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This study, a practice forum article, aims to presents the lessons learned and the development of a discrete event simulation model to support the funerary system management of São Paulo City, Brazil, during the COVID-19 pandemic.

A discrete event simulation model was developed by the authors as soon as the pandemic affected the city of São Paulo, Brazil. Based on the model, several scenarios with varying minimum, median and peak demands (i.e. the number of deaths) were tested and evaluated. The lessons learned from the scenario analysis and implementation of the decision-making of the city government of São Paulo are discussed in this article.

The lessons learned about the coordination, inventory management and other operational characteristics in funerary logistics during the pandemic are shared with a model, which quantifies the demand for vehicles, coffins, graves and teams in the cemeteries in different simulated scenarios.

The São Paulo State Civil Defense used this information during the pandemic to prepare the funerary system of the municipality.

The study presents methods to mitigate the sanitary, environmental and psychosocial problems related to the funerary system.

Studies on funerary systems are scarce. This study presents the results that supported the dimensioning of the funerary system during the pandemic and operational lessons about the logistics to support decision-making in future events.

The purpose of this paper is to analyze demand forecast strategies to support a more sustainable management in a pallet supply chain, and thus avoid environmental impacts, such as reducing the consumption of forest resources.

Since the producer presents several uncertainties regarding its demand logs, a methodology that embed zero-inflated intelligence is proposed combining fuzzy time series with clustering techniques, in order to deal with an excessive count of zeros.

A comparison with other models from literature is performed. As a result, the strategy that considered at the same time the excess of zeros and low demands provided the best performance, and thus it can be considered a promising approach, particularly for sustainable supply chains where resources consumption is significant and exist a huge variation in demand over time.

The findings of the study contribute to the knowledge of the managers and policymakers in achieving sustainable supply chain management. The results provide the important concepts regarding the sustainability of supply chain using fuzzy time series and clustering techniques.