Solutions for the Improvement of the Failure Mode and Effects Analysis in the Automotive Industry

Transcription

1 Solutions for the Improvement of the Failure Mode and Effects Analysis in the Automotive Industry NEAGOE B.S. Advanced Technologies and Manufacturing Systems Department University Transilvania of Braşov Bld. Eroilor, Nr. 29, , Braşov ROMANIA Abstract: The Failure Mode and Effects Analysis represents one of the most important and used engineering inductive analysis techniques used in the automotive industry, which in spite of the simplicity of the methodology has demonstrated in time the capacity to generate important results from the technical, financial and competitive points of view. After a rigorous research of the literature concerning the subject, and using the personal observations as a member of the FMEA team in the automotive industry, an analysis has been performed in regard to the specific characteristics of the methodology, with an emphasis on the limitations and the drawbacks of the traditional approach. The purpose of the paper is to report the findings and establish a series of developments and measures that should be considered in order to reach the maximum potential of the benefits that can be obtained with the application of the FMEA in the automotive industry. As a conclusion, several research opportunities will be presented. Key-words: Failure Mode and Effects Analysis, Automotive industry, Cost-based risk evaluation, Automation, Knowledge management. 1 Introduction The automotive industry is one of the most competitive fields of the modern economy, especially in the difficult financial context that characterizes the last period of time. In order to achieve a competitive advantage, the companies active in this field have to insure the best conditions from a technological point of view, as far as the quality, reliability and safety of the products they design and produce is concerned. For this purpose, the Failure Mode and Effects Analysis is one of the risk management and quality improvement methods with a very good potential in addressing the risks linked to the design and manufacturing processes. The ultimate goal is to make sure, through a methodical and in-depth approach that no expected failures reach the client, while simultaneously increasing the overall system efficiency and reliability. In an early form, the method was developed during the 1940 by the US Army in an effort to determine the effects of failures on the systems and equipments, by making a classification in relation to their effects on the mission success and the equipment and personnel safety. Later on, during the 1963 the FMEA was applied formally by NASA during the development of the Apollo Program and adopted at a general level in the aeronautical industry in As far as the automotive industry is concerned, the Failure Mode and Effects Analysis was introduced by Ford Motor Company in 1972 to address the engineering challenges it faced during that period, but it was only after 1990 that the methodology has been accepted and successfully implemented in the field, as a viable and essential methodology for the prevention of failures and quality improvement. That is how it became a required technique as subject of industry standards and regulations, such as ISO- 9000, QS-9000, ISO-TS/16949, APQP, PPAP, Six- Sigma, etc. [1]. In order to be properly applied, the Failure Mode and Effects Analysis has to be performed as a preventive measure and to constitute a continuous improvement effort, based on the defined functions of the subject system, process or design, in accordance to the specifications and requirements of the client (the customer or the following operation), whilst respecting the principle of the different degree of importance of the issues that are determined

2 Although it was introduced by many companies in the automotive industry as early as 1980, several important aspects such as the long time necessary for the application of the analysis process, the ambiguity of certain technical aspects and the tedious project management, lead to a difficult acceptance of the method as a real means to achieve the desired and real improvements. As it is also revealed in [2], many suppliers in the automotive field still use the FMEA only because it is required by standards and regulations or specifically requested in their clients demands, and thus fail to consider and obtain the highest level of benefits. Properly applied though, the FMEA aims to identify existent or potential failure modes and analyze them in order to evaluate the corresponding risks so that the problems and issues can be addressed before they occur, or, where possible to reduce the impact of their potential effects. In the same time, as a group effort, the method facilitates communication and enhances inter-department relations and allows for a thorough and critical system, process or design analysis. The result of the FMEA is a set of actions meant to reduce risks, development time and costs, increase customer satisfaction and as a result improve the company image and competitiveness. Several types of FMEA are used in the automotive industry. The most representative are: the Design FMEA (DFMEA) that is aimed at products and components; the Process FMEA (PFMEA) that evaluates process functions and Concept or System FMEA (SFMEA) that is applied in the early phases of the manufacturing process. Other types and variations of FMEA in this field include Machinery, Service or Software FMEAs. The basic FMEA process is illustrated in figure 1. The following sections aim to individually analyze several specific elements of the classic FMEA process and offer solutions and future developments for their improvement. Figure 1. The FMEA Process 2. Risk assessment in FMEA The risk evaluation and ranking methodology specific to the FMEA in the automotive industry consists of individually assessing failure modes on a Likert-type 10 point scale, in relation to the Severity (S) of their effects, the probability of Occurrence (O) of their causes and the likeliness of their Detection (D). The product of the three factors is the Risk Priority Number or RPN (1), which takes an integer value ranging from 1 to 1000 [3]. RPN = S O D (1) There is a double purpose for the determination of the RPN: first to determine which problems require elimination (failure modes that affect the passenger safety or with catastrophic effects on the vehicle), mitigation of their effects (for risks that can be reduced to an acceptable level) or simply which can be safely ignored. Secondly, but just as important, the numerical value of the RPN is used to evaluate the efficiency of the actions performed to reduce the risks, by reducing individually the value of the S,O,D factors (as described in table 1, for DFMEA). It must also be mentioned that besides the numerical value of the RPN (for which a threshold is established), the nature of the component factors must be taken into consideration, thus actions have to be taken, at any value of the RPN, for failure modes with high Severity (>9), high Occurrence (>7) or a high value of the S O combination [4]

3 Table1. Actions for the reduction of the RPN Factor Recommended actions for reduction - reducing the effects by design modifications; - protection or warning systems; S - design-enhancing features (e.g. doubling less reliable components); - designing-out safety hazards. - inclusion in the design of security or surveillance features; O - modification of the design to eliminate certain causes or mechanisms of failure. - improving the design validation; - introducing trainings and control D procedures; - warning systems. Although this method is accepted and successfully used (mainly because of the simplicity of the calculus and the transparency of the results), there are several issues that have to be taken into consideration. First, we must analyze the nature of the evaluation of the 3 parameters, S,O,D. The Severity determines the level at which the effect of a failure is perceived by the client or by the nextlevel assembly in the case of components. According to [3], the Severity is evaluated on a scale that ranks the effects from the highest level - Failure to meet safety and/or regulatory requirements, continuing with different levels of loss or degradation of primary functions (that consider operability, usage comfort and convenience functions) and at the lowest level the annoyances (such as appearance and noise). It is to be noted that although Severity can be considered the most critical factor of the RPN, its evaluation is not based on strictly objective elements but mostly on engineering common sense and the experience of the evaluation team. During the case studies the author performed, a series of ambiguities were identified in the establishment of a proper Severity level, on many occasions the members of the team preferring to over-evaluate the Severity factor in order to highlight important issues. The Occurrence is evaluated after the determination of the causes/mechanisms of failure and should reflect the likelihood that these take place, the criteria of evaluation being the number of incidents per items/vehicles [3]. The estimation should be based on historical data, client reclamations or warranty and service information. However, in practice, such information is often not available and the nature of the evaluation is subjective to a certain degree. The Detection is a measure of the efficiency of the control actions to detect the failure mode considering it could occur. This evaluation has a high degree of subjectivity, and different values could be obtained for the same failure mode, as it estimates the trust that the evaluation team has in the design control measures. A major flaw of the traditional RPN evaluation, besides the subjectivity of the three factors, is the nature of the RPN scale itself. Table 2. RPN scale characteristics Interval No.of values % First of all, as a product of three numbers, the RPN scale is not continuous and presents a series of holes as mentioned in [5]. Actually, 88% of the scale is empty with only 120 unique values, with the distribution presented in table 2 [6]. Several issues result from the characteristics of the RPN scale: 1. Identical values of the RPN can be obtained from different combinations of the S, O, D parameters, which can result in an erroneous estimation of the real importance of a failure mode. 2. Because of the gaps between consecutive values of the RPN, misinterpretations of the relative importance of different failure modes are likely to occur. 3. Due to the RPN determination methodology, the effect of the modifications of the estimated values is multiplied, i.e. small variations in the values of the S, O, D parameters generate disproportionate modifications of the RPN. As the RPN are compared to threshold values when corrective actions are considered, the results can be manipulated this way without real improvements to the overall risk. For these reasons, and also considering the relative degree of subjectivity of the estimation of the three parameters and the fact that their relative importance is not taken into consideration, it can be stated that the traditional RPN does not offer a consistent evaluation of risks and corrective actions

4 There are several studies concerning these issues, and several recommendations can be determined: In order to reduce the subjectivity of the estimation of the RPN, the rating scales must be optimized (especially in the case of the Detection); to increase the precision of the risk evaluation, other factors have to be considered (such as costs) and also the relative importance between the factors. Several modern approaches to enhance the prioritization method have been proposed in the literature, most of which based on uncertainty treatment theories such as fuzzy logic [7], grey theory, Bayesian nets, etc. 3. FMEA Automation The FMEA is in the traditional approach the result of the efforts of a cross-functional team that includes design, process or quality engineers, specialists and even legal, purchase or client representatives. Because it is a thorough and methodical process, the activity of the team is subject to human error, such as omissions and misinterpretations. It is also to be mentioned that the inter-dependencies between failure modes are also omitted. The complexity of automotive designs and processes often surpass the capacity of human teams, which leads to great efforts, time and financial expenses. For the complex electrical systems used in modern cars this approach is not sufficient, and as the procedure itself is repetitive, it is well suited for computer automation, both for storing and management of FMEA data and also the reasoning steps of the process. Possible variants for the automation of the FMEA process have been identified in [8], such as numerical simulation, expert systems and causal reasoning. Several researches on the automation of FMEA reporting have been made, e.g. [9] in which the author defines a system that analyses not only single failures but also the effects of multiple failures in the case of automotive electrical systems. Such developments, in spite of several drawbacks, such as complexity and necessity to translate the FMEA information into system-specific terms should be able to overcome the limitations of the conventional approach, especially in the context of the development of the processing power of the IT infrastructure. 4. Cost-based FMEA In section 2, several drawbacks of the conventional FMEA risk assessment were presented, among which the need for the integration of costs as a factor of the evaluation, as an objective means to evaluate the real effects of failure modes. The necessity for the introduction of a cost based FMEA model resides in the following: - From a managerial point of view, the inclusion of financial aspects to reliability improvement gives the opportunity to balance the costs of corrective actions with expected revenues, allowing an optimized resource allocation and evaluation of changes. - As previously stated in section 2, the traditional RPN determination is flawed by the lack of consistency in the RPN scale; thus the integration of a financial dimension to the evaluation allows for a more realistic view upon the real effects of failure modes. The literature on the subject reveals that these issues have been taken into consideration. For example, Von Ashen presents in [10] a model of cost-oriented FMEA, applied to operator and control units for vehicle air conditioning and engine cooling, blower control units and electrical heaters. The model proposed in the research takes into consideration not only the costs associated to failures detected externally (at client level) but also the costs of faults identified before delivery (both real and false positive inspection), for a complete financial risk assessment. Figure 2 graphically depicts the types of costs associated to failures. Figure 2. Costs of failures The benefits of using a cost-based FMEA model come with several drawbacks, among which: the necessity for an integral and realistic cost estimation for failure modes; the necessity to integrate an computer-aided FMEA management model, in order

5 to be able to efficiently process all the extra information and computations; the necessity to evaluate the relations between the costs associated to inter-dependant failure modes. 5. Knowledge management in the FMEA The FMEA process involves the manipulation of a massive amount of data and information, resulting in the development of a potential failure mode database, a historical documentation of design and process changes and a forum for recommending and tracking risk reducing actions [1]. In a classical approach, an FMEA process can be managed using only a spreadsheet application to generate the FMEA sheet, making the data management process very difficult. Another issue resides in the nature of FMEA projects that are independent by nature and often the information is subject to interpretation, because it is recorded in natural language, dependant on the knowledge and experience of the FMEA team members. In order to be able to improve the FMEA process by reducing the duration, possibility of errors and personnel allocation needs, but mostly to benefit from the positive results of previous FMEAs it is necessary to integrate a knowledge-base system. One approach to this problem, as exemplified in [11] is to use knowledge engineering techniques such as ontologies to define concepts, relations and rules and and offer a possibility for the FMEA team to have starting base for new projects. Another aspect that has to be considered is the access to the information. In multi-national enterprises, it is possible that the experts required to participate in the brainstorming process are located at great distances, and their relocation would imply great time and financial expenses. Other limitations of a traditional spreadsheet management solution for FMEA are: - the difficult management of design and process changes; - the absence of an automated system of information retrieval from previous FMEAs; - the difficulty of adaptation to FMEA standards changes. In order to eliminate these drawbacks, an efficient solution would be to use a computer-aided web-based application that uses a knowledge management system. Such a system, described in figure 3 would allow the participation of team members over distance, well suited for automotive international companies, with an efficient data management system and capability to reuse valuable FMEA information. Figure 3. Web-based FMEA system Other advantages of using a computer-aided FMEA management system could be the integration with other reliability tools, various result reporting capabilities and the possibility to develop more complex risk-assessment models (integrating new factors such as costs) and automation capabilities. 5. Conclusions In order to insure the delivery at the desired time, the required quality level and at a competitive value, automotive companies have to implement a proper quality management system, insuring that their suppliers do the same, from the early stages of vehicle design. The Failure Mode and Effect Analysis provides the tools for quality and reliability improvement, by determining solutions for the elimination of failure modes or mitigation of their effects. The FMEA methodology requires a teameffort, in-depth knowledge of the designs and processes, time and financial expenses. The global industry-wide integration of the FMEA as a reliability management process is due to the proved efficiency of the method, as well as the simplicity and transparency of the analysis. However, certain

6 aspects limit the benefits, accuracy and efficiency of the FMEA and open pathways for future research and improvements, demonstrated by the number of studies and articles dedicated to the subject. A set of conclusions can be formulated based on this study: The risk evaluation based on the conventional RPN generates inconsistent results and permits the application of corrective actions that do not translate in real improvement. By developing new risk evaluation models, such issues can be avoided. As the complexity of the analyzed designs increases, human effort is no longer sufficient and for certain parts of the analysis process an automated system can be applied. The traditional method of risk assessment does not include a financial evaluation of the failure modes. A cost-oriented FMEA model offers the management opportunity to determine the financial risk of failure modes and to weigh the failure costs with the expenses needed for improvements. In order to increase the efficiency and reduce the costs and time necessary for the analysis, previous FMEAs can be used by integrating a knowledge management system. The geographic distribution of automotive production sites and the development of the IT and communication infrastructure offer the possibility of using computer-aided management systems for the FMEA, with a web-based infrastructure that facilitates long-distance collaboration, an essential requirement in multi-national companies. Acknowledgment This paper is supported by the Sectoral Operational Programme Human Resources Development (SOP HRD), financed from the European Social Fund and by the Romanian Government under the contract number POSDRU/88/1.5/S/ References: [1] Stamatis DH., Failure mode and effect analysis: FMEA from theory to execution. 2nd ed. Milwaukee, WI: ASQ Quality Press, [2] Johnson, K.G.; Khan, M.K., A study into the use of the process failure mode and effects analysis (PFMEA) in the automotive industry in the UK, Journal of Materials Processing Technology, Vol.139, No.1-3, 2003, pp [3] Chrysler Corporation, Ford Motor Company, General Motors Corporation, Potential Failure Modes and Effects Analysis (FMEA). Reference Manual, 4 th ed., [4] Farris, J., Letens, G., Van Aken, E.M., Ellis, K., and Boyland, J., A Structured Approach for Assessing the Effectiveness of Engineering Design Tools in New Product Development, Engineering Management Journal, Vol. 19(2), 2007, pp [5] Bowles, J. An assessment of RPN prioritization in a failure modes effects and criticality analysis, Annual Reliability and Maintainability Symposium, pag , [6] Yang G., Life Cycle Reliability Engineering, Wiley, [7] Yang, Z.; Bonsall, S.; Wang, J. Fuzzy rule-based Bayesian reasoning approach for prioritization of failures in FMEA. IEEE Transactions on Reliability, v. 57, n. 3, p , [8] Bell, D., Cox, L., Jackson, S., Schaeffe, P., Using causal reasoning for automated failure modes and effects analysis (FMEA), Reliability and Maintainability Symposium, IEEE Xplore, Jan. 1992, pag , 1992 [9] Price, C.J., Taylor, N.S., Automated multiple failure FMEA, Reliability Engineering & System Safety, vol.76, nr.1, pag.1-10, [10] Von Ahsen, A., Cost-oriented failure mode and effects analysis, International Journal of Quality & Reliability Management, vol.25, nr.5, pag , [11] Dittmann L, Rademacher T, Zelewski S. Performing FMEA using ontologies. In: The 18th international workshop on qualitative reasoning, Northwestern University, Evanston, Illinois, USA, August 2-4,