A two phase multi-attribute decision-making approach for new product introduction

Kahraman, Cengiz; Büyüközkan, Gülçin; Ateş, Nüfer Yasin

doi:10.1016/j.ins.2006.09.008

Cited by 149 publications

(69 citation statements)

References 44 publications

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“…This approach extends the fuzzy TOPSIS method to a hierarchical one and has been conducted by many researchers, also named as hierarchical fuzzy TOPSIS method [31][32][33]. This method inherits the hierarchy mechanism of AHP method and provides greater superiority to classical fuzzy TOPSIS methods [34,35]. The hierarchical fuzzy TOPSIS does not have the disadvantages of the pairwise comparisons among criteria, sub-criteria and alternatives, while it also simultaneously overcomes the disadvantages for the necessity to assign an initial weight in fuzzy TOPSIS [31].…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluating the Sustainability of Community-Based Long-Term Care Programmes: A Hybrid Multi-Criteria Decision Making Approach

et al. 2016

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Sustainability is a crucial factor in Long-Term Care (LTC) programmes, which implies whether the programmes have the capability of sustaining a quality service over the long term. To evaluate the sustainability of community-based LTC programmes, a novel hybrid framework has been demonstrated with a mixed Multi-Criteria Decision Making (MCDM) technique. According to extensive literature review and the fuzzy Delphi method, four pillars of initial criteria and twelve sub-criteria have been determined. Then a weighted hierarchy has been constructed with Analytic Hierarchy Process (AHP) to constitute the evaluation index system. In order to prove our framework, a case study of four community-based LTC programmes in Michigan is presented by applying the fuzzy technique for order preference by similarity to an ideal solution (TOPSIS) method. The results indicate that programme P2 has the best potential of sustainability, and sub-criteria associated with economy outweigh other sub-criteria. The sensitivity analysis verifies that the result of the ranking remains stable regardless of the fluctuation in sub-criteria weights, which proves the evaluation results and proposed model to be accurate and effective. This study develops a comprehensive and effective framework for evaluating community-based LTC programmes from the sustainability perspective.

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Section: Literature Reviewmentioning

confidence: 99%

Evaluating the Sustainability of Community-Based Long-Term Care Programmes: A Hybrid Multi-Criteria Decision Making Approach

et al. 2016

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“…To minimise the risk of failure of a newly launched product, the best attribute design, among many alternatives, that satisfies various consumers' preferences should be found during the product development process. Conjoint analysis [14,15] and fuzzy multi-attribute decision techniques [16][17][18] have been introduced to describe consumers' preferences. Optimisation techniques, such as genetic algorithms [15], Lagrangian relaxation with a branch and bound algorithm [19], and particle swarm optimisation [20,21], which are all associated with preference measures, have been applied to determine the combination of product attributes that maximises a company's expected profit or the consumers' total utility.…”

Section: Related Researchmentioning

confidence: 99%

Product Attribute Design Using an Agent-Based Simulation of an Artificial Market

Kim¹,

Kim²

2014

Int. j. simul. model.

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New product development is a high-risk decision-making problem in which similar products compete with each other to expand their market shares. Brand-level diffusion predictions can help product design managers to analyse how product attribute specifications impact total market shares, which can, in turn, aid managers in choosing the designs that yield maximum profits. In this paper, we develop a product attribute design method in which an artificial market consisting of consumer agents in an interaction network is created to simulate the diffusion process of products, and a genetic algorithm is integrated with the artificial market to support the product design decision-making process. The contribution of this research is that the predicted market response to product alternatives is incorporated into the product design optimisation. Two empirical experiments were conducted on the Korean laptop computer market to demonstrate the potential of this integrated method. Preliminary experiment showed that our prediction diffusion curves had an average error of 3.04 %. In the primary experiment, five designs were recommended, and a comparison with the 31 best-selling laptop computers resulted in an average error of less than 8 % when the "Price" attribute was excluded.

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Section: Introductionmentioning

confidence: 99%

“…temporal, financial, and human) and result in the difficulties to meet the project goals, including product innovativeness. Unsatisfactory success rate of product development projects can also be considered from the perspective of inherent feature of NPD, that is, it is a relatively risky activity [5], as market competition and product technology advancement are often intense [6].Although the success of a new product depends on the environmental uncertainties that are beyond a firm's control, companies should take into account both external and internal indices that can impact on the product success. Internal indices can be acquired from company's databases, including Enterprise Resource Planning (ERP) system, project management software, customer relationship management system, etc.…”

mentioning

confidence: 99%

Estimating New Product Success with the Use of Intelligent Systems

Relich

Bzdyra

2014

Foundations of Management

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The paper presents identifying success factors in new product development and selecting new product portfolio. The critical success factors are identified on the basis of an enterprise system, including the fields of project management, marketing and customer's comments concerning the previous products. The model of measuring the success of a product includes the indicators such as duration and cost of product development, and net profit from a product. The proposed methodology is based on identification of the relationships between product success and project environment parameters with the use of artificial neural networks and fuzzy neural system that is compared with the results from linear model. The presented method contains the stages of knowledge discovery process such as data selection, data preprocessing, and data mining in the context of an enterprise resource planning system database. The illustrative example enhances a performance comparison of intelligent systems in the context of data preprocessing.Keywords: project management, new product development, knowledge acquisition, data mining, ERP system. IntroductionNew product development (NPD) is one of the most important processes in maintaining a company's competitive position and continuing business success. New products and innovations impact on sales volume, employment, technological process, and economic progress. Contribution of NPD to the growth of the companies, its influence on profit performance, and its role as a key factor in business planning has been widely considered [e.g. 1-3]. Nevertheless, it is still reported that the success rate of product development projects is unsatisfactory, with more cost and time than expected having been consumed to achieve the project goals.The main reasons why most companies have failed in the development of new products derive from extrinsic and intrinsic problems. Extrinsic problems include flops in the market, changes in regulations or simply competition develops product first [4]. Intrinsic problems concern the limited resource constraints (e.g. temporal, financial, and human) and result in the difficulties to meet the project goals, including product innovativeness. Unsatisfactory success rate of product development projects can also be considered from the perspective of inherent feature of NPD, that is, it is a relatively risky activity [5], as market competition and product technology advancement are often intense [6].Although the success of a new product depends on the environmental uncertainties that are beyond a firm's control, companies should take into account both external and internal indices that can impact on the product success. Internal indices can be acquired from company's databases, including Enterprise Resource Planning (ERP) system, project management software, customer relationship management system, etc.The proper choice of critical success factors and metrics can improve the accuracy of new product evaluation, determine an optimal set of new products for development, m...

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A two phase multi-attribute decision-making approach for new product introduction

Cited by 149 publications

References 44 publications

Evaluating the Sustainability of Community-Based Long-Term Care Programmes: A Hybrid Multi-Criteria Decision Making Approach

Evaluating the Sustainability of Community-Based Long-Term Care Programmes: A Hybrid Multi-Criteria Decision Making Approach

Product Attribute Design Using an Agent-Based Simulation of an Artificial Market

Estimating New Product Success with the Use of Intelligent Systems

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