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

Abstract 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.

[1]  Xin Wang,et al.  Decentralized Cyber-Physical Systems: A Paradigm for Cloud-Based Smart Factory of Industry 4.0 , 2017 .

[2]  Ying-Han Chen,et al.  Effect of digital transformation on organisational performance of SMEs: Evidence from the Taiwanese textile industry's web portal , 2016, Internet Res..

[3]  Chen-Fu Chien,et al.  A UNISON framework for knowledge management of university-industry collaboration and an illustration , 2019, Comput. Ind. Eng..

[4]  George Chryssolouris,et al.  On a Predictive Maintenance Platform for Production Systems , 2012 .

[5]  Thomas H. Davenport,et al.  Business Intelligence and Organizational Decisions , 2010, Int. J. Bus. Intell. Res..

[6]  Chen-Fu Chien,et al.  A UNISON framework for analyzing alternative strategies of IC final testing for enhancing overall operational effectiveness , 2007 .

[7]  Chen-Fu Chien,et al.  UNISON DECISION ANALYSIS FRAMEWORK FOR WORKFORCE PLANNING FOR SEMICONDUCTOR FABS AND AN EMPIRICAL STUDY , 2015 .

[8]  Elena Deza,et al.  Encyclopedia of Distances , 2014 .

[9]  Manoj Kumar Tiwari,et al.  Next generation smart manufacturing and service systems using big data analytics , 2019, Comput. Ind. Eng..

[10]  Chen-Fu Chien,et al.  UNISON analysis to model and reduce step-and-scan overlay errors for semiconductor manufacturing , 2011, J. Intell. Manuf..

[11]  C. Kruse,et al.  Barriers Over Time to Full Implementation of Health Information Exchange in the United States , 2014, JMIR medical informatics.

[12]  Chen-Fu Chien,et al.  Overlay Error Compensation Using Advanced Process Control With Dynamically Adjusted Proportional-Integral R2R Controller , 2014, IEEE Transactions on Automation Science and Engineering.

[13]  Chen-Fu Chien,et al.  A hybrid multi-subpopulation genetic algorithm for textile batch dyeing scheduling and an empirical study , 2018, Comput. Ind. Eng..

[14]  Walter Brenner,et al.  How AUDI AG Established Big Data Analytics in Its Digital Transformation , 2017, MIS Q. Executive.

[15]  Marinko Maslarić,et al.  Achieving competitive supply chain through business process re-engineering: A case from developing country , 2010 .

[16]  Tzu-Chuan Chou,et al.  Resource fit in digital transformation , 2011 .

[17]  Chen-Fu Chien,et al.  A Framework for Root Cause Detection of Sub-Batch Processing System for Semiconductor Manufacturing Big Data Analytics , 2014, IEEE Transactions on Semiconductor Manufacturing.

[18]  Yi Xie,et al.  A method for estimating the cycle time of business processes with many-to-many relationships among the resources and activities based on individual worklists , 2013, Comput. Ind. Eng..

[19]  W. J. Chen.  Scheduling with dependent setups and maintenance in a textile company , 2009, Comput. Ind. Eng..

[20]  Rajkumar Roy,et al.  Operations strategy for the effective delivery of integrated industrial product‐service offerings , 2011 .

[21]  Prasanta Kumar Dey.  Re-engineering materials management: A case study on an Indian refinery , 2001, Bus. Process. Manag. J..

[22]  Sigurdur Olafsson,et al.  Learning effective new single machine dispatching rules from optimal scheduling data , 2010 .

[23]  Yi Xie,et al.  A dynamic task assignment approach based on individual worklists for minimizing the cycle time of business processes , 2016, Comput. Ind. Eng..

[24]  E Ponce de León,et al.  Optimisation of the colour sequence in the dyeing process: industrial applications , 2000 .

[25]  Ying-Jen Chen,et al.  Bayesian inference for mining semiconductor manufacturing big data for yield enhancement and smart production to empower industry 4.0 , 2017, Appl. Soft Comput..

[26]  Filipe Portela,et al.  Step towards of a Homemade Business Intelligence Solution - A Case Study in Textile Industry , 2015, WorldCIST.

[27]  Marlene Amorim,et al.  Digital Transformation: A Literature Review and Guidelines for Future Research , 2018, WorldCIST.

[28]  Thomas Hess,et al.  Digital Transformation Strategies , 2015, Business & Information Systems Engineering.

[29]  Antonio Padovano,et al.  Smart operators in industry 4.0: A human-centered approach to enhance operators' capabilities and competencies within the new smart factory context , 2017, Comput. Ind. Eng..

[30]  Chen-Fu Chien,et al.  Modeling strategic semiconductor assembly outsourcing decisions based on empirical settings , 2008, OR Spectr..

[31]  Ingrid Schirmer,et al.  Bimodal IT: Business-IT alignment in the age of digital transformation , 2016 .

[32]  Chen-Fu Chien,et al.  An empirical study for smart production for TFT-LCD to empower Industry 3.5 , 2017 .

[33]  Chen-Fu Chien,et al.  A Novel Route Selection and Resource Allocation Approach to Improve the Efficiency of Manual Material Handling System in 200-mm Wafer Fabs for Industry 3.5 , 2016, IEEE Transactions on Automation Science and Engineering.

[34]  David J. Brailer,et al.  Economic Perspectives on Health Information Technology , 2005 .

[35]  Rose-Marie Dröes,et al.  User evaluation of the DEMentia-specific Digital Interactive Social Chart (DEM-DISC). A pilot study among informal carers on its impact, user friendliness and, usefulness , 2010, Aging & mental health.

[36]  Louis-Martin Rousseau,et al.  A two-phase approach to solve the synchronized bin–forklift scheduling problem , 2018, J. Intell. Manuf..

[37]  Luis B. Morales,et al.  OPTIMISATION OF THE COLOUR SEQUENCE IN THE PROCESS OF FABRIC DYEING , 2008 .

[38]  Chen-Fu Chien,et al.  Analysing semiconductor manufacturing big data for root cause detection of excursion for yield enhancement , 2017, Int. J. Prod. Res..

[39]  Liang Gao,et al.  Evolving scheduling rules with gene expression programming for dynamic single-machine scheduling problems , 2010 .

[40]  Mitsuo Gen,et al.  A Multiobjective Hybrid Genetic Algorithm for TFT-LCD Module Assembly Scheduling , 2014, IEEE Transactions on Automation Science and Engineering.

[41]  Nathália Cristina Ortiz da Silva,et al.  Online single machine scheduling with setup times depending on the jobs sequence , 2019, Comput. Ind. Eng..

[42]  Anandhi S. Bharadwaj,et al.  A Resource-Based Perspective on Information Technology Capability and Firm Performance: An Empirical Investigation , 2000, MIS Q..

[43]  Verónica Bolón-Canedo,et al.  Predictive Maintenance in the Metallurgical Industry: Data Analysis and Feature Selection , 2018, WorldCIST.

[44]  Thomas Hess,et al.  Options for Formulating a Digital Transformation Strategy , 2020, MIS Q. Executive.

[45]  Catherine Tucker,et al.  Health Information Exchange, System Size and Information Silos , 2013, Journal of health economics.

[46]  Chen-Fu Chien,et al.  UNISON framework of data-driven innovation for extracting user experience of product design of wearable devices , 2016, Comput. Ind. Eng..