Port connectivity indices: an application to European RoRo shipping

In recent years, there has been significant interest in the development of connectivity indicators for ports. For short sea shipping, especially in Europe, Roll-on Roll-off (RoRo) shipping is almost equally important as container shipping. In contrast with container shipping, RoRo shipments are primarily direct, thus the measurement of its connectivity requires a different methodology. In this paper, we present a methodology for measuring the RoRo connectivity of ports and illustrate its use through an application to European RoRo shipping. We apply the methodology on data collected from 23 different RoRo shipping service providers concerning 620 unique routes connecting 148 ports. We characterize the connectivity of the ports in our sample and analyze the results. We show that in terms of RoRo connectivity, neither the number of links nor the link quality (frequency, number of competing providers, minimum number of indirect stops) strictly dominate the results of our proposed indicator. The highest ranking ports combine link quality and number. Finally, we highlight promising areas for future research based on the insights obtained.</p

Adoption of Electric Trucks in Freight Transportation

Transportation sector is the largest contributor of global greenhouse gas emissions in the USA. Disruptive technological changes in this sector, such as alternative fuel vehicles, are crucial for emission reduction. We analyze how a cost-minimizing strategic transition plan can be developed for a transportation firm that aims to adopt electric trucks in their fully diesel fleet, over time. We consider the case in which the firm needs to invest in charging infrastructure required to support this transition, as the public charging infrastructure is currently inadequate. The congestion effect at the charging stations, the charging times, and the potential loss of productive driving time due to detours to reach charging stations are explicitly considered. By developing an independence property, we are able to model this problem as a linear integer program without any need to explicitly specify origins and destinations. We illustrate the resulting transition plan with a realistic data set. Our results indicate that a transportation firm that operates with high demand density over a given service region significantly benefits from adoption of electric trucks, while also enjoying substantial carbon emissions savings. High demand density also favors smaller battery capacity with shorter ranges under the optimized charging network capacity, even though larger battery capacity would increase productivity with extended ranges. Our analysis also offers insights for governments and regulators regarding the impact of several influential factors such as carbon cost, content of renewable energy in electricity mix, diesel engine efficiency, and subsidizing the charging infrastructure

Transitioning to Sustainable Freight Transportation by Integrating Fleet Replacement and Charging Infrastructure Decisions

The transportation sector is the largest contributor to global greenhouse gas emissions. Disruptive technological changes in this sector, such as alternative fuel vehicles, are crucial for emission reduction. We show how a cost-minimizing strategic transition plan to adopt electric trucks over time can be developed for a firm that owns and operates a fleet of diesel trucks. We consider the case in which the firm decides to invest in the charging infrastructure required to support this transition, either because the public charging infrastructure is currently inadequate or for strategic reasons. The congestion effect at the charging stations, the charging times, and the potential loss of productive driving time due to detours to reach charging stations are explicitly considered. By developing an independence property, we are able to model this problem as a linear integer program without specifying origins and destinations. We illustrate the resulting transition plan with realistic parameter configurations. Our results indicate that a firm with high transportation demand density over a given service region significantly benefits from adoption of electric trucks, while also enjoying substantial carbon emissions savings. High demand density also favors smaller battery capacity with shorter ranges under the optimized charging network capacity, even though larger battery capacity would increase productivity with extended ranges. Our analysis also offers insights for governments and regulators regarding the impact of several influential factors such as carbon cost, content of renewable energy in electricity mix, diesel engine efficiency, and subsidizing the charging infrastructure. Additionally, we present an extension to the model that allows for different modalities of partnership in the infrastructure investment; notably public-private and private-private partnerships. While in general our results suggest that such partnerships are beneficial to all involved, the amount and relative distribution of the potential gains depend on the topography and on the density of charging infrastructure

A stochastic program to evaluate disruption mitigation investments in the supply chain

Supply chain risk management is becoming increasingly important due to a variety of natural and man-made uncertainties. We develop a methodology to evaluate the costs of disruptions and the value of supply chain network mitigation options based on a two-stage stochastic program. To solve the model, we rely on a solution scheme based on sample average approximation. We explicitly differentiate between disruption periods and business as usual periods to decrease the model size and computational requirements by approximately 85% and 95%, respectively. Furthermore, the decrease in model complexity allows us to include the conditional value at risk in the objective function to incorporate the risk aversion of decisions makers. Based on a case study of a chemical supply chain, this study shows the trade-off between long-term expected costs minimization and short term risk minimization, where the latter leads to a more aggressive investment policy

Global Sourcing under Tariffs: Time Series Analysis of Product-Level Evidence

Global sourcing is a complex process to acquire products and services from international sources, and therefore is subject to various disruptions. This paper focuses on the potential disruptions arising from the large-scale tariffs during 2018-2019. Drawing on tariff implications from analytic models of global supply network design, we specifically examine the patterns of global sourcing and how tariffs could disrupt global supply chains by investigating time series of monthly sourced amounts. We draw 222 manufacturing firms from FactSet Shipping database, with 3,348,595 unique observations covering time period between January 2014 and December 2019. By aggregating sourcing amounts on each firm, and applying multivariate time series clustering algorithm, we identify seven unique clusters for these firms. We further examine the disruptive effects of these tariffs by using intervention analysis of time series for each cluster. We find that firms in each cluster increase their sourcing amounts before or during tariff time periods. Our results further show that while some firms have a significant disruptive effect, other firms still maintain pre-tariff sourcing behaviors. An additional analysis reveals that firm size, growth potential, and firm profitability are associated with firms’ ability to deal with disruptions. Overall, our results have important implications for global supply chain management

Increased bullwhip in retail: A side effect of improving forecast accuracy with more data?

Can there be side effects of improved forecast accuracy? In this study of the Belgian food retailer Colruyt Group, we show how adding explanatory variables (such as promotions, weather forecasts, national events, etc.) increases forecast accuracy compared to methods using only historical sales data. Furthermore, when using these sales forecasts to determine inventory levels and order decisions in a numerical experiment, we see that these more accurate forecasts require less inventory to maintain a target service level, indicating that more accurate predictions may reduce stockouts and operational costs related to high inventories. These are expected findings. We also found the use of explanatory variables makes the sales forecasts (and consequently the replenishment) more responsive towards changes in customer demand patterns. This creates a higher bullwhip effect regarding the variability of the supermarket’s replenishment orders -- a less desirable outcome of more accurate forecasting using explanatory variables