Urban solid waste management in developing countries from the sustainable supply chain management perspective: A case study of Brazil's largest slum

Urban solid waste management (SWM) is a central problem in major cities worldwide. This topic is particularly relevant for developing countries, given the constant growth of slums. SWM is an essential part of sustainable supply chain management (SSCM), which appears to be a viable method of incorporating social, environmental and economic goals as pillars for the sustainable development of an organization and its supply chain. Although the literature offers a large and growing number of publications in SSCM and SWM, there is not yet a study that addresses SWM in a slum of a developing country from the SSCM perspective and there still a need for more sustainable waste management strategies involving all relevant stakeholders. Through a longitudinal case study conducted in the largest slum of Brazil (Rocinha), this paper aims to address these research gaps by developing a framework for SWM from the SSCM perspective in areas of slums. The research findings indicate that to tackle environmental problems related to SWM in such areas, it is necessary to solve basic social issues related to education, security, and infrastructure, with the integration and support of government, local community and industry. Although some progress has been observed in the last 10 years in Rocinha, the studied real life setting shows that in practice, the performance of SWM stakeholders, particularly industry, is far from fulfilling the social and environmental pillars of SSCM. The proposed framework encompasses government and the community viewpoints in addition to the industrial focus and points to practical actions based on SSCM enablers, reinforcing a holistic and multi-perspective view, and the need for integrated management for better SWM in slum areas in developing countries. As it is a first attempt in this direction, additional research in similar settings is required to evolve the analysis towards more generalizable findings. (C) 2019 Elsevier Ltd. All rights reserved.