Supply Chain Management and Logistics in Emerging Markets

In the past decade, firms have become more aware of supply chain disruptions and their impact on the firm. Developing a supply chain resilience organizational culture has been proposed as an effective way to manage supply chain risks. This study intends to explore how the geographical location risks impact the decision to develop a supply chain resilience strategy, in particular, to anticipate the disruption proactively and have a business continuity plan in place. Using a unique database including thousands of manufacturing locations that belong to over 7,000 firms across 102 countries, we test three hypotheses to understand if geographical location risks, frequency of disruptive events, and the region in which a site is located are factors for the likelihood of a firm having a business continuity plan at their locations. The study also seeks to understand if there are regional effects and firm effects affecting the decision to develop resilience. With a particular focus in Latin America and the firms with a manufacturing presence in that region. The main findings of the study are that natural disaster risks do tend to develop a culture of resilience, while macroeconomic risks tend to do the opposite. These results remain stable for firms' effects. The Latin America region shows no observable statistical difference in developing resilience compared to the Asia region. While the Northern America region shows more resilience compared to Asia. We conclude that economic risk is less predictable and harder to develop a plan for than disruptions, such as natural disasters. The findings of this study present an opportunity for governments to develop resilience plans that can make their countries more attractive for investment to multinational firms looking to establish new manufacturing locations around the world.

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.

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.

The functional strategy map (FSM) is a tool used to capture the organizations' supply chain strategy. Its product, the strategy map, allows the organizations to apply the Conceptual System Assessment and Reformulation methodology (CSAR) with a multi-objective perspective to rethink the business strategy. The aim of this study is to optimize a company business strategy of corrugate cardboard boxes, with its strategic and tactical relations and problems obtained with the CSAR methodology and the FSM tool, as well as its operational priorities identified by the analytical hierarchy process, which is a tool to support multicriteria decision-making. This proposal, with a mixed methodology approach, generates multiple improvements, such as the reduction of the overall cost, the optimal fill rate operations, and the articulation of the strategic and functional decisions in this organization, which are based on a cost competitive strategy. The results were validated by the financial assessments that ensure an attractive return of the investment and the articulation between the business strategies with its functional plans.

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.

Identifying and managing supply chain risk is crucial for the competitiveness of a company. However, research focused on the risks of supply chain operations in Brazil is scarce. The purpose of this study is to analyze and assess the risk of cargo theft in the country. The methodology adopted is deductive and based on an analysis of historical data from January 2015 to November 2017, aiming to evaluate risk based on probability and impact. The findings unveil a scenario of criminality of transporting goods in Brazil, where the use of force, violence, and threats to steal goods is most likely to occur en route or when parked in key locations on the way to the distribution center. On the other hand, the higher impact cargo crimes are concentrated en route to the customer. This chapter provides a better understanding of the risks of transporting goods by road in Brazil and contributes to a more efficient supply chain design by identifying the risks and assessing the primary locations of the crimes along with their modi operandi and the period of the day during which the crime occurs.

Brazil has been increasing its participation in the international trade market, mainly due to agricultural and forestry products, as in the case of soybeans and cellulose. This growth led to the expansion of the logistics infrastructure and its use. An important example of this trend is the port of São Luís, MA, in northern Brazil, which saw an increase in exports via rail (more than 200% growth in 6 years) and, consequently, an increase in the circulation of trains within its port complex.

This work proposes a mixed-integer linear programming model for the daily train scheduling problem at this port. All trains operated by VLI, a logistics company, are scheduled to minimize the departure times in order to improve the dwell time of freight train cars.

The railroad system in this Brazilian port consists of two classification yards, five terminals and a double-track railway for circulation. Different products such as grains, minerals, cellulose, and fuels are transported. The model also incorporates different operations at terminals and occupation restrictions due to maintenance and the physical flow of other third-party logistics companies. These features are modeled through a preprocessing step. In this phase, a series of auxiliary sets are defined to simplify constraints, circulation options are mapped, and the double-track is divided into segments based on the transit time with the objective to control track occupation.

This preprocess step also reduces the model complexity and, consequently, the computational time to solve it, as shown in the numerical tests using real-world operational data.

The main gains of the project were a reference train timetable for peak days, standardization of train crossing options, and a support tool for traffic adjustments with other rail operators.

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 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.

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.

The key performance indicators (KPIs) are frequently used in organizations, and they help to transmit the strategy at all levels of the organization. However, the implementation of these indicators in small- and medium-sized companies remains a challenge. Many studies reveal two challenges faced by these firms, the lack of knowledge about the KPIs and the lack of alignment of these with the business strategy. For this reason, this chapter investigated the current level of knowledge about KPIs in managers of small and medium enterprises in the wood and timber sector in Peru.

The level of knowledge was measured using the framework of Bloom's Taxonomy in 21 firms. The use and importance that managers assign to performance indicators were evaluated, in order to identify gaps that exist between the strategy and its use.

The results of a survey study show a high degree of variability in the knowledge of KPI-related concepts as well as an average low level of usage. The importance attributed to KPIs was seen as a necessary but not sufficient condition for attaining higher levels of KPI usage.

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 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.