Mass Customization Research Articles

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

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.

Transforming architecture: The synergy of digital fabrication and parametric design

This paper delves into the transformative synergy between digital fabrication and parametric design in the field of architecture, tracing their evolution, principles, and integration that herald a new era of architectural innovation. Digital fabrication, with its roots in the mid-20th century, has evolved from basic CNC machinery to advanced 3D printing and robotic assembly, enabling a shift from mass production to mass customization. Parametric design, through its algorithmic nature, allows for the manipulation of complex geometries based on set parameters, facilitating designs that adapt to environmental data for enhanced sustainability. The integration of these two methodologies has fostered a powerful design-to-production workflow, enabling architects to materialize complex designs with unprecedented precision and efficiency. Highlighted through cutting-edge techniques and notable case studies, this paper provides a comprehensive overview of the current landscape and potential future directions of architectural practices shaped by these technologies. It underscores the significant efficiency gains, structural optimization, and innovation potential that this integration offers, backed by quantitative analysis and mathematical modeling. This exploration not only emphasizes the shift towards more sustainable, efficient, and innovative architectural solutions but also reflects on the broader implications for the future of architectural design and construction.

Matheuristic and learning-oriented multi-objective artificial bee colony algorithm for energy-aware flexible assembly job shop scheduling problem

With the increase of mass customization, flexible job shop scheduling problem considering assembly stage has widely existed in many manufacturing industries, such as die-casting mould factories. This problem is to find a reasonable machine assignment and operation sequence both in fabrication and assembly stages and simultaneously maximize production efficiency. In reality, energy shortages and environmental pollution have given an impetus to the development of energy-aware production scheduling problems. In this study, we address an energy-aware flexible assembly job shop scheduling problem (EFAJSP) with the objectives of minimizing flow time and energy consumption and first develop a mixed-integer linear programming (MILP) model to solve EFAJSP problem. Then, the model-specific characteristics are extracted and applied to a matheuristic decoding method for exploring the Pareto optimal solution. Due to the complexity of EFAJSP problem, a matheuristic and learning-oriented multi-objective artificial bee colony algorithm (MLABC), which combines the advantages of mathematical programming, reinforcement learning and meta-heuristic algorithm, is proposed. In addition, an initialization, destruction/construction operator and population update operator are proposed and work together to improve the exploration and exploitation performance of the proposed MLABC. Finally, numerical experimental results demonstrate the effectiveness of the proposed MILP model and the superiority of the MLABC over other algorithms in the literature.

Advanced IGZO Phototransistor Arrays: Enhancing Visible Light Detection Through Selectively Electrohydrodynamic Jet‐Printed Photocatalytic Layer Formation

AbstractExploring new horizons in phototransistors with an emphasis on mass customization is essential. Herein, a method for enhancing the performance of indium‐gallium‐zinc‐oxide (IGZO) phototransistors by incorporating a selectively formed tungsten trioxide (WO3) photocatalytic layer, utilizing electrohydrodynamic (EHD) jet‐printing is proposed. This approach aims to achieve significant manufacturing advantages and enhance functionality. An oxidizing solution of hydrogen peroxide (H2O2) doped with hydroxyethyl cellulose (HEC) is introduced to selectively oxidize tungsten (W) electrodes. In this mixture, H2O2 is responsible for the selective oxidation of W, whereas HEC contributes to the viscosity regulation and the formation of subgap states. The resulting selectively oxidized photocatalytic layer significantly expands its visible light absorption capacity to red light. Therefore, the optimized device, particularly under red light (635 nm) 5 mW mm−2, shows a remarkable enhancement in its optoelectronic characteristic: photoresponsivity increases from 29.71 to 124.54 A W−1, photosensitivity from 1.99 × 10‐1 to 1.79 × 104, and detectivity from 1.31 × 107 to 1.98 × 108 Jones compared to IGZO phototransistors without selectively oxidized photocatalytic layer. This advancement not only enhances the visible light detection capacity but also minimizes the persistent photoconductance issue inherent in IGZO devices. The demonstration of 6 × 6 phototransistor array illustrates the potential for image sensor applications, showcasing the practicality of mass customization.

Learning outcome evaluation in manual assembly

Mass customization and shorter product life cycles are causing ever more variants in production, especially in manual assembly. At the same time, more diverse personnel structures are emerging due to demographic change and labor shortages. This is causing different challenges to production managers, e.g., competence gaps. To meet these challenges, learning in manual assembly becomes increasingly important. The design of the learning process can only be improved by checking whether the processes fulfill their purpose. Various learning evaluation measures are described in general vocational education and competence development, but it is hard to select the right one for the learning process. This paper shows a procedure, how learning evaluation measures can be selected, and how they can measure learning progress. For this, a test person study was conducted to compare different learning evaluation measures and show their usability and advantages in manual assembly. The results support making learning in assembly easier to apply and controllable. In the long term, feeding back the results improves the learning process design.

A sensor-driven operations and maintenance planning approach for large-scale leased manufacturing systems

The rise of mass customisation drives manufacturers to adopt leasing agreements for their machinery rather than outright their ownership. This industrial trend results in large-scale leased manufacturing systems, where the asset conditions and maintenance requirements for a fleet of machines and/or production lines from several manufacturers are continuously monitored and managed by a lessor. In this paper, we propose an operations and maintenance planning model that explicitly models (i) dynamic real-time failure rate predictions for machines based on sensor-driven degradation data, (ii) optimal routes for maintenance teams across a number of geographically-distributed manufacturing sites, and (iii) operational outcomes. The model can handle complex maintenance and operational outcome interdependencies between multiple machines, and incorporate demand for different products, heterogeneous machine capacities and capabilities, factory topology, and the resulting production flow capacities of products. To demonstrate the model’s practicality, it is applied to three experimental case studies with various numbers of machines, operating schedules, product types, and flow capacities. The results show that the proposed model significantly outperforms the traditional reliability-based maintenance model in key metrics such as the number of unexpected failures, the cost of preventive maintenance actions, machine downtime, the percentage of unsatisfied demand, and total cost.

The Promises and Challenges toward Mass Customization of Healthcare Services

The healthcare industry is confronted with the challenge to offer an increasing variety of healthcare services while in the meantime controlling rapidly increasing healthcare costs. Mass customization has been proven to be an effective strategy to fulfill customers’ individual specific needs with high efficiency and low cost in the manufacturing industry. This paper investigates the theoretical feasibility and practical applicability of adopting mass customization as a conceptual framework for designing a healthcare service delivery system. The nature of healthcare delivery systems and their evolution are discussed relative to those of manufacturing systems. Recent research in personalized medicine, consumer-driven healthcare, consumer healthcare informatics, and integrated healthcare delivery is reviewed as enabling technologies towards mass customization of healthcare services. By synthesizing these scattered efforts in different streams of literature, this paper concludes that mass customization can contribute to the redesign of healthcare service systems, and delineates a roadmap for future research.

НОВЫЕ РОЛИ РАБОТНИКОВ В УСЛОВИЯХ ЦИФРОВОЙ ТРАНСФОРМАЦИИ ПРОМЫШЛЕННОСТИ

Global changes taking place in the economy under the influence of digitalization and automation are inevitable, and society needs to accept and take into account their positive and negative aspects. The research objective is to study the structure and specifics of the interaction between people and technology and the impact of new technologies on the work environment and process, highlighting the positive and negative aspects. The theoretical and methodological basis of the study is the work of domestic and foreign researchers devoted to issues of digitalization and automation in industry, opportunities and threats of the transition to Industry 5.0. Results. The main idea of the concept is human-centricity – people and artificial intelligence as a team. This will ensure sustainable and stable production, reducing costs in the future. The structure of this interaction between the two subsystems affects the productivity and efficiency of the entire system, as well as the health, motivation and satisfaction of employees and has a positive impact on future development. It is emphasized that collaboration between humans and machines is aimed at leveraging the unique strengths of each participant. Automation is not about replacing humans, but about expanding their capabilities. This is especially true for tasks that require creativity, critical thinking, and complex decision making – areas that require human intuition and ingenuity. The subject of the study of the Industry 5.0 concept was the new role of people in the production process. The human worker is more connected to information and less vulnerable to attack thanks to technology. The object of the study was digital technologies that help, first of all, to solve the main problem of labor shortages by transferring complex and tedious tasks to robots. This allows people to concentrate and develop more informed management decisions. Thanks to advanced analytics and artificial intelligence, manufacturers can understand the individual needs and preferences of customers. This data is then used to customize products during production, provi¬ding a level of personalization that was previously considered impractical at scale. Mass customization allows for a wider range of product types to be produced without huge amounts of waste and pollution because such customization can be done on the same production lines using reused materials and components. Conclusion. The article summarizes the advantages of Industry 5.0: reduced time and increased efficiency of processes, their visibility and transparency in real time, high customer satisfaction, better occupational safety and greater risk management capabilities, ergonomics and environmental sustainability. The biggest barriers to technology adoption are also highlighted: high investment costs, as well as integration and lack of qualified employees.

A STUDY ON CUSTOMER AWARENESS ON E- BANKING SERVICES AT UNION BANK OF INDIA, MANGALORE

E-banking is fast becoming a norm in the developed world, and is being implemented by many banks in developing economies around the globe. The main reason behind this success is the numerous benefits it can provide, both to the banks and to customers of financial services. For banks, it can provide a cost effective way of conducting business and enriching relationship with customers by offering superior services, and innovative products which may be customized to individual needs. For customers it can provide a greater choice in terms of the channels they can use to conduct their business, and convenience in terms of when and where they can use e- banking. Over the last decade India has been one of the fastest adopters of information technology, particularly because of its capability to provide software solution to organizations around the world. This capability has provided a tremendous impetuous to the domestic banking industry in India to deploy the latest in technology, particularly in the Internet banking and e-commerce areas. Technology is playing a major role in increasing the efficiency, courtesy and speed of customer service. An Online Banking user is expected to perform transactions online such as Checking account balance and transaction history, Paying bills, Transferring funds between accounts, Requesting credit card advances, ordering checks, Managing investments and stocks trading. From a bank‟s perspective, using the Internet is more efficient than using other distribution mediums because banks are looking for an increased customer base. Moreover Internet delivery offers customized service to suit the needs and the likes of each user. Mass customization happens effectively through Online Banking. It reduces cost and replaces time spent on routine errands with spending time on business errands. Online Banking means less staff members, smaller infrastructure demands, compared with other banking channels. From the customers‟ perspective, Online Banking provides a convenient and effective way to manage finances that is easily accessible 24 hours a day, seven days a week. In addition information is up to date. Nevertheless Online Banking has disadvantages for banks like how to work the technology, set- up cost, legal issues, and lack of personal contact with customers. And for customers there are security and privacy issues.

Sustainable tool technology: Wood-based forming tools for deep drawing of sheet metal

In order to improve adaptability to evolving manufacturing processes in an era marked by mass customization and individualization, while also meeting geopolitical climate objectives, it is necessary to employ more sustainable methods in sheet metal forming to enhance flexibility. Metal based rigid tooling systems require a lot of material and energy resources, when aiming for high flexibility and scalability with respect to small batch size production. To reduce the overall resource consumption and to meet climate restrictions, non-metallic tooling approaches can counteract these issues when aiming for individualization in sheet metal forming. Compared to conventional metal-based tooling approaches, wood-based forming tools may enable a potential resource reduction (e.g., carbon footprint) and offer an alternative tooling approach for sheet metal forming. Although the mechanical performance of wood is lower compared to conventional tooling steel, certain wood-based forming tools can potentially be implemented for sheet metal forming. In this work a feasibility study of wood-based tooling materials is executed to investigate the mechanical performance on drawn sheet metal parts in a deep drawing operation. Different composite structures are investigated for wooden forming tools of black locust (robinia pseudoacacia).

Management of rule-based product-portfolios with high variance: a systematic literature review

AbstractThis paper conducts a systematic literature review about the management of rule-based product-portfolios with high variance. This type of portfolio is particularly distinctive in the chosen use case of the German automotive industry since it satisfies the requirements of mass customization and modularization. However, the research field of variant- and complexity management is manifold and multidimensional. This paper systematically searches the databases Scopus and Web of Science using the PRISMA method and briefly summarizes the main contributions and comparing them by elaborated topics.

Supporting decision-making of collaborative robot (cobot) adoption: The development of a framework

Collaborative robots (cobots) are emerging in manufacturing as a response to the current mass customization production paradigm and the fifth industrial revolution. Before adopting this technology in production processes and benefiting from its advantages, manufacturers need to analyze the investment. Therefore, this study aims to develop a decision-making framework for cobot adoption, incorporating a comprehensive set of quantitative and qualitative criteria, to be used by decision-makers in manufacturing companies. To achieve that objective, a qualitative study was conducted by collecting data through interviews with key actors in the cobot (or advanced manufacturing technologies) adoption decision process in manufacturing companies. The main findings of this study include, firstly, an extensive list of decision criteria, as well as some indicators to be used by decision-makers, some of which are new to the literature. Secondly, a decision-making framework for cobot adoption is proposed, as well as a set of guidelines to use it. The framework is based on a weighted scoring method and can be customizable by the manufacturing company depending on its specific context, needs, and resources. The main contribution of this study consists in assisting decision-makers of manufacturing companies in performing more complete and sustained decision analyses regarding cobots adoption.

Strategy design of fresh e-commerce pre-warehouse based on mass customization

Pre-warehouse is the last link in the delivery process of fresh e-commerce. The purchase link of pre-warehouse needs to balance two contradictory goals: meeting the diverse individual needs of customers and reducing the delivery cost through scale. The idea of mass customization is a trade-off between economy of scope and economy of scale. Based on this idea, this paper constructs a mass customization model, and formulates the purchase strategy for the pre-warehouse based on the prediction of customer demand, so as to maximize the long-term profit of enterprise. Through the simulation experiment, this paper compares the comprehensive performance of the mass customization model with five other contrastive strategy models: the highest satisfaction strategy, the demand priority strategy, the lowest purchasing price strategy, the highest selling price strategy and the highest gross margin strategy. Experimental results show that no matter how the parameters change, the mass customization model performs better than other models. What’s more, when the demand forecast is accurate, the substitutability between commodities is strong, and the delivery cost is high, the mass customization model has biggest advantage over other strategy models. The main innovation of this paper is introducing the idea of mass customization in pre-warehouse purchase problem, which not only expands the scope of application of mass customization, but also proposes a stably better and appliable pre-warehouse purchase strategy.

Tailoring Garment Fit for Personalized Body Image Enhancement: Insights from Digital Fitting Research

In the context of the Fashion Apparel Industry 4.0, a transformative evolution is directed towards the Online Apparel Mass Customization (OAMC) strategy, which provides efficient and personalized apparel product solutions to consumers. A critical challenge within this customization process is the determination of sizes. While existing research addresses comfort evaluation in relation to wearer and garment fit, little attention has been given to how garment fit influences the wearer’s body image, which is also an important purchase consideration. This study investigates the impact of garment fit on the wearer’s body scale perception using quantitative research design. A digital dataset of avatars, clothed in varying sizes of T-shirts, were created for the body scale perception experiment, and an Artificial Neural Network (ANN) model was developed to predict the effect of T-shirt fit on body image. With only a small number of garments and body measurements as inputs, the ANN model can accurately predict the body scales of the clothed persons. It was found that the effect of apparel fit on body image varies depending on the wearer’s gender, body size, and shape. This model can be applied to enhance the online garment shopping experience with respect to personalized body image enhancement.

Advancements in multi-material additive manufacturing of advanced ceramics: A review of strategies, techniques and equipment

Ceramics possess notable attributes including excellent wear resistance, exceptional high-temperature strength, superior hardness, and outstanding biocompatibility, rendering them indispensable for the fabrication of key industrial components. Additive manufacturing presents significant benefits such as precise fabrication, design flexibility, and the ability for mass customization. This technology facilitates the creation of intricate structural or functional ceramic components with complex geometries, effectively addressing issues that are inadequately managed by conventional methods, such as the inherent brittleness of ceramics and challenges associated with ceramic machining. As advancements in aerospace, special industry, and biomedical sectors progress, the performance limitations of uniform ceramic materials and structures have emerged as a bottleneck constraining the broadening of their application range. There is a pressing need to transition from single-material ceramic additive manufacturing to multi-material printing. This paper highlights and compares various notable advancements in typical single-material ceramic additive manufacturing. In addition, it delves into the present status of multi-material ceramic additive continuous forming and the development of associated equipment, focusing on different forming mechanisms. Finally, the paper concludes by identifying the primary challenges and research priorities in multi-material ceramic additive manufacturing, and suggests potential directions for future development.

Feature-based energy consumption quantitation strategy for complex additive manufacturing parts

Additive manufacturing (AM) has significant advantages, including design freedom, mass customization, and complex-structure manufacturing capabilities. As reducing the manufacturing energy is challenging in terms of the industrial sustainability, the AM energy consumption must be further explored. Existing energy consumption quantitation methods for AM require complex models that considering the energy characteristics of equipment. Therefore, we propose a feature-based energy consumption quantitation method for complex AM parts using simple models and suitable for different AM technologies. First, a feature segmentation method is proposed to divide complex AM parts into typical AM features. Then, the energy consumption model for each AMF is developed for energy consumption quantification during part fabrication. Finally, the energy consumption of a typical mechanical part manufactured via three different AM processes is investigated using the proposed method and measured experimentally. The results show that the proposed method can effectively and rapidly predict the energy consumption of AM processes with an accuracy of more than 95 %. Furthermore, the efficiency of the three AM processes is compared and discussed to address suitable efficiency improvement methods. In general, the proposed method can be integrated into three-dimensional AM models, providing a reference for the structural optimization of AM parts and sustainable manufacturing.

Securing industry 5.0 using 6σ CYBERNETIC framework

The data-driven digital economy highly relies on immersive and emerging technologies, mass customisation, autonomous systems, and seamless connectivity. Enabling such an Industrial IoT/Industry 5.0 environment requires streamlined and end-to-end transparent methods for insights, visibility, and control. However, it is important to note that its success depends on data security metrics. The recent cyber-attacks in healthcare and industrial infrastructures have led service providers to high-risk scenarios. From supply chain to service delivery, remote functionality variables, enabling a fully connected factory is a major cybersecurity concern as emerging technologies employ different security requirements. To mitigate these risks, strategic, operational, technical, and cybersecurity alignment is a must, where the gaps between the production and process environments must be bridged to achieve the prime goal of a sustainable, secure, and technologically innovative factory. This research provides a systematic approach to bridging Industry 5.0’s QoS metrics and security gaps by implementing a Six Sigma (6σ) approach in a manufacturing environment. The approach further maps IT/OT, cloud, and cybersecurity standards, thereby enabling insights, visibility, and control. A healthcare 5.0 use-case is demonstrated to show how a 6σ implementation can improve the QoS metrics, unifying standards to achieve a secure, sustainable, resilient, and high-performance environment.

A matheuristic-based multi-objective evolutionary algorithm for flexible assembly jobs shop scheduling problem in cellular manufacture

The flexible assembly job shop scheduling problem (FAJSP) and its extensions have received increased attention. However, multi-objective FAJSP in cellular manufacture (FAJSP-CM), a real-world extension of FAJSP with important applications in the mass customization of complex products, is barely considered in most previous studies. Hence, this study first addresses the multi-objective FAJSP-CM problem via a mixed-integer linear programming model (MILP). Then, a matheuristic-based multi-objective evolutionary algorithm (MMOEA), with a matheuristic decoding method, knowledge-guided initialization and learning-based local intensification method, is presented to efficiently tackle the FAJSP-CM problem in polynomial-time. The matheuristic decoding method, which hybrids a decomposition linear programming model (D-LP) that can quickly and optimally allocate the execution times of all operations, to search for the optimal solution in a specific space. The knowledge-guided initialization is extended to get a high-quality and great-diversity initial population for accelerated convergence. Meanwhile, the learning-based local intensification method is developed to adaptively coordinate five problem-specific local search operators to enhance the exploitation ability of the MMOEA algorithm. Finally, comprehensive experiments based on 720 instances are conducted to evaluate the proposed MILP model and MMOEA algorithm. The results of the first experiments illustrate the MILP model can obtain the Pareto solutions of small-scale instances. The remaining experiments demonstrate that the proposed MMOEA algorithm outperforms its competitors by 0.21 and 0.72 in hypervolume ratio and coverage indictors, along with approximately a 25 % improvement in success rate.

Collective intelligence-driven 3D printing factory for social manufacturing: implementing a testbed for industrial application

ABSTRACT The emergence of 3D printing technology has imbued the mass customization production model with novel implications. Concurrently, investigations into social manufacturing (SocialM) and collective intelligence present a fresh challenge for the 3D printing industry in their pursuit of realizing customized mass production. However, there is still a lack of investigation on the technical implementation and application scenarios of SocialM, and it hinders the development of SocialM from theory to industrial application. To mitigate this gap, firstly a five-layer framework based on collective intelligence for the configuration of design-production-service integrated 3D printing factory is established, together with the key enabling techniques that support the configuration and operation of the factory from social interaction software perspective and cyber-physical-social interconnection perspective. Secondly, the running logic of the 3D printing factory is demonstrated, and it starts from order generation to order completion. Thirdly, a testbed of the 3D printing factory is built, which contains both social interaction software and physical production hardware environments, and a production order of a 3D printed robotic arm is used to verify the feasibility of the testbed and the configuration and operation theories of SocialM. The work in this paper provides technical solutions for the industrial application of SocialM.

Mass individualism: The snowman effect

This study critically evaluates contemporary menswear, with a particular focus on the ‘snowman effect’ and mass individualism, to understand the complex dynamics between performativity, gender and socio-styles. It is inspired by the story of ‘Frosty the Snowman’, which illustrates how garments, initially mass-produced, are given life and identity through personalization. This transformation metaphorically represents the fashion industry’s complex relationship between individual expression and societal norms. The article addresses the issue of overconsumption, arguing that it leads to uniform fashion styles despite an abundance of clothing options, thereby increasing the demand for more authentic and personalized fashion. The core of this research examines ‘mass individualism’ within the global fashion industry, exploring its potential to offer sustainable choices while highlighting the challenges and opportunities it presents to major fashion brands. It argues for a shift away from traditional marketing, emphasizing the need for tailored services that meet consumers’ desires for authenticity and individuality. The article also examines the historical constraints on menswear, the evolution of socio-styles and the impact of ‘fit’ on the appropriateness of clothing. It also examines consumer habits and the growing trend towards customization in men’s fashion, particularly in terms of performative individuality and social norms. Finally, the study emphasizes the importance of recognizing the contemporary consumer’s desire for uniqueness and suggests mass customization as a vital strategy for revitalizing the menswear sector, reflecting the changing relationship between fashion, gender performance and socio-styles in contemporary society.