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The increase in the supply chain complexity demands new professionals who are able to deal with the new challenges faced nowadays. The purpose of this work is to propose an international university–industry collaboration model to develop supply chain management competences in students as a tool for the training of future professionals.

This study proposes an international collaboration model to develop supply chain competences. The model consists of three main phases from the genesis of the collaboration to the assessment of the competence development. This study validates the model collaborating with one of the largest retailer companies in Mexico.

Results identify collaboration good practices and point at possible improvements for the next model iteration. This study identifies four key supply chain competences as part of this model. Three didactic approaches (i.e. guidance methods) and two student’s involvement schemes were tested. The results show that professors acting as an advisor (i.e. acting as a guider without telling student directly what to do or how to do it) plus a voluntary student’s involvement in the project promote better competence development.

The first contribution of this research is the definition of an international collaboration model that promotes competence-based education. Also, this study documents good practices for this type of partnership. The second one refers to a large-scale model validation (i.e. 14-week experiment in nine different regions of Mexico involving a retail company, 20 professor-researchers and more than 100 students). The third contribution includes the assessment of different levels of competences development using diverse students’ participation schemes and professor’s guidance methods.

The fast growth of urban areas in major cities worldwide is undoubtedly one of the biggest concerns for city officials. In Latin America, data show that currently 81% of its inhabitants live in urban areas, and calculations forecast an increase in this percentage. In this context, urban logistics would become increasingly important in the overall performance of the region and its cities. The main objective of this study was to develop a set of key performance indicators (KPIs) to complement the square kilometer (km²) methodology developed by MIT, applying it in a highly intensive HORECA (hotels, restaurant, coffee shops) area known as “Zona T (The T Zone)” in Bogotá, Colombia, as this is a critical area where distribution needs to be performed at its best. Data such as shop inventory (stores, restaurants, drugstores, etc.), vehicle counting (cars, buses, motorbikes, trucks, etc.), product deliveries (by type: perishables, groceries, cleaning supplies, etc.), and traffic disruptions were collected through observation. Based on literature review and results of the study, 13 KPIs are proposed in three categories: operational (average distance and store coverage, service time & service rate, store density, delivery points, easiness for delivery and vehicle delivery factor), energy and environmental (fuel consumption and emissions), and traffic (traffic density, speed and traffic per lane). The main results show a 62-m average distance from truck to store, service time of 18:36 min, 1.019 gal and 9.1 kg CO₂e of fuel consumption and emission, respectively, traffic density of 421 vehicles/h, and other results described throughout the document.

This note explores the applicability of evaluation criteria to the problem of evaluating the supply chain strategy (SCS) of an organization. A discussion of SCS evaluation is relevant today, as the validity of the dominant approach – proposed two decades ago and based on matching types – has come into question. While evaluation criteria have a long history in other disciplines, they are new to SCS evaluation. To help supply chain (SC) scholars assess the applicability of evaluation criteria to SCS, this note proposes a tentative set of criteria and provides insights derived from the authors’ recent experience. We propose that the use of criteria for the evaluation of SCS may be a useful alternative, or at least a complement, to the dominant approach. These proposed criteria are currently being validated in a project with a company in Uruguay; we invite further empirical validation by third parties.

This chapter proposes a hybrid heuristic method combining a clustering search (CS) metaheuristic with an exact algorithm to solve a two-stage capacitated facility location problem (TSCFLP). The TSCFLP consists of defining the optimal locations of plants and depots and the product flow from plants to depots (first stage) and from depots to customers (second stage). The problem deals commonly with cargo transportation in which products must be transported from a set of plants to meet customers’ demands passing out by intermediate depots. The main decisions to be made are related to define which plants and depots must be opened from a given set of potential locations, which customer to assign to each one of the opened depots, and the amount of product flow from the plants to the depots and from the depots to the customers. The objective is to minimize costs satisfying demand and capacity constraints. Computational results demonstrate that our method was able to find good solutions when comparing it directly with a commercial solver and a genetic algorithm (GA) reported in a recent chapter found in the literature, requiring less than 1.5% and 41% of the computational time performed by these methods, respectively. Thus, our hybrid method combining CS with an exact algorithm can be considered as a new matheuristic to solve the TSCFLP.

The objective of inventory management models is to determine efficient policies for managing the trade-off between customer satisfaction and the cost of goods. This chapter presents a methodology that uses the Monte Carlo Method (MCM) to estimate the behavior of a raw material supply model, considering uncertain variables such as demand, prices, and exchange rates. In order to show how to use this methodology, we analyze the case of a Colombian company in the aluminum industry. This company imports aluminum sheets from China. In this case, we analyze the financial impact of the raw material supply contract proposed by the Chinese supplier. The model considers different supply scenarios for the raw material. We calculate robust indicators such as Value at Risk (VaR), the Conditional Value at Risk (CVaR) and the probability of success for each scenario analyzed. Finally, we conduct a sensitivity analysis with respect to the sales price to validate the proposed models and solution approaches. The results show that considering risk metrics to evaluate the impact of endogenous factors over the supply process is a useful approach to improve decision-making related to this process and also can help to ensure the profitability of the company.

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.

Waste production is one of the most important problems that humankind faces. Human-based activities generate diverse waste types that have to be treated and disposed differently. This results in the need to build more facilities to manage the waste and to avoid further environmental damage. Colombia established a successful policy to close open dumps and to control pollution. Notwithstanding the advances that have been made in final disposal, it is necessary to extend the life of the final disposal sites and increase the closure of open landfills. Valle del Cauca is the third most populated Colombian province, and it is also considered the third province that generates more waste. This chapter addresses the problem of locating solid waste disposal centers in Valle del Cauca by applying the analytic hierarchy process (AHP) with fuzzy logic, a multicriteria method that compares opinions of a decision-making group. Additionally, each potential location area is characterized by considering industrial and environmental issues, societal dynamics, infrastructure and topography, costs, and taxes. After applying a variant of AHP, the decision-making group was able to find that Jamundi is the best location to open the disposal center. The method shows strong potential to identify and prioritize alternative locations for a diverse group of stakeholders. Most importantly, the methodology lets us structure better qualitative and quantitative data, as well as to link multiple levels to avoid choosing locations that will affect society, environment, and other stakeholders, without considering the trade-offs among diverse criteria considering benefits, opportunities, costs, and risks (BOCR).