AI-based methodology of integrating affective design, engineering, and marketing for defining design specifications of new products

Kwong, C. K.; Jiang, Huimin; Luo, Xiangfeng

doi:10.1016/j.engappai.2015.04.001

Cited by 78 publications

(40 citation statements)

References 57 publications

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“…Likewise, also in the domain of engineering, it is acknowledged that AI can be used to support the product development process (see e.g. Kwong, Jiang, and Luo 2016;Pham and Pham 1999;Yan Chan et al 2016). Hence, there seems to be a good match between an idea that can contribute to an improved purchasing performance and the applicability of AI.…”

Section: Discussion: Prospects Of Success Substantially Depend On Datmentioning

confidence: 99%

Artificial Intelligence in Purchasing: Facilitating Mechanism Design-based Negotiations

Schulze-Horn

Hueren

Scheffler

et al. 2020

Applied Artificial Intelligence

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Negotiations are central to reach consensus between supply chain partners while, simultaneously, meeting internal cost and quality targets. Purchasing prices can be improved by inducing competition in the supply base. In this context, the application of mechanism design theory in negotiations gained enhanced attention. While such approaches can result in high cost reductions, mechanism design-based negotiations are very complex. The paper aims at answering the question whether artificial intelligence (AI) can facilitate the execution of mechanism design-based negotiations. To this end, a World Café has been conducted at an automotive original equipment manufacturer. A group of 20 experts from the fields of purchasing and AI discussed the potentials of AI for the purchasing function. The results indicate that the application of AI can indeed facilitate the execution of mechanism design-based negotiations and help overcoming bounded rationality problems. Even more, AI might be a game changer for the purchasing function. Introduction: Could Artificial Intelligence Support the Design and Execution of Complex Negotiations? Artificial intelligence (AI) is considered to be one of the pacemaker technologies of the 4 th industrial revolution (Monostori 2014). With AI's promise to simulate human-like behavior (Russell and Norvig 2010), it could conceptionally be imagined to support complex problems like negotiations. Given the importance of business-to-business negotiations, previous research has focused on various influencing factors in the negotiation process as well as their outcomes. However, literature concentrating on how negotiations are executed remains scarce (Geiger 2017). Recent studies have tried to take up on this gap by addressing buying organizations' application of mechanism design theory in negotiations (e.g Huang et al. 2013; Schulze-Horn et al. in press). Mechanism design theory represents the inverse of game theory: the development and implementation of economic incentivesso-called mechanismscan lead to the achievement of desired objectives (Nisan

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Section: Discussion: Prospects Of Success Substantially Depend On Datmentioning

confidence: 99%

Artificial Intelligence in Purchasing: Facilitating Mechanism Design-based Negotiations

Schulze-Horn

Hueren

Scheffler

et al. 2020

Applied Artificial Intelligence

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show abstract

“…Unfortunately, the way that consumers view a product is quite different from the way product designers view that product (Bahn, Lee, Nam, & Yun, ; Lai, Lin, Yeh, & Wei, ). Whether consumers choose a product depends largely on their affective preferences of the product (particularly having a wide variety of appearances; Kwong, Jiang, & Luo, ; Lin, Chen, & Yeh, ; Seva, Duh, & Helander, ; Wang, ), while product designers design a product by considering its physical elements (Borsci, Kuljis, Barnett, & Pecchia, ; Lin et al., ).…”

Section: Introductionmentioning

confidence: 99%

“…Whether consumers choose a product depends largely on their affective preferences of the product (particularly having a wide variety of appearances; Kwong, Jiang, & Luo, 2016;Lin, Chen, & Yeh, 2014;Seva, Duh, & Helander, 2007;Wang, 2011), while product designers design a product by considering its physical elements (Borsci, Kuljis, Barnett, & Pecchia, 2014;.…”

Section: Introductionmentioning

confidence: 99%

A hybrid consumer‐oriented model for product affective design: An aspect of visual ergonomics

Lin

Wei

2016

Hum Ftrs & Erg Mfg Svc

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Crucial issues for product designers include how to capture consumers’ attention, evoke their pleasurable preferences, and affect their purchase decisions. In this article, we focus on consumers’ affective preferences in relation to visual ergonomics to propose a new hybrid consumer‐oriented model using gray relational analysis (GRA), gray prediction (GP), and the technique for order preference by similarity to ideal solution (TOPSIS). The GRA is used to identify the most influential elements of the product form to help product designers focus their attention more on these elements without compromising the predictive performance. The GP is used in conjunction with the GRA to obtain a better structure for the hybrid consumer‐oriented model, and TOPSIS is performed to determine the optimal alternatives for best matching consumers’ affective preferences. These experimental results show that the new hybrid consumer‐oriented model incorporated with the CAD/CAM system can facilitate the product affective design process.

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“…For the decision of the product itself, a model has been introduced, which combines functions of design, development and marketing as early as in the concept phase for the purpose of optimal end product. Study has been carried out on the case study of the iron (Kwong et al, 2016). In 2015 an article has been published in which optimisation process with the help of advanced methods has been introduced (Noorossana et al, 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

Product Development Using an Intelligent Supporting System

Buchmeister¹,

Palčić²

2017

DAAAM International Scientific Book

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AI-based methodology of integrating affective design, engineering, and marketing for defining design specifications of new products

Cited by 78 publications

References 57 publications

Artificial Intelligence in Purchasing: Facilitating Mechanism Design-based Negotiations

Artificial Intelligence in Purchasing: Facilitating Mechanism Design-based Negotiations

A hybrid consumer‐oriented model for product affective design: An aspect of visual ergonomics

Product Development Using an Intelligent Supporting System

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