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This chapter describes and discusses the main results of the successful off-hour delivery (OHD) pilot test in the city of São Paulo, Brazil, which took place between October 2014 and March 2015. The pilot engaged major stakeholders in urban distribution, including local authorities, shippers, carriers, and receivers, with the aim to determine what are the main requirements, constraints, opportunities, and threats for establishing a public policy related to shifting deliveries to late night in order to mitigate traffic congestion.

Differently from the former City of New York OHD pilot, here all participant companies were volunteers, with no need for cash incentives. The primary focus in São Paulo was on the issues of safety and noise, besides productivity aspects of travel time, truck speed, and delivery time.

The pilot was very successful, with no registered complaints of noise or security incidents. Travel speeds were obtained from global positioning system (GPS) tracking data and internal delivery systems. The chapter compares daytime and night operations and shows that productivity in some chains would improve significantly, but noise and safety must be carefully controlled to guarantee the expansion of the concept.

The field of Supply Chain Management (SCM) has mainly focused on applications for large firms, where significant amount of theory has been developed in the last decades. Little attention has been received by micro and small enterprises (MSEs) that in Latin America represent approximately 99% of all businesses and are the key for the development of the economy, employment, and growth of the region. Due to MSEs' lack of productivity, only a fraction of them survive and thus contribute to Latin America's economic growth. In this chapter, we discuss the connection between MSEs' productivity growth and SCM. We present key takeaways from the literature and summarized different research approaches used to study this emerging field, specifically related to the impact of the size of the company, the use of surveys to gather data, and the importance of field interventions. We also present a large-scale project (i.e., MIT GeneSys) that focuses on improving survival of MSEs in developing countries and discuss some preliminary learnings gained via conducting shadowing/immersion of ∼250 MSEs from Mexico, Colombia, Chile, Ecuador, Peru, and Bolivia. We conclude the chapter by presenting some recommendations for the future research agenda for the emerging field of SCM for MSEs in Latin America.

Supplier selection is a complex and strategic activity needed in every organization, involving many stakeholders and different attributes as price, delivery performance, and product quality. Globalization, in the last decades, increased the competitiveness between vendors, enhancing the use of decision models to support the best choice based on optimizations and bidding variations due to specific needs. This chapter presents three models of multi-dimensional auctions to improve an international humanitarian NGO process procurement efficiency by reducing procurement costs and the decision-making process time. These models have the advantage to be easily implementable in typically complex environments where there is a large number of categories, suppliers, and other features.

The first proposed model uses combinatorial auctions and is suited for procurement, where suppliers can benefit from cost complementarity. The second one uses volume discount auctions and is suited for volumetric purchases, where discounts for large quantities are common. The third one is a multi-attribute model, which computes the best possible solution considering several criteria and can be used in case of complex purchases that involve various categories and trade-offs and are subject to spot prices.

Several design considerations for this type of auctions are reviewed, as well as the mathematical formulation to determine the best alternative (i.e., winner) that can be solved using simple tools like Microsoft Excel. The models are optimized by a mixed-integer programming, and the multi-attribute one is developed using multi-criteria decision analysis (MCDA). All three models developed in this research showed superior results compared to the baseline, being between 9% and 20% more efficient than a regular supplier selection (singly choosing the lowest price) and improving the bidding compliance.

With an increasing urbanization trend over the last decades, urban agglomerations are facing different challenges that affect its inhabitants: pollution, traffic congestion, thriving population growth rates, and economic uncertainty. In the context of Latin America, where less than 20% of its inhabitants live in rural areas and with a projection to decrease to close to 10% by the year 2030, providing solutions to reduce the impact of this increase of population, on at least one of the issues, seems logical.

This study focuses on the urban logistics component to propose a classification method for homogeneous areas, using Factor Analysis (FA) and analysis of variance (ANOVA) as the main supporting tools. The proposed methodology builds up on the square kilometer (KM2) methodology developed by MIT Center for Transportation and Logistics, applying it in a neuralgic section of the downtown area of a mid-sized city in Latin America: Córdoba, Argentina. The selection was made considering the logistic restrictions, commercial density, and the relevance of the area for the city. Our proposed methodology uses relevant variables for urban logistics to perform the statistical analysis. The main goal is to develop a data-driven methodology to identify clusters to guide Córdoba's urban logistics policy and decision-making processes.

The results suggest a clear relationship between the different commercial activities and the location inside the area, splitting the area under study clearly into two main sections with similar overall characteristics and two subsections inside each one of them, which should be considered as a basis for further urban logistic analysis and implementation of specific best practices that fit the particular needs.

Micro and small enterprises (MSEs) represent 99% of Peruvian firms, contribute 42% of Peru's Gross Domestic Product, and employ half of the country's labor force. Despite their relevance for the Peruvian Economy, they have low survival rates and are characterized by low productivity and processes inefficiencies. This chapter explores whether the adoption of Business and Supply Chain Management (SCM) practices influences MSEs' performance. We conducted a field study using data from 50 MSEs located in Piura, Peru, specifically from trade, service, and manufacturing sectors. We used the data collection guidelines from the MIT GeneSys to measure the firms' adoption of Business and SCM practices. Our results show that MSEs with higher adoption of Financial Planning, Supply Chain Planning, Supplier Relationship Management, Marketing, Procurement, and Stock Control practices are more likely to have higher revenue (i.e., sales). In addition, a multiple regression analysis reveals that while SCM practices do not seem to directly explain productivity growth in MSEs (as business practices do), they, however, seem to influence the performance of business practices, and, thus, have an indirect effect on the productivity growth of MSEs.

Since 2016, organized retailers in Colombia have struggled against a new retail format: Hard-discount stores. This sales channel fulfills essential shopping basket products with consistent low prices. To be competitive and preserve their market position, organized retailers must improve their processes and their pricing decisions. Promotions and discounts have been considered as an effective alternative to compete. This study analyzes the impact of joint prices decisions over the individual and global financial key performance indicators when a collaborative strategy is adopted. Our case study comprises a supermarket chain Colombian retailer and a consumer packaged-goods manufacturer to analyze its supply chain performance. The analysis considers different product categories (food, personal care, and cosmetics) and country regions. The results highlight that benefits are unequally distributed along different echelons and supply chain performance is affected when pricing decisions are made independently.