A Strategic Empty Container Logistics Optimization in a Major Shipping Company

Abstract

In this paper, we present a system that Compañía Sud Americana de Vapores (CSAV), one of the world's largest shipping companies, developed to support its decisions for repositioning and stocking empty containers. CSAV's main business is shipping cargo in containers to clients worldwide. It uses a fleet of about 700,000 TEU containers of different types, which are carried by both CSAV-owned and third-party ships. Managing the container fleet is complex; CSAV must make thousands of decisions each day. In particular, imbalances exist among the regions. For example, China often has a deficit of empty containers and is a net importer; Saudi Arabia often has a surplus and is a net exporter. CSAV and researchers from the University of Chile developed the Empty Container Logistics Optimization System (ECO) to manage this imbalance. ECO's multicommodity, multiperiod model manages the repositioning problem, whereas an inventory model determines the safety stock required at each location. CSAV uses safety stock to ensure high service levels despite uncertainties, particularly in the demand for containers. A hybrid forecasting system supports both the inventory and the multicommodity network flow model. Major improvements in data gathering, real-time communications, and automation of data handling were needed as input to the models. A collaborative Web-based optimization framework allows agents from different zones to interact in decision making. The use of ECO led to direct savings of $81 million for CSAV, a reduction in inventory stock of 50 percent, and an increase in container turnover of 60 percent. Moreover, the system helped CSAV to become more efficient and to overcome the 2008 economic crisis.

An Overview of Empty Container Repositioning Studies and Research Opportunities

“…Another reason is the high purchasing and using cost of the softwares. The former Chilean shipping company Compañía Sud Americana de Vapores (CSAV) developed a system called Empty Container Optimization (ECO) that optimizes ECR by integrating operational and commercial decisions at its all regional offices (Epstein et al, 2012). However, there is no clear information about the availability, effectivity and sustainability of this system.…”

Revenue Management for Intermodal Transportation: The Role of Dynamic Forecasting

“…12 Indeed, commonly used forecasting models-for example, IID, AR(1) time series, MMFE, and empirical distribution-are all special cases of the Markov framework (Elliott and Timmermann 2008, Hwang et al 2010, Li et al 2013, Song and Zipkin 1996, Xu et al 2007, Zhang et al 2010. 13 Epstein et al (2012): "The uncertainty in the main problem parameters and the lack of tools to quantify this uncertainty led decision makers to hold high levels of stock of empty containers to maintain their high level standards." 14 Instead of directly calculating x t for all states ðK t ; D t ; h t Þ, we can now pre-specify a list of thresholds for each class j, and then obtain x i t via a simple transformation.…”

Socially Responsible Supply Chain Management for a Competitive Advantage

“…As a result of these new processes, CSAV was able to increase its fuel efficiency by maximising container space in its shipping operations. The project has generated cost savings in excess of $100 million since 2010 (Epstein et al 2012). While CSAV did not calculate the precise amount of GHG emissions it reduced through this new system of empty container optimisation, the reduction in fuel consumption certainly led to a corresponding reduction in GHG emissions.…”