This comparative study analyzes the impact of the COVID-19 pandemic on motorized mobility in eight large cities of five Latin American countries. Public institutions and private organizations have made public data available for a better understanding of the contagion process of the pandemic, its impact, and the effectiveness of the implemented health control measures. In this research, data from the IDB Invest Dashboard were used for traffic congestion as well as data from the Moovit© public transport platform. For the daily cases of COVID-19 contagion, those published by Johns Hopkins Hospital University were used. The analysis period corresponds from 9 March to 30 September 2020, approximately seven months. For each city, a descriptive statistical analysis of the loss and subsequent recovery of motorized mobility was carried out, evaluated in terms of traffic congestion and urban transport through the corresponding regression models. The recovery of traffic congestion occurs earlier and faster than that of urban transport since the latter depends on the control measures imposed in each city. Public transportation does not appear to have been a determining factor in the spread of the pandemic in Latin American cities.

Objectives: An analysis of forecast system SAP-APO of a company producing food demand is made Methods/Statistical analysis: redefining the parameters of statistical models l module demand for SAP-APO, change the statistical model adaptive average seasonality and trend of historical data and categorize products according segmentation analysis to assign a different range of tolerance to each when comparing their demand forecasts with sales in recent months. Findings: After implementation the percentage improvement product to manually adjust decreased by 12.58%, compared to the previous level of attention. Application/Improvements: This decreased attention, resulting in an increase in the utility system more than 85%.