Organizational |
Industry 4.0 |
Lack of a digital strategy (Cavata et al., 2020Cavata, J. T., Massote, A. A., Maia, R. F., & Lima, F. (2020). Highlighting the benefits of industry 4.0 for production: an agent-based simulation approach. Gestão & Produção, 27(3), e5619. http://dx.doi.org/10.1590/0104-530x5619-20. http://dx.doi.org/10.1590/0104-530x5619-...
; Raj et al., 2020Raj, A., Dwivedi, G., Sharma, A., Jabbour, A. B. L. S., & Rajak, S. (2020). Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: an inter-country comparative perspective. International Journal of Production Economics, 224, 107546. http://dx.doi.org/10.1016/j.ijpe.2019.107546. http://dx.doi.org/10.1016/j.ijpe.2019.10...
). |
Strategic Road Map, Simulations, Artificial Intelligence, Big Data, Machine Learning. |
Integration and digitalization of the quality management system (Goecks et al., 2020Goecks, L. S., Santos, A. A., & Korzenowski, A. L. (2020). Decision-making trends in quality management: a literature review about Industry 4.0. Production, 30, 20190086. http://dx.doi.org/10.1590/0103-6513.20190086. http://dx.doi.org/10.1590/0103-6513.2019...
; Korzenowski et al., 2020Korzenowski, A. L., Simões, W. L., Goecks, L. S., Gerhard, M., Fogaça, P., & Noronha, R.S. (2020). Economic sustainability of the X̅ implementation in uncapable processes. International Journal of Qualitative Research, 14(3), 881-894. http://dx.doi.org/10.24874/IJQR14.03-15. http://dx.doi.org/10.24874/IJQR14.03-15...
). |
Big Data, Decision-Making Models, Machine Learning, Neural Networks, Cyber-Physical Systems, Automated Process Control. |
Business model |
It requires in-depth knowledge of customer needs and the technological and organizational resources that might meet those needs (Sousa & Barros, 2020Sousa, A. M. H., & Barros, J. D. P., No. (2020). Is it possible to implement ERP in the production function of civil construction? Gestão & Produção, 27(3), e4445. http://dx.doi.org/10.1590/0104-530x4445-20. http://dx.doi.org/10.1590/0104-530x4445-...
; Teece, 2018Teece, D. J. (2018). Business models and dynamic capabilities. Long Range Planning, 51(1), 40-49. http://dx.doi.org/10.1016/j.lrp.2017.06.007. http://dx.doi.org/10.1016/j.lrp.2017.06....
). |
Customer Relational Management (CRM), Enterprise Resource Planning (ERP), Big Data, Artificial Intelligence, Machine Learning. |
Dynamic Capabilities |
How dynamic capabilities contribute to digital transformation (Vial, 2019Vial, G. (2019). Understanding digital transformation: a review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118-144. http://dx.doi.org/10.1016/j.jsis.2019.01.003. http://dx.doi.org/10.1016/j.jsis.2019.01...
). |
Sensing: assessment of technological opportunities in relationship to customer needs; Seizing: address needs and opportunities, and to capture value; Transformation: continued renewal. |
Informational |
Supply Chain Management |
Lack of specification can affect coordination (Vosooghidizaji et al., 2020Vosooghidizaji, M., Taghipour, A., & Canel-Depitre, B. (2020). Supply chain coordination under information asymmetry: a review. International Journal of Production Research, 58(6), 1805-1834. http://dx.doi.org/10.1080/00207543.2019.1685702. http://dx.doi.org/10.1080/00207543.2019....
). |
Simulations, Artificial Intelligence, Big Data, Machine Learning, Blockchain, IoT. |
Consideration of supply chain data analytic approaches (Kakhki & Gargeya, 2019Kakhki, M., & Gargeya, V. B. (2019). Information systems for supply chain management: a systematic literature analysis. International Journal of Production Research, 57(15-16), 5318-5339. http://dx.doi.org/10.1080/00207543.2019.1570376. http://dx.doi.org/10.1080/00207543.2019....
). |
Machine learning technique, neural networks and data-driven approaches to integrate the production and distribution planning in the supply chain. |
Industry 4.0 |
Fashion manufacturing companies and their Industry 4.0 strategies to extract potential success factors (Braglia et al., 2020Braglia, M., Marrazzini, L., Padellini, L., and Rinaldi, R. (2020). Managerial and Industry 4.0 solutions for fashion supply chains. Journal of Fashion Marketing and Management: An International Journal, 25(1), 184-201.). |
Artificial Intelligence, Big Data, Machine Learning. |
Support the production strategy using data processing aspects (Campos & Alves, 2020Campos, F. C., & Alves, A. G., Fo. (2020). Proposal for a framework for production strategy utilizing Big Data: illustrative case in public service. Gestão & Produção, 27(3), e4651. http://dx.doi.org/10.1590/0104-530x4651-20. http://dx.doi.org/10.1590/0104-530x4651-...
). Intelligent hospitals are optimizing the use of resources and reducing hospital stay (Flórez et al., 2020Flórez, C. A. C., Rosário, J. M., & Hurtado, D. A. (2020). Application of Automation and Manufacture techniques oriented to a service-based business using the Internet of Things (IoT) and Industry 4.0 concepts. Case study: smart Hospital. Gestão & Produção, 27(3), e5416. http://dx.doi.org/10.1590/0104-530x5416-20. http://dx.doi.org/10.1590/0104-530x5416-...
). |
Big Data and IoT (Flórez et al., 2020Flórez, C. A. C., Rosário, J. M., & Hurtado, D. A. (2020). Application of Automation and Manufacture techniques oriented to a service-based business using the Internet of Things (IoT) and Industry 4.0 concepts. Case study: smart Hospital. Gestão & Produção, 27(3), e5416. http://dx.doi.org/10.1590/0104-530x5416-20. http://dx.doi.org/10.1590/0104-530x5416-...
; Campos & Alves, 2020Campos, F. C., & Alves, A. G., Fo. (2020). Proposal for a framework for production strategy utilizing Big Data: illustrative case in public service. Gestão & Produção, 27(3), e4651. http://dx.doi.org/10.1590/0104-530x4651-20. http://dx.doi.org/10.1590/0104-530x4651-...
) |
Decentralization of the productive system’s control by autonomous and intelligent devices interconnected by a communication system (Guirro et al., 2020Guirro, D. N., Asato, O. L., Santos, G. A., & Nakamoto, F. Y. (2020). Manufacturing operational management modeling using interpreted Petri nets. Gestão & Produção, 27(2), e3920. http://dx.doi.org/10.1590/0104-530x3920-20. http://dx.doi.org/10.1590/0104-530x3920-...
). |
Enterprise Resource Planning (ERP); Big Data, Artificial Intelligence, Machine Learning (Guirro et al., 2020Guirro, D. N., Asato, O. L., Santos, G. A., & Nakamoto, F. Y. (2020). Manufacturing operational management modeling using interpreted Petri nets. Gestão & Produção, 27(2), e3920. http://dx.doi.org/10.1590/0104-530x3920-20. http://dx.doi.org/10.1590/0104-530x3920-...
). |
Humans |
Management |
Investigate how Top Managers stimulate internal/external debate without generating conflicts (Pereira et al., 2019Pereira, G., Tzempelikos, N., Trento, L. R., Trento, C. R., Borchardt, M., & Viegas, C. V. (2019). Top managers’ role in key account management. Journal of Business and Industrial Marketing, 34(5), 977-993. http://dx.doi.org/10.1108/JBIM-08-2018-0243. http://dx.doi.org/10.1108/JBIM-08-2018-0...
). |
Big data insights for more effective relational selling; Leverage artificial. intelligence for relational selling (Arli et al., 2018Arli, D., Bauer, C., & Palmatier, R. W. (2018). Relational selling: Past, present and future. Industrial Marketing Management, 69(1), 169-184. http://dx.doi.org/10.1016/j.indmarman.2017.07.018. http://dx.doi.org/10.1016/j.indmarman.20...
). |
Industry 4.0 |
Lack of firms’ internal capabilities in order to overcome the challenges of implementing Industry 4.0 (Raj et al., 2020Raj, A., Dwivedi, G., Sharma, A., Jabbour, A. B. L. S., & Rajak, S. (2020). Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: an inter-country comparative perspective. International Journal of Production Economics, 224, 107546. http://dx.doi.org/10.1016/j.ijpe.2019.107546. http://dx.doi.org/10.1016/j.ijpe.2019.10...
). |
Building roadmaps and planning strategically to invest in suitable resources. Framework to prioritize their allocation of resources. |
Consumers Intentions |
Factors such as price and time might influence their actual behavior. Future research might be able to measure subjects actual usage and purchasing behavior (Wu et al., 2015Wu, J., Kang, J. Y. M., Damminga, C., Kim, H. Y., & Johnson, K. K. P. (2015). MC 2.0: testing an apparel co-design experience model. Journal of Fashion Marketing and Management, 19(1), 69-86. http://dx.doi.org/10.1108/JFMM-07-2013-0092. http://dx.doi.org/10.1108/JFMM-07-2013-0...
). |
Software’s for decision-making in properties portfolio (Baierle et al., 2020Baierle, I. C., Schaefer, J. L., Sellitto, M. A., Fava, L. P., Furtado, J. C., & Nara, E. O. B. (2020). MOONA software for survey classification and evaluation of criteria to support decision-making for properties portfolio. International Journal of Strategic Property Management, 24(4), 226-236. http://dx.doi.org/10.3846/ijspm.2020.12338. http://dx.doi.org/10.3846/ijspm.2020.123...
). Customer Relational Management (CRM), Big Data, Artificial Intelligence, Machine Learning. |