DOI : https://doi.org/10.18517/ijaseit.11.4.14280

Developing I4.0 Readiness Index for Factory Operation in Indonesia to Enhance INDI 4.0

Hasbullah Hasbullah (1), Salleh Ahmad Bareduan (2), Sawarni Hasibuan (3)
(1) Department of Industrial Engineering, Universitas Mercu Buana, Jakarta 11650 Indonesia
(2) Department of Manufacturing and Industrial Engineering, University Tun Hussein Onn, Malaysia 86400 Parit Raja, Johor, Malaysia
(3) Department of Industrial Engineering, Universitas Mercu Buana, Jakarta 11650 Indonesia
Fulltext View | Download
How to cite (IJASEIT) :
Hasbullah, Hasbullah, et al. “Developing I4.0 Readiness Index for Factory Operation in Indonesia to Enhance INDI 4.0”. International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 4, Aug. 2021, pp. 1668-77, doi:10.18517/ijaseit.11.4.14280.
Citation Format :
Adopting Industry 4.0 (I4.0) is an effort to gain competitiveness through technological innovation for enhancing productivity and efficiency. Indonesia left behind in launching the policy timeline of the I4.0 initiative, compared to Singapore, Thailand, Malaysia, and Vietnam. In an official government report, Indonesia’s I4.0 index showed a low score at an average of 1.992 (scale 0 to 4).  Indonesia designed INDI 4.0 (Industry 4.0 readiness index Indonesia) in 2018 to prepare industry readiness. It lacks accuracy and is less comprehensive in capturing I4.0 readiness, especially in the factory operation aspect. INDI 4.0 just provides very few questions to capture extensive information in measuring I4.0. This study aimed to develop a comprehensive I4.0 index by enhancing INDI 4.0 in the factory operation aspect. By exploring issues in the I4.0 readiness index, the research extensively searched the journal articles and some other I4.0 indexes used in some countries. Finally, the paper designed a comprehensive I4.0 index with determinant indicators comprising data life cycle (sources, collection, storage, analysis, and transmission) and smart product life cycles (designing, planning, monitoring, quality, and maintenance). This model is expected to be an essential contribution to improve INDI 4.0 in Indonesia. The I4.0 phenomena will undoubtedly influence all countries, and more research into this topic and other critical variables affecting I4.0 preparation are required to complete this study. To improve this research, additional research from other academics is needed to fill in the gaps, incompleteness, and loopholes.

K. Schwab, “The Fourth Industrial Revolution,” World Economic Forum in Davos-Switzerland, 2015. .

B. Marr et al., “Why Everyone Must Get Ready For The 4th Industrial Revolution,” Forbes. 2016, doi: 10.22201/fq.18708404e.2004.3.66178.

H. Kagermann, W. Wahlster, and J. Helbig, “Securing the future of German manufacturing industry: Recommendations for implementing the strategic initiative INDUSTRIE 4.0,” Final Rep. Ind. 4.0 Work. Gr., no. April, pp. 1-84, 2013.

D. Klitou, J. Conrads, M. Rasmussen, L. Probst, and B. Pedersen, European Comission. Digital Transformation Monitor Germany: Industrie 4.0, no. January. 2017.

Q. Li et al., “Smart manufacturing standardization: Architectures, reference models and standards framework,” Comput. Ind., vol. 101, no. April 2017, pp. 91-106, 2018, doi: 10.1016/j.compind.2018.06.005.

F. Tao, Q. Qi, A. Liu, and A. Kusiak, “Data-driven smart manufacturing,” J. Manuf. Syst., vol. 48, pp. 157-169, 2018, doi: 10.1016/j.jmsy.2018.01.006.

C.-C. Kuo, J. Z. Shyu, and K. Ding, “Industrial revitalization via industry 4.0 - A comparative policy analysis among China, Germany and the USA,” Glob. Transitions, vol. 1, pp. 3-14, 2019, doi: 10.1016/j.glt.2018.12.001.

Indonesian Ministry of Industry, “Ministry of Industry Making Indonesia 4.0 Making Indonesia 4.0 Making Indonesia 4.0,” p. 55, 2018.

S. Singapore Economy Development Board, “THE SINGAPORE SMART INDUSTRY READINESS INDEX,” 2017.

Ministry of Industry, Indonesia, “Indonesia Industry 4 . 0 Readiness Index,” 2018.

M. of I. BPPI, “A Roadmap for Indonesia ’ s Strategies in the Era of Industry 4.0,” 2020, pp. 1-19.

A. G. Frank, L. S. Dalenogare, and N. F. Ayala, “Industry 4.0 technologies: Implementation patterns in manufacturing companies,” Int. J. Prod. Econ., vol. 210, pp. 15-26, 2019, doi: 10.1016/j.ijpe.2019.01.004.

M. Akerman, Implementing Shop Floor IT for Industry 4 . 0 Implementing Shop Floor IT for Industry 4 . 0 Department of Industrial and Materials Science, no. July. 2018.

L. Stefan, W. Thom, L. Dominik, K. Dieter, and K. Bernd, “Concept for an evolutionary maturity based Industrie 4.0 migration model,” Procedia CIRP, vol. 72, pp. 404-409, 2018, doi: 10.1016/j.procir.2018.03.155.

D. Ibarra, J. Ganzarain, and J. I. Igartua, “Business model innovation through Industry 4.0: A review,” Procedia Manuf., vol. 22, pp. 4-10, 2018, doi: 10.1016/j.promfg.2018.03.002.

G. Erboz, “HOW TO DEFINE INDUSTRY 4 . 0 : The Main Pillars of Industry 4 . 0,” no. November 2017, 2018.

M. Hermann, T. Pentek, and B. Otto, “Design principles for industrie 4.0 scenarios,” Proc. Annu. Hawaii Int. Conf. Syst. Sci., vol. 2016-March, pp. 3928-3937, 2016, doi: 10.1109/HICSS.2016.488.

B. Gí¤rtner, “Industry 4.0 maturity index,” Assembly, vol. 61, no. 12, pp. 32-35, 2018.

X. F. Shao, W. Liu, Y. Li, H. R. Chaudhry, and X. G. Yue, “Multistage implementation framework for smart supply chain management under industry 4.0,” Technol. Forecast. Soc. Change, vol. 162, no. September 2020, 2021, doi: 10.1016/j.techfore.2020.120354.

C. Leyh, K. Bley, T. Schaffer, and S. Forstenhausler, “SIMMI 4.0-a maturity model for classifying the enterprise-wide it and software landscape focusing on Industry 4.0,” Proc. 2016 Fed. Conf. Comput. Sci. Inf. Syst. FedCSIS 2016, vol. 8, pp. 1297-1302, 2016, doi: 10.15439/2016F478.

S. Mittal, M. A. Khan, D. Romero, and T. Wuest, “Building blocks for adopting smart manufacturing,” Procedia Manuf., vol. 34, pp. 978-985, 2019, doi: 10.1016/j.promfg.2019.06.098.

P. Anna De Carolis, “A toolkit to guide manufacturing companies towards digitalization,” SMARS SPACE, Politec. Milan, 2018.

W. W. Gí¼nther Schuh, Reiner Anderl, Jí¼rgen Gausemeier, Michael ten Hompel, Industrie 4.0 Maturity Index. .

K. Schweichhart, “RAMI 4.0 reference architectural model for Industrie 4.0,” InTech, vol. 66, no. 2, 2019.

H. Arksey and L. O’Malley, “Scoping studies: Towards a methodological framework,” Int. J. Soc. Res. Methodol. Theory Pract., vol. 8, no. 1, pp. 19-32, 2005, doi: 10.1080/1364557032000119616.

J. Hussey and R. Hussey, Business Research: A Practical Guide for Undergraduate and Postgraduate Students. 2014.

A. P. T. Pacchini, W. C. Lucato, F. Facchini, and G. Mummolo, “The degree of readiness for the implementation of Industry 4.0,” Comput. Ind., vol. 113, p. 103125, 2019, doi: 10.1016/j.compind.2019.103125.

P. O’Donovan, K. Leahy, K. Bruton, and D. T. J. O’Sullivan, “An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities,” J. Big Data, vol. 2, no. 1, pp. 1-26, 2015, doi: 10.1186/s40537-015-0034-z.

Y. Liu and X. Xu, “Industry 4 . 0 and Cloud Manufacturing : A Comparative Analysis,” vol. 139, no. March, pp. 1-8, 2017, doi: 10.1115/1.4034667.

A. Siddiqa et al., “A survey of big data management: Taxonomy and state-of-the-art,” J. Netw. Comput. Appl., vol. 71, pp. 151-166, 2016, doi: 10.1016/j.jnca.2016.04.008.

F. Ungermann, A. Kuhnle, N. Stricker, and G. Lanza, “Data analytics for manufacturing systems - A data-driven approach for process optimization,” Procedia CIRP, vol. 81, pp. 369-374, 2019, doi: 10.1016/j.procir.2019.03.064.

C. Leyh, T. Schí¤ffer, K. Bley, and L. Bay, “The Application of the Maturity Model SIMMI 4 . 0 in Selected Enterprises Full Paper Chair of Information Systems Chair of Information Systems,” Twenty-third Am. Conf. Inf. Syst., no. August, pp. 1-10, 2017.

A. Schumacher, S. Erol, and W. Sihn, “A Maturity Model for Assessing Industry 4.0 Readiness and Maturity of Manufacturing Enterprises,” Procedia CIRP, vol. 52, pp. 161-166, 2016, doi: 10.1016/j.procir.2016.07.040.

D. Agung and H. Hasbullah, “Reducing the Product Changeover Time Using Smed & 5S Methods in the Injection Molding Industry,” Sinergi, vol. 23, no. 3, p. 199, 2019, doi: 10.22441/sinergi.2019.3.004.

G. Schuh, T. Potente, and A. Hauptvogel, “Sustainable increase of overhead productivity due to cyber-physical-systems,” 11th Glob. Conf. Sustain. Manuf., pp. 332-335, 2013.

D. Mourtzis, A. Gargallis, and V. Zogopoulos, “Modelling of customer oriented applications in product lifecycle using RAMI 4.0,” Procedia Manuf., vol. 28, pp. 31-36, 2019, doi: 10.1016/j.promfg.2018.12.006.

D. Gorecky, M. Schmitt, M. Loskyll, and D. Zí¼hlke, “Human-machine-interaction in the industry 4.0 era,” Proc. - 2014 12th IEEE Int. Conf. Ind. Informatics, INDIN 2014, pp. 289-294, 2014, doi: 10.1109/INDIN.2014.6945523.

M. Awais and D. Henrich, “Human-robot interaction in an unknown human intention scenario,” Proc. - 11th Int. Conf. Front. Inf. Technol. FIT 2013, pp. 89-94, 2013, doi: 10.1109/FIT.2013.24.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).