Risk Assessment Framework for Reverse Logistics in Waste Plastic Recycle Industry: A Hybrid Approach Incorporating FMEA Decision Model with AHP-LOPCOW- ARAS Under Trapezoidal Fuzzy Set

Failure Mode Effects Analysis (FMEA) is the primary tool for Dynamic Positioning (DP) system reliability assessment and is required by the Classification Societies for DP-Class Notation. However, many DP vessels have experienced faults that either were not identified in their FMEAs or were more severe than their FMEAs indicated. This paper's authors, and others, have questioned why DP FMEAs have not been as effective as expected. As a result, the paper will discuss:Post-FMEA experience of DP vessels;Conclusions of gap analysis between DP FMEA-expected results and actual experience;Specific steps to upgrade the quality of DP FMEAs on existing vessels. Upgraded FMEAs of three vessels are compared to previous FMEAs, demonstrating higher effectiveness with additional single-point failures identified;Ways that vessel personnel can utilize the FMEA to improve responses to unplanned events. The summary conclusion shows that FMEA effectiveness is sensitive to several predictable factors. FMEA History The U.S. military has employed FMEA techniques since the 1970s as a design aid to enhance equipment reliability. An early application of FMEAs in offshore drilling on the Transocean vessel Discoverer Seven Seas identified DP reliability improvements in 1985. By the early 1990s, FMEA techniques were in wider use in the marine industry. The UK trade organization Dynamic Position Vessel Owners Association (DPVOA) cited the FMEA technique in its 1991 ‘Guidelines for the Design and Operation of Dynamically Positioned Vessels.’ 1 After the 1994 publication of International Maritime Organization (IMO) MSC CIRC 6452 the Classification Societies, including ABS, DNV, and Lloyds Register of Shipping, subsequently included FMEAs in their DP Class Notation requirements as the primary means of identifying failure modes. FMEAs were then widely employed to satisfy those requirements. For most vessels, physical trials were used toverify the FMEA. Once these vessels began their working lives, however, equipment failures sometimes resulted in effects more severe than those identified in the vessel's FMEA. Also, Some failure modes experienced were not even identifiedin the FMEA. Evidence and recognition of such issues grew through the early 2000s and produced reaction by industry groups. For example, the International Marine Contractors Association (IMCA) attempted to address these developments in two papers titled: ‘Guidelines on Failure Modes and Effects Analysis (FMEAs)’3 and ‘FMEA Management Guide.’4 Another important document was a study commissioned by the UK HSE (Health and Safety Executive) titled ‘Review of Methods for Demonstrating Redundancy in Dynamic Positioning Systems for the Offshore Industry.’5 Gap Analysis Believing that FMEAs could deliver better results, we looked for the root causes of the disparity between the predicted and actual results and found the following:Failure by an owner to specify, or adequately specify, an FMEA's scope and depth, which often led to a shallow, ineffective FMEA.Failure of the FMEA vendor to perform sufficient analytical work. The default tended to be an experienced- based FMEA performed by one or two individuals who looked for familiar faults that identified only a worst-case fault for each system.

Failure Modes Effect Analysis (FMEA), or more specifically, Failure Modes Effect and Criticality Analysis (FMECA) has been accepted as an effective condition monitoring assessment tool used widely by the mili-tary, traditional industries and reliability relevant engineering systems. A successful FMEA assists to identity, evaluate and report component failure modes, their severity and impact on the systems. FMEA has been al-ready applied to onshore wind turbines, but there is a lack of offshore wind turbine applications. FMEA can be quantified by using the metric of Risk Priority Number (RPN), defined as the product of the levels of event severity, occurrence frequency and detectability. This paper presents an approach that allows the application of RPN to offshore wind energy by identifying correction factors to existing onshore RPN values taken from previous research. This approach estimates offshore failure rates for key wind turbine components from onshore data.

The objective of this contribution is to provide a review and suggest possible extensions of the Failure Mode Effects Analysis (FMEA), Hazard Risk Assessment (HRA) [2] and to demonstrate the importance of these tools to general probabilistic design for reliability (PDfR) [8]. FMEA was first introduced in the 1960s by the U.S. National Aeronautics and Space Administration (NASA) and is currently used extensively across many industries. FMEA is useful in understanding the failure modes of various products, qualifying the effects of failure and aiding in the development of mitigation strategies. It is a useful tool in improving quality, reliability, and the maintainability of designs, and is a critical component in risk management strategies and evaluations. This is, actually, the approach of the prognostics and health monitoring/management (PHM) engineering. Failure mode effects and criticality analysis (FMECA) [1] is an extension of (FMEA). While FMEA is a bottom-up, inductive analytical method which may be performed at either the functional or piece-part level, FMECA extends FMEA by including a criticality analysis that is aimed, like PDfR is, at charting the probability of failure modes against the severity of their consequences. The result highlights failure modes with relatively high probability and severity of consequences, allowing remedial effort to be directed where it will produce the greatest value. FMECA tends to be preferred over FMEA in space and North Atlantic Treaty Organization (NATO) military applications, while various forms of FMEA predominate in other industries. Being extensions of the FMEAs, FMECAs add severity and probability ranking aspects to the problems of interest. This is accomplished through an appropriate HRA - an engineering process of where the risk of an event is quantified by examining the chain of the preceding events, starting with, e.g., the failure mode, then stepping through to the end effects. The approach allows quantification of risk through the use of probabilistic risk analysis (PRA) and is addressed and discussed in detail. Failure oriented accelerated testing (FOAT) [9] could and should be viewed as an important constituent part of the effort. It is shown that care must be taken to establish the appropriate probabilities, to identify the statistical independence of the random variables of importance, as well as to assess the trustworthiness of the available or obtained data. It is indicated that an important drawback of the FMEA is the lack of pure operational (field) failure data. These data are frequently utilized from the computerized maintenance management system (CMMS) software, which does not always provide a true snapshot of the Mean Time Between Failures (MTBF) or other critical characteristics of the product. This results in the situation that personal judgment plays a large part in the development of the FMEA. Several papers have been published recently on development of Fuzzy FMEA methodologies (see, e.g., [7]). This application of fuzzy logic to Hazard Risk Analysis will allow additional uncertainty and inaccuracy to be modeled throughout FMECA development, leading to a more robust decision making with consideration of various uncertainties.

PurposeThe purpose of this paper is to establish a Process-aware FMEA (PAFMEA) development environment in order to face the main Failure Mode Effect Analysis (FMEA) deficiencies concerning failure analysis in maintenance.Design/methodology/approachThe proposed framework integrates Delphi methodology to obtain consensus of specialists’ opinions, analytic hierarchy process (AHP) to perform multiple criteria-based risk assessment and a business process management system to instantiate the development cycle. A conceptual model is presented and analyzed through a case study.FindingsPAFMEA reveals a new perception in the evaluation and prioritization of failure modes during maintenance failure analysis, such as risk definition and resource availability, dealing with conflicting characteristics in decision-making approaches.Practical implicationsThe PAFMEA environment includes requirements that are grouped with a process instantiation of an AHP structure, providing a high degree of applicability and performance to the development cycles of the FMEA. The new method confronts the classical risk assessment approach and contributes to the literature, adding new perspectives to the FMEA analysis.Originality/valuePAFMEA brings new and promising perspectives to the FMEA development cycle, which, in short, means adding on a multi-criteria failure analysis method (AHP) through a process-aware platform, with performance impacts in FMEA knowledge sharing, decision making and delivery.

PurposeThe purpose of this paper is to explore the various disposition alternatives and to develop a framework for the optimal disposition decisions in reverse logistics.Design/methodology/approachIn reverse logistics, once the products are collected and inspected, decision is to be taken regarding their disposition for reuse, re-manufacture or recycle or other possible alternatives. A combination of analytical hierarchy process (AHP) and fuzzy technique for order preference by similarity to ideal solution (TOPSIS) approach is proposed for the selection of best disposition alternative based on criteria economic benefits, environmental benefits, corporate social responsibility, stakeholder’s needs and reverse logistics resources.FindingsA case of electronics firm was illustrated for the demonstration of the approach for the disposition of mobile phones. Returned mobile phones must be disposed for repairing or reuse in current business scenario, if possible. Otherwise, the firm may prefer to recycle them rather than dispose or remanufacture.Research limitations/implicationsThe study is limited to mobile manufacturing firm. Also, these findings may vary depending on the sector and products. Further, empirical studies and case studies can be carried out to validate the findings.Practical implicationsThe proposed framework provides useful tool to the practitioners and researchers in decision-making for disposition in reverse logistics.Originality/valueVery few studies related to disposition decisions in reverse logistics were found in the previous research literature review. The study will add value to the very limited research on reverse logistics disposition. Also, the AHP-Fuzzy TOPSIS approach is first time being used for the disposition decisions in reverse logistics.

Failure Modes Effects Analysis (FMEA) has been utilized in engineering since the 1940s with the main goal of identifying failure modes through systematic evaluation of a system, product, or process. This method has been widely adopted across many industries and provides valuable input to more detailed analyses such as failure analysis, risk analysis, root cause analysis, and design improvement. FMEA also forms part of the Design for Reliability (DfR) process when utilized early during product design. The approach is utilized extensively in methodologies such as Reliability Centered Maintenance (RCM) and provides valuable input to Condition Based Maintenance (CBM) programs — both important elements within product sustainment and design processes Although FMEA can be an effective tool within Reliability and Maintainability, the results of this approach are often limited by its susceptibility to human errors that can degrade the quality of an analysis. Data overload, wherein an analyst becomes overwhelmed by the sheer volume of failure modes to be evaluated, potential biases toward extreme severity ratings, the variance in team dynamics, individual past experience (operator versus designer), and years of experience have all been identified as possible sources of analysis error. Researchers have proposed FMEA quality improvement approaches ranging from simple solutions, such as best practices, to the application of fuzzy logic. While these methods can potentially improve FMEA quality, they offer no solid quantification of human error within the process. Specifically, potential subjectivity can be present during the analysis process when severity rankings are chosen, and superfluous information can bias the analysis. This paper will present initial results from a recent study that investigated sources of variation in FMEA analyses due to variances in the human decision making process. The study specifically investigated the impact of data availability (failure and mishap) and analyst experience on several FMEA variables, e.g., severity and mitigation selection. The paper will provide valuable insight into the improvement of the FMEA process, thus improving the reliability and maintainability in a technology-reliant world.

In the recent years, there has been a significant attention among researchers and practitioners to new product development (NPD) strategies. The development of a new product also has long been categorized as the key function of companies in an increasingly competitive global market. However, the initiation of a new product is a process involving risk and uncertainty. That is why companies needs to adapt more accurate product development strategies and evaluate the launching of a new product carefully. One way to cope with this risk is to use novel product development strategies. Therefore, this control problem is formulated as a systematic decision process in order to select the more rational candidate to be launched as a new product. Basically in this chapter, the determination of a comparable new product alternatives and the selection of the best one is done through an integrated approach based on a heuristic multi-criteria decision methodology. The Pythagorean Fuzzy sets (PFSs) are used as an objective world environment since its definite advantages in handling vagueness and uncertainty. A significant focus of the chapter is the dependency of decision criteria and to reflect this situation, the Pythagorean Fuzzy based heuristic approach is proposed for the first time as a combination of AHP (Analytic Hierarchy Process) and ARAS (Additive Ratio Assessment). A production system is considered where manufacturing of a new product is performed in a Group Decision Making (GDM) setting. Literature reviewed in this chapter presents the current state of the art and discusses the potential future research trends. Finally, a practical case study is presented to demonstrate the potential of the methodology and validate the outcome.

Failure mode effect analysis applied to hospital TB program

Failure mode effect analysis for a better functional composite rocket motor casing

In today’s global environment, supplier selection is one of the critical strategic decisions made by supply chain management. The supplier selection process involves the evaluation of suppliers based on several criteria, including their core capabilities, price offerings, lead times, geographical proximity, data collection sensor networks, and associated risks. The ubiquitous presence of internet of things (IoT) sensors at different levels of supply chains can result in risks that cascade to the upstream end of the supply chain, making it imperative to implement a systematic supplier selection methodology. This research proposes a combinatorial approach for risk assessment in supplier selection using the failure mode effect analysis (FMEA) with hybrid analytic hierarchy process (AHP) and the preference ranking organization method for enrichment evaluation (PROMETHEE). The FMEA is used to identify the failure modes based on a set of supplier criteria. The AHP is implemented to determine the global weights for each criterion, and PROMETHEE is used to prioritize the optimal supplier based on the lowest supply chain risk. The integration of multicriteria decision making (MCDM) methods overcomes the shortcomings of the traditional FMEA and enhances the precision of prioritizing the risk priority numbers (RPN). A case study is presented to validate the combinatorial model. The outcomes indicate that suppliers were evaluated more effectively based on company chosen criteria to select a low-risk supplier over the traditional FMEA approach. This research establishes a foundation for the application of multicriteria decision-making methodology for unbiased prioritization of critical supplier selection criteria and evaluation of different supply chain suppliers.

Preparing a good Failure Mode Effects Analysis (FMEA) has always been time consuming and problematic, because even small and simple systems often have a few to several hundred potential failure modes. This becomes a struggle between Effectiveness vs. Efficiency. Until now it has been hard to achieve Effectiveness with limited time, money and resources, and the push for Efficiency. Speed, deadlines and human frailty, often cause potentially critical Failure Modes (FMs) to be missed all together - not so effective. FMEA preparation can consume 50-80 hours, or more, for the FMEA Facilitator alone. This time is then matched or exceeded by each of the FMEA team members. This paper presents a solution to the problem, the “Decomposition Table” (Decomp Table), a simple structured and rapid method for systematically capturing all potential failure modes and prioritizing by criticality, before populating the FMEA. The Decomp Table establishes confidence that fai lure modes are not being missed (overlooked) in the FMEA and provides an audit path to check the FMEA for content and completeness. FMEAs can thus be completed in less than half the time. Time saved can then be focused on mitigation, to optimize system reliability. The 2013 case study applying this Decomp method revealed $4,205,817 in FMEA team labor hours.

Study objective: Resuscitation is a fast paced highly complex process which makes considerable demands on the re- suscitation team; even minor errors and failures may reduce the chance of a successful outcome. Failure Mode Effects Analysis (FMEA) is a team-based, systematic proactive step-by-step process which identifies failure modes/hazards within the process which could compromise the progression and outcome of the process and highlights where improvements need to be imple- mented to mitigate failures from occurring. We applied FMEA to the resuscitation process and highlight specific areas within the currently accepted resuscitation protocol which require further assessment. Methods: We followed the procedure for FMEA outlined by the Joint Commission but with some modifications of the basic process. Our modified approach made use of 16 individual interviews with healthcare professionals experienced in resuscitation and focus groups in order to gain a more detailed understanding of the different perspectives on the resuscitation process. The interviews prioritised potential failures that exist in the on-ward resuscitation process which enabled the team to identify specific areas which require further assessment and improvements. Results: In total the FMEA found 28 failure modes that carry a degree of risk that require action. It was found that staff perceived failure modes that relate to the patient's airway, breathing or circulation as ones which would result in a severe effect on the patient's outcome, and those that relate to tasks involved in the running of the process were often perceived as likely to occur. Conclusion: The modified FMEA approach proved practical, and was well received by clinicians within the context of resuscitation and fundamentally highlighted areas where quality improvements are necessary. Our research group and design team at the Helen Hamlyn Centre used the FMEA to develop design cues and technology innovations to address current issues of design duplicity that would be incorporated into a new 'intelligent resuscitation trolley' which we foresee will support the team by improving communication, coordination and overall efficiency.

This paper presents a modular variant of Failure Mode Effects Analysis (FMEA), Failure Mode Modular De-Composition (FMMD), a methodology which can be applied to software, and is compatible and integrable with FMMD performed on mechanical and electronic systems. Software generally sits on top of most modern safety critical control systems and defines its most important system wide behaviour and communications. Currently standards that demand FMEA for hardware (e.g. EN298, EN61508), do not specify it for software, but instead specify good practise, review processes and language feature constraints. This is a weakness. Where FMEA traces component failure modes to resultant system failures, software has been left in a non-analytical limbo of best practises and constraints. If software and hardware integrated FMEA were possible, electro-mechanical-software hybrids could be modelled, and so we could consider `complete' failure mode models. Presently FMEA, stops at the glass ceiling of the computer program: FMMD seeks to address this, and offers additional test efficiency benefits. (6 pages)

Nata de coco production has a GMP aspect that needs to be implemented so that the production process can run well. The Failure Mode and Effect Analysis (FMEA) method is the stage of identifying the severity of product defects (severity), the incidence rate of product defects (occurrence), and the detection rate of product defects (detection), then calculating the Risk Priority Number (RPN) value, namely by multiplying the severity value (severity), the value of the event (occurrence), and detection value. Nata is a collection of cellulose with a chewy white texture that produces pieces of gel that float on the surface of the liquid. Nata is cellulose, the result of the synthesis of sugar in the form of agar by Acetobacter xylinum, which is white in color and contains about 98% water. The material observed was Good Manufacturing Practice (GMP) at nata de coco and observed how GMP was applied to locations, buildings, sanitation facilities, machinery and equipment, materials, process supervision, final products, laboratories, employees, packaging, product labels and descriptions, storage, maintenance, product recall and implementation of guidelines. The method used in calculating the observation results is the Failure Mode Effect Analysis (FMEA) method, the conclusion obtained is that the GMP score obtained in the manufacture of Nata de coco is less than optimal, which is 80 out of a total score of 100. FMEA obtained the results of problems that must be corrected immediately because they get a high RPN score, namely the pasteurization, cooling, drying, secondary packaging and storage of finished products Corrective actions and improvements need to be carried out immediately, especially in areas that receive high (critical) RPN scores, namely in the pasteurization, cooling, drying, secondary packaging and storage of finished products to avoid too many rejected products and product contamination. the lack of maximum GMP score can be identified as the source of the problem using the FMEA quantitative method. Failure Mode Effect Analysis (FMEA) is an effective tool in managing the potential for failure (failure mode), the results that arise from the failure mode and the level of criticality of the effect of the failure mode of the system of a product.

Analyse des risques a priori en unité de rétrocession hospitalière : focus sur le processus de dispensation