantitative dataset of a defined system, thus through 10 steps, each one of them corresponding to interacting subcomponents. None of these steps can be skipped, which makes MOSAR not a flexible method [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Fault tree analysis (FTA) is an analytical method that relies on deductive reasoning to determine the occurrence of an undesired event. It is time-consuming, requires experts\u0026rsquo; analysis, does not detect all failure modes, especially those with common cause, is not accurate, and in many real-world applications, does not allow to easily determine the exact values to the probabilities of occurrence [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Preliminary hazard analysis is a comparative method effective to identify and evaluate hazards in a system. However, its qualitative assessment leads to results that may not be detailed and reasonable enough as they lack precision due to fuzziness and randomness of information. It also may conduct to subjective assessment [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. When it comes to FMEA, it was proven to be the most effective and easy to use analytical method to assess and eradicate potential failures of a product, process or service. Thanks to its proactivity, FMEA is the technique that perfectly matches safety and reliability requirements in industrial manufacturing sector [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], which makes it the most suitable method to use for our study, especially that it is focused on production flows. In addition to that, FMEA approach is an IATF (International Automotive Task Force) requirement and one of the Quality Core Tools. Its usage is therefore mandatory in the automotive industry [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, the traditional FMEA was much criticized due to weaknesses in measurement scale, computation of risk priority numbers (RPN), absence of risk factors\u0026rsquo; weight, weak mathematical formulation and more [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], which generates a high level of uncertainty. Nevertheless, these weaknesses can be treated by the fusion of FMEA with decision-making approaches such as Fuzzy logic. Choosing to use Fuzzy Logic in this research comes from results of a previous comparison study performed by the authors, and its effectiveness observed during its usage for the risk assessment of maintenance activities also studied by the authors [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMany other methods exist for risk analysis such as SAM (System-Action-Management) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], Cognitive Reliability and Error Analysis Method (CREAM) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], TRIPOD [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], etc., but are poorly considered as their main purpose is to represent the organizational aspect during the risk assessment process. In addition to that, these methods do not support complexity of processes as well as the different interactions between the elements of a given system.\u003c/p\u003e \u003cp\u003eAcross the literature review, many decision-making methods were used to judge RPN score of failure modes according to traditional FMEA method. The contribution of our study is that, on the contrary of the other studies in risk assessment area, it considers the new version of FMEA (VDA\u0026thinsp;+\u0026thinsp;AIAG) in accordance with automotive requirements, and presents a model for a better decision-making in the context of assessing risks. Additionally, the use case was chosen in a way to emphasize an industry 4.0 technology that was not adopted previously in the same context: the RFID system.\u003c/p\u003e \u003cp\u003eTo accomplish this research, the 40 papers selected for the literature review were extracted from the most reliable multidisciplinary databases like Scopus and Web of Science, alongside with popular and reliable publishers such as ScienceDirect, springer and IEEE, using specific key words (Risk assessment methods, Risk analysis, Risk prediction, Actions prioritization, FMEA, AIAG/VDA, Decision-making methods, Fuzzy-Logic, etc.). After the analysis of the selected papers, results allowed the authors to identify research gap and therefore constitute the purpose of our study described previously. When it comes to the challenges and difficulties faced during this research, it mainly concerns resource and information availability and accessibility for analysis, complexity of studied processes, diversity of decision-making and risk assessment methods and the lack of case studies within the automotive industry.\u003c/p\u003e \u003cp\u003eAn intense literature review was also conducted to evaluate FMEA usage evolution over the years, especially with the appearance of MCDM methods and innovative technologies that has seen the light through the fourth industrial revolution. FMEA traditional approach was widely used in different industries since its appearance, but recently, due to its limitations (factors\u0026rsquo; weight equality, reliability on experts\u0026rsquo; judgment, etc.), researchers adapted this approach to their need by bringing some modifications such as additional determinants (quality costs, system capabilities, economic aspect, etc.) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] or sub-factors [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], or the combination with MCDM methods. Indeed, researches showed that using MCDM methods to define FMEA criteria, such as AHP and TOPSIS, ends up with more precise results, and more accurate priorities attribution compared to the traditional methods [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Nevertheless, in decision-making discipline, Fuzzy Logic appears as most adopted method for risk assessment. Considering the ranking method adopted in FMEA approach, decision-making process is confronted to a lot of uncertainties [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In this context, Fuzzy TOPSIS, applied to a nuclear reheat valve installation, was adopted for failures ranking considering subjectivity and objectivity while affecting weights, which prevents the under/overestimation of these failures [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Fuzzy FMEA was applied to a Liquefied Petrol Gas supply chain system to evaluate risk of failures in maintenance [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], to enhance safety of an aircraft landing project in combination with AHP and DEA methods [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], to analyze risks in health, safety and environment [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], to determine failure modes of an internet banking service quality [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and so on. Which shows that the fuzzy FMEA combination is used in many different disciplines and industries thanks to its efficiency and reliability.\u003c/p\u003e \u003cp\u003eIn the automotive field, which is the context of our study, FMEA approach was also frequently adopted to proceed with risk assessment of different systems. For example, it was applied to predict maintenance strategies for equipment aging in the automotive industry for a company of motors production [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], the latest being one of the most critical and risky departments in every industry. FMEA was also applied to Brake Oil Filling Machines assembly lines to evaluate and prevent risks that may occur during leakage tests performance and the processes that goes with it (pressure/vacuum, fill/charge, leveling of various fluids, etc.) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In another researches, the adoption of FMEA was effective to support decision-making while performing Vehicle Flexible Component risk assessment to identify and prioritize quality issues [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], but also to generate needed prediction in a vehicle braking system [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In terms of safety, even if it is not much aborded by the literature, Health, Safety and Environment (HSE) management is also concerned by risks which can be evaluated and prioritized using FMEA. Indeed, HSE risk score was evaluated in an enterprise specialized in production of automotive spare parts using a DEA-FMEA approach [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e"},{"header":"3 Description of the proposed model","content":"\u003cp\u003eFollowing this literature review, this section develops the methods that were identified as the most suitable for an improved risks assessing and decision-making process: FMEA and Fuzzy-logic.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Failure mode and effects analysis (FMEA)\u003c/h2\u003e \u003cp\u003eFMEA, following its latest version (AIAG and VDA fusion), is a 7 steps risk assessment approach [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] used to identify and prioritize root causes based on 3 factors: Severity, occurrence and detection. As an input (data base), it relies on lessons learned from previous projects, customer claims and several meetings with all departments (Quality, logistics, production, process, etc.) to evaluate the potential defects. The FMEA database is updated upon each design/process change, and is revised yearly.\u003c/p\u003e \u003cp\u003eRanking standards as defined by AIAG [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSeverity [S]: AIAG evaluates severity by taking into consideration the effect on the product from two perspectives. The first one is customer effect, which represents the impact that directly effects the final product (the sailable car) whether when it comes to its primary/secondary functions or to its safety. The second perspective is the effect from manufacturer side, and it concerns the impact on product disruption during the assembly step and on operators\u0026rsquo; safety. Ranking values are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Due to the lack of expertise and experimentation possibilities, impact cannot be clearly evaluated especially from customer side. In this case, fuzzy logic can be applied to evaluate severity ranking;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eOccurrence [O]: The occurrence of a defect is judge by its probability to happen, but also on the process capability (Cpk) that measures how close a process is running to its specification limits. Historical data and similar process data can also be taken into consideration while evaluating occurrence. The ranking values by intervals are presented by Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Nevertheless, the experts\u0026rsquo; judgment can still be confronted to uncertainty. Without a reliable data, the intervals can only be an assumption and therefore lead to wrong ranking. To overcome this, the authors suggest the usage of sensors linked to FMEA database in a way to automatically update occurrence ranking;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDetection [D]: When it comes to detection, it represents at which point the defect can be detected during the process, and is judged according to the inspection type adopted in the process. The ranking values are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eOnce rankings are defined and agreed on, next step is to set the Action Priority (AP). This method is introduced to prioritize severity, then occurrence and finally detection. Thus, according to the failure prevention intent. The extraction of AP scores (Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e1\u003c/span\u003e) presents AIAG/VDA judgement using High/Medium/Low priority for action, and is based on the score obtained by concatenating S, O and D rankings.\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Extraction of Action Priority ranking standard [3].\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"324\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS\u0026amp;O\u0026amp;D\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e101010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e10109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e10108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e10107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e10106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e10105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e10104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e10103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e10102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e10101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e10910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e10810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e10710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n 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\u003cp\u003e1065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e10510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e10410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n 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\u003cp\u003e1041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e10310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e10210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e10110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e91010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e9109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e9108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e9107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e9106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e9105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e9104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e9103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e9102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e9101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e9910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n 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style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n 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48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n 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style=\"width: 78px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n 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style=\"width: 48px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n 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\u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e9510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ePriority High (H): need for reviews and actions. Appropriate actions must be identified to enhance prevention and/or detection means or explain then record the reason that makes used controls proper ones;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePriority Medium (M): should for reviews and actions. Appropriate actions should be identified to enhance prevention and/or detection means or explain then record the reason that makes used controls proper ones;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePriority Low (L): must for reviews and actions. Appropriate actions may or may not be identified to improve prevention or detection means [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Fuzzy-logic\u003c/h2\u003e \u003cp\u003eBased on a comparative study held on 2024 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Fuzzy logic was identified as the most efficient approach to solve uncertainty issues. Indeed, Fuzzy logic, through favorizing the condition of being partially true and partially false at once, is suitable to resolve ambiguity and uncertainty [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], thus by using linguistic terms represented by membership functions [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDiverse fuzzy number shapes are available, but triangular fuzzy one is mostly adopted [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. A triangular fuzzy set (TFS) is shaped using a triplet [c, a, b] limited by a [0\u0026ndash;1] range, where c and b respectively correspond to the left and right vertex of the TFS as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003e [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. A value of zero (0) is out of range, a value of one (1) on the other hand is fully representative of the set, while a value higher than zero and lower than one is not completely among the range [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The fuzzy sets are associated with their corresponding class and represented by a triangular membership function [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTwo definitions can be used for calculations:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDefinition 1\u003c/strong\u003e \u003cp\u003e[\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/p\u003e \u003c/p\u003e \u003cp\u003eConsidering Y a triangular fuzzy number, A = (c\u003csub\u003e1\u003c/sub\u003e, a\u003csub\u003e1\u003c/sub\u003e, b\u003csub\u003e1\u003c/sub\u003e), and B = (c\u003csub\u003e2\u003c/sub\u003e, a\u003csub\u003e2\u003c/sub\u003e, b\u003csub\u003e2\u003c/sub\u003e), (a\u003csub\u003ei\u003c/sub\u003e, b\u003csub\u003ei\u003c/sub\u003e and c\u003csub\u003ei\u003c/sub\u003e: positive real numbers):\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;A\u0026otimes; B = (c\u003csub\u003e1\u003c/sub\u003ec\u003csub\u003e2\u003c/sub\u003e, a\u003csub\u003e1\u003c/sub\u003ea\u003csub\u003e2\u003c/sub\u003e, b\u003csub\u003e1\u003c/sub\u003eb\u003csub\u003e2\u003c/sub\u003e) (1)\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDefinition 2\u003c/strong\u003e \u003cp\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe TFS Y = (c, a, b) is a specific instance of a generalized trapezoidal fuzzy number. The representation of the graded mean integration for Y is expressed as:\u003c/p\u003e \u003cp\u003eP(Y) = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{c\\:+\\:4a\\:+\\:b}{6}\\)\u003c/span\u003e\u003c/span\u003e (2)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Fuzzy-FMEA model\u003c/h2\u003e \u003cp\u003eAs highlighted previously, the purpose of the research is combining Fuzzy Logic and FMEA methods to overcome uncertainty and reinforce the risk assessment process. This combination, as described by the model in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e5\u003c/span\u003e, consists on following the AIAG/VDA FMEA 7 steps for risk identification and then exploit Fuzzy logic to evaluate severity parameter, the latest being the one exposed to uncertainty.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Use case: Risk evaluation using Fuzzy-FMEA for an RFID system implementation","content":"\u003cp\u003eIn order to concretize our risk assessment model, an RFID system implemented within an automotive company in Morocco was chosen as an example to identify how and at which step the fuzzy-logic can be used in combination with the FMEA approach. In this study, we will use the new version of PFMEA (Process Failure Modes and Effects Analysis) according to AIAG/VDA strategy (7 steps analysis).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.1 PFMEA approach according to AIAG/VDA standard\u003c/h2\u003e \u003cp\u003e \u003cb\u003eFirst Step: Planning \u0026amp; Preparation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAs the basis of the analysis, this step consists on describing the process to be reviewed at first, which will represent the scope of the study, to then set a timing plan for the execution of PFMEA strategy. It is during this step that the header of PFMEA document is filled according to the template presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e6\u003c/span\u003e. For confidentiality matters, the filled header will not be presented in this paper.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis example is based on an RFID system already studied by the authors in a previous work in the context of improving boxes feeding and control flow within an automotive company [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Feeding process is ensured by a dedicated operator and starts when a box gets empty. The operator (distributor) picks the empty boxes from the assembly line and puts them in the RFID structure so that the concerned reference (mentioned in each box) gets scanned. The reference then appears on the warehouse system supervised by a warehouse agent in charge of preparing a trolley with replacement of the empty boxes. Before putting the replacement box in the trolley, warehouse agent scans it to be removed from the picklist. Every 2 hours, the distributor gets the emptied boxes trolley to the warehouse, and brings back the one with replacement boxes back to the assembly line. In the warehouse picking area, the gate does not open unless all needed components mentioned in the picklist are scanned. In case of missing components in the plant, to not block replacement of the remaining components, warehouse operator fills and alert in the system and the trolley can be released.\u003c/p\u003e \u003cp\u003eThe risk assessment previously performed on this system showed some weaknesses and threats of the RFID system such as dependance to the network, Reading range/perturbation, cyber-attacks and scan of wrong label. These elements, alongside with other risks detected during the study, will serve as an input and will therefore be reflected on the PFMEA analysis. The study will focus only on the failure modes that may be directly related to the RFID system and will not include those related to the feeding flow.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSecond Step: Structure Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThrough the process structure analysis, a breakdown is performed to identify process items, steps/sub-steps and work elements. This will serve as a basis to the function analysis step. It is mandatory to be completed before moving forward. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, risk assessment of our study was focused on the \u0026ldquo;Components order preparation\u0026rdquo; and \u0026ldquo;Forwarding of components to assembly area\u0026rdquo; processes, with \u0026ldquo;Empty boxes scan in assembly line\u0026rdquo;, \u0026ldquo;Trolley feeding\u0026rdquo; and \u0026ldquo;Feeding of components in assembly stations\u0026rdquo; as process steps.\u003c/p\u003e \u003cp\u003eAIAG/VDA strategy necessitate the consideration of the 4M\u0026rsquo;s (Man, Method, Machine, Material) during the review of activity taking place within the process, which was not considered previously in the traditional PFMEA.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStructure analysis (step 2).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Process Item System, Subsystem, Part Element or Name of Process\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2. Process Step\u003c/p\u003e \u003cp\u003eStation No. and Name of Focus Element\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3. Process Work Element\u003c/p\u003e \u003cp\u003e4M Type\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"10\" rowspan=\"11\"\u003e \u003cp\u003eComponents order preparation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEmpty boxes scan in assembly line\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMachine\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMachine\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMachine\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eTrolley feeding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eForwarding of components to assembly area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eFeeding of components in assembly stations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMany tools can be used in this step to perform the breakdown such as process diagram or structure tree. In this use case, we will rely on the process flow chart already available and developed by the company. Due to confidentiality matters, the process flow chart will not be presented in this paper.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThird Step: Function Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA function defines what the process item or step was designed for. One single process item or step can have many functions. Each function must be clearly described. When it comes to the characteristics, it is defined as a distinguishing feature directly related to a function and can be shown by the work instructions, manufacturing drawings, etc. The function analysis of the RFID system is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Concerned function is to \u0026ldquo;Ensure conform feeding of components.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFunction analysis (step 3).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Function of the Process Item\u003c/p\u003e \u003cp\u003eFunction of System, Subsystem, Part Element or Process\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2. Function of the Process Step and Product Characteristic\u003c/p\u003e \u003cp\u003e(Quantitative value is optional)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3. Function of the Process Work Element and Process Characteristic\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eEnsure conform feeding of components\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEnsure empty boxes trolley preparation and generate picking list\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGenerate the pick list\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCheck the preparation of the needed reference for each assembly line\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEnsure feeding of the empty trolley with the correct component and according to the needed quantity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnsure empty boxes trolley preparation and generate picking list\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnsure empty boxes trolley preparation and generate picking list\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnsure empty boxes trolley preparation and generate picking list\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEnsure correct feeding method of components in assembly lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRespect \u0026amp; follow the Work instruction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCorrect identification\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRespect \u0026amp; follow the Work instruction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOnce again, completing the function analysis step will push the analysis to the next step which is the failure analysis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFourth Step: Failure Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAt this step, we enter the core of the risk analysis approach in which we define the failures causes, modes and effects, alongside with their relationship to deduce the risk assessment. A failure can either be a non-conformity, an incomplete task or an unintentional/unnecessary activity. Based on the failure effect, and as presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, severity rank can already be defined.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFailure analysis (step 4).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Failure Effects (FE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeverity (S) of FE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2. Failure Mode (FM) of the Process Step\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3. Failure Cause (FC) of the Work Element\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAssembly line stoppage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eConnection to the system failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNetwork issue\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLoss of power supply\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCyber-attack\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStock inaccuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWrong box detection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRFID structure location in assembly line\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAssembly line stoppage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIncorrect qty transfer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnknown qty needed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWarehouse operator inattention\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStock inaccuracy\u003c/p\u003e \u003cp\u003eAssembly line stoppage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMixing between dedicated assembly lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLocations mixture\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAssembly line stoppage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCollecting the wrong components\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eManual operation\u003c/p\u003e \u003cp\u003eUntrained operator\u003c/p\u003e \u003cp\u003eUnclear/missing work instruction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMixing similar components\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMissing / Wrong identification\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot collecting all components needed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMissing box\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAssembly line stoppage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFeeding the component in the wrong location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eManual operation\u003c/p\u003e \u003cp\u003eNon respect of WI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMixing similar components\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncorrect / Missing cells identification in the side of feeding operator\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent Information tracking loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLost identification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWrong feeding by operator\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAt this step, we can already determine the severity score as the effects are now identified, and this is where authors are facing uncertainty. Each stakeholder, based on its experience, background and knowledge, has a different vision and attributes a different severity rank. In addition to that, sometimes the situation does not match with the ranking table of AIAG/VDA standard as the ranking from customer effects is lower or higher than the one from manufacturing effect. In this case, the higher rank is used, which may lead to a misjudgment and therefore reflect an incorrect action priority score. For example, with a wrong severity rank, we can result in a Low action priority when the risk must be ranked as Medium or High. In this situation, the risk evaluated will not be prioritized and no preventive or corrective actions will be considered. The risk will then not be eliminated and will probably lead to a customer claim and therefore impact the customer satisfaction. At this step, fuzzy logic will be used to re-evaluate the severity ranks attributed to each failure effect. A comparison and observation will then be performed to evaluate the impact of uncertainty in severity judgment without.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFifth Step: Risk Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo complete the risk analysis, occurrence and detection needs to be rated. To do so, prevention and detection controls currently established must be evaluated. Once it is done, the action priority number is automatically calculated enabling the classification of risks into: High, Medium and Low. The result of the risk analysis step is presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRisk analysis (step 5).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent Prevention Control (PC)\u003c/p\u003e \u003cp\u003eof FC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOccurrence (O)\u003c/p\u003e \u003cp\u003eof FC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCurrent Detection Controls (DC)\u003c/p\u003e \u003cp\u003eof FC or FM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDetection (D)\u003c/p\u003e \u003cp\u003eof FC or FM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePFMEA AP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecial Characteristics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBack-up servers\u003c/p\u003e \u003cp\u003eRegular system updates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequent network maintenance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePower supply back-up (Generator)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequent electrical maintenance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyber-security system (Firewall)\u003c/p\u003e \u003cp\u003eLimited and controlled access\u003c/p\u003e \u003cp\u003eOperator sensitization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEffective design and code reviews\u003c/p\u003e \u003cp\u003eReport unusual and mysterious pop-ups/emails\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeeding operators training\u003c/p\u003e \u003cp\u003eAdd counter for boxes scanning\u003c/p\u003e \u003cp\u003eAdd isolation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelf-control\u003c/p\u003e \u003cp\u003eDisplay last scanned reference\u003c/p\u003e \u003cp\u003eProcess audit\u003c/p\u003e \u003cp\u003eSemestrial physical inventory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdd box capacity in the RFID tag\u003c/p\u003e \u003cp\u003eWarehouse operators training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCheck by warehouse operators\u003c/p\u003e \u003cp\u003eCheck by feeding operators\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdd box capacity in the RFID tag\u003c/p\u003e \u003cp\u003eWarehouse operators training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCheck by warehouse operators\u003c/p\u003e \u003cp\u003eCheck by feeding operators\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdd project name to the RFID tag\u003c/p\u003e \u003cp\u003eSeparation of picking area by project\u003c/p\u003e \u003cp\u003eIdentification of Racks in picking area\u003c/p\u003e \u003cp\u003eWarehouse operators training\u003c/p\u003e \u003cp\u003eFeeding operators training\u003c/p\u003e \u003cp\u003eWork Instruction update\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelf-control\u003c/p\u003e \u003cp\u003eVisual check by feeding operators\u003c/p\u003e \u003cp\u003eStorage process audit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScan RFID tag before putting it in the trolley\u003c/p\u003e \u003cp\u003eFeeding Operators training\u003c/p\u003e \u003cp\u003eLocations identification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelf-control\u003c/p\u003e \u003cp\u003eDetection in subsequent operations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdd visual aids\u003c/p\u003e \u003cp\u003eFeeding Operators training\u003c/p\u003e \u003cp\u003eLocations identification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelf-control\u003c/p\u003e \u003cp\u003eDetection in subsequent operations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-identified boxes\u003c/p\u003e \u003cp\u003eAdd visual aids\u003c/p\u003e \u003cp\u003eTeam training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelf-control\u003c/p\u003e \u003cp\u003eQuality Audit\u003c/p\u003e \u003cp\u003eProcess audit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWH gate opens only if all needed components in the picklist are scanned\u003c/p\u003e \u003cp\u003eTeam training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelf-control\u003c/p\u003e \u003cp\u003eProcess audit\u003c/p\u003e \u003cp\u003eDetection in subsequent operations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperators training\u003c/p\u003e \u003cp\u003eLocations identification\u003c/p\u003e \u003cp\u003eDistribution list\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelf-control\u003c/p\u003e \u003cp\u003eDetection in subsequent operations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeeding Operators training\u003c/p\u003e \u003cp\u003eDistribution list\u003c/p\u003e \u003cp\u003eLocations identification\u003c/p\u003e \u003cp\u003eSeparation between similar components in line\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelf-control\u003c/p\u003e \u003cp\u003eDetection in subsequent operations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcedure\u003c/p\u003e \u003cp\u003eTeam training\u003c/p\u003e \u003cp\u003eQuality audit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelf-control\u003c/p\u003e \u003cp\u003eDetection in subsequent operations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeeding Operators training\u003c/p\u003e \u003cp\u003eWork instruction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelf-Control\u003c/p\u003e \u003cp\u003eVisual check by assembly operator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe occurrence rank can easily be determined by calculating the failure rate and the Cpk based on the database gathered by keeping trace of each failure that occurs. In the context of industry 4.0, to make it more reliable and to eliminate the risk of human mistake due to manual work, the authors suggest the usage of smart sensors that can be directly connected to the ERP system and will automatically be updated after each occurrence by calculating the failure rate and the Cpk. These values can then be reflected on the ranking table presented by AIAG/VDA standard to determine the correct occurrence rank.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSixth Step: Optimization\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe interest of this step is defining actions that will potentially eliminate the failure causes, reduce its rate of occurrence, or enhance detection ability, and then, once implemented, asses their effectiveness by recalculating the AP score by updating S, O and D rankings in a different column as shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e, without bringing any modifications to the previous rankings (except in case of PFMEA revision). Defined actions must be assigned to concerned responsible with a clear closure date.\u003c/p\u003e \u003cp\u003eThe status can be set on open (No action defined yet), decision pending (Actions were defined but are still waiting approval), implementation pending (Actions were defined and approved but are still waiting implementation), completed (Actions were defined, approved and implemented, and the final assessment was done) or not implemented (Actions defined but implementation was not approved due to practical or technical limitations).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOptimization (step 6).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecommended Actions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponsible Person's Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTarget Completion Date\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStatus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAction Taken with Pointer to Evidence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCompletion Date\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSeverity (S)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOccurrence (O)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDetection (D)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSpProd Char\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePFMEA AP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eRemarks\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs we can observe in the Table \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e8\u003c/span\u003e, all AP scores are judged as Low. Therefore, no recommended actions were determined.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSeventh Step: Results Documentation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFinally, documentation step consists on emphasizing, through a report, results of the PFMEA analysis performed. According to AIAG/VDA, this report includes below statements:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe final status compared to original defined goals;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe analysis scope and identification of what is new;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHow functions were improved;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe high-risk determined failures;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eActions taken or planned for addressing the high-risk failures including their status;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePlan for ongoing PFMEA improvement actions.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Fuzzy-PFMEA approach according to AIAG/VDA standard\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThis step consists on repeating the fourth, fifth and sixth step of the PFMEA approach using Fuzzy logic to re-evaluate the severity ranking, recalculate the AP score, and analyse the results.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFourth Step: Failure Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAs described previously, defining the severity rank of a failure effect is constrained by uncertainty. It is therefore very important to overcome it in order not to misjudge the risk. For that, this step will be re-evaluated using fuzzy logic approach to define a proper severity rank.\u003c/p\u003e \u003cp\u003eIn our case, and according to AIAG/VDA severity ranking table, our parameters will be defined as Customer effect (C) and Manufacturing/Assembly Effect (M). Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e7\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e8\u003c/span\u003e describes the meaning and linguistic terms used for each rank of C and M respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCustomer effect (C).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnoyance\u0026thinsp;\u0026gt;\u0026thinsp;20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnoyance\u0026thinsp;\u0026gt;\u0026thinsp;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnnoyance\u0026thinsp;\u0026gt;\u0026thinsp;75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDegradation of secondary function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLoss of secondary function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDegradation of primary function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLoss of primary function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePredictable safety risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUnpredictable safety risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLinguistic term\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eH4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eManufacturing/Assembly Effect (M).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMinor Disruption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate Disruption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate Disruption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModerate Disruption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModerate Disruption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSignificant Disruption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMajor Disruption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePredictable safety risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUnpredictable safety risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLinguistic term\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eL5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eL6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eH2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccordingly, the fuzzy sets will be defined as described in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFuzzy sets.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.0, 1.0, 2.0)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.0, 2.0, 3.0)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.0, 3.0, 4.0)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3.0, 4.0, 5.0)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(4.0, 5.0, 6.0)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(5.0, 6.0, 7.0)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(6.0, 7.0, 8.0)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(7.0, 8.0, 9.0)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(8.0, 9.0, 10.0)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(9.5, 10.0, 10.0)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eH4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eL5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eL6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eH2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe graphical representation of these fuzzy sets, proper to the parameter C for example, is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBy applying the equations 1 and 2, we can calculate the new severity rank. For example, if a failure effect is judged as C\u0026thinsp;=\u0026thinsp;M1 and M\u0026thinsp;=\u0026thinsp;L6, the numeric application will be as below:\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;C x M = (4.0, 5.0, 6.0) x (5.0, 6.0, 7.0) = (20.0, 30.0, 42.0)\u003c/p\u003e \u003cp\u003eSeverity (S) = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{1}{6}\\)\u003c/span\u003e\u003c/span\u003e (20.0\u0026thinsp;+\u0026thinsp;120.0\u0026thinsp;+\u0026thinsp;42.0) = 30,5\u003c/p\u003e \u003cp\u003eAccordingly, and following the same approach, Severity ranking for all the failure effects were re-calculated. Results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFailure analysis (step 4) using fuzzy-logic.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Failure Effects (FE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeverity (S) of FE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2. Failure Mode (FM) of the Process Step\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3. Failure Cause (FC) of the Work Element\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAssembly line stoppage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eConnection to the system failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNetwork issue\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLoss of power supply\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCyber-attack\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStock inaccuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWrong box detection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRFID structure location in assembly line\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAssembly line stoppage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIncorrect qty transfer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnknown qty needed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWarehouse operator inattention\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStock inaccuracy\u003c/p\u003e \u003cp\u003eAssembly line stoppage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMixing between dedicated assembly lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLocations mixture\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAssembly line stoppage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCollecting the wrong components\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eManual operation\u003c/p\u003e \u003cp\u003eUntrained operator\u003c/p\u003e \u003cp\u003eUnclear/missing work instruction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMixing similar components\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMissing / Wrong identification\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot collecting all components needed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMissing box\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAssembly line stoppage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFeeding the component in the wrong location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eManual operation\u003c/p\u003e \u003cp\u003eNon respect of WI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMixing similar components\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncorrect / Missing cells identification in the side of feeding operator\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent Information tracking loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLost identification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWrong feeding by operator\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003cp\u003e\u003cb\u003eResults analysis\u003c/b\u003e\u003c/p\u003e \n \u003cp\u003eOnce the new severity ranks are defined and freed from fuzziness, we now get to determine the new AP scores. For that, to align with AIAG/VDA AP score determination, severity ranks must be converted into an interval of [\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8 CR9\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], thus by calculation all possible combinations. The max value obtained (98.375) is the equivalent of the highest severity rank in AIAG/VDA table (10). Accordingly, 16.33 will be equivalent to 2, 12.33 to 1, 2.5 to 1, and 30.33 to 3.\u003c/p\u003e \u003cp\u003eAfter reflecting the converted severity ranks on the PFMEA table, hopefully, there was no impact on the AP score. All of them remained \u0026ldquo;Low\u0026rdquo; as the severity ranks decreased compared to the initial ones. Next step is to analyze and evaluate the impact of using fuzzy logic, thus based on the comparison presented in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e11\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of severity ranking with and without using Fuzzy logic.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard severity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFuzzy severity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2. Failure Mode (FM) of the Process Step\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3. Failure Cause (FC) of the Work Element\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eConnection to the system failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNetwork issue\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLoss of power supply\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCyber-attack\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWrong box detection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRFID structure location in assembly line\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIncorrect qty transfer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnknown qty needed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWarehouse operator inattention\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMixing between dedicated assembly lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLocations mixture\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCollecting the wrong components\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eManual operation\u003c/p\u003e \u003cp\u003eUntrained operator\u003c/p\u003e \u003cp\u003eUnclear/missing work instruction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMixing similar components\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMissing / Wrong identification\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot collecting all components needed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMissing box\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFeeding the component in the wrong location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eManual operation\u003c/p\u003e \u003cp\u003eNon respect of WI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMixing similar components\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncorrect / Missing cells identification in the side of feeding operator\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLost identification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWrong feeding by operator\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs we can observe from the comparison, all severity ranks decreased. This is explained by the fact that all these failure effects have more impact on the manufacturing site than the customer. During the FMEA meetings, the team had to choose the highest rank since AIAG/VDA standard does not separate rankings, when in reality, considering that customer effect has more importance than the manufacturing effect, the severity was overestimated as proven by Fuzzy logic approach.\u003c/p\u003e \u003cp\u003eFor example, if we consider the failure modes \u0026ldquo;Wrong box detection\u0026rdquo; and \u0026ldquo;Mixing between dedicated assembly lines\u0026ldquo;, severity was initially ranked at 4. After using fuzzy PFMEA, the new rank decreased to 1. Indeed, putting a box in the wrong assembly line can impact production lines as this will generate a line stoppage and may also lead to a rework of all parts produced. But on the other hand, as we are here talking about components that are not similar, the probability of their assembly on the product is very low as they will not fit. If they do, the process is equipped by several Poka-Yokes that will detect the usage of a wrong component. It is then very difficult for such product to be sent to the customer and there\u0026rsquo;s therefore no impact from customer side, which explains that the new rank is 1.\u003c/p\u003e \u003cp\u003eIf we consider the failure mode \u0026ldquo;Collecting the wrong components\u0026rdquo;, here we are talking about similar components, which makes the probability to reach the customer higher as the Poka-yokes, if not frequently checked and controlled, cannot be a 100% reliable. Sending a product with wrong components may lead to some annoyance at the customer side, which justifies the new severity rank of 3.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eLately, the industrial world, especially the automotive sector, has grown strong enough to generate a competitive environment with high diversity and more complex systems. With this complexity comes doubts and contradictory opinions monitored by each ones\u0026rsquo; experience, expertise and point of view, which makes the decision process more challenging and complex. Many solutions were developed over the years to overcome this challenge, but depending on the factors involved in the decision-making process, and following a comparison study previously held by the authors, Fuzzy Logic is most suitable to use for this problematic.\u003c/p\u003e \u003cp\u003eAnother necessity to consider nowadays is the risk assessment process. A safe production environment first starts with a proper management of risks. This paper, following a constructive literature review, identified and studied the most used risk assessment methods based on their effectiveness and were they fit best. FMEA approach was without any doubts the most suitable one to this study.\u003c/p\u003e \u003cp\u003eWithing this scope, this paper introduces a new concept which is Fuzzy-FMEA approach considering the latest updates from AIAG/VDA edition. A tool to properly overcome uncertainty and accurately define the AP score during risk assessment process. For this, and from a digitalization aspect, the study focused on the analysis of an RFID system were PFMEA approach was applied. During the ranking process, uncertainty was faced during the severity judgment step as many rankings were suggested, each based on a different opinion. Sometimes, the decision-makers face a situation where the effect from customer and manufacturing sides are not at the same level, and therefore choose the highest rank. At this step, the Fuzzy logic approach can help the stakeholders to align on a single ranking for each risk and therefore overcome the uncertainty that occurred. Indeed, by applying the suggested Fuzzy model to the use case of the RFID system, results showed that the severity ranks were overestimated as the effect on manufacturing was higher than the effect on customer, which pushed the team to choose the highest rank. By analyzing the new ranks obtained after applying Fuzzy logic approach, results seemed more logical.\u003c/p\u003e \u003cp\u003eIn terms of perspectives, the model can be developed further by combining it with artificial intelligence algorithms for prioritization and classification of actions. The model will then be as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor\u0026nbsp;contributions\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eL.N. and M.G.; methodology, L.N.; validation, M.G., A.S., S.M. and M.D.N.; formal analysis, L.N., M.G., S.M. and M.D.N.; investigation, L.N. and M.G.; resources, L.N., M.G. and M.D.N.; data cura-tion, L.N. and M.G.; writing\u0026mdash;original draft preparation, L.N.; writing\u0026mdash;review and editing, M.G., S.M. and M.D.N.; visualization, M.G., A.S., S.M. and M.D.N.; supervision, M.G. and M.D.N. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData\u0026nbsp;availability\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eNo datasets were generated or analyzed during the current study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting\u0026nbsp;interests\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics, Consent to Participate, and Consent to Publish declarations\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOpen Access\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third-party material in this article are included in the article\u0026rsquo;s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article\u0026rsquo;s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublisher\u0026rsquo;s\u0026nbsp;Note\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMIL-P 1629, 1949. USA Military Standard, Procedure for Performing a Failure Mode, Effects and Criticality Analysis. 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Even though this evolution plays an important role in enhancing companies’ performance, they also present many risks to manage, which leads stakeholders to the need of implementing a strong strategy to assess risks such as Failure Modes and Effects Analysis (FMEA). Nowadays, manufacturing flows confronts different types of variables continuously and dynamically interacting (social, environmental, financial, political, educational, cultural, etc.), leading to uncertainty in the decision-making approach, specifically during the ranking of a failure severity. Accordingly, this article explores how the latest version of FMEA (AIAG/VDA) approach can be strengthened through Fuzzy-logic to overcome uncertainty in judgments. It also presents a use case based on an RFID system implemented within an automotive company.\u003c/p\u003e","manuscriptTitle":"Addressing Uncertainty in Digital Risk Evaluation Using a Fuzzy-FMEA Methodology","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-13 06:36:48","doi":"10.21203/rs.3.rs-6423322/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5edb15ed-907d-41c2-a6d8-576fa0b68ffc","owner":[],"postedDate":"May 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-23T14:23:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-13 06:36:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6423322","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6423322","identity":"rs-6423322","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}