Last-mile operations in forward and reverse logistics are responsible for a large part of the costs, emissions, and times in supply chains. These operations have increased due to the growth of electronic commerce and direct-to-consumer strategies. We propose a novel data- and model-driven framework to support decision making for urban distribution. The methodology is composed of diverse, hybrid, and complementary techniques integrated by a decision support system. This approach focuses on key elements of megacities such as socio-demographic diversity, portfolio mix, logistics fragmentation, high congestion factors, and dense commercial areas. The methodological framework will allow decision makers to create early warning systems and, with the implementation of optimization, machine learning, and simulation models together, make the best utilization of resources. The advantages of the system include flexibility in decision making, social welfare, increased productivity, and reductions in cost and environmental impacts. A real-world illustrative example is presented under conditions in one of the most congested cities: the megacity of Bogota, Colombia. Data come from a retail organization operating in the city. A network of stakeholders is analyzed to understand the complex urban distribution. The execution of the methodology was capable of solving a complex problem reducing the number of vehicles utilized, increasing the resource capacity utilization, and reducing the cost of operations of the fleet, meeting all constraints. These constraints included the window of operations and accomplishing the total number of deliveries. Furthermore, the methodology could accomplish the learning function using deep reinforcement learning in reasonable computational times. This preliminary analysis shows the potential benefits, especially in understudied metropolitan areas from emerging markets, supporting a more effective delivery process, and encouraging proactive, dynamic decision making during the execution stage.

Supply chains constantly face new problems that go way beyond the traditional issues of supply and demand uncertainty, particularly due to the impact of worldwide disruptive events. This paper aims at mapping academic literature examining so far issues related to the impact on supply chains of the COVID-19 outbreak based on a thorough literature review of peer-reviewed papers published in 2020. Following criteria established by the systematic approach, we build a framework based on the most recent papers of the discipline to address major disruptions that challenge SCM operations. We consider features from individual organisations, characteristics of the whole supply chain, performance metrics for long-term sustainability, and attributes from external disruptions. We derive a list of topics that deserve further investigation such as collaboration, technology adoption and knowledge creation together with their diffusion, strategies to shape and promote awareness along supply chain stakeholders.

This work is about sustainability-related learning experiences for the discipline of supply chain management (SCM) in Higher Education. It arises from the need to motivate students with relevant and interesting activities to improve their learning performance. Higher Education must respond to dynamic demands to keep impactful topics for students, organizations, and society over time. This work addresses the relevance of contemporary challenges in real-world SCM situations concerning Sustainable Development Goals (SDGs). It also provides an actionable framework integrating experiential learning ideas, the ADDIE model for instructional design, the Triple Bottom Line for sustainability, the continuous improvement cycle, and the SDGs into an SCM model. In a case study, the article illustrates the use of this framework for instructional design in a learning experience from an undergraduate course in an Industrial and Systems Engineering program. The application describes the impact of food ecosystems on cities and communities during the COVID-19 crisis. The results suggest positive attainment levels in students’ learning outcomes and highly favorable opinions regarding learning relevance, interest, motivation, and the recommendation of the course. Therefore, this work contributes to SCM education by including sustainability-related challenges and disciplinary topics in novel instructional designs that will actively prepare future professionals and decision-makers.

This Research-to-Practice full paper refers to academic perspectives on educational innovation for industrial engineering education. Two common views prevail in educational innovation that turn into different results. One view refers to the use of pedagogical approaches to improve in-classroom students’ learning. This is an operational perspective about teaching activities, instructional facilitation and the use of academic resources. The second view refers to educational value creation for students, educational partners, society and to improve the academic positioning of universities. However, both views complement each other and can articulate a holistic approach on educational innovation. To proceed in this direction, this work unfolds in three parts. First, a literature review illustrates the differences between the two complementary views. Second, a conceptual framework is provided to connect the two perspectives and guide further educational innovation efforts. Third, a descriptive and exploratory application case is offered to exemplify the framework at the MIT Supply Chain and Logistics Excellence (SCALE) Latin America Network for industrial engineering education. This work contributes to educational practice with a tool to reflect upon innovation efforts, identify instances and align initiatives with intended educational purposes.