A modular product design framework for the home appliance industry

Lima, Matheus Bólico; Kubota, Flávio Issao

doi:10.1007/s00170-022-08896-9

Cited by 16 publications

(6 citation statements)

References 35 publications

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“…The manufacturer's objective is to meet individualized customer demands by reducing development time [30] without compromising on product quality and minimizing manufacturing cost. Workers will see different parts for multiple variants requiring specific assembly steps.…”

Section: Conceptual Framework Elementsmentioning

confidence: 99%

Intelligent planning process with adaptive quick response system for low volume manufacturing

Dcoutho

Rahman²,

Eisenbart³

et al. 2022

Preprint

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The automotive manufacturing industry in recent years has seen a paradigm shift in production. Increased customer individualization demands and shorter product life cycles have become the norm in the market. Traditionally, production planning methods in this sector are based on high volumes; thus, production lines used to be relatively rigid. With the current demand for individualized low-volume production, the line must be altered frequently, leading to increased downtime and additional cost. This shift in automotive manufacturing requires production planning to cater to faster, cost-effective adoption to changing low-volume individualized demands. This research discusses a novel Intelligent Planning Process (IPP) to address low-volume individualized manufacturing. The IPP model harnesses transformative technologies such as extended reality (xR) to facilitate faster and more adaptive planning. Further, artificial intelligence is embedded through xR models using various response nodes (e.g., quick response) This provides a critical advantage in developing and evaluating multiple production layouts with considerably reduced efforts. A case studyon positioning preloaded planning data to the real world with quickresponse nodes resulted in one-fourth of the time required by manualinteractive positioning of physical assets. In addition, real-timecontrol and synchronous optimization were other intangible outcomes.

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Section: Conceptual Framework Elementsmentioning

confidence: 99%

Intelligent planning process with adaptive quick response system for low volume manufacturing

Dcoutho

Rahman²,

Eisenbart³

et al. 2022

Preprint

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“…-MODULARITY. The modularity of Ps/Ss is essential to be able to adapt them to customer's needs (see, e.g., Lima & Kubota, 2022). In our own proposal, we model this modularity as the different criteria of a Multi-Criteria Decision-Making (MCDM) process.…”

Section: State-of-the-art About Design Of Ps/ssmentioning

confidence: 99%

Decision-Making Model for Designing Telecom Products/Services Based on Customer Preferences and Non-Preferences

Cid-López

Hornos

Carrasco

2022

Technological and Economic Development of Economy

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The design of the packages of products/services to be offered by a telecom company to its clients is a complex decision-making process that must consider different criteria to achieve both customer satisfaction and optimization of the company’s resources. In this process, Intuitionistic Fuzzy Sets (IFSs) can be used to manage uncertainty and better represent both preferences and non-preferences expressed by people who value each proposed alternative. We present a novel approach to design/develop new products/services that combines the Lean Six Sigma methodology with IFSs. Its main contribution comes from considering both preferences and nonpreferences expressed by real clients, whereas existing proposals only consider their preferences. By also considering their non-preferences, it provides an additional capacity to manage the high uncertainty in the selection of the commercial plan that best suits each client’s needs. Thus, client satisfaction is increased while improving the company’s corporate image, which will lead to customer loyalty and increased revenue. To validate the presented proposal, it has been applied to a real case study of the telecom sector, in which 2135 users have participated. The results obtained have been analysed and compared with those obtained with a model that does not consider the non-preferences expressed by users.

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“…Its applications span various industries, including electronics, computers, construction, and automobiles [6][7][8][24][25][26][27][28]. In the field of engineering, particularly in industrial design, modular design serves as an essential technology for meeting customer demands for diversification and rapid customized delivery in basic product design [29,30].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Decision-Making Approach for Strategic Product Module Planning in Mass Customization

Liu,

Wang,

Tung

2024

Mathematics

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This paper explores the integrated optimization of complex coupled industrial manufacturing systems and production strategies based on user customization needs. Two optimization metrics are considered: one is whether the production process of engineering manufacturing is simplified, and the other is whether it is based on the customization requirements of the customer. These two metrics are interrelated, and cases may even be conflicting. Considering the interdependence between engineering manufacturing and user requirements, this paper develops an integrated customized modular engineering manufacturing process to minimize production and maintenance costs and improve efficiency while meeting user customization requirements. This paper takes expert evaluation as an important decision indicator and optimizes the production process strategy on this basis. Finally, a case study is given to illustrate the applicability of the proposed process model.

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A modular product design framework for the home appliance industry

Cited by 16 publications

References 35 publications

Intelligent planning process with adaptive quick response system for low volume manufacturing

Intelligent planning process with adaptive quick response system for low volume manufacturing

Decision-Making Model for Designing Telecom Products/Services Based on Customer Preferences and Non-Preferences

A Comprehensive Decision-Making Approach for Strategic Product Module Planning in Mass Customization

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