Delivery Schedule Reliability in Container Liner Shipping Chung-Piaw Teo NUS Business School, National University of Singapore Joint work with Jasmine, Siu Lee Lam School of Civil and Environmental Engineering, Nanyang Technological University Abraham Zhang Waikato Management School, University of Waikato Zhichao Zheng Lee Kong Chuan School of Business, Singapore Management University

Vessel Farm/Factory Introduction – Liner Shipping Economic contribution –90% of international trade takes place by sea, and the liner shipping industry is responsible for 60% of them by value (IHS Global Insight 2009)

Introduction – Schedule Unreliability Schedule reliability –From December 2005 to June 2010, average schedule delay (more than one day) ranged from 32% to 54% (Drewry 2010) –50% to 70% schedule reliability Impacts on supply chain –Difficulties in resource coordination –Increase in safety stocks –Impossible to implement just-in-time/lean strategies Vessel Farm/Factory

Introduction – New Initiatives Daily Maersk Schedule Reliability in Container Liner Shipping Using Copositive Cones414 July IFORS [Figure from Maersk Line website on 12 Jul 2014]

Introduction – New Initiatives Daily Maersk –98% reliability in the first year –Six months after, its market share of Asia-Europe trade increased from 21% to 25% (Leach 2012) –“Reliability is the new rate war; we need an end-to-end view on reliability” – Eivind Kolding (former CEO of Maersk Line, 2011) Schedule Reliability in Container Liner Shipping Using Copositive Cones5 [Figure from Maersk Line website on 12 Jul 2014]

Introduction – New Initiatives Slow steaming since 2007 –Fuel (bunker) cost –Now becomes the new standard (fully adopted by 2010) –Confounding effects on schedule reliability More vulnerable to uncertainties Ability to speed up to recover delays Willingness to speed up? –New generation of vessels: designed for slow steaming Maersk Triple E class Schedule Reliability in Container Liner Shipping Using Copositive Cones6 [“Slow Steaming: The Full Story” by Maersk Line]

Introduction – Schedule Reliability Strategic value of schedule reliability is now widely recognized, but –Industry average in Q is still 70.0% … Why? –Challenges from uncertainties Extreme weather conditions Current and tides Availability of empty containers Port congestion (propagation effect) Etc. Schedule Reliability in Container Liner Shipping Using Copositive Cones7

Introduction – Research Questions 1.Given a service route with fixed journey time and default sailing speeds, how to schedule port arrival and departure times to maximize schedule reliability? 2.How do the total journey time, default sailing speed, and sailing frequency affect schedule reliability? 3.What are the cost implications of improving scheduling reliability? Schedule Reliability in Container Liner Shipping Using Copositive Cones8

How is the schedule derived? Schedule Reliability in Container Liner Shipping9

Literature Impact of schedule reliability on shippers –Notteboom (2006), Vernimmen et al. (2007), Lam et al. (2011), Zhang & Lam (2013), etc. Influential factors for schedule reliability –Notteboom (2006), Vernimmen et al. (2007), Sözer and Dogan (2007), Chung & Chiang (2011),etc. Schedule design –Focus on cost minimizing (fuel cost, operating costs) –Stochastic programming –Christiansen et al. (2004), Qi & Song (2012), Wang & Meng (2012a, 2012b), etc. Schedule Reliability in Container Liner Shipping Using Copositive Cones10

Basic Model – Assumptions A vessel is always on time to start a round-trip voyage from its home port –Practice: sufficient buffer times between two consecutive round-trip voyages A vessel maintains a constant sailing speed at sea or be still –Relaxed in model extensions A port will not service a vessel until its scheduled arrival time –Terminal handling capacity is a bottleneck in liner shipping Each port of call is scheduled to be visited during the same time window every week –Analysis on increasing sailing frequency Schedule Reliability in Container Liner Shipping Using Copositive Cones11

Basic Model – Notation Schedule Reliability in Container Liner Shipping Using Copositive Cones12

Basic Model – Notation (Cont.) Schedule Reliability in Container Liner Shipping Using Copositive Cones13

Basic Model – Notation (Cont.) Schedule Reliability in Container Liner Shipping Using Copositive Cones1414 July IFORS

Basic Model – Notation (Cont.) Schedule Reliability in Container Liner Shipping Using Copositive Cones15

Basic Model – Objective Schedule Reliability in Container Liner Shipping Using Copositive Cones16

Cost Computation – Network Flow Schedule Reliability in Container Liner Shipping Using Copositive Cones1714 July IFORS

Worst-Case Expected Cost Theory based on Natarajan et al. (2011) and Kong et al. (2013) Schedule Reliability in Container Liner Shipping Using Copositive Cones18

Model Extensions Variable speed – bunker cost –Dynamic Speed up if there was a delay at previous port Different speeds for different scenarios –Static Optimize speeds at different legs Extreme weather conditions –Separate the scenarios with extreme weather –Conditional moments and multiple cones Schedule Reliability in Container Liner Shipping Using Copositive Cones19

Numerical Studies – Case Analysis Schedule Reliability in Container Liner Shipping Using Copositive Cones20 Westbound [Figure from Maersk Line website on 12 Jul 2014]

Numerical Studies – Case Analysis Schedule Reliability in Container Liner Shipping Using Copositive Cones21 Eastbound [Figure from Maersk Line website on 12 Jul 2014]

Numerical Studies – Data Data source: Maersk Line (2013), SeaRates.com (2013), portworld.com (2013) Port time includes pilotage in/out, berthing, cargo handling, etc. Extreme weathers may cause up to 24 hours delay in the South China Sea, the Indian Ocean and the English Channel Schedule Reliability in Container Liner Shipping Using Copositive Cones22

Numerical Studies – Data 1251 historical data points Schedule Reliability in Container Liner Shipping Using Copositive Cones23

Comparison of Schedules Schedule Reliability in Container Liner Shipping Using Copositive Cones24 < 20s CPU time

Performance of COP Schedule > 97% on-time probability under four common distributions Practice: 98% (Daily Maersk ports) Schedule Reliability in Container Liner Shipping Using Copositive Cones25

Performance Comparison Schedule Reliability in Container Liner Shipping Using Copositive Cones26 Port/Sea Time Distribution PatternUniformNormalGammaTwo-point COP Schedule Vessel Reliability (Avg)98.0% 97.8%95.9% - Rotterdam98.0%97.6%97.2%93.8% - Bremerhaven100.0% Bunker (tons)8,327 Maersk AE2 Schedule Vessel Reliability (Avg)97.4% 97.2%92.2% - Rotterdam98.1%97.6%97.3%93.7% - Bremerhaven100.0% Bunker (tons)8,327

Impacts of Sailing Speeds Schedule Reliability in Container Liner Shipping Using Copositive Cones27

Impacts of Total Journey Time Schedule Reliability in Container Liner Shipping Using Copositive Cones28 Total journey time (weeks)

Concluding Remarks Copositive cone optimization model for schedule reliability problem Robust performance comparable to existing Daily Maersk schedules Cost-reliability trade-off analysis Future work (on-going) –Variable speeds –Changing sailing frequency –Cargo reliability (overbooking and transhipment) Schedule Reliability in Container Liner Shipping Using Copositive Cones29

Thank you!