Competitive advantage by adjusting design characteristics to satisfy cost targets

Iranmanesh, Hossein; Thomson, Vince

doi:10.1016/j.ijpe.2008.05.006

Cited by 31 publications

(13 citation statements)

References 20 publications

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“…In other words, is interpreted as the design quality level of DR . In addition, based on previous studies (e.g., [3][4][5][6][7][8][9][10][11][12][13][14]), using the normalized information from the HOQ, norm and norm in (2) and (3), respectively, the customer satisfaction can be formulated as…”

Section: Satisfaction Function With Quality Levelmentioning

confidence: 99%

“…A number of researchers have proposed various quantitative approaches for applying QFD to a variety of problems. Among quantitative approaches, mathematical programming is usually applied to construct models to find the optimum design requirements for maximizing customer satisfaction under the limits of budget, time, and/or other resources [3][4][5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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A QFD-Based Mathematical Model for New Product Development Considering the Target Market Segment

Chen

2014

Journal of Applied Mathematics

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Responding to customer needs is important for business success. Quality function deployment provides systematic procedures for converting customer needs into technical requirements to ensure maximum customer satisfaction. The existing literature mainly focuses on the achievement of maximum customer satisfaction under a budgetary limit via mathematical models. The market goal of the new product for the target market segment is usually ignored. In this study, the proposed approach thus considers the target customer satisfaction degree for the target market segment in the model by formulating the overall customer satisfaction as a function of the quality level. In addition, the proposed approach emphasizes the cost-effectiveness concept in the design stage via the achievement of the target customer satisfaction degree using the minimal total cost. A numerical example is used to demonstrate the applicability of the proposed approach and its characteristics are discussed.

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Section: Satisfaction Function With Quality Levelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A QFD-Based Mathematical Model for New Product Development Considering the Target Market Segment

Chen

2014

Journal of Applied Mathematics

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“…Based on the above perspectives, some researchers (Shidpour, Shahrokhi & Bernard, 2013;Singh, 2014;Soroor, Tarokh, Khoshalhan & Sajjadi, 2012;Iranmanesh & Thomson, 2008;Aissaoui et al, 2007;Kannan, Khodaverdi, Olfat, Jafarian & Diabat, 2013;Awasthi, Chauhan & Goyal, 2011) pointed out that in the present reality decision making issues in (SSSS) problems, one often takes place in an uncertain environment. It is hard to report the problem parameters as known because of the complexity of environment socially and economically and at the same time some unpredictable factors for example bad weather and machine or vehicle breakdowns.…”

Section: Key Problem Descriptionmentioning

confidence: 99%

Application of multi-objective optimization based on genetic algorithm for sustainable strategic supplier selection under fuzzy environment

Hashim

Nazam

Yao

et al. 2017

JIEM

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Abstract:Purpose: The incorporation of environmental objective into the conventional supplier selection practices is crucial for corporations seeking to promote green supply chain management (GSCM).Challenges and risks associated with green supplier selection have been broadly recognized by procurement and supplier management professionals. This paper aims to solve a Tetra "S" (SSSS) problem based on a fuzzy multi-objective optimization with genetic algorithm in a holistic supply chain environment. In this empirical study, a mathematical model with fuzzy coefficients is considered for sustainable strategic supplier selection (SSSS) problem and a corresponding model is developed to tackle this problem.Design/methodology/approach: Sustainable strategic supplier selection (SSSS) decisions are typically multi-objectives in nature and it is an important part of green production and supply chain management for many firms. The proposed uncertain model is transferred into deterministic model by applying the expected value measure (EVM) and genetic algorithm with -188-Journal of Industrial Engineering and Management -https://doi.org/10.3926/jiem.2078 weighted sum approach for solving the multi-objective problem. This research focus on a multiobjective optimization model for minimizing lean cost, maximizing sustainable service and greener product quality level. Finally, a mathematical case of textile sector is presented to exemplify the effectiveness of the proposed model with a sensitivity analysis.Findings: This study makes a certain contribution by introducing the Tetra 'S' concept in both the theoretical and practical research related to multi-objective optimization as well as in the study of sustainable strategic supplier selection (SSSS) under uncertain environment. Our results suggest that decision makers tend to select strategic supplier first then enhance the sustainability. Research limitations/implications:Although the fuzzy expected value model (EVM) with fuzzy coefficients constructed in present research should be helpful for solving real world problems. A detailed comparative analysis by using other algorithms is necessary for solving similar problems of agriculture, pharmaceutical, chemicals and services sectors in future. Practical implications:It can help the decision makers for ordering to different supplier for managing supply chain performance in efficient and effective manner. From the procurement and engineering perspectives, minimizing cost, sustaining the quality level and meeting production time line is the main consideration for selecting the supplier. Empirically, this can facilitate engineers to reduce production costs and at the same time improve the product quality. Originality/value:In this paper, we developed a novel multi-objective programming model based on genetic algorithm to select sustainable strategic supplier (SSSS) under fuzzy environment. The algorithm was tested and applied to solve a real case of textile sector in Pakistan. The experimental results and comparative sensitivity an...

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“…Quality function deployment (QFD) translates the voice of the customer into appropriate technical requirements for each product development stage [2,9]. However, customers often express product requirements in their own terms; thus, requirements must be translated into product characteristics or subfunctions, where they are expressed in technical language.…”

Section: Product Characteristicsmentioning

confidence: 99%

Ensuring correct product characteristics during the design process

Jing

Hisarciklilar

Thomson

2014

Int J Interact Des Manuf

Self Cite

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For many companies, the design process and product design are treated as two separate domains. However, the design process needs to be controlled in such a way that desired product characteristics are attained. This paper proposes a decision support system (DSS) where management techniques develop linkages between process and product, and where the system identifies possible conflicts between product characteristics and design objectives. Product characteristics and the design process are decomposed into three levels and their relationship is identified. The DSS ensures that design activities accomplish product characteristics successfully. A goal-directed negotiation method that coordinates decisions is presented. An example of an aircraft pylon demonstrates the DSS method.

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Competitive advantage by adjusting design characteristics to satisfy cost targets

Cited by 31 publications

References 20 publications

A QFD-Based Mathematical Model for New Product Development Considering the Target Market Segment

A QFD-Based Mathematical Model for New Product Development Considering the Target Market Segment

Application of multi-objective optimization based on genetic algorithm for sustainable strategic supplier selection under fuzzy environment

Ensuring correct product characteristics during the design process

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