Search Results (239)

Background: This paper presents an analysis and a structured framework for improving inventory accuracy in an automotive factory, considering the current context of global disruptions. In 2023, the company recorded 20,340 inventory adjustments (1695 per month) and a 0.24% monthly net value discrepancy (EUR 256,594 YTD), with a baseline absolute discrepancy of 2.21% of sales. The project aimed to reduce adjustments to below 700 per month and the net value discrepancy to 0.1%. Methods: The research followed the Six Sigma methodology’s Define, Measure, Analyze, Improve and Control (DMAIC) phases, integrating Root Cause Analysis (RCA) and Failure Mode and Effects Analysis (FMEA) to enhance inventory accuracy in manufacturing operations. Results: Implementation significantly improved inventory accuracy: monthly adjustments decreased from 1695 to 971, the highest RPN was reduced from 576 to 144, and the absolute discrepancy-to-sales ratio stabilized at 0.98% (a 56% improvement). Financial variance was reduced to EUR 1948.10 in Q4 2024, while organizational discipline, role clarity and process control also increased. Conclusions: The integrated DMAIC–RCA–FMEA framework proved effective and replicable, enabling systematic identification of root causes, targeted corrective actions and sustainable KPI-driven improvements. The results demonstrate a scalable approach to inventory optimization that supports operational resilience and supply chain performance. Full article

Logistics involves planning and managing resources to meet customer demands. Its effectiveness depends not only on time and process coordination but also on the performance of logistics operators, whose actions directly affect customer satisfaction. Although operational risks are inherent to logistics, customer-oriented service failures are often overlooked in traditional risk assessment. To address this gap, this study proposes an integrated approach that combines a Pareto Diagram and Failure Mode and Effects Analysis (FMEA) within the ISO 31000 risk assessment framework. This structured method enables the identification and prioritization of logistics failures based on customer complaints, thereby supporting data-driven decision-making and continuous service improvement. Applied to a real-world case in a ceramic production line specializing in tableware manufacturing, the method identified and evaluated key logistics failures; particularly those related to late deliveries and damaged goods. Based on these findings, improvement actions were proposed to reduce the recurrence of these issues. This study contributes a structured, practical, and replicable approach for organizations to introduce risk assessment practices and enhance the service quality of logistics management. This study advances the literature by shifting the focus from internal production failures to customer-driven service risks, offering strategic insights for improving reliability and operational performance. Full article

The main purpose of the article is to demonstrate how large language models (LLMs) can enhance and automate the Failure Modes and Effects Analysis (FMEA) method to improve the identification, assessment, and management of operational risk in modern technological systems. The study aims to show that integrating AI into FMEA increases the efficiency, precision, and reliability of detecting potential failures and evaluating their consequences, provided that expert supervision and model transparency are maintained. The research combines a literature review with a case study using OpenAI’s model to generate an automated FMEA for a manufacturing process. The methodology defines process components, identifies potential failure modes, and evaluates their risk impact. Five specialized libraries—structure, function, failure, risk, and optimization—serve as structured inputs for the LLM. A feedback mechanism allows the system to learn from previous analyses, improving future risk assessments and supporting continuous process optimization. The developed platform enables engineers to initiate projects, input data, generate and validate AI-based FMEA reports, and export results. Overall, the study demonstrates that the integration of LLMs into FMEA can transform operational risk management, making it more intelligent, adaptive, and data-driven. Full article

Accurate fault risk assessment is essential for maintaining wind turbine reliability. Traditional failure modes and effects analysis (FMEA)-based approaches struggle to handle the fuzziness, uncertainty, and conflicting nature of multi-criteria evaluations, which may lead to delayed fault detection and increased maintenance risks. To address these limitations, this paper proposes an enhanced risk assessment framework that integrates subjective-objective weighting and fuzzy decision-making. First, a combined subjective–objective weighting (CSOW) model with adaptive fusion is developed by integrating the analytic hierarchy process (AHP) and the entropy weight method (EWM). The CSOW model optimizes the weighting of severity (S), occurrence (O), and detection (D) indicators by balancing expert knowledge and data-driven information. Second, a fuzzy decision-making model based on interval-valued intuitionistic fuzzy numbers and VIKOR (IVIFN-VIKOR) is established to represent expert evaluations and determine risk rankings. Notably, the overlap rate between the top 10 failure modes identified by the proposed method and a fault-tree-based Monte Carlo simulation incorporating mean time between failures (MTBF) and mean time to repair (MTTR) reaches 90%, substantially higher than other methods. This confirms the superior performance of the framework and provides enterprises with a systematic approach for risk assessment and maintenance planning. Full article

This study introduces a sensor-centric cybersecurity framework for railway infrastructure that extends Failure Mode and Effects Analysis (FMEA) from traditional reliability evaluation into the domain of cyber-induced failures affecting data integrity, availability and authenticity. The contribution lies in bridging regulatory obligations of the NIS2 Directive with field-layer monitoring by enabling risk indicators to evolve dynamically rather than remain static documentation artefacts. The approach is demonstrated using a scenario-based dataset collected from approximately 250 trackside, rolling-stock, environmental and power-monitoring sensors deployed over a 25 km operational segment, with representative anomalies generated through controlled spoofing, replay and injection conditions. Risk was evaluated using RPN scores derived from Severity–Occurrence–Detectability scales, while anomaly-detection performance was observed through detection-latency variation, changes in RPN distribution, and qualitative responsiveness of timestamp-based alerts. Instead of presenting a fixed benchmark, the results show how evidence from real sensor streams can recalibrate O and D factors in near-real-time and reduce undetected exposure windows, enabling measurable compliance documentation aligned with NIS2 Article 21. The findings confirm that coupling FMEA with streaming telemetry creates a verifiable risk-evaluation loop and supports a transition toward continuous, evidence-driven cybersecurity governance in railway systems. Full article

Failure Mode and Effects Analysis (FMEA) is a systematic risk assessment tool that effectively evaluates the safety and reliability of products prior to their deployment. However, traditional FMEA fails to consider and leverage inherent system-specific information during risk assessment, while also neglecting the weights of risk factors (RFs) when processing data related to the Risk Priority Number (RPN). This leads to significant subjectivity in the final risk ranking of failure modes. To overcome these drawbacks, this study proposes an improved FMEA risk assessment method based on load sharing, aiming to develop an improved FMEA method that addresses the critical limitations of traditional approaches by integrating load sharing principles and systematic weight determination, thereby enhancing risk assessment objectivity and accuracy in complex multi-component systems. First, probabilistic linguistic terms are adopted to quantify experts’ risk assessment information, and the geometric mean method is then used to aggregate assessments from multiple experts. Second, the Fuzzy Best–Worst Method (FBWM) is employed to determine the relative weights of the three RPN factors (Occurrence, Severity, and Detection). Additionally, partial system structural data are obtained through load sharing, and these data—combined with the calculated factor weights—are integrated into the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to generate the final risk ranking of failure modes. Finally, a case study of a magnetic crane is conducted to verify the feasibility and effectiveness of the proposed method, supplemented by comparative experiments to demonstrate its superiority. Full article

Effectively managing Construction and Demolition Waste (CDW) requires prioritizing multi-dimensional risks, a task complicated by the inherent uncertainty and subjectivity of expert judgments. While classical methods like Failure Modes and Effects Analysis (FMEA) and Fine–Kinney (FK) provide a diagnostic structure, they struggle to capture the vagueness in subjective assessments. This study addresses this gap by developing an integrated framework that couples the classical FMEA/FK criteria (Severity, Exposure, Probability, Detectability, Frequency) with Picture-Fuzzy (PiF) multi-criteria decision making. The methodology first elicits criterion importances from 15 experts using PiF Stepwise Weight Assessment Ratio Analysis (PiF-SWARA), which retains approval, indeterminacy, rejection, and refusal degrees to reduce information loss. Subsequently, it ranks 40 risk factors using the PiF Technique for Order Preference by Similarity to Ideal Solution (PiF-TOPSIS). Results show severity is the most influential criterion, followed by exposure and probability. The framework identifies the highest-priority risks as cumulative pollution with rising complaints, groundwater leakage, and insufficient investment/operating budgets. A sensitivity analysis confirms that environmental and financial risks remain consistently prominent across various weighting scenarios. This harmonized FMEA/FK–PiF-SWARA–TOPSIS approach yields a transparent and defensible prioritization, offering a practical tool for managers to allocate resources effectively, focusing on critical environmental controls and addressing core financial deficiencies in CDW systems. Full article

Aircraft maintenance delays (AMD) remain a significant challenge in commercial aviation, adversely affecting operational efficiency, flight punctuality, and passenger satisfaction. Despite advancements in maintenance strategies, recurring disruptions continue to generate financial losses and reputational risks. This study proposes an integrated five-step framework that combines failure mode and effects analysis (FMEA) with the Define–Measure–Analysis–Improve–Control (DMAIC) methodology to systematically address and reduce AMD. The framework involves the definition of problems, the identification of contributing factors and failure modes, the assessment of risk and root cause analysis, the mitigation of risk, and continuous monitoring. The main contribution of this study lies in the integration of FMEA and DMAIC into a unified data-driven system that proactively reduces maintenance delays, offering a novel approach to continuous process improvement in aviation operations. Its practical applicability is demonstrated through a case study of the AFRIQIYAH Airways Airbus A320 fleet, which represents the majority of the airline’s operations. High-risk landing gear failure modes were identified, evaluated and addressed through targeted improvement projects, including predictive maintenance, supplier diversification, inventory optimization, and improved quality assurance for critical spare parts. Implementing these initiatives is expected to reduce the overall Risk Priority Number (RPN) by approximately 59%, highlighting the effectiveness and potential to minimize AMD. Full article

Detailed planning of vessel traffic on the fairway, carried out by Vessel Traffic Service (VTS) operators, is a complicated task, especially when there are restrictions for two-way ship traffic. Such restrictions take place on the dredged Świnoujście–Szczecin fairway in Poland. After the dredging of the fairway to 12.5 m, vessel traffic regulations introduced in a Port Regulations document have changed, which impacted the course of the decision-making process related to planning vessel traffic on the fairway performed by VTS operators. The aim of the article is to assess the probability of making erroneous decisions related to the admission of non-compliant vessels to traffic on the dredged Świnoujście–Szczecin fairway after the introduction of new vessel traffic regulations. In the article, the tasks carried out by VTS operators during vessel traffic planning were described and analyzed using Failure Mode and Effects Analysis (FMEA) method. The probability of making an erroneous decision at each stage of the planning process was calculated using the Human Error Assessment and Reduction Technique (HEART) method. An event tree was developed in relation to VTS operators’ decision-making on vessel traffic planning performed before and after the introduction of a decision support system (DSS). An expert method was used to determine the probability values. Recommendations were proposed to reduce the risk of making erroneous decisions by VTS operators while vessel traffic planning. The research results contributed to the expansion of knowledge on the impact of new regulation implementation on vessel traffic safety and the risk of making erroneous decisions related to the admission of non-compliant vessels to traffic on the dredged Świnoujście–Szczecin fairway, considering the implementation of a DSS. The results of the study may be of interest to VTS operators, port authorities and maritime administrations. Full article

Differing stakeholder interests often lead to the application of varying criteria when evaluating green technology projects. This heterogeneity can impede project outcomes by making it challenging to reconcile conflicting perspectives. The present study empirically examines stakeholder alignment in relation to the perceived benefits and barriers to green technology implementation. Insights from a focus group comprising 15 project stakeholders were used to identify key barriers, which were subsequently ranked using survey data collected from 286 UAE-based stakeholders. A customised fuzzy-based Failure Mode and Effects Analysis tool (FMEA–FST) was applied to prioritise these factors. The results reveal significant variation in the salience of factors across stakeholder groups, highlighting both notable differences and shared framing biases. The study’s originality lies in its use of the bespoke FMEA–FST model to prioritise factors, thereby identifying the relative importance of benefits, barriers, and challenges. Notably, ‘Lack of support from senior management’ emerged as the most critical factor across all categories, while ‘Potentially lower benefits for small or less complex projects’ was deemed the least important. To foster greater stakeholder alignment, the study recommends strengthening social relationships to bridge divergent perspectives. Limitations include the inability to account for changes in factor salience across different stages of the project lifecycle, as well as the exclusion of temporal and typological effects. These limitations present opportunities for future research. Full article

This paper investigates the reliability of a dual-axis solar tracking system using Failure Mode and Effects Analysis (FMEA), Fault Tree Analysis (FTA), and Reliability Block Diagrams (RBD). The system’s control and data transfer subsystems are evaluated under indoor and outdoor conditions using failure rate data. Key vulnerabilities—particularly sensor degradation—are modeled through probabilistic analysis. Results show a significant drop in reliability (to 15.02%) in harsh environments, primarily due to light sensor failures. However, mitigation strategies such as Built-In Self-Test (BIST) architectures improve test coverage, thereby increasing the chance of fault detection. The findings highlight the need for reliability-focused design in solar trackers to ensure long-term energy efficiency and fault resilience. Full article

This study investigates the determinants of service demand in the packaging and printing industry, identifying 19 key factors through expert evaluation. These factors were analyzed using the Importance–Satisfaction (I–S) Model to pinpoint areas requiring enhancement, with four elements classified within the improvement zone. Considering resource constraints, improvement priorities were established through a modified Risk Priority Number (RPN) framework derived from Failure Modes and Effects Analysis (FMEA), expressed as RPN = I × F × E. The highest-priority areas for improvement included product pricing, flexibility in meeting customer requirements, suppliers’ emergency response capabilities, and proactive communication regarding raw material price fluctuations. The findings indicate that consumers balance price against sustainability value, highlighting the necessity of setting prices that align with perceived value to sustain trust and meet expectations. Strengthening firms’ emergency response mechanisms and developing an online standard operating procedure (SOP) notification system for raw material price changes can enhance communication efficiency, increase transparency in pricing, and ultimately improve organizational competitiveness. Full article

The implementation of Digital Twins (DTs) represents a significant advancement for the Oil and Gas (O&G) industry. A DT virtually replicates a physical asset, enabling the monitoring, diagnosis, prediction, and optimization of its outcomes. Since failures are undesirable outcomes, investigations into potential failure modes are often integrated into the development. Traditional methods, such as Failure Modes and Effects Analysis (FMEA) and Failure Mode, Effects, and Criticality Analysis (FMECA), are widely used to identify, assess, and mitigate risks. However, there is still a lack of specific guidelines for studying potential failures in complex systems. This article introduces a framework for Failure Modes, Mechanisms, and Effects Analysis (FMMEA) as a tool for identifying and assessing failures in early DT development. Exploring failure mechanisms is highlighted as essential for effective prediction and management We also propose adjustments to FMMEA for complex, predictable systems, such as using a DPR (Detectable Priority Risk) instead of RPN (Risk Priority Number) for prioritizing risks. A comprehensive case illustrates the framework’s application in developing a DT for an intelligent completion system in a major O&G company. The approach enables mechanism-oriented failure analysis and more detailed prognostic health management, providing greater transparency in the failure identification process. Full article

In a dynamic and uncertain environment, maintaining a high level of business process (BP) reliability represents a key long-term objective for organizations. The manufacturing process, as the most critical business process in manufacturing enterprises, is emphasized due to its potential to cause significant disruptions across other BPs if it fails. This paper proposes a two-stage model. In the first stage, failures leading to lean waste are evaluated and ranked using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) combined with interval-valued intuitionistic fuzzy numbers (IVIFNs), referred to as IF-TOPSIS. The model is grounded in the Failure Mode and Effect Analysis (FMEA) framework. In the second stage, a modified fuzzy logic system with IVIFN-based rules is applied to determine the risk level of the manufacturing process. This approach is based on the property of symmetry in the decision-making process, ensuring that criteria are treated in a balanced manner and inference rules are applied consistently. A case study based on real-life data demonstrates that the obtained results identify measures that can enhance business strategy and reduce failure rates. Thus, the model is validated and shown to contribute to lean waste reduction. It can be concluded that the proposed methodology provides clear and practical guidance to enterprise management, as well as to all sectors and individuals involved in ensuring a reliable manufacturing process, for defining failure priorities and implementing preventive measures. Full article

This study evaluated a novel methodology for assessing food safety vulnerabilities in shopping malls by integrating Hazard Analysis and Critical Control Points (HACCP), Threat Assessment and Critical Points (TACCP), and Failure Mode and Effects Analysis (FMEA). Inspections were conducted in nine shopping centers across Poland, the Czech Republic, Slovakia, and Spain to identify the risk of intentional/unintentional contamination with chemical, biological, radiological, and nuclear agents. The assessment considered key operational areas, including food delivery, transportation, staff security, back-office access, product handling, and inspection protocols. Risk levels were quantified using FMEA parameters. The findings revealed an overall high to average risk score with the most critical vulnerabilities linked to back-office access, unauthorized personnel entry, and susceptibility to fraudulent inspections. Observations also highlighted infrastructural shortcomings, insufficient monitoring, and procedural gaps that could facilitate contamination. The proposed methodology offers a structured, quantitative framework for identifying and prioritizing food safety hazards in public environments. Implementing targeted countermeasures—such as enhanced surveillance, strict access control, staff training, and dedicated food handling protocols—can substantially reduce risks, thereby strengthening public health protection and operational resilience. This approach may serve as a promising framework for integrating food defense and safety assessments for food defense in high-density commercial facilities. Full article