Digital transformation to empower smart production for Industry 3.5 and an empirical study for textile dyeing

• Published in: Computers & industrial engineering Vol. 142; p. 106297
• Main Authors: Ku, Chien-Chun, Chien, Chen-Fu, Ma, Kang-Ting
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2020
• Subjects: Decision support system; Digital transformation; Industry 3.5; Smart production; Textile dyeing machine scheduling; Traditional industry; Traditional industry; Smart production; Digital transformation; Textile dyeing machine scheduling; Decision support system; Industry 3.5
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2020.106297

Digital transformation for traditional industry is proposed to empower Smart Production for Industry 3.5. Decision support system integrating the related decisions is developed to provide effective scheduling solutions and remove information silos. An empirical study that was conducted for textile dyeing has validated practical viability of the developed solution. [Display omitted] •Digital transformation to empower intelligent manufacturing for Industry 3.5.•Information silos are reduced.•Decision support system is developed for integrated decisions.•A single-machine-scheduling using sorting priority dispatching rules is developed.•An empirical study was conducted in textile for validation. Most of traditional industries in emerging countries may not be ready to migrate for Industry 4.0 directly. There is a need of effective solutions to support digital transformation of traditional industries. Textile industry is facing global competition for mass customization to address dynamic customer demands. To enable the challenge from mass production to build-on-demand with small lot size and diversified product mixes, this study aims to develop a solution to support traditional industries to adopt smart manufacturing and empower digital transformation. Following a framework as systematic approach to collect, identify, and analyze related steps and decisions for an organization, a decision support system for dyeing machine scheduling is developed to empower smart manufacturing and break down information silos. In particular, setup time for textile dyeing operations is sequence-dependent, and products of different types and colors require setups for tank cleaning. The results have shown the practical viability of the proposed approach and Industry 3.5. Indeed, the developed solution has been implemented in a textile company in Taiwan.