A Strategic Empty Container Logistics Optimization in a Major Shipping Company

1. A Strategic Empty Container Logistics Optimization in a Major Shipping Company Fatih Kara Berkan Kesen Ali Yapar

2. OUTLINE • Brief About The Company • The Existing System • The Empty Container Problem • The Empty Container Logistics Optimization System • Implementing the ECO system • Impact • Conclusion

3. CompaniaSudAmericana de Vapores • Largestlogisticoperator in Latin America • Operations in over 100 countries • 700,000 20-foot equivalentunitcontainers of over 10 types • Thisfleetservescustomerneeds of 2.9 million TEU(about 50 millionstons) • Rents 95% of containers on longtermcontracts • Seven regionaloffices:Valparaiso,SaoPaulo, Hong Kong, Hamburg, New Jersey, Barcelona,Mumbai

4. Theexistingsystem • Multimodal network with 40 container transport services • Services havefixedroutesandcarriedoutby 180 containerships • CSAV professionalsare of differentnationalities=> Operationcentersare in different time zones • Companymanageditscontanierfleetbased on decentralizeddecisions

5. Theexistingsystemhad shortcomingsledby; -highlevel of interconnection of decisions -emptycontainerimbalanceamongregions -uncertainty in main parameters • Toadressthis problem CSAV andresearchersbegan a project:emptycontainerlogisticssystem(ECO) • Itoptimizesemptycontainerlogisticssystembyintegratingtheoperationalandbusinessdecisions of allregionaloffices • Main objectivewasto minimize global emptycontainer-relatedcostswhileguaranteeing a high service level

6. Cycle of a Cargo Container

7. TheEmptyContainer Problem • Similardiscussions in literatureforthis problem -Crainic et al.(1993) givethefirstcharacterizations of theseproblems=>developsingleand multicommodity network formulations -Erera et al present a largescalemulticommodity problem on a time-discretized network model integratingcontainerroutingandrepositioningdecisions. -Choong et al. discuss a tacticalplanning problem formanagingemptycontainers on barges forintermodaltransportation

8. Four Main Problems • Imbalance of demandamongregionsrelatedwithefficientrepositioning • Multiplesources of uncertaintyrelatedwithdependance on market conditions • Handling andsharingoperationsinformation • Lack of coordinationbetweenregionaloffices

9. EmptyContainerLogisticsOptimizationSystem • Two stage solution : • Network-Flow Model • Inventory Model • Based on forecastingsystem • Network FlowModel: • Multi Commodity-Multi Period Network Flow

10. EmptyContainerLogisticsOptimizationSystem • Network-Flow Model: • Imbalance problem andsupportsdailyemptycontainerrepositioningandinventorylevels • Inventory Model: • Fordealingwithuncertainty problem, • Determiningthesafetystock • Service Quality: • Managedbyimposingthesafetystock as constraints in thenetwrokflow problem

11. EmptyContainerLogisticsOptimizationSystem • DesicionVariables • How & When ? • Repositioningemptycontainerstofulfill • Whatlevel ? • Emptycontainers at eachpoint • When & Where • Leaseandreturncontainers

12. EmptyContainerLogisticsOptimizationSystem • Multi Commodity-Multi Period Network Flow • Main Constraints • ContainerFlowBalanceEquations • Demandsatisfaction • Capacityconstraints • SafetyStockLimits • Initialconditions • GOAL OF THE MODEL • Achievepredefined Service Level inventoryvariables • Safetystock as lowerbounds • Ifthesafetystockcannot be fulfilledwithcurrentavailableemptycontainers • Variablesrepresentsshortagesforadditionalrequirements

13. EmptyContainerLogisticsOptimizationSystem • Multi Commodity-Multi Period Network Flow

14. EmptyContainerLogisticsOptimizationSystem • Inventory Model • Importantpart of Project wasunderstandingtherealtionshipbetweenforecasterrorandsafetystock • Tocaluclatesafetystock, varianceandforecasterror is included in thesafetystock. • ForecastApproaches • Time-Series Forecast • Sales Force Forecast • Logisticplanners’ forecast

15. EmptyContainerLogisticsOptimizationSystem • Inventory Model

16. Implementing the ECO System • Implementing is a long process • Gradually implement starting with headquarters office • Understanding problems • Approach allowed to gradually improve models

17. Implementing the ECO System Difficulties of implementation; • Different cultures • Different processes • Different concepts • Different language but a global unique process and tool !

18. Implementing the ECO System • In early 2007, tested first prototype with historical data shows the opportunities. • This results encourage to move to the next step. • In late 2007, implemented to Valparaiso in Chile and Sao Paulo in Brazil. • This prototype shows that Web-based interface.

19. Implementing the ECO System • In 2008, financial crisis hit the industry. • Many company went bankrupt, volume is decreased. • Between July 2008 and March 2009; Freight cargo fell 18 % Sales dropped 38 % Market value fell 66 %

20. Implementing the ECO System • A competitive advantage is that ECO System. • In January 2010, the system launched globally. • The weekly sessions with staff for determining features of the model.

21. Implementing the ECO System ECO uses four server; • Parameter Server • Store Server • Web Server • Application Server

22. Impact The project has a great impact than orginally intended; • Data gathering • Real time communication • Automation of data handling • Quality of decision processes

23. Impact In quantitative terms;

24. Impact

25. Impact On the qualitative side; • ECO provided decision makers with a trustworthy methodology that gives them target inventory levels. • OR made CSAV’s tracking and information system useful.

26. Impact ECO made contributions to; • Data quality, management and availibility • Personnel efficiency • Unification of processes • Better reporting and control

27. Conclusion • ECO provided a decision support framework • OR allowed CSAV to coordinate empty container logistics under uncertainty changing containers and imperfect information • Managers became convinced of the significant benefits of an OR approach

28. Conclusion • The importance of using prototype • The results proved that the approach is right. • Being able to centralize the the coordination • Web based framework assisted to cope with distinct time zones