Mass Customization Research Articles

Data-driven analysis and human-centric assignment for manual assembly production lines

Flow production-oriented assembly production lines have become crucial in various industries with the development of Industry 4.0, leading to mass customization and personalized production. In addition, Industry 5.0 has emerged, which emphasizes human-centric design. Manual assembly production line balancing, considering worker ergonomics, has also gained substantial attention. This study aims to solve the line balancing problem through combination optimization considering ergonomic risks based on many studies on human-centric production lines. A framework of the data-driven analysis and assignment for manual assembly production lines is proposed herein. The proposed framework defines and analyzes the process difficulty and the worker’s process ability and assigns a skilled worker to a process through the presented heuristic algorithm. To demonstrate the applicability of the proposed framework, a case study was conducted on an experiment imitating a home appliance assembly line in the United States. This paper provides empirical evidence of a framework aimed at realizing human-centric business objectives through case study. Future studies on the data-driven analysis and assignment for manual assembly production lines are anticipated to exhibit a range of variations such as line balancing problems and evaluation methods.

A review of parametric clothing pattern CAD software methodology

PurposeThe main issue in the mass customization of apparel products is how to efficiently produce products of various sizes. A parametric pattern-making system is one of the notable ways to rectify this issue, but there is a lack of information on the parametric design itself and its application to the apparel industry. This study compares and analyzes three types of parametric clothing pattern CAD (P-CAD) software currently in use to identify the characteristics of each, and suggest a basic guideline for efficient and adaptable P-CAD software in the apparel industry.Design/methodology/approachThis study compared three different types of P-CAD software with different characteristics: SuperALPHA: PLUS(as known as YUKA), GRAFIS and Seamly2D. The authors analyzed the types and management methodologies of each software, according to the three essential components that refer to previous studies about parametric design systems: entities, constraints and parameters.FindingsThe results demonstrated the advantages and disadvantages of methodology in terms of three essential components of each software. Based on the results, the authors proposed five strategies for P-CAD development that can be applied to the mass customization of clothing.Originality/valueThis study is meaningful in that it consolidates and organizes information about P-CAD software that has previously been scattered. The framework used in this study has an academic value suggesting guidelines to analyze P-CAD systems.

Embracing the Digital Intelligence: A Strategic Approach to Optimizing Mass Customization

This paper provides an insightful exploration of Mass Customization (MC), a rising trend in the modern manufacturing landscape that couples the efficiency of large-scale production with the allure of personalized products. It elucidates MC’s operational, market-related, and customer engagement challenges, offering optimization strategies grounded in digital intelligence technology and Supply Chain Agility (SCA). The paper delves into the role of 3D printing, Flexible Manufacturing Systems, data-driven designs, and Digital Twin Technology, illustrating how these technologies can enhance MC processes. It also underscores the importance of SCA in mediating the complex relationship between these technologies and MC capabilities, thus offering a nuanced understanding of its implementation. The study concludes that the integration of digital intelligence technology, paired with the enhancement of SCA, can effectively navigate the complexities of MC, providing a competitive edge in today’s volatile market landscape. This research is a significant theoretical and practical guide for organizations aspiring to transition toward MC amidst the digital revolution.

Intelligent and self- reconfigurable manufacturing system

Customer demand and profit potential have pushed companies to offer diverse customization options in their products and move towards mass customization. To move towards mass customization reconfigurable manufacturing systems (RMS) have been prosed as a solution. However, it comes with challenges, including labor, costs, time, and complexity. Introducing intelligence, smartness and state-of-the-art industry 4.0 technologies into reconfigurable manufacturing systems (RMS) can be a solution to these challenges. Combining these concepts can be used to develop an intelligent RMS that can be automatically reconfigured without any human intervention. In this paper, a novel architecture has been proposed that explains all the details from the idealization phase to design and physical implementation. The architecture covers individual elements of the RMS, introduces the technologies that enable intelligence and follows through the steps that add up to the complete functionality of the system. In addition, we propose an intelligent process that automates the reconfiguration phase of our proposal. Based on this architecture and intelligent recon-figuration, a physical demonstration is also presented that shows how the system can be implemented. The constructed system is demonstrated as an intelligent RMS capable of mass customization without human effort.

Trustworthiness of Artificial Intelligence Applications for Quality Optimisation in Metal Additive Manufacturing

Market demand is driving the trend towards more individualised products, a phenomenon known as mass customisation. This has spurred the development of innovative technologies, collectively referred to as Industry 4.0. Within this framework, Metal Additive Manufacturing and Artificial Intelligence serve as cornerstone technologies. In manufacturing, Metal Additive Manufacturing promises the ability to efficiently produce low-volume products with reduced lead times. At the same time, it promises greater sustainability, reduced production complexity and improved supply chain resilience. However, the complex physical processes involved are beyond the capabilities of traditional quality improvement techniques such as Statistical Quality Control, necessitating the incorporation of Artificial Intelligence. Although the AI Act seeks to establish a robust regulatory framework to improve the trustworthiness of AI applications, empirical studies substantiating this trust in the context of quality optimisation remain scarce. To address this gap, this study explores the implementation of trustworthy AI in quality optimisation for Metal Additive Manufacturing, evaluating established frameworks and employing state-of-the-art methods to validate the trustworthiness of AI-based quality optimisation techniques. In addition, the physical plausibility and reliability of the AI models are qualitatively checked by process experts.

Improving the Competitiveness of the Manufacturing Industry using Mass Customization

Improving the Competitiveness of the Manufacturing Industry using Mass Customization

Modular design of new products and remanufacturing of used products under mass customization

Modular architecture design can not only increase the product variety and thus make mass customization (MC) easier, but also improve interchangeability and then lower the remanufacturing cost. This paper models a remanufacturing MC supply chain with one manufacturer and one retailer. The manufacturer, as a Stackelberg game leader, designs and then sells the new and modular MC products via its brand retailer, and meanwhile the remanufacturing of used MC products would be carried out by either the manufacturer or the retailer. We study the optimal modular design for new MC products and the best remanufacturing mode for used MC products. One centralized and two decentralized decision models are formulated, and the equilibrium solutions of proposed models are compared and analyzed. Findings show that, compared to the retailer-remanufacturing mode, the manufacturer-remanufacturing mode can be superior in terms of the ability to cope with customer customization requirements (i.e., the modularity level), total supply chain profit, and consumer surplus. Moreover, both supply chain members and consumers would prefer the manufacturer-remanufacturing mode when the parameters meet certain conditions. We also extend the model to two scenarios: the remanufacturing is bounded by cores supply and the retailer acts a leader, so as to verify the robustness of main results. The above theoretical results are verified by numerical examples.

Digital fabrication with FDM 3D printing on prototype development of toys: Additive manufacturing and sustainability

Additive manufacturing, commonly referred to as 3D printing, is currently one of the most dominant developments in the manufacturing sector. Consumer demands for more customized goods and services, in conjunction with the emergence of advanced manufacturing technologies, are influencing changes in the scope and distribution of manufacturing. In this work, the function of digital fabrication, one of these advanced manufacturing process technologies, is examined. The Digital Manufacturing and even other advanced manufacturing technologies have a significant footprint in all future manufacturing endeavours because of the robustness and mass customization that they provide. The Fused Deposition Modelling (FDM) is a sustainable method used extensively for the development of prototypes for toys. The present study investigates the potential of FDM process as a method for prototype development. Additionally the advantages of FDM process from the viewpoint of sustainable manufacturing integrating innovativeness and do-it-yourself approach at home driven by consumer demand for products such as toys will also be investigated. 3D printing technology such as FDM process, where objects such as toy prototypes are constructed by adding materials layer by layer has been in the forefront of various Digital Manufacturing techniques as it reduces material wastage thereby optimizing resource utilization.

Implementing Additive Manufacturing for Sustainability in Operations: Analysis of Enabling Factors

Additive manufacturing (AM) is heralded as a new manufacturing paradigm with significant environmental, economic, and social sustainability consequences. The purpose of this study is to identify and evaluate the sustainable enablers of AM. The research has identified 19 sustainable enablers of AM belonging to three areas: social, economic, and environmental, through an in-depth literature review. The decision-making trial and evaluation laboratory (DEMATEL) approach has been applied after taking inputs from industry experts to develop causal relationships (cause–effect diagram) and to analyze each enabler's influence. The cause–effect diagram has been developed based on threshold values. Our observations revealed that weight reduction, reduced time to market, mass customization, and personalization are the most critical enablers for sustainability. From visual representation, reduced toxicity of materials, no need for tooling, and ease of accessibility for remote part production have a substantial impact on effect enablers. This study will facilitate AM practitioners to systematically identify and prioritize essential enablers from a sustainability perspective. Practitioners should focus on these cause group enablers because it will help in controlling the effect of group enablers to improve sustainability in operations. Furthermore, insights from the study will enable policymakers to make policies leading to sustainable development. The novelty lies in identifying 19 enablers and incorporating the DEMATEL model to assess the causal relationship.

How Do Goal Orientations Affect Organizational Agility? The Mediating Effects of Ambidextrous Operational Capabilities

Organizational agility is critical to the firms’ survival and prospects in today's complex environment. Although the firms’ goal orientations regulate their efforts in response to market changes and influence organizational agility, existing studies remain inconclusive on the effects of goal orientations on organizational agility and leave the underlying mechanisms of such effects unclear. In this article, we draw upon the orientation–action—outcome framework to look inside the black box of how two types of goal orientations (i.e., learning and performance goal orientation) affect organizational agility by examining the mediating effects of ambidextrous operational capabilities. We also investigate the moderating effects of competitive intensity on the mediating effects of ambidextrous operational capabilities. Using archival data and two-wave multirespondent survey data from 387 top managers of 129 firms, the results reveal important findings. First, the total impact of learning goal orientation on organizational agility is insignificant, but that of performance goal orientation is significant. Second, mass customization and innovative ambidexterity significantly mediate the indirect impacts of learning goal orientation and performance goal orientation on organizational agility. Third, competitive intensity does not influence the indirect effects of learning goal orientation, whereas it weakens the indirect effects of performance goal orientation. We also conducted the fsQCA, the results of which reinforced and refined the findings of mediation and moderated-mediation analysis. These results provide new insights into the underlying mechanisms through which goal orientations contribute to agility and the boundary condition of these mechanisms as well as various configurations associated with high/low agility.

A user-friendly toolkit to select and design multi-purpose grippers for modular robotic systems

Robotic grasping is a billion-dollar industry with significant growth potential for the next decade. Unsurprisingly, technology providers and integrators are increasingly assisting in optimising production efficiency by implementing industrial robots and the necessary end-effectors into production lines. However, still too often in today’s robotic applications, dedicated grippers are designed to perform single tasks therewith adversely increasing cycle times and robot cell spaces due to constantly switching grippers from a tool magazine. In order to reduce handling costs and to cope with mass customisation, versatile grasping of various objects with multi-purpose grippers is key in evolving to modular assembly systems.Nowadays, thoughtful selection and design of robotic grippers is still mainly restricted to expert knowledge due its ad hoc and multi-objective nature. However, gripper selection and design should be tailored to the properties of the objects to be grasped, and the tasks to be executed. As a result, current solutions often end up being partly appropriate and/or over-dimensioned. The use of software tools to support this complex decision-making process is needed but limited so far.This paper presents a user-friendly toolkit that assists in selecting appropriate robotic grippers for grasping pre-specified ranges of objects. For this, a web API has been established for accessing the available gripper database and selection methodology. Additionally, this toolkit assists in designing multi-functional jaws for impactive grippers, and lay-outing individually positioned astrictive grippers. The underlying optimiser will try to find the most ideal grasping points on each object in accordance with quality metrics and constraints, and this for each of the selected grippers. To demonstrate the functionality of the presented toolkit, two case studies on impactive and astrictive gripper designs have been worked out involving 3D mechatronic and 2D sheet metal parts.

Reconfigurable Manufacturing Systems through Intelligent Workpiece Handling and Artificial Intelligence

In recent years, businesses have strived to align their manufacturing goods with the unique needs and preferences of their customers. However, the mass customization paradigm introduces challenges mostly associated to the manipulation of small product batches and knowledge management. This paper proposes a framework for intelligent reconfigurable manufacturing systems, that leverages robotic automation supported by a collaborative dual-arm manipulator, cognitive mechatronic devices, and machine-learning algorithms. In more detail, pioneer end-effectors and machinery provide the capacity of grasping and handling a variety of products without time-consuming hardware modifications. The backbone of this manipulation scheme is a set of machine learning tools for: a) the planning and synchronization of handling agents, b) identification and localization of workpieces, c) the generation of optimal packaging configurations based on customer orders and identified workpieces, and d) the allocation of the respective handling operations to robots, operators, and machinery. The proposed framework is evaluated through its implementation on a use case deriving from the metal industry. The particularities of this use case, as expressed in product variety, workpiece complexity, customer orders and intralogistics, provide a fertile ground for the validation of the discussed technologies.

Modularity-Based Mass Customization for the Competitiveness of the Manufacturing Industry

Modularity-Based Mass Customization for the Competitiveness of the Manufacturing Industry

Research on key technology of mass customization based on flexible manufacturing in the context of deep learning

Abstract In this paper, we first use particle swarm optimization to improve the BP neural network algorithm to optimize the neural network connection weights and thresholds and then apply it to the flexible manufacturing system to build a complete, flexible manufacturing process of mass customization. Based on the nonlinear mapping relationship between customer demand and product structure, a three-layer network is used to identify the effective demand of customers as well as the actual risk of the enterprise in order to satisfy the common users who do not care about the product structure and the optimization of industrial structure. After completing the configuration, the system is applied to an automobile company and a manufacturing company for testing purposes. After a long time of use, the average accuracy error is 0.0061. The value of the body module ignored by the customer is as high as 0.129, and the weight of the indicator system of the traveling control module is as low as 0.072, but it has become one of the most important indicators of the potential demand of the customer. Supply chain risk has a maximum difference of 6% when compared to the actual values. The manufacturer’s expectation value of 3.8967 for SCRF11 is “low”.

Comparison of Safety Mechanisms for Human-Robot Collaboration in Assembly using a Top-View RGB-D Camera System

The paradigm shift from mass production to mass customization is leading to new requirements in today's manufacturing environment. Especially in manual manufacturing processes, these requirements are characterized by high-cost pressure, increasing variant diversity and required lot size. In contrast to full-automation settings with industrial robots, human-robot collaboration offers the potential to design adaptable hybrid solutions and generate individual levels of automation in rapidly changing production lines. The safety of the operator in collaborative robot applications is one of the key factors for facilitating their use in industrial practice. This paper addresses this issue and presents a solution for safe human-robot collaboration using a top-view RGB-D camera system that continuously monitors the shared workspace. Based on the detection of visual markers and human hand tracking, different safety strategies according to DIN ISO/TS 15066 are implemented and successively compared in a paradigmatic assembly scenario as a part of a user study with 12 test persons. The utilized safety strategies include safety-rated monitored stop, speed and separation monitoring, and active contact prevention. Compared to the incorporated safety features of collaborative robots, the results show that the acceptance and fluency of collaborative robot applications improves, but the execution time increases at the same time.

Design and Additive Manufacturing of a Prototype Using Fused Deposition Modeling Technique

A screw jack is a tool for lifting heavy loads with little effort. In this paper, the design, model and fabrication of a Screw jack using 3D Printing Technology is discussed. Digital fabrication, or 3D printing or additive manufacturing, creates physical objects from a geometrical representation by successive addition of materials. In agriculture, health care, motor vehicles, locomotives and aviation, 3D Printing is increasingly being applied for mass customization or production for all types of open-source design. Design is an important industrial activity which influences the quality of the product. The parts of a screw jack are modelled by using the modelling software Creo Parametric. The CAD model of the screw jack is then converted into. STL (Standard Tessellation Language) format in which the 3D Printer receives the printing input. The STL files are then imported to modelling software to analyze mesh and rectify errors. The rectified STL files of parts of the screw jack are imported to Makerbot software, and parts are made ready for 3D Printing using Makerbot FDM (Fused Deposition Modelling) 3D Printer. The parts printed in an FDM 3D printer are assembled based on the tolerances obtained, and the final prototype is fabricated.

The Impact of ERP Assimilation on Mass Customization Capability: A Dynamic Capabilities View

The Impact of ERP Assimilation on Mass Customization Capability: A Dynamic Capabilities View

GET WITH THE PROGRAM: PROGRAMMATIC ADVERTISING AND THE DATAFICATION OF PODCAST AUDIENCES

The podcasting landscape has been reshaped in the past several years by acquisitions and mergers among players in the industry. Major platform services like Spotify, SiriusXM, iHeartMedia, Google, and Apple have all attempted to more closely bind consumers to their proprietary services, threatening the open architecture of distribution via RSS. While control and monetization of intellectual property is one key driver of platformization in podcasting, another key institutional shift is being accelerated these changes: the datafication of the audience. In short, datafication involves the quantification of human activity to enable surveillance, prediction, and mass customization of advertising. In this paper, I explore one significant impact of widescale platformization within podcasting: the emergence of programmatic advertising markets. By essentially “listening in” to these industry discourses about podcast advertising (in podcasts and in the Podcast Upfront presentations from Spring 2022), this essay outlines the importance of platform-to-platform data transactions and highlights the resulting shifts in the podcasting ecosystem: away from the intimate, relationship-driven ethos of the medium and toward a quantitative, surveillance-driven ecosystem.

Big Data Analytics for Supply Chain Mass Customization: Prospects, Challenges, and Opportunities

In today's world, the topic of economic globalization is still on the rise, and trade between countries is becoming more and more frequent. At the same time of economic prosperity, customers' demand for products is clearer and more perfect, which brings more intense competition between countries and enterprises. As one of the four most important strategic resources in the 21st century, the supply chain also shows increasingly important positions in the wave of international competition. More accurate customization of customer demand has become particularly important to save resources, create better revenue, and accurately meet customer demand. Mass customization is built through big data analysis; supply chain management is mass customization's basis and starting point. Nowadays, flexible and changeable customer demand greatly improves the risk and pressure of enterprises, standing on the tip of the wind of digitalization, intelligence, and the use of information technology to improve the enterprise organizational structure and operational processes to become a problem for enterprises to change their problems, the use of big data for customization in the supply company to analyze the point of focus.

Learning to schedule dynamic distributed reconfigurable workshops using expected deep Q-network

Distributed manufacturing has become common in the globalized production environment. Facing the tendency of mass customization, distributed workshops are required to have the reconfiguration capability to produce multiple products. Based on the scheduling demands of distributed workshops, this paper studied the dynamic distributed reconfigurable flowshop scheduling problem (DRFSP) with new job arrivals using deep reinforcement learning (DRL) for the first time. The DRL-based intelligent scheduling and reconfiguration systems are modeled by designing efficient rewards, actions, and state features for both scheduling and reconfiguration agents. A new state feature normalization approach is proposed to address the variability in production configurations and dynamics. A novel expected deep Q-network (EDQN) algorithm is proposed to provide more precise state-action values rather than using estimations. Moreover, the DRL-based training and execution procedures for the dynamic DRFSP are provided by combining decision-making, agent learning, and reconfigurable production. Training curves validate the reasonableness of system modeling and the good learning efficiency of the EDQN algorithm. Extensive comparison experiments show that our EDQN algorithm outperforms three popular DRL algorithms and four well-known priority dispatching rules (PDRs) by around 20 % and 89 % respectively, with good generalization ability and time efficiency. The average decision time under dynamics is only 0.65 ms, which enables real-time scheduling.