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In the context of increasing competition between chained retailers and family-owned retailers, it is key to understand the customer's format choice. Using a logistics regression (i.e., binary logit) model, we explain customers' preference to buy in supermarkets or in small-scale, mom-and-pop stores like nanostores. We collect a representative sample of over 110 surveys from customers in the district of Surco, Lima, Perú, which is a representative area of the features of Lima's residents. We asked customers to focus on analyzing their preference between two retail formats: modern channel (i.e., big-box retailers, supermarkets, and hypermarkets) and traditional channel (i.e., mom-and-pop stores, nanostores). Our surveys included factors pertaining retail format attributes as well as factors related to the purchasing process. The results showed that time available for purchase and a comparatively better perceived service at a mom-and-pop store (i.e., nanostore) are significant factors that explain a higher probability of selecting these retailers, while a better store's ambience benefits more supermarkets. The overall discrete choice model is able to explain 65% of the variance using pseudo R-squared of the actual format choice decisions.

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.

This chapter explores how customer's attributes, shopping behavior, and preferences affect the retail choice in fiercely competitive retail environments of megacities from developing countries. We study how small, family-owned retailers (i.e., nanostores) compete against organized chains from the modern channel (i.e., convenience stores and supermarkets) at different socioeconomic levels in 9 out of 16 boroughs from Mexico City. Primary data were collected using a combination of instruments (i.e., observation, interviews, and surveys) that were applied to relevant stakeholders of the retail footprint where nanostores develop their operations. We analyze the data via statistical tools such as descriptive statistics and independent nonparametric tests to understand the significant factors of the competitive landscape in which nanostores are immersed. We supplement our research methodology by using causal loop diagrams to identify opportunities in the way suppliers, shopkeepers, competitors, and customers interact with each other and new business models for the nanostore supply chains. By breaking down our result analysis into low-, middle-, and high-income areas, we provide insightful recommendations to increase nanostores' survival, improve their operations, and grow them in Mexico City by addressing issues from the supply, store management, and customer service.

In line with the knowledge-based view of organizations, this paper aims to analyze how supply chain (SC) employees contribute to the creation of competitive advantage through knowledge acquisition and utilization activities. The authors consider SC employees' skills and competencies, their external network of relationships, their job satisfaction and company investments in training and test how they relate to SC-level outcomes (i.e. SC growth).

The authors design a research model including the aforementioned variables, and the authors apply structural equation modeling (SEM) to survey data collected from 246 SC professionals in Latin America. The authors also use multi-group analysis to evaluate how the relationships between these variables change with different levels of company investment in training.

The results show that a broad professional network of relationships contributes to increasing the skills and competencies of SC professionals, which, in turn, impact job satisfaction and SC performance. This reinforces the value of investing in skilled human talent, who can contribute to knowledge acquisition, utilization, and, ultimately, to SC competitiveness. Companies that invest more in training to develop their SC employees benefit from stronger SC outcomes.

This study contributes to broadening the understanding of the impact of human resource management (HRM) on supply chain management (SCM). One of the added original foci of this research is the emphasis on developing countries where these HRM-to-SCM performance relationships have not been studied before.

With the increasing urbanization rates in emerging countries such as the ones in Latin America and the Caribbean, urban logistics solutions and initiatives are widely needed. Urban planners often consider only passenger transportation and leave freight transportation unattended, thus increasing externalities and degrading the transportation of goods. This chapter presents three urban logistics solutions, which intend to tackle problems related to urbanization and last mile delivery operations challenges by evaluating location models for loading and unloading bays, urban transfer centers location models, and freight trip generation models. The presented solutions were proposed by several researchers of the Institute of Innovation in Productivity and Logistics CATENA-USFQ over the last four years and remain theoretical at the moment. However, we present estimated results of potential implementations in three districts of Quito: Historic Center, Entertainment District, and Corporate District. This chapter not only presents the mentioned urban logistics solutions in Quito but also gives an overview of the followed methodology, which can be replicated in countries and cities of similar characteristics of the region.

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.

This chapter discusses a collaborative strategy for noncompetitive small- and medium-sized enterprises (SME's) aiming to reduce their logistics costs by means of a joint replenishment of multiple items. The proposed approach is an extension of the classical joint replenishment problem, named as a Stochastic Collaborative Joint Replenishment problem (S-CJRP) because it considers stochastic demand, warehouse and transport capacity constraints, and multiple buyers and vendors. Operating this method implies three main challenges: (1) determining the frequency with which each buyer should replenish the products; (2) allocating investments and benefits between partnering buyers; and (3) deciding whether to coordinate the supply chain internally or outsource its coordination. The S-CJRP is solved through a heuristic approach, which deals with uses of the Shapley Value Function to allocate the investments and benefits, and it explores the coordination through several simulation scenarios, all of which exhibit prospective cost reductions in inventory management. Preliminary results show that third-party logistics providers could be a valuable resource in coordinating SMEs along a supply chain.

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.