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

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

doi:10.1590/0103-6513.20190086

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Sumário

Thematic Section - Present and Future of Production Engineering • Prod. 30 • 2020 • https://doi.org/10.1590/0103-6513.20190086 copy

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

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

Paper aims

Due to the scarcity of research on current scenarios of quality management in the 21^st 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.

Item	Definition
1	The basic role that quality plays in the economic performance of companies;
	New performance measures and metrics for peer-driven target settings and customer expectations;
	Sophistication in quality standards, practices and methods to gain sustainable competitive advantage.
2	Decision models related to contemporary quality management;
2	Costs of poor-quality decision models in terms of customer expectations and credibility.
3	Contemporary lean techniques and economic influences in manufacturing and services;
	Outsourcing/offshoring quality challenges in logistics and supply chain operations in the era of Industry 4.0;
	Continuous improvement strategies and models that result in minimal viable change and pivoting;
	Emerging philosophies and business tactics that look beyond total quality management and six sigma;
	The role of product innovation, design (R&D) and grassroots autonomy in quality management.
4	Capturing message credibility and supplier involvement aspects in qualify function deployment;
	Leadership emphasis and process flexibility in achieving economic sustainability;
	Business models and tactics for peer involvement of employees within enterprises;
	Business models that study the influence of quality management in a multicultural environment.
5	The role of technology, automation and IT/IS in quality management and its relationship with employee empowerment.

Item	Definition
6	The economic aspects include the basic role that quality plays in the economic performance of companies;
	Aspects of decision models including the costs of poor-quality decision models in terms of customer expectations and credibility;
	Aspects of business models regarding the continuous improvement strategies and models that result in minimal viable change and pivoting.
7	Human aspects include i) leadership emphasis and process flexibility in achieving economic sustainability and ii) business model and tactics for peer involvement of employees within enterprises and capturing messages. Credibility and supplier involvement in qualify function deployment;
	Decision model aspects include decision models related to contemporary quality management;
	Business model aspects include: i) contemporary lean techniques and economic influences in manufacturing and services and ii) emerging philosophies and business tactics that look beyond total quality management and six sigma.
8	Economic aspects include: i) new performance measures and metrics for peer-driven target settings and customer expectations and ii) sophistication in quality standards, practices and methods to gain sustainable competitive advantage;
8	Business model aspects include: i) outsourcing/offshoring quality challenges in logistics and supply chain operations in the era of Industry 4.0 and ii) the role of product innovation, design (R&D) and grassroots autonomy in quality management.
9	Technological aspect: the role of technology, automation and IT/IS in quality management and its relationship with employee empowerment.

Item	Definition
1	What changes need to be made in quality management to commit to micro level human involvement with respect to emerging new technologies, such as blockchain, the Internet of Things, big data, business analytics, cyber physical systems and smart supply chains?
2	What are the new quality-related performance measures and metrics for the futuristic supply chains?
3	What sophisticated methods have been developed to capture new emerging quality challenges, such as quality risk, security, trust and compliance?
4	How can service outsourcing be dealt with in the era of Industry 4.0?
5	How can radical quality innovation be encouraged by including grassroots autonomy in quality management?
6	What contemporary lean management and techniques have been developed in the present industrial revolution?
7	What are emerging philosophies other than six sigma and total quality management?
8	How can leadership emphasis and process agility be created to achieve economic sustainability in the era of Industry 4.0?
9	How can employees be encouraged to meet quality standards in the era of Industry 4.0?
10	How can quality trade-offs be made with the support of sophisticated methods in futuristic supply chains?
11	How can the cost of poor-quality decision models be scaled in terms of customer expectations and credibility to suit the futuristic supply chains?

Author	Area	Country
Ngo & Schmitt (2016)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. http://dx.doi.org/10.1016/j.procir.2016....	Academic	Germany
D’Emilia et al. (2018)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. http://dx.doi.org/10.4018/IJSSMET.201804...	Manufacture	Italy
Ding (2018)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. http://dx.doi.org/10.1016/j.psep.2018.06...	Pharma Industry	UK
Irani et al. (2018)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. http://dx.doi.org/10.1016/j.cor.2017.10....	Food Industry	UK
Kampker et al. (2018)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. http://dx.doi.org/10.1016/j.promfg.2018....	Manufacture	Germany
Kozjek et al. (2018)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. http://dx.doi.org/10.1016/j.procir.2018....	Auto Industry	Slovenia
Lin et al. (2018)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. http://dx.doi.org/10.1108/IMDS-09-2017-0...	Auto Industry	China
Melnyk et al. (2018)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. http://dx.doi.org/10.1080/00207543.2017....	Supply Chain	Australia
Müller et al. (2018)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. http://dx.doi.org/10.1016/j.techfore.201...	Germany SME;	Germany
Telukdarie et al. (2018)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. http://dx.doi.org/10.1016/j.psep.2018.06...	Supply Chain	China
Tsai & Lai (2018)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. http://dx.doi.org/10.3390/su10082932...	Sustainability	China
Yadegaridehkordi et al. (2018)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. http://dx.doi.org/10.1016/j.techfore.201...	Manufacture	Malaysia
Nascimento et al. (2019)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. http://dx.doi.org/10.1108/JMTM-03-2018-0...	Academic	Argentina
Para et al. (2019)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. http://dx.doi.org/10.1016/j.engappai.201...	Auto Industry	Spain
Ren et al. (2019)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. http://dx.doi.org/10.1016/j.jclepro.2018...	Sustainability	China
Rossit et al. (2019)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. http://dx.doi.org/10.1016/j.jii.2019.04....	Supply Chain	Brazil
Tsai et al. (2019b)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. http://dx.doi.org/10.3390/su11030899...	Sustainability	Taiwan

Author	Classification									Questions
Author	1	2	3	4	5	6	7	8	9	1	2	3	4	5	6	7	8	9	10	11
Ngo & Schmitt (2016)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. http://dx.doi.org/10.1016/j.procir.2016....					x			x							x
D’Emilia et al. (2018)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. http://dx.doi.org/10.4018/IJSSMET.201804...					x		x							x
Ding (2018)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. http://dx.doi.org/10.1016/j.psep.2018.06...			x				x								x
Irani et al. (2018)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. http://dx.doi.org/10.1016/j.cor.2017.10....		x							x			x				x
Kampker et al. (2018)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. http://dx.doi.org/10.1016/j.promfg.2018....					x		x							x
Kozjek et al. (2018)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. http://dx.doi.org/10.1016/j.procir.2018....		x						x							x		x
Lin et al. (2018)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. http://dx.doi.org/10.1108/IMDS-09-2017-0...			x				x			x				x
Melnyk et al. (2018)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. http://dx.doi.org/10.1080/00207543.2017....					x	x
Müller et al. (2018)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. http://dx.doi.org/10.1016/j.techfore.201...			x			x
Telukdarie et al. (2018)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. http://dx.doi.org/10.1016/j.psep.2018.06...			x				x				x								x
Tsai & Lai (2018)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. http://dx.doi.org/10.3390/su10082932...		x							x			x		x	x	x
Yadegaridehkordi et al. (2018)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. http://dx.doi.org/10.1016/j.techfore.201...		x							x			x
Nascimento et al. (2019)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. http://dx.doi.org/10.1108/JMTM-03-2018-0...					x		x				x
Para et al. (2019)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. http://dx.doi.org/10.1016/j.engappai.201...		x							x			x
Ren et al. (2019)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. http://dx.doi.org/10.1016/j.jclepro.2018...					x			x						x	x	x	x
Rossit et al. (2019)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. http://dx.doi.org/10.1016/j.jii.2019.04....		x						x				x			x
Tsai et al. (2019b)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. http://dx.doi.org/10.3390/su11030899...	x								x			x				x

Author	Future research
Tsai et al. (2019b)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. http://dx.doi.org/10.3390/su11030899...	They suggest that ABSC be used to integrate internal and external systems into the MES to connect all the real-time information about relevant requirements that will help all industries in the digital age.
Para et al. (2019)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. http://dx.doi.org/10.1016/j.engappai.201...	They recommend the inclusion and hybridization of existing production models for data mining (e.g., CRISPDM) and the development of agile software (SCRUM or lean software, among others) within the ASPPID methodology, with a particular emphasis on learning how to incorporate multiple criteria of conflicting decisions for decision-making in the production process.
Nascimento et al. (2019)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. http://dx.doi.org/10.1108/JMTM-03-2018-0...	Limitations of the research refer to the perceptions of the participants in the focus groups, which introduces subjectivity, and the small sample of specialists considered, which does not allow generalization of the results. Therefore, it is suggested to focus on these two areas.
Yadegaridehkordi et al. (2018)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. http://dx.doi.org/10.1016/j.techfore.201...	Future studies may consider other MCDM techniques, such as TOPSIS and ANP, combined with soft computing techniques to produce more interesting results or even to compare the results of different techniques.
Lin et al. (2018)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. http://dx.doi.org/10.1108/IMDS-09-2017-0...	Comparative studies can be conducted to investigate different responses to Industry 4.0 technologies in automotive industries across countries.
Telukdarie et al. (2018)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. http://dx.doi.org/10.1016/j.psep.2018.06...	Future studies may develop advanced data analysis algorithms focused on industry-specific combinations of business and Industry 4.0, which can be applied globally.
Kozjek et al. (2018)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. http://dx.doi.org/10.1016/j.procir.2018....	The authors identified challenges in the development and implementation of analytical data tools in real manufacturing environments.
Kampker et al. (2018)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. http://dx.doi.org/10.1016/j.promfg.2018....	Future research may focus on harnessing the full potential of manufacturing data and extending the concepts of adaptive availability of these to provide a more significant knowledge base.
Irani et al. (2018)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. http://dx.doi.org/10.1016/j.cor.2017.10....	They suggest other authors to design and build big data research in food supply chains.
Tsai & Lai (2018)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. http://dx.doi.org/10.3390/su10082932...	When planning their profit targets, companies should also consider the multi-objective considerations of customer service levels and supplier profits.
Ngo & Schmitt (2016)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. http://dx.doi.org/10.1016/j.procir.2016....	To conduct quality regulation, based on data in practice, further research is needed in four fields of action: (1) identification of quality-relevant data and data sources, (2) development of an IT-architecture, (3) application of data mining methods for analyzing and prediction of quality progressions, and (4) derivation of measures for quality regulation.

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