Systematic continuous improvement model for variation management of key characteristics running with low capability

Estrada, Gabriela; Shunk, Dan L.; Ju, Feng

doi:10.1080/00207543.2017.1369599

Cited by 9 publications

(8 citation statements)

References 15 publications

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“…In future research, we will study a systematic method of identification of KDCs and KPCs to support product life cycle development. The latest studies on identification of KPCs published by Estrada et al [23] and Etienne et al [24] can be used for reference.…”

Section: Discussionmentioning

confidence: 99%

“…Lee and Thornton [22] proposed a variation model to calculate the importance degree of a part KC, which was dependent on the sensitivity to the variation of product quality. Recently, Estrada et al [23] created a variation model to calculate the rework cost for KCs as a variable in function of the expected amount of material to be removed. This cost plus scrap cost was used to prioritize KCs running with low capability.…”

Section: Quantitative Analysis For Kc Prioritymentioning

confidence: 99%

“…However, at present, there is no mutual definition for a KC. Thornton [8] referred to the previous definitions of KCs and defined KCs as the product, subassembly, part, and process features that significantly impact the real cost [23,24], performance, or safety of a product when the KCs vary from nominal. According to the main phases of product development, KCs can be divided into design KCs and process KCs [9].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, most of these studies focus on the KPCs, less on KDCs. 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].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Quantitative Analysis For Kc Prioritymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identification of key design characteristics for complex product adaptive design

Han

Wang

et al. 2017

Int J Adv Manuf Technol

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“…The second literature gap concerns the relationship between systematic knowledge management and different aspects of innovation outcomes [34]. There has been considerable research establishing the positive relationship between absorptive capacity and innovation output such as number of new products launched and financial performance exclusive of new product launch [28], [35], [36], [37], [38], and in technology sourcing [39] However, it is uncertain whether new product launch necessarily results in better financial performance [28], [40].…”

Section: Theoretical Background and Hypothesesmentioning

confidence: 99%

Evolving Absorptive Capacity: The Mediating Role of Systematic Knowledge Management

Dabić

Vlačić

Ramanathan

et al. 2020

IEEE Trans. Eng. Manage.

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Absorptive capacity is mediated through knowledge management capacity on innovation output and performance in technology-oriented firms. While prior research has focused on the direct effect of absorptive capacity on innovation, our model posits that absorptive capacity is more efficient in promoting firms' innovation provided that it is supported by systematic knowledge management practices. We tested this model that included all four components of absorptive capacity using a sample of 127 manufacturing and technology firms in Croatia. Structural equation modelling procedures were used to test hypotheses. Our findings confirm the significance of the relationship between absorptive capacity and knowledge management within firms. Further, we found that firms with higher acquisition and transformation dimensions of absorptive capacity can enhance and replenish their knowledge management practices, which in return results in higher innovation output. These findings extend previous research by explaining the sometimescontradictory findings concerning knowledge management practices, which firms may adopt to enhance their absorptive capacity.

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Risk-Informed Design Verification and Validation Planning Methods for Optimal Product Reliability Improvement

2020

Handbook of Advanced Performability Engineering

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Systematic continuous improvement model for variation management of key characteristics running with low capability

Cited by 9 publications

References 15 publications

Identification of key design characteristics for complex product adaptive design

Identification of key design characteristics for complex product adaptive design

Evolving Absorptive Capacity: The Mediating Role of Systematic Knowledge Management

Risk-Informed Design Verification and Validation Planning Methods for Optimal Product Reliability Improvement

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