Variation Mode and Effect Analysis: a Practical Tool for Quality Improvement

Johansson, Per; Chakhunashvili, Alexander; Barone, Stefano; Bergman, Bo

doi:10.1002/qre.773

Cited by 63 publications

(48 citation statements)

References 14 publications

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“…), resulting in expensive reclamations and dissatisfaction that may lead to loss of customers [8]. Therefore, a method called Variation Mode and Effect Analysis (VMEA), a relatively new methodology, which is a deductive method of identifying and managing sources of variation, has been developed.…”

Section: Variation Mode and Effects Analysismentioning

confidence: 99%

A comparative critical study between FMEA and FTA risk analysis methods

Cristea

Constantinescu

2017

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. Today there is used an overwhelming number of different risk analyses techniques with acronyms such as: FMEA (Failure Modes and Effects Analysis) and its extension FMECA (Failure Mode, Effects, and Criticality Analysis), DRBFM (Design Review by Failure Mode), FTA (Fault Tree Analysis) and and its extension ETA (Event Tree Analysis), HAZOP (Hazard & Operability Studies), HACCP (Hazard Analysis and Critical Control Points) and What-if/Checklist. However, the most used analysis techniques in the mechanical and electrical industry are FMEA and FTA. In FMEA, which is an inductive method, information about the consequences and effects of the failures is usually collected through interviews with experienced people, and with different knowledge i.e., cross-functional groups. The FMEA is used to capture potential failures/risks & impacts and prioritize them on a numeric scale called Risk Priority Number (RPN) which ranges from 1 to 1000. FTA is a deductive method i.e., a general system state is decomposed into chains of more basic events of components. The logical interrelationship of how such basic events depend on and affect each other is often described analytically in a reliability structure which can be visualized as a tree. Both methods are very time-consuming to be applied thoroughly, and this is why it is oftenly not done so. As a consequence possible failure modes may not be identified. To address these shortcomings, it is proposed to use a combination of FTA and FMEA.

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Section: Variation Mode and Effects Analysismentioning

confidence: 99%

A comparative critical study between FMEA and FTA risk analysis methods

Cristea

Constantinescu

2017

IOP Conf. Ser.: Mater. Sci. Eng.

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“…Etienne et al [24] established a cost model for variation management to identify key process characteristics, which could support tolerance design, computer-aided process planning (CAPP), and computer-aided inspection planning (CAIP). As for VMEA, Chakhunashvili et al [25] and Johansson et al [26] used VMEA to analyze the sensitivity of product performance or quality to the variation of KCs, and the relative importance degree of KCs could be determined by the sensitivity degree. Similar to the VMEA method, Ibrahim et al [27,28] recently proposed a VRMM methodology to prioritize KCs and quantify their associated risk of variation.…”

Section: Quantitative Analysis For Kc Prioritymentioning

confidence: 99%

“…KPCs are identified by establishing and analyzing the relations between process characteristics and cost/quality [23,24], which are not completely applicable to the identification of KDCs, because the KDCs are not only related to the quality and cost but also mainly related to the functions and performances. From the perspective of research methods, research on the identification of KCs has focused on either qualitative analysis for KC acquisition [18][19][20] or quantitative analysis for KC priority [21][22][23][24][25][26][27][28]. However, both have some defects (see details in next section).…”

Section: Introductionmentioning

confidence: 99%

Identification of key design characteristics for complex product adaptive design

Han

Wang

et al. 2017

Int J Adv Manuf Technol

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Key design characteristics (KDCs) are important information related to the product and part designs, which significantly influence on the product's functions, performances, and quality. Identifying KDCs for a complex product will help designers to focus on key design parameters during the design process and rapidly obtain design schemes based on their close relationships to the product's functions, performances, and quality. Although there are some researches on key characteristic (KC) identification, most of them are focused on key process characteristics (KPCs) and few on KDCs. There also lacks a KDC identification framework to support KDC identification with better completeness and diverse usages. Adaptive design is the most important pattern of complex product design. Therefore, this paper presents a systematic method to identify KDCs for complex product adaptive design, in which KDCs can be determined by two related phases. Firstly, a product design specification (PDS)-KDC Candidates Network (PKCN) is constructed by using existing product instance data, cluster analysis, KC flow-down, and network analysis approaches. Then, the result from the first phase is used as a basis to identify KDCs for adaptive design. Three KDC identification techniques: similarity reasoning technique, breadth-first search (BFS), and the gray relational analysis approach are applied to find out KDCs from the PKCN, which are the most sensitive to the variation of a PDS. These identified KDCs can help designers to understand the relationships between KDCs and PDS and rapidly develop a design scheme. The effectiveness and the feasibility of the proposed method are verified by a case study via the development of an electric multiple unit (EMU)'s bogie.

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“…A more suitable method for estimating the epistemic uncertainty is Variation Mode and Effect Analysis (VMEA) which uses second order moment statistics which is more easily accessible. A more crude way to account for epistemic uncertainties is through the use of safety factors Svensson and Johannesson 2013;Johansson et al 2006).…”

Section: Uncertaintymentioning

confidence: 99%

Road tunnel fire safety and risk: a review

Gehandler

2015

Fire Sci Rev

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A review concerning road tunnel fire safety and risk is presented. In particular different perspectives and methods on safety and risk are discussed. Road tunnel fire safety usually involves high uncertainty and high-stakes decisions. Thus, a wider group of stakeholders and different types of knowledge should be included in the fire safety analysis and evaluation, than what is required by technical risk analyses. It is argued that the decision process should not be separated from the design and safety evaluation as they are strongly dependent and iterative processes. Decision theory can guide the design and decision process in negotiation with stakeholders. Key parameters for the decision can be analysed through a combination of functional requirements, societal and political values, safety engineering, safety factors and systems theory. By taking an organisational viewpoint, potential latent and active errors can be analysed and a good safety culture can be engineered. In order to improve the safety culture of truck companies, regulation ensuring proper maintenance, training and quality management may be necessary in a competitive global economy.

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Variation Mode and Effect Analysis: a Practical Tool for Quality Improvement

Cited by 63 publications

References 14 publications

A comparative critical study between FMEA and FTA risk analysis methods

A comparative critical study between FMEA and FTA risk analysis methods

Identification of key design characteristics for complex product adaptive design

Road tunnel fire safety and risk: a review

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