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Small- and medium-sized manufacturing enterprises (SMMEs) face intense competitiveness, necessitating ever greater productivity. Enterprises struggle to meet the demand for customized products while maintaining their productivity. The transition from mass customization (MC) to mass personalization (MPe) leads to a further increase in product variety and, thus, complexity. Digital transformation alone is not sufficient to achieve MPe and traditional adoption of modularity no longer ensures enterprise competitiveness in this context of increased variety. The synergy between modularity concepts could enhance the efficiency of this design strategy. This study is part of a research plan to develop an effective modularity implementation strategy addressing MPe. The aim of this article is to identify the main concepts and tools to be considered in an implementation strategy. Concepts and tools are grouped into four combinations according to the level of product variety in different production strategies. This preliminary work serves as the foundational research for a larger research plan aimed at adapting and validating a modular product development strategy that incorporates these modularity concepts. [ABSTRACT FROM AUTHOR]

Industry 5.0 manufacturing requires a highlighted emphasis on resilience, sustainability, and human-centricity. Circular bioeconomy could play an important role in practicing Industry 5.0 through using natural materials and minimizing the waste. Mass production with natural materials focuses on utilizing materials of standard geometry, resulting in under-utilized non-standard materials. To facilitate the transition to Industry 5.0 and practicing circular bioeconomy, this study proposes a framework to utilize non-standard natural materials for mass customization using an integrated computational design – digital fabrication methodology. The proposed framework offers opportunities for waste reduction, sustainability in production, and human-centric design for customized manufacturing. Two case study projects were discussed to further explain the framework, followed by the challenges of the proposed integrated approach and the direction for future studies.

In the Internet era, the industrial Internet platform has become a bridge between customers and enterprises. In addition, with the continuous improvement of customers' living standards, customers not only meet the basic needs for the functionality of products, but also yearn to participate in the production process of products and interactive experience activities with enterprises while pursuing high quality. In view of the difficulties in identifying and capturing the dynamically changing needs of customer- product interaction whole life cycle under the new environment of industrial Internet, and the problem of large amount and redundancy of demand data, this paper develops a set of models and methods covering the identification, definition, acquisition and parsing of customer needs, which provides theoretical support and decision-making basis for enterprises to accurately capture and predict customer needs, excavates potential market customers of enterprises, and realizes value-added of the value chain. Moreover, the methodology of personalized requirements for CILC proposed in this paper is helpful to the transformation of enterprise manufacturing model from mass manufacturing to mass customization and even to mass personalization. It can reduce enterprise production costs, and improve customer satisfaction and service experience. [ABSTRACT FROM AUTHOR]

After mass production and then mass customization, the time is almost ripe for mass personalization. The goal is to offer unique products designed for the needs of each customer. However, production in larger series of products also has its advantages, and the promise of "lot size one" is still far from being the norm in several sectors of the economy. As a result of an action research project in a small household ceramic producer, this paper explores the potential of a hybrid strategy. Augmented digital engineering is adopted to (1) ensure customer participation along the entire product design lifecycle, (2) maintain the benefits of modularization and low cost, (3) minimize the waste of time and materials during product design, and (4) seek a minimum trade‐off between customer desires and engineering strategy. For theory, our work describes Industry 4.0 technology's role in achieving individual customer interaction and value co‐creation in hybrid strategies of mass customization and mass personalization. For practice, we present an example of technological architecture to implement augmented digital engineering in Industry 4.0, accessible to scenarios of hand‐intensive work and creative design processes. [ABSTRACT FROM AUTHOR]

Small- and medium-sized manufacturing enterprises (SMMEs) face intense competitiveness, necessitating ever greater productivity. Enterprises struggle to meet the demand for customized products while maintaining their productivity. The transition from mass customization (MC) to mass personalization (MPe) leads to a further increase in product variety and, thus, complexity. Digital transformation alone is not sufficient to achieve MPe and traditional adoption of modularity no longer ensures enterprise competitiveness in this context of increased variety. The synergy between modularity concepts could enhance the efficiency of this design strategy. This study is part of a research plan to develop an effective modularity implementation strategy addressing MPe. The aim of this article is to identify the main concepts and tools to be considered in an implementation strategy. Concepts and tools are grouped into four combinations according to the level of product variety in different production strategies. This preliminary work serves as the foundational research for a larger research plan aimed at adapting and validating a modular product development strategy that incorporates these modularity concepts.

In order to meet the changing needs of customers, the manufacturing model of products is constantly changing from mass manufacturing model to mass customization model, and to mass personalization model, which is the research object of this paper. Using the research method of system engineering, the system model, characteristic, technology, application blueprints, application scenarios, implementation paths of S-MMP from the perspective of top-level planning are studied. The complete system model of S-MMP proposed in this paper is in line with the development trend of today's industry and meets the urgent needs of enterprises, and it can provide theoretical guidance for enterprises in policy upgrading, model transformation, and business process reengineering. The search results of this paper can provide a reference value for companies to implement high-level planning of the smart system in manufacturing with mass personalization. [ABSTRACT FROM AUTHOR]

With the development of intelligent manufacturing driven by new-generation information technology, manufacturing service collaborative chains (MSCC) based on Industrial Internet Platforms have become the main way to cope with mass personalized production. To adapt the dynamics and variability of personalized demands, manufacturing services are required to be frequently reorganized. This leads to dynamic changes in manufacturing service availability and ambiguous reliable collaboration mechanisms, making it challenging for existing methods to consistently maintain stable MSCC. Therefore, this paper proposes a three-stage deep reinforcement learning method (TSDRL) to improve the reliability of MSCC while guaranteeing the satisfaction of personalized demands. The method comprehensively considers the dynamic changes of manufacturing services and service reliability, and solves the reliable MSCC recommendation problem. Specifically, in stage one, high-quality services are selected through DRL state. In stage two, the improved NSGA-III algorithm is used to optimize the MSCC. In stage three, the BP+GA algorithm is used to mine user preferences and recommend high-satisfaction MSCC. By leveraging the exploratory process of DRL, cyclic optimization of MSCC is performed. Through comprehensive analysis and experimental research, it is shown that the method of this paper is robust and practical in dynamic environment. [ABSTRACT FROM AUTHOR]

The aim of the article is to philosophically address and expand the contemporary discourse of the increasingly digitalized education by examining the problem of matter of education and its conceptualizations in this context. With the digital transformation of education a significant change occurs not only in terms of what is considered to be a matter of education, but also in the very principles of interaction between the subjects of education. In order to do this, in the first section several examples of conceptual justifications of applying digital technologies in education are examined with the task of demonstrating how in such justifications the activity of education is reduced to the process of learning and what risks such instrumental interpretation engenders. Then, these issues are analyzed and contextualized utilizing the material of the philosophical discussion of the 20th century - the critique of the progressive education in the analytic philosophy of education. By outlining the relevance of the arguments of discussion on manner/matter to realities of digital education, the issue of the dominance of the logic of learning in current education is analyzed. In the last section several adjustments to the contemporary discourse on digitalization of education are proposed, aimed at finding a balance between individual and social dimensions of the problem of matter of education, given the current tendency of mass personalization in education. Particularly, the importance of the idea of education as a common good in the contemporary educational discourse is claimed.

There is a recognized need for mass personalization for sustainability at scale. Mass personalization is becoming a leading research trend in the latest Industrial Revolution, whereas substantial research has been undertaken on the role of Industry 4.0 enabling technologies. The world is moving beyond mass customization, while manufacturing has led to mass personalization ahead of other industries. However, most studies have not treated human capabilities, machines, and technologies as sustainable collaboration. This research investigates mass personalization as a common goal under the latest Industrial revolutions. Also, it proposes a Reference Architecture Model for achieving mass personalization that contributes to understanding how Industry 5.0 enhances Industry 4.0 for higher resilience and sustainability through a human-centric approach. The study implies that Human Capital 5.0 leads collaboration with machines and technologies, bringing more value-added and sustainable products. [ABSTRACT FROM AUTHOR]

• A framework process for MP-PSS is constructed. • Theory for MP-PSS driven by industrial intelligence is proposed. • Industrial implementation path for MP-PSS is given. • Industrial modular configuration method for MP-PSS is proposed. With the advancement of industrial intelligence technology, the Mass Personalization (MP) model is converging with Product Service Systems (PSS) to form a novel large-scale service model oriented towards personalized products, which is Mass Personalization Product Service Systems (MP-PSS). The MP-PSS, grounded in Personalized Product Service Systems, is primarily manifested in the areas of personalized orders, modular design, personalized product design and production, and personalized services throughout the entire product lifecycle. This paper provides a detailed study of MP-PSS driven by industrial intelligence from the perspectives of processes, frameworks, methods, and cases. A detailed analysis is conducted on configuration for MP-PSS, focusing on industrial modular configuration for MP-PSS. Finally, an enterprise in the automotive industry is selected as a case study for validation. The top-level framework and theory of MP-PSS can provide theoretical guidance for the transformation, upgrading, and model innovation of enterprises in the industrial value chain, and provide theoretical decision-making for enterprise managers. Moreover, the product modular configuration method based on comprehensive fuzzy evaluation, entropy value method, and analytical hierarchy process can improve the efficiency of customer customization of products and improve customer satisfaction. [ABSTRACT FROM AUTHOR]

• A method of hybrid order priority determination for MPFM model is developed. • A method of production batch optimization for MPFM model is developed. • It can improve equipment utilization and save the total production cost. • It is helpful for enterprises to set up, plan, build and implement MPFM. In terms of manufacturing model, it is from the initial stage of manual production model to mass production model, then to mass customization model, and finally to mass personalization model. It is the future development trend for enterprises to change from the traditional mass manufacturing model based on inventory to the mass personalization flexible manufacturing (MPFM) model driven by customer demand. Therefore, an innovation manufacturing model and industrial application for MPFM are studied. Aiming at the problems of hybrid order priority processing and equipment flexibility adjustment, it is urgent for enterprises to develop a set of models and methods covering hybrid order priority determination and production batch optimization. The focus of this paper is to study the hybrid order priority determination and production batch optimization under the flexible manufacturing mode of personalized products. In this paper, a hybrid order priority determination method based on comprehensive fuzzy cluster evaluation method in MPFM model is proposed. Moreover, a production batch optimization method based on cat swarm optimization (CSO) optimization algorithm in MPFM model is proposed. Through the research of this paper, it can help enterprises realize the flexibility of production layout, orders and processes, improve the utilization rate of equipment and save the total production cost. [ABSTRACT FROM AUTHOR]

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Researchers of the Mass Customization domain face not only challenges of proper and timeless identification of latest practical trends, but also difficulties in rational analyses on the numerous existing scientific studies in this field as well as a need for a comprehensive and multidimensional state-of-the-art overview of the Mass Customization research domain in the last three decades. Therefore, the present research article aims to provide a critical standpoint and reveal the main research directions and content at systemic, bibliometric and historical research levels in the period of 1990-2020. Four types of bibliometric clustering techniques and a visualization of results in a format of two-dimensional maps by the VOSviewer software were applied in the analysis on 1783 scientific papers from the Clarivate Analytics Web of Science Core Collection. The analysis reveals six historical periods in the Mass Customization research domain, from which, in the last three decades, three are identified as influencing modern Mass Customization research areas and objects. Results confirmed a shift from a stand-alone scientific approach to the customization of tangible products in the manufacturing field and their risk management, to a hybrid scientific approach with a focus on the customization of non-tangible products and personalized customer behavior in online environments. [ABSTRACT FROM AUTHOR]

The Fourth Industrial Revolution (Industry 4.0) leads to an age of extraordinary changes through digital transformation. High customer demands and market competitions drive almost all business sectors to meet individuals' requirements with a cost close to mass production. This paper aims to get the best out of Digital Twin capabilities for meeting mass personalization. A cross-sectional study was undertaken to explore the potential relationship between Industry 4.0, Information and communication technologies (ICT), and Digital Twin towards mass personalization. This study identifies cutting-edge technologies for building a Digital Twin reference model. The results reveal that Digital Twin fulfils mass personalization under Industry 4.0. The findings can contribute to a better understanding of new industrial applications for a wide range of Digital Twin integration levels. [ABSTRACT FROM AUTHOR]

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Technisches Messen is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Growing environmental awareness in society increasingly influences individual everyday decisions, such as which product to buy or how to sustainably use it. Yet, available information to support these decisions is often limited, or difficult to understand particularly regarding sustainability. Effective ways of communicating environmental impacts of individual decisions are required to close this gap. While Life Cycle Assessment (LCA) is an established tool to evaluate environmental impacts of products and services and support environmental decision-making, the results are typically standardized and based on statistical or averaged data. However, for individuals, this information might be irrelevant, as it neglects personal situation, behavior, information need, or individual level of expertise. In tackling those central issues of personalization in LCA, this article focuses on two main questions: How can individual aspects be addressed in LCA and at which stages of the methodology can LCA be personalized? For this purpose, the ISO 14040/44 standards are analyzed regarding individuality, and current approaches in literature are presented. In an explorative approach, this research identifies two general approaches of personalizing LCA. A personalized Life Cycle Inventory (LCI) enables evaluating the environmental impacts of personal(ized) products and conditions. A broader personalization approach based on the flexibility of the methodological framework of LCA aims at providing understandable and relevant results for individual stakeholders. This article provides an overview, outlines key aspects of this vision, and points out further research needs to bring the concept into application. [ABSTRACT FROM AUTHOR]

With constantly development of customer personalized needs, enterprises will undergo a series of transformations and upgrades around the product value chain to adapt to the flexible and ever-changing market environment, meet customer personalized needs, and enhance their competitiveness. In existing research, the study of PSS (Product Service System) does not simultaneously consider three factors: the product manufacturing mode oriented towards mass personalization in the product manufacturing process, the open community collaborative supply oriented towards community resources in the product supply process, and the product service system oriented towards personalized orders in the product service process. Therefore, the research object of this paper is an innovative model of personalized product service system (PPSS) for open community collaborative supply (OCCS). Firstly, an innovative model of PPSS for OCCS is proposed through business model, platform model, and data model. Secondly, the Process framework of PPSS for OCCS is studied. Thirdly, in-depth research is conducted on selection, configuration, and optimization for OCCS, including selection oriented customer requirements, configuration oriented supplier network, and optimization oriented personalized product of PPSS for OCCS. Finally, a case study is conducted using the automotive industry as an example to analyze the proposed research. By leveraging networked online supply resources and fully leveraging the collaborative effects of community-based division of labour and openness, it can achieve innovation in supply models, improve supply efficiency, and save supply resource costs. • A personalized product open collaborative supply method is proposed. • It is an extension and supplement to the theory of PPSS. • It can give a reference for enterprise to carry out OCCS. • It can achieve innovation in product supply model. • It has a certain reference value for industry and government. [ABSTRACT FROM AUTHOR]

Saxophonists have different expectations from the saxophone mouthpiece, as it significantly affects the playability and the sound of the instrument. A mass personalization paradigm provides unique products to cater to their needs, using the flexibility of additive manufacturing. The lack of quantitative knowledge on mouthpiece design hinders the personalization attempts. This study aims to lay out how design parameters affect mouthpiece characteristics. Twenty-seven 3D-printed mouthpieces with varying design parameters are used in conjunction with an artificial blowing machine, to determine the acoustical relevance of the various mouthpiece designs on four selected mouthpiece features. The influence of the design parameters is evaluated statistically and via a case study with five saxophonists. The analysis shows that seven out of nine parameters tested affect the mouthpiece characteristics by relatively different amounts. A user study demonstrates that saxophonists confirm the results in 7 of 10 cases, and they prefer personalized mouthpieces in 4 of 5 cases. The results present a key contribution to the understanding of mouthpiece design. The findings provide valuable insights for new mouthpiece design and mouthpiece personalization.

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Graphical models are widely used to model the statistical relationships among variables in a system. Existing graphical models can be used to model the relationships among scalar variables, but cannot be directly applied to model a system with functional variables. In this paper, a novel functional graphical model is proposed to model complex systems where functional variables are measured. To cope with the small sample size problem, we further develop a special sparsity penalization approach to robustly learn the graphical model from limited sample size, and develop a difference from the mean penalization for functional variables. Simulation studies and a case study in a plasma spray manufacturing process are used to demonstrate the effectiveness of the proposed method.

Note to Practitioners—Emerging sensing and information technologies have provided unprecedented functional data collection capacity from systems over time. It is important and challenging to model the relationships among these variables. Among many different modeling alternatives, graphical models are powerful tools to characterize the underlying relationships among variables in systems. However, traditional graphical models cannot directly model a system with a mixture of functional and scalar variables. The proposed model aims to address this challenge by proposing a functional graphical model. Based on simulation studies and a case study for a plasma spray manufacturing process, it is shown that the proposed method performs well under various conditions. [ABSTRACT FROM PUBLISHER]

Nowadays, with the rapid development of information technologies (e.g. web 2.0, cloud computing and virtual reality) and manufacturing technologies (e.g. additive manufacturing), users become more actively involved in the product development stage to create personalized products with higher efficiency. This emerging manufacturing paradigm is known as mass personalization, of which user experience (e.g. emotional factors and product utility), co-creation (e.g. user participation), and product change (e.g. modular design) are regarded as three key characteristics. In previous studies, researchers often treat each characteristic separately with an illustrative example to demonstrate its significant effect respectively. They, however, cannot fully reflect the product development process for mass personalization, and may cause inconsistency in implementing the existing methods into real cases. To fill the gap, this work proposes a three-model based (i.e. physical model, cyber model and user experience model) generic framework for conducting user experience based product development for mass personalization. A case study of a personalized smart wearable product development is described, of which the three key characteristics are considered collectively. It can also be seen as a typical case integrating the frontier techniques to enable mass personalization. The authors hope this work can provide some useful guidance to product designers and engineer-to-order companies. [ABSTRACT FROM AUTHOR]

• A Workflow for OCCM in MP model is constructed. • A method of resource allocation for OCCM is proposed. • A method of resource matching for OCCM is proposed. • A case study for OCCM in the automotive industry is giving. • It has a certain reference value for industry and government. In order to meet the requirements of customers' pursuit of personalized and diversified needs, enterprises need to change from the traditional mass manufacturing model to mass personalization manufacturing model. On the other hand, collaborative manufacturing based on stakeholders will effectively solve the problems of information asymmetry, low operational efficiency and insufficient resource utilization in the process of manufacturing. Moreover, the new model of open community manufacturing is conducive to the planning, allocation, optimization and management of various networked and social resources. Therefore, combined with above three innovative models of mass personalization manufacturing, collaborative manufacturing and open community manufacturing, this paper studies the online merchant resource allocation and matching of open community collaborative manufacturing (OCCM) for mass personalization (MP) model to realize the innovation of design model, improve design efficiency and save design resource cost. Firstly, the workflow for OCCM in MP model based on system engineering analysis method is studied. Secondly, online merchant resource allocation for OCCM based on social network analysis (SNA) and online merchant resource matching for OCCM based on fuzzy clustering are proposed. At last, a case study for OCCM in the automotive industry is given to verify the effectiveness of the proposed model and methods. [ABSTRACT FROM AUTHOR]

Nowadays, manufacturing enterprises are struggling to satisfy personalized and dynamic user requirements. Smart Product-service System (Smart PSS), as a promising and sustainable business model, can respond to personalized and dynamic demands through effective configuration/reconfiguration processes. Mass personalization (MP), which emphasizes diversified values to improve user experience by considering both functional and affective elements, can significantly contribute to Smart PSS. However, Smart PSS field still lacks a unified knowledge representation framework and an effective configuration method in the MP context. Aiming to fulfill the gap, this research proposes a novel Smart PSS configuration method oriented to MP to meet personalized and dynamic requirements of users, especially satisfy affective and functional requirements simultaneously throughout the reconfiguration life-cycle. Due to the extreme complexity of heterogeneous data from Smart PSS and MP, Knowledge Graph (KG) is introduced as a powerful tool for managing intricate system knowledge. The common schemas of three KGs are constructed as the basis of configuration, with design knowledge, related field knowledge, and user information integrated comprehensively. KG-based question answering is combined with similarity calculation as a hybrid method in the configuration process to provide satisfactory and complete solutions. Moreover, the configuration process is placed in the reconfiguration life-cycle of Smart PSS to deal with dynamic changes through the latest knowledge in updating KGs. Furthermore, an illustrative case study of the intelligent management system for family medication is demonstrated. Through the comparison with other approaches, the proposed method is proved to be valuable for the implementation of Smart PSS configuration for MP. [ABSTRACT FROM AUTHOR]

Personalized healthcare, which uses individual characteristics to better predict and prevent disease and customize therapies, is a potential solution to our current healthcare crisis. Personalized care aims to improve quality of care and reduce overall healthcare costs. Despite its potential, adoption of personalized healthcare has been slow for several reasons, one of which is related to financial incentives toward change. This perspective piece discusses how the Affordable Care Act (ACA), through support for preventive care and comparative effectiveness research, begins to align the right incentives toward innovation around personalized risk prediction, innovation that is much needed as we aim to improve the health of individuals and communities. [ABSTRACT FROM AUTHOR]