Mass customized/personalized manufacturing in Industry 4.0 and blockchain: Research challenges, main problems, and the design of an information architecture

Pérez, Andrea Teresa Espinoza; Rossit, Daniel Alejandro; Tohmé, Fernando; Vásquez, Óscar C.

doi:10.1016/j.inffus.2021.09.021

Cited by 59 publications

(16 citation statements)

References 95 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Industry 4.0 enables the manufacturing system with a highly dedicated and autonomous control and monitoring system resulting in mass personalization and customization (Perez et al, 2022). Therefore, integrating Industry 4.0 with LSS is all set to strengthen the continual improvement approach.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of critical success factors for successful integration of lean six sigma and Industry 4.0 for organizational excellence

Samanta

Virmani

Singh

et al. 2023

TQM

View full text Add to dashboard Cite

PurposeManufacturing industries are facing dynamic challenges in today’s highly competitive world. In the recent past, integrating Industry 4.0 with the lean six sigma improvement methodologies has emerged as a popular approach for organizational excellence. The research aims to explore and analyze critical success factors of lean six sigma integrated Industry 4.0 (LSSI).Design/methodology/approachThis research study explores and analyzes the critical success factors (CSFs) of LSSI. A three-phase study framework is employed. At first, the CSFs are identified through an extensive literature review and validated through experts’ feedback. Then, in the second phase, the initial list of CSFs is finalized using the fuzzy DELPHI technique. In the third phase, the cause-effect relationship among CFSs is established using the fuzzy DEMATEL technique.FindingsA dyadic relationship among cause-and-effect category CSFs is established. Under the cause category, top management commitment toward integrating LSSI, systematic methodology for LSSI and organizational culture for adopting changes while adopting LSSI are found to be topmost CSFs. Also, under the effect category, organizational readiness toward LSSI and adaptability and agility are found to be the uppermost CSFs.Practical implicationsThe study offers a framework to understand the significant CSFs for LSSI implementation. Insights from the study will help industry managers and practitioners to implement LSSI and achieve organizational excellence.Originality/valueTo the best of the authors’ knowledge, CSFs of LSSI are not much explored in the past by researchers. Findings will be of great value for professionals in developing long-term operations strategies.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Analysis of critical success factors for successful integration of lean six sigma and Industry 4.0 for organizational excellence

Samanta

Virmani

Singh

et al. 2023

TQM

View full text Add to dashboard Cite

show abstract

“…Companies are enabling collaborative sharing and personalized services through digital platforms, which has emerged as one of the most important strategies for companies seeking to achieve innovative growth ( Matthews et al, 2021 ; Espinoza Pérez et al, 2022 ; Liu Z. et al, 2022 ; Zhang H. et al, 2022 ). For smart manufacturing, the industrial Internet connects the physical world and cyberspace ( Srinidhi et al, 2019 ; He L. et al, 2020 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Investigate the Influencing Factors of Industrial Design Platform Demand: From the Perspective of Emotional Interaction

Zhang¹,

Yang²,

Tong³

et al. 2022

Front. Psychol.

View full text Add to dashboard Cite

With the deep integration of industries brought about by big data technology, users’ design needs are diversifying and individuating. Thanks to big data technology, users’ diverse design needs can be precisely met. Meanwhile, big data can be used to realize emotional interaction for personalized design needs of users, resulting in a better user experience. Using grounded theory to mine user demand text data, this paper investigates the influencing factors of emotional interaction and dynamic resource allocation in the digital design supply chain. The results show that government-driven factors have a direct impact on the demand for industrial design in user emotional interactions. Market factors are the most fundamental in the development of an industrial design service platform, and universities play an important role in this. Furthermore, a lack of market sensitivity stems from a lack of emotional interaction with users, resulting in a schism between industry, university, and research, which has become a major impediment to the development of China’s industrial design industry. This study not only lays the theoretical groundwork for understanding the mechanisms of user emotional interaction on IDSPs, but it also points the way forward for future industrial design service platform development.

show abstract

“…To this aim, a shift from the mass production to the mass customization paradigm is required [10,11], which also necessitates a transformation in the manufacturing planning and control (MPC) structure. The primary challenge in a mass customization scenario is the variability introduced by highly personalized customer orders.…”

Section: Introductionmentioning

confidence: 99%

Generalised Performance Estimation in Novel Hybrid MPC Architectures: Modeling the CONWIP Flow-Shop System

et al. 2023

View full text Add to dashboard Cite

The ability to supply increasingly individualized market demand in a short period of time while maintaining costs to a bare minimum might be considered a vital factor for industrialized countries’ competitive revival. Despite significant advances in the field of Industry 4.0, there is still an open gap in the literature regarding advanced methodologies for production planning and control. Among different production and control approaches, hybrid architectures are gaining huge interest in the literature. For such architectures to operate at their best, reliable models for performance prediction of the supervised production system are required. In an effort to advance the development of hybrid architecture, this paper develops a model able to predict the performance of the controlled system when it is structured as a controlled work-in-progress (CONWIP) flow-shop with generalized stochastic processing times. To achieve this, we employed a simulation tool using both discrete-event and agent-based simulation techniques, which was then utilized to generate data for training a deep learning neural network. This network was proposed for estimating the throughput of a balanced system, together with a normalization method to generalize the approach. The results showed that the developed estimation tool outperforms the best-known approximated mathematical models while allowing one-shot training of the network. Finally, the paper develops preliminary insights about generalized performance estimation for unbalanced lines.

show abstract

Mass customized/personalized manufacturing in Industry 4.0 and blockchain: Research challenges, main problems, and the design of an information architecture

Cited by 59 publications

References 95 publications

Analysis of critical success factors for successful integration of lean six sigma and Industry 4.0 for organizational excellence

Analysis of critical success factors for successful integration of lean six sigma and Industry 4.0 for organizational excellence

Investigate the Influencing Factors of Industrial Design Platform Demand: From the Perspective of Emotional Interaction

Generalised Performance Estimation in Novel Hybrid MPC Architectures: Modeling the CONWIP Flow-Shop System

Contact Info

Product

Resources

About