Production
https://prod.org.br/article/doi/10.1590/0103-6513.20190086
Production
Thematic Section - Present and Future of Production Engineering

Decision-making trends in quality management: a literature review about Industry 4.0

Lucas Schmidt Goecks; Alex Almeida dos Santos; André Luis Korzenowski

Downloads: 2
Views: 44

Abstract

Abstract: Paper aims: Due to the scarcity of research on current scenarios of quality management in the 21st century, this article addresses the concepts of big data and Industry 4.0 for decision-making in quality control.

Originality: This article contributes to completing categorizations and answering questions that have been previously suggested.

Research method: This study presents a systematic literature review and qualitative data. The methodological framework shows the process of the selection and review of articles according to their alignment with the objective of the study.

Main findings: Seventeen articles were selected to structure the study and were classified according the categories presented in the literature. The vast majority of the research gaps pointed out in previous review have been filled since their publication.

Implications for theory and practice: In addition, this article presents new gaps to be filled and complements the literature and concepts about quality management and Industry 4.0.

Keywords

Quality management. Decision-making. Industry 4.0. Big data.

References

Almada-Lobo, F. (2016). The industry 4.0 revolution and the future of manufacturing execution systems (MES). Journal of Innovation Management, 3(4), 16-21. http://dx.doi.org/10.24840/2183-0606_003.004_0003.

D’Emilia, G., Gaspari, A., & Galar, D. P. (2018). Improvement of measurement contribution for asset characterization in complex engineering systems by an iterative methodology. International Journal of Service Science, Management, Engineering, and Technology, 9(2), 85-103. http://dx.doi.org/10.4018/IJSSMET.2018040104.

Ding, B. (2018). Pharma industry 4.0: Literature review and research opportunities in sustainable pharmaceutical supply chains. Process Safety and Environmental Protection, 119, 115-130. http://dx.doi.org/10.1016/j.psep.2018.06.031.

Gunasekaran, A., Subramanian, N., & Ngai, W. T. E. (2019). Quality management in the 21st century enterprises: Research pathway towards industry 4.0. International Journal of Production Economics, 207, 125-129. http://dx.doi.org/10.1016/j.ijpe.2018.09.005.

Irani, Z., Sharif, A. M., Lee, H., Aktas, E., Topaloğlu, Z., van’t Wout, T., & Huda, S. (2018). Managing food security through food waste and loss: small data to big data. Computers & Operations Research, 98, 367-383. http://dx.doi.org/10.1016/j.cor.2017.10.007.

Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2018). Sustainable industry 4.0 framework: a systematic literature review identifying the current trends and future perspectives. Process Safety and Environmental Protection, 117, 408-425. http://dx.doi.org/10.1016/j.psep.2018.05.009.

Kampker, A., Heimes, H., Bührer, U., Lienemann, C., & Krotil, S. (2018). Enabling data analytics in large scale manufacturing. Procedia Manufacturing, 24, 120-127. http://dx.doi.org/10.1016/j.promfg.2018.06.017.

Kozjek, D., Vrabič, R., Rihtaršič, B., & Butala, P. (2018). Big data analytics for operations management in engineer-to-order manufacturing. Procedia CIRP, 72, 209-214. http://dx.doi.org/10.1016/j.procir.2018.03.098.

Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business & Information Systems Engineering, 6(4), 239-242. http://dx.doi.org/10.1007/s12599-014-0334-4.

Li, L. (2018). China’s manufacturing locus in 2025: With a comparison of “made-in-china 2025” and “industry 4.0”. Technological Forecasting and Social Change, 135, 66-74. http://dx.doi.org/10.1016/j.techfore.2017.05.028.

Li, X., Li, D., Wan, J., Vasilakos, A. V., Lai, C. F., & Wang, S. (2015). A review of industrial wireless networks in the context of industry 4.0. Wireless Networks, 23(1), 23-41. http://dx.doi.org/10.1007/s11276-015-1133-7.

Lin, D., Lee, C., Lau, H., & Yang, Y. (2018). Strategic response to industry 4.0: an empirical investigation on the Chinese automotive industry. Industrial Management & Data Systems, 118(3), 589-605. http://dx.doi.org/10.1108/IMDS-09-2017-0403.

Melnyk, S. A., Flynn, B. B., & Awaysheh, A. (2018). The best of times and the worst of times: empirical operations and supply chain management research. International Journal of Production Research, 56(1-2), 164-192. http://dx.doi.org/10.1080/00207543.2017.1391423.

Miller, W. J., Duesing, R. J., Lowery, C. M., & Sumner, A. T. (2018). The quality movement from six perspectives. The TQM Journal, 30(3), 182-196. http://dx.doi.org/10.1108/TQM-10-2017-0113.

Müller, J. M., Buliga, O., & Voigt, K. I. (2018). Fortune favors the prepared: how SMEs approach business model innovations in industry 4.0. Technological Forecasting and Social Change, 132, 2-17. http://dx.doi.org/10.1016/j.techfore.2017.12.019.

Nascimento, D. L. M., Alencastro, V., Quelhas, O. L. G., Caiado, R. G. G., Garza-Reyes, J. A., Rocha-Lona, L., & Tortorella, G. (2019). Exploring industry 4.0 technologies to enable circular economy practices in a manufacturing context. Journal of Manufacturing Technology Management, 30(3), 607-627. http://dx.doi.org/10.1108/JMTM-03-2018-0071.

Ngo, Q. H., & Schmitt, R. H. (2016). A data-based approach for quality regulation. Procedia CIRP, 57, 498-503. http://dx.doi.org/10.1016/j.procir.2016.11.086.

Para, J., Del Ser, J., Nebro, A. J., Zurutuza, U., & Herrera, F. (2019). Analyze, sense, preprocess, predict, implement, and deploy (ASPPID): An incremental methodology based on data analytics for cost-efficiently monitoring the industry 4.0. Engineering Applications of Artificial Intelligence, 82, 30-43. http://dx.doi.org/10.1016/j.engappai.2019.03.022.

Park, S. H. (1995). A new method of analysis for parameter design in quality engineering. Total Quality Management, 6(1), 13-20. http://dx.doi.org/10.1080/09544129550035549.

Raut, R. D., Mangla, S. K., Narwane, V. S., Gardas, B. B., Priyadarshinee, P., & Narkhede, B. E. (2019). Linking big data analytics and operational sustainability practices for sustainable business management. Journal of Cleaner Production, 224, 10-24. http://dx.doi.org/10.1016/j.jclepro.2019.03.181.

Ren, S., Zhang, Y., Liu, Y., Sakao, T., Huisingh, D., & Almeida, C. M. (2019). A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: a framework, challenges and future research directions. Journal of Cleaner Production, 210, 1343-1365. http://dx.doi.org/10.1016/j.jclepro.2018.11.025.

Rossit, D.A., Tohmé, F. and Frutos, M. A data-driven scheduling approach to smart manufacturing. Journal of Industrial Information Integration, 2019, 15, 69-79. http://dx.doi.org/10.1016/j.jii.2019.04.003.

Sanders, A., Elangeswaran, C., & Wulfsberg, J. (2016). Industry 4.0 implies lean manufacturing: research activities in industry 4.0 function as enablers for lean manufacturing. Journal of Industrial Engineering and Management, 9(3), 811. http://dx.doi.org/10.3926/jiem.1940.

Telukdarie, A., Buhulaiga, E., Bag, S., Gupta, S., & Luo, Z. (2018). Industry 4.0 implementation for multinationals. Process Safety and Environmental Protection, 118, 316-329. http://dx.doi.org/10.1016/j.psep.2018.06.030.

Tsai, W. H., & Lai, S. Y. (2018). Green production planning and control model with ABC under industry 4.0 for the paper industry. Sustainability, 10(8), 2932. http://dx.doi.org/10.3390/su10082932.

Tsai, W. H., Chu, P. Y., & Lee, H. L. (2019a). Green activity-based costing production planning and scenario analysis for the aluminum-alloy wheel industry under industry 4.0. Sustainability, 11(3), 756. http://dx.doi.org/10.3390/su11030756.

Tsai, W.-H., Lan, S. H., & Huang, C. T. (2019b). Activity-based standard costing product-mix decision in the future digital era: green recycling steel-scrap material for steel industry. Sustainability, 11(3), 899. http://dx.doi.org/10.3390/su11030899.

Yadegaridehkordi, E., Hourmand, M., Nilashi, M., Shuib, L., Ahani, A., & Ibrahim, O. (2018). Influence of big data adoption on manufacturing companies’ performance: an integrated DEMATEL-ANFIS approach. Technological Forecasting and Social Change, 137, 199-210. http://dx.doi.org/10.1016/j.techfore.2018.07.043.
 

5eb59e030e8825353afd124b production Articles
Links & Downloads

Production

Share this page
Page Sections