Efficiency determinants in Industry 4.0: a two-stage DEA approach in the Brazilian 3PL industry
Antonio Carlos Rodrigues; Roberta de Cássia Macedo; Aline Rodrigues Fernandes
Abstract
Supplementary Material
Supplementary Material – DEA Concepts and Returns to Scale Classification
Keywords
References
Abdel-Basset, M., Chang, V., & Nabeeh, N. A. (2021). An intelligent framework using disruptive technologies for COVID-19 analysis.
Akbari, M., & Hopkins, J. L. (2022). Digital technologies as enablers of supply chain sustainability in an emerging economy.
Akbari, M., Kok, S. K., Hopkins, J., Frederico, G. F., Nguyen, H., & Alonso, A. D. (2023). The changing landscape of digital transformation in supply chains: impacts of industry 4.0 in Vietnam.
Arroyo, P., Gaytan, J., & de Boer, L. (2006). A survey of third party logistics in Mexico and a comparison with reports on Europe and USA.
Associação Brasileira de Operadores Logísticos – ABOL. (2023).
Azadi, M., Moghaddas, Z., Farzipoor Saen, R., & Hussain, F. K. (2021). Financing manufacturers for investing in Industry 4.0 technologies: internal financing vs. external financing.
Azadi, M., Moghaddas, Z., Cheng, T. C. E., & Farzipoor Saen, R. (2023). Assessing the sustainability of cloud computing service providers for Industry 4.0: a state-of-the-art analytical approach.
Azadi, M., Toloo, M., Ramezani, F., Saen, R. F., Hussain, F. K., & Farnoudkia, H. (2024). Evaluating efficiency of cloud service providers in era of digital technologies.
Balcombe, K., Fraser, I., Latruffe, L., Rahman, M., & Smith, L. (2008). An application of the DEA double bootstrap to examine sources of efficiency in Bangladesh rice farming.
Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis.
Bogetoft, P., & Otto, L. (2011).
Brasil. (2019, June 25).
Brasil. (2020, November 3).
Caiado, R. G. G., Scavarda, L. F., Gavião, L. O., Ivson, P., Nascimento, D. L. M., & Garza-Reyes, J. A. (2021). A fuzzy rule-based industry 4.0 maturity model for operations and supply chain management.
Carbone, V., & Stone, M. A. (2005). Growth and relational strategies used by the European logistics service providers: Rationale and outcomes.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units.
Chen, Z., Zhao, J., & Jin, C. (2023). Business intelligence for Industry 4.0: predictive models for retail and distribution.
Cook, W. D., Tone, K., & Zhu, J. (2014). Data envelopment analysis: Prior to choosing a model.
Cooper, W. W., Seiford, L. M., & Tone, K. (2007).
Cooper, W. W., Seiford, L. M., & Zhu, J. (2011). Data Envelopment Analysis: History, Models, and Interpretations. In W. W. Cooper, L. M. Seiford & J. Zhu (Eds.),
Dalenogare, L. S., Benitez, G. B., Ayala, N. F., & Frank, A. G. (2018). The expected contribution of Industry 4.0 technologies for industrial performance.
Figueiredo, K. F., Fleury, P. F., & Wanke, P. (2000).
Fleury, P. F., & Ribeiro, A. F. M. (2003). A indústria de provedores de serviços logísticos no Brasil. In K. F. Figueiredo, P. F. Fleury & P. F. Wanke (Eds.),
Fosso Wamba, S., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study.
Frank, A. G., Dalenogare, L. S., & Ayala, N. F. (2019). Industry 4.0 technologies: implementation patterns in manufacturing companies.
Gujarati, D. (2021).
Instituto de Logistica e Supply Chain – ILOS. (2024).
Ivanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics.
Ivanov, D., Sethi, S., Dolgui, A., & Sokolov, B. (2018). A survey on control theory applications to operational systems, supply chain management, and Industry 4.0.
Javaid, M., Khan, I. H., Singh, R. P., Rab, S., & Suman, R. (2022). Exploring contributions of drones towards Industry 4.0.
Kalantary, M., & Farzipoor Saen, R. (2019). Assessing sustainability of supply chains: an inverse network dynamic DEA model.
Kumawat, P. (2024). Using 3rd -party logistics services or developing own capability to cater to ecommerce demands: a comparative analysis.
Lieb, R., & Bentz, B. A. (2005). The use of third-party logistics services by large american manufacturers: the 2004 survey.
Lieb, R. C., & Lieb, K. J. (2016). 3PL CEO perspectives on the current status and future prospects of the third-party logistics industry in north america: the 2014 survey.
Maghazei, O., Lewis, M. A., & Netland, T. H. (2022). Emerging technologies and the use case: a multi‐year study of drone adoption.
Marchet, G., Melacini, M., Sassi, C., & Tappia, E. (2017). Assessing efficiency and innovation in the 3PL industry: an empirical analysis.
Marchetti, D., & Wanke, P. (2017). Brazil’s rail freight transport: efficiency analysis using two-stage DEA and cluster-driven public policies.
Mastos, T. D., Nizamis, A., Terzi, S., Gkortzis, D., Papadopoulos, A., Tsagkalidis, N., Ioannidis, D., Votis, K., & Tzovaras, D. (2021). Introducing an application of an industry 4.0 solution for circular supply chain management.
Min, H., & Joo, S. J. (2006). Benchmarking the operational efficiency of third party logistics providers using data envelopment analysis.
Min, H., & Joo, S. J. (2009). Benchmarking third‐party logistics providers using data envelopment analysis: an update.
Min, H., DeMond, S., & Joo, S. (2013). Evaluating the comparative managerial efficiency of leading third party logistics providers in North America.
Muniz Junior, J., Moschetto, G. P., & Wintersberger, D. (2023). Industry 4.0 at Brazilian modular consortium: work, process and knowledge in engine supply chain.
Panayides, P. M., Maxoulis, C. N., Wang, T. F., & Ng, K. Y. A. (2009). A critical analysis of DEA applications to seaport economic efficiency measurement.
Pishdar, M., Danesh Shakib, M., Antucheviciene, J., & Vilkonis, A. (2021). Interval Type-2 fuzzy super SBM network DEA for assessing sustainability performance of third-party logistics service providers considering circular economy strategies in the era of Industry 4.0.
Rahman, S., & Jim Wu, Y. C. (2011). Logistics outsourcing in China: the manufacturer‐cum‐supplier perspective.
Raj, A., Dwivedi, G., Sharma, A., Lopes de Sousa Jabbour, A. B., & Rajak, S. (2020). Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: an inter-country comparative perspective.
Rajnoha, R., & Hadač, J. (2024). Strategic key elements in big data analytics as driving forces of IoT manufacturing value creation: a challenge for research framework.
Rodrigues, A. C., Martins, R. S., Wanke, P. F., & Siegler, J. (2018). Efficiency of specialized 3PL providers in an emerging economy.
Rosin, F., Forget, P., Lamouri, S., & Pellerin, R. (2020). Impacts of Industry 4.0 technologies on Lean principles.
Sahay, B., & Mohan, R. (2006). 3PL practices: an Indian perspective.
Simar, L., & Wilson, P. W. (2000). Statistical inference in nonparametric frontier models: the state of the art.
Simar, L., & Wilson, P. W. (2007). Estimation and inference in two-stage, semi-parametric models of production processes.
Simar, L., & Wilson, P. W. (2011). Two-stage DEA: caveat emptor.
Simm, J., & Besstremyannaya, G. (2020).
Singh, H., & Singh, B. (2023). Industry 4.0 technologies integration with lean production tools: a review.
Singh, M., Goyat, R., & Panwar, R. (2024). Fundamental pillars for industry 4.0 development: implementation framework and challenges in manufacturing environment.
Sony, M., & Naik, S. (2020). Industry 4.0 integration with socio-technical systems theory: a systematic review and proposed theoretical model.
Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing.
Tran, T. H., Lu, W.-M., & Kweh, Q. L. (2023). Sustainable investment initiatives and the performance of stakeholders involved in multinational technology companies’ supply chains: linear or nonlinear effects?
Vivaldini, M., & Pires, S. R. I. (2020). The brazilian logistics service provider on the internet.
Wang, Y., Lin, Y., Zhong, R. Y., & Xu, X. (2019). IoT-enabled cloud-based additive manufacturing platform to support rapid product development.
Wanke, P. F. (2012). Determinants of scale efficiency in the Brazilian 3PL industry: a 10-year analysis.
Wanke, P. F., & Affonso, C. R. (2011). Determinantes da eficiência de escala no setor brasileiro de operadores logísticos.
Wanke, P., & Barros, C. P. (2016). New evidence on the determinants of efficiency at Brazilian ports: a bootstrapped DEA analysis.
Wanke, P., Arkader, R., & Fernanda Hijjar, M. (2007). Logistics sophistication, manufacturing segments and the choice of logistics providers.
Woo, J. H., Zhu, H., Lee, D. K., Chung, H., & Jeong, Y. (2021). Assessment framework of smart shipyard maturity level via data envelopment analysis.
Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: state of the art and future trends.
Yang, C.C., Wang, C.N., & Ngo, T.T. (2024). Evaluating the relative efficiency of third party logistics companies using data envelopment analysis: a case study in road transportation and warehousing.
Zhou, G., Min, H., Xu, C., & Cao, Z. (2008). Evaluating the comparative efficiency of Chinese third‐party logistics providers using data envelopment analysis.
Zhou, X., Wang, Y., Chai, J., Wang, L., Wang, S., & Lev, B. (2019). Sustainable supply chain evaluation: a dynamic double frontier network DEA model with interval type-2 fuzzy data.
Submitted date:
07/01/2024
Accepted date:
06/22/2025