In this paper, an alternative multi-attribute decision-making approach for prioritizing failures in failure mode, effects and criticality analysis (FMECA) is presented. The technique is specifically intended to overcome some of the limitations concerning the use of the conventional US MIL-STD-1629A method. The approach is based on a fuzzy version of the 'technique for order preference by similarity to ideal solution' (TOPSIS). The use of fuzzy logic theory allows one to avoid the intrinsic difficulty encountered in assessing 'crisp' values in terms of the three FMECA parameters, namely chance of failure, chance of non-detection, and severity. With the proposed approach, the definition of a knowledge base supported by several qualitative rule bases is no longer required. To solve the fundamental question of ranking the final fuzzy criticality value, a particular method of classification is adopted, allowing a fast and efficient sorting of the final outcome. An application to an important Italian domestic appliance manufacturer and a comparison with conventional FMECA are reported to demonstrate the characteristics of the proposed method. Finally, a sensitivity analysis of the fuzzy judgement weights has confirmed that the proposed approach gives a reasonable and robust final priority ranking of the different causes of failure

This paper presents a tool for reliability and failure mode analysis based on an advanced version of the popular failure mode, effects and criticality analysis (FMECA) procedure. To help the analyst formulating efficiently effective criticality assessments of the possible causes of failure, the fuzzy logic technique is adopted. Particular attention has been devoted to support the maintenance staff with a fuzzy criticality assessment model easy to implement and design. To test the proposed methodology, an actual application concerning a process plant in milling field for human consumption flour is showed in the paper

Purpose -Overall equipment effectiveness (OEE) is the key metric to measure the performance of individual equipment. However, when machines operate jointly in a manufacturing line, OEE alone is not sufficient to improve the performance of the system as a whole. The purpose of this paper is to show how to overcome this limitation, by presenting a new metric (overall equipment effectiveness of a manufacturing line -OEEML) and an integrated approach to assess the performance of a line. Design/methodology/approach -An alternative losses classification structure is developed to divide the losses that can be directly ascribed to equipment, from the ones that are spread in the line. Starting from this losses classification structure, an approach based on OEE is developed to evaluate the criticalities and the effectiveness of the line. Findings -This method has been applied to an automated line for engine basements production. Results show that OEEML successfully highlights the progressive degradation of the ideal cycle time, explaining it in terms of: bottleneck inefficiency, quality rate, and synchronisation-transportation problems.Research limitations/implications -OEEML alone fails to explain to which extent effectiveness is supported by in process-inventories and should be integrated with additional metrics to estimate the inventories-related costs. Practical implications -OEEML provides practitioners with an operative tool useful to highlight the points where the major inefficiencies take place and to foresee the potential benefits of corrective actions. Originality/value -In relation to other methodologies, OEEML presents two main advantages: it detects and quantifies the line's critical points and it can be applied even in presence of buffers, without underestimating the efficiency of the system.

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In this paper an experience dealing with the analysis of maintenance outsourcing by means of multi-criteria decision methods (MCDM) is reported. In particular, the analytic hierarchy process technique (AHP) is used as a managerial decision support system to select the best alternative between different outsourcing contracts in terms of maintenance services. The proposed methodology has been tested on an industrial case study dealing with an important italian brickwork. This application shows how the AHP is able to support the choice of the correct level of the maintenance activities outsourcing. In particular, the hierarchic decisional structure developed represents an instrument able to give a well balanced synthesis of several different factors that must be taken into account during this type of decision problem

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PurposeThe purpose of this paper is to provide a structured methodology for performing build‐in reliability (BIR) investigation during a new product development cycle.Design/methodology/approachThe methodology in this paper represents an extension of the Quality Functional Deployment/House of Quality (QFD/HoQ) concepts to reliability studies. It is able to translate the reliability requisites of customers into functional requirements for the product in a structured manner based on a Failure Mode And Effect Analysis (FMEA). Besides, it then allows it to build a completely new operative tool, named House of Reliability (HoR), that enhances standard analyses, introducing the most significant correlations among failure modes. Using the results from HoR, a cost‐worth analysis can be easily performed, making it possible to analyse and to evaluate the economical consequences of a failure.FindingsThe paper finds that the application of the proposed approach allows users to identify and control the design requisites affecting reliability. The methodology enhances the reliability analysis introducing and managing the correlations among failure modes, splitting the severity into a detailed series of basic severity aspects, performing also cost/worth assessments.Practical implicationsIt is shown that the methodology enables users to finely analyse failure modes by splitting severity according to the product typology and the importance of each Severity criterion according to laws or international standards. Moreover the methodology is able to consider the “domino effects” and so to estimate the impact of the correlation between the causes of failure. Finally a cost/worth analysis evaluates the economical consequences of a failure with respect to the incurred costs to improve the final reliability level of the product.Originality/valueThe paper proposes a completely new approach, robust, structured and useful in practice, for reliability analysis. The methodology, within an integrated approach, overcomes some of the largely known limits of standard FMECA: it takes into account multiple criteria, differently weighted, it analyses the product considering not only the direct consequence of a failure, but also the reaction chain originated by a starting failure.