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Chapter 2 • Foundations and Technologies for Decision Making 105

for empty containers create uncertainty in the location increased from a record low of 3.8 cycles in 2009 to 4.8 cycles
and availability of containers. in 2010. Moreover, when the ECO system was implemented in
• Information handling and sharing. Huge loads of 2010, the excess cost per full voyage became $35 cheaper than
data need to be processed every day. CSAV processes the average cost for the period between 2006 and 2009. This
400,000 container transactions every day. Timely deci- resulted in cost savings of $101 million on all voyages in 2010.
sions based on accurate information had to be gener- It was estimated that ECO’s direct contribution to this cost
ated in order to help reduce safety stocks of empty reduction was about 80 percent ($81 million). CSAV projected
containers. that ECO will help generate $200 million profits over the next
• Coordination of interrelated decisions worldwide. 2 years since its implementation in 2010.
Previously, decisions were made at the local level.
Consequently, in order to alleviate the empty container case Questions
problem, decisions regarding movement of empty con- 1. Explain why solving the empty container logistics
tainers at various locations had to be coordinated. problem contributes to cost savings for CSAV.
2. What are some of the qualitative benefits of the optimi-
methodology/solution zation model for the empty container movements?
CSAV developed an integrated system called Empty Container 3. What are some of the key benefits of the forecasting
Logistics Optimization (ECO) using moving average, trended model in the ECO system implemented by CSAV?
and seasonal time series, and sales force forecast (CFM) meth- 4. Perform an online search to determine how other ship-
ods. The ECO system comprises a forecasting model, inven- ping companies handle the empty container problem.
tory model, multi-commodity (MC) network flow model, and Do you think the ECO system would directly benefit
a Web interface. The forecasting model draws data from the those companies?
regional offices, processes it, and feeds the resultant informa- 5. Besides shipping logistics, can you think of any other
tion to the inventory model. Some of the information the fore- domain where such a system would be useful in reduc-
casting model generates are the space in the vessel for empty ing cost?
containers and container demand. The forecasting module
also helps reduce forecast error and, hence, allows CSAV’s What We can Learn from this end-of-
depot to maintain lower safety stocks. The inventory model chapter application case
calculates the safety stocks and feeds it to the MC Network
Flow model. The MC Network Flow model is the core of the The empty container problem is faced by most shipping
ECO system. It provides information for optimal decisions companies. The problem is partly caused by an imbalance
to be made regarding inventory levels, container reposition- in the demand of empty containers between different geo-
ing flows, and the leasing and return of empty containers. graphic areas. CSAV used an optimization system to solve
The objective function is to minimize empty container logis- the empty container problem. The case demonstrates a situ-
tics cost, which is mostly a result of leasing, repositioning, ation where a business problem is solved not just by one
storage, loading, and discharge operations. method or model, but by a combination of different opera-
tions research and analytics methods. For instance, we realize
that the optimization model used by CSAV consisted of differ-
results/benefits
ent submodels such as the forecasting and inventory models.
The ECO system activities in all regional centers are well coor- The shipping industry is only one sector among a myriad of
dinated while still maintaining flexibility and creativity in their sectors where optimization models are used to decrease the
operations. The system resulted in a 50 percent reduction cost of business operations. The lessons learned in this case
in inventory stock. The generation of intelligent information could be explored in other domains such as manufacturing
from historical transactional data helped increase efficiency and supply chain.
of operation. For instance, the empty time per container cycle
decreased from a high of 47.2 days in 2009 to only 27.3 days Source: R. Epstein et al., “A Strategic Empty Container Logistics
the following year, resulting in an increase of 60 percent of Optimization in a Major Shipping Company,” Interfaces, Vol. 42, No.
the average empty container turnover. Also, container cycles 1, January–February 2012, pp. 5–16.

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Alter, S. L. (1980). Decision Support Systems: Current Practices Propositions.” Journal of the Academy of Marketing Science,
and Continuing Challenges. Reading, MA: Addison-Wesley. Vol. 24, pp. 338–349.

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