Port Congestion and Economies of Scale: The Large Containership Factor

Transcription

1 Guan, Yahalom and Yu 1 Port Congestion and Economies of Scale: The Large Containership Factor Changqian Guan Department of Marine Transportation United States Merchant Marine Academy 300 Steamboat Road Kings Point, New York Phone number: cguan68@gmail.com Shmuel (Sam) Yahalom (Corresponding Author) Department of International Trade and Transportation State University of New York Maritime College 6 Pennyfield Avenue Throggs Neck, New York Phone Number: or yahaloms@aol.com syahalom@sunymaritime.edu Jun Yu Department of Business Administration School of Economics and Management Shanghai Maritime University 1550 Haigang Avenue Shanghai, P. R. China Tel: junyu@shmtu.edu.cn ABSTRACT Large containerships provide economies of scale at sea. Consequently, very large containerships and ultra large containerships are the largest percentage of containerships on order and are deployed in Asia Europe and Transpacific routes. However, do the economies of scale prevail for the overall operation, i.e., at sea and port?

2 Guan, Yahalom and Yu 2 The paper tests the relationship between vessel size and port time, using multiple regression analysis. The test uses a case analysis for containerships deployed in the Asia Europe route. The analysis captures the changes in vessel size and industry practice and their impact on port time. The results indicate that altogether a one percent increase in ship size and its auxiliary industry operations will increase port time by nearly 2.9%, i.e., diseconomies of scale at the port. After the literature review the paper provides data analysis, test results, a conclusion and recommendations. Keywords: Economies of scale, diseconomies of scale, port time, containership size, ports of call, number of ports

3 Guan, Yahalom and Yu 3 1. INTRODUCTION The launching of larger and larger containerships that started years ago is expected to continue, including a large annual growth for the next three years, 12.8% and 40.4% respectively, for all the containership size categories (1). Driving this trend is the desire to take advantage of the economies of scale that large containerships provide at sea. The weak global trade market and excess carrying capacity due to the increase in the number of containerships which caused firms to operate with less than full containerships forced companies into alliances. An alliance consolidates containership space and improves utilization of the containership fleet. As a result, the alliance s average ship size by trade route increased. Since the demand for container discharge and load by port is less than a full containership, the containership calls multiple ports on every voyage. The actual number of ports of call depends on the liner service and demand for freight. Globally, major container ports have been reporting severe congestion and delays. Much of the congestion and delays are associated with containerships larger than 10,000 TEUs (2). This indicates diseconomies of scale in the port due to the increase in containership size. Thus, what are the economies of scale at the port with the vessel size increase? The paper tests the relationship between vessel size and port time using multiple linear regression analysis. The test is designed to capture the changes in vessel size and industry practice and their impact on the port. After the literature review the paper provides data analysis, test results, conclusions and recommendations. 2. LITERATURE REVIEW The key change in the containership market that impacts the transport system is economies of scale. Other impacts come from environmental concerns, integration of information technologies, progress in terminal automation, ship financing, security issues, and real estate pressures around terminal facilities (3). Wijnolst (4) states that the driving force is the creation of a competitive advantage through economies of scale. Economies of scale in liner shipping have been increasing in response to technology-driven productivity growth, regulatory changes, and higher world-wide trade flows (5). The Financial Times reported Maersk s Triple E Class containership (18,000 TEU) to be 26 percent more cost efficient at sea than the E class (15,000 TEU) (6). However, OECD/ITF (2) reports that a 19,000 TEU modern vessel is only about 15 percent more efficient than a 15,000 TEU at a speed of 22 knots. Economies of scale from operating larger containerships can be achieved if: vessels are full, the number of ports of call decreases, shipping distance increases, the relative costs of large ships decrease, and port productivity improves (7) (8). The deployment of the new generation of containerships is mainly due to economies of scale, assuming high utilization of the larger ships (9). However, due to the lack of ship capacity utilization, shipping companies try to reduce

4 Guan, Yahalom and Yu 4 operating costs. The reduction of operating cost includes: optimization of the ship size, speed, fuel efficiency, sailing frequency, different routes and port calling mode (9) (10) (11) (12) (7). The aggregate economies of scale in containership operation is a trade-off between the positive returns at sea and the negative returns in port (13). The obvious optimizing factors are the design and capability of the quay (draught, strength) and the quay cranes (outreach, air draft). Other factors are the yard and the yard handling equipment, especially in response to cascading (2). Yahalom and Guan (14) demonstrated that the minimum port time for containerships is a function of a vessel s beam size; the larger the vessel s beam size, the more time a vessel needs to discharge and load. Thus, ports can mitigate this factor with higher gantry crane productivity. Ports control factors such as berth length and the number of gantry cranes. However, terminals with an excess number of gantry cranes could face high idle costs due to excess idle time. Furthermore, the larger vessels improve crane productivity initially up to a point of diminishing returns, when the crane-working cycle time increases because the container bays are larger and deeper (Yahalom and Guan, 14, OECD/ITF, 2, and Le, 18). Additional diminishing effects are due to the larger hatches and therefore the lessening of gantry crane movements. Cullinane and Khanna (15) argued that there are no diseconomies of scale in port for ship sizes of less than 1500 TEU and that the economies of container ship operations are crucially dependent on port productivity. Two years later, Cullinane and Khanna (8) argued that at least in the vessel size range of 6000 to 7000 TEU, there are net positive returns to scale such that the cost savings while at sea outweigh the additional costs in port. There is no doubt that the productive capacity of the port has improved. As a result, the critical vessel size (number of TEUs) also increased. However, vessel size has a ceiling depending on the port s productivity. Larger vessels experience longer vessel arrival delays and longer berth times than smaller vessels. Over 60% of vessel arrival delays exceed 24 hours for vessels with a capacity of 8,000 TEU at both ports LA/LB. The impact is worldwide. In Asia, ships of more than 10,000 TEU had the highest average arrival delay, with an average delay of 19 hours in Shenzhen and 23 hours in Hong Kong (16). This is no surprise, as indicated by Yahalom and Guan (14). The bigger the ship, the larger the number of hours spent in port; an increased port stay is a diseconomy of scale (8). In order to guarantee the economies of scale for bigger container ships, the average turnaround time (ATT) is important. It includes the time spent entering the port, loading, unloading, and departing, i.e., ship-to-shore operations, other terminal operations and port functions as a whole (17). The studies mentioned above focused on containership economies of scale based on voyage costs per TEU. This paper focuses on port time based on the number of vessels in an alliance service, average vessel size and the number of ports of call. There is no similar analysis in the literature.

5 Guan, Yahalom and Yu 5 3. METHODOLOGY Vessel size is an important input in determining the number of ports of call and the amount of time a containership spends in each port. Large containerships, due to their size and the limited accommodating conditions in many ports, call several ports. A voyage time of a containership is divided between sea-time and port-time. A voyage includes several ports of call, i.e., Total Voyage Time (TVT). The economies of scale of TVT determination is by analyzing Total Port Time per voyage by vessel size (TPTi) which is the time spent in the port/terminal from the moment a vessel is tied to the pier and Total Sea Time per voyage by vessel size (TSTi) which is TVTi less TPTi. Voyages are by different vessel sizes i. As vessel size changes, so does the number of ports of call and the time a vessel spends in each port. TVTi is the sum of TSTi and TPTi, formally, TVTi = TSTi + TPTi (1) where i indicates the average vessel size in TEU. Containership port time has two players: the port and the shipowners/operators (SOO). The focus of the analysis is from the SOO perspective and control. The analysis provides the details of TPT implications of the SOO vessel deployment strategy due to port limitations in accommodating large vessels. This strategy is based on the number of vessels in the liner service, average vessel size (in TEU) and number of ports of call. Obviously, the SOO deployment strategy is also in response to demand for containers discharge and load per port along the voyage and the port inefficiencies and limitations, some of which are: The number of containers discharged and loaded in the port The number of gantry cranes used to discharge and load a vessel Gantry crane operators technology and productivity Yard operation and technology The distribution between 20 and 40 containers The stowing plan for discharge and load in each port/terminal The terminal management and design This focus highlights the impact of SOO deployment strategy on port time. 4. DATA ANALYSIS The data for analysis was obtained from major shipping lines websites in The shipping lines used for the study include Maersk, APL, CMA and Evergreen. The vessels for all the alliance services are as follows: Asia-Europe routes (M2, G6, O3 and CKYHE) Three Asia-Europe loop services within Maersk (AE1, AE2, and AE10) Five Asia-Europe loop services within APL, i.e. LP1, LP4, LP5, LP6, and LP7

6 Guan, Yahalom and Yu 6 Ten French-Asia Lines within CMA, i.e. French-Asia Line 1, 2, 3, 6, 7, 8, 11, 12, 15, and 16 The Asia-Europe loop services within Evergreen, i.e. China-Europe-Mediterranean Service (CEM), China-Europe Shuttle Service(CES), Europe French-Asia Line (FAL3), MD2 Service (MD2), Asia-North Europe Service VI(NE6), Asia/North Europe Weekly Express Service 3 (NE3), ASIA/North-Europe Express 2 (NE2), Asia/North-Europe Weekly Express Service 7 (NE7), ASIA/MED Express Service (AMX1), Asia-Europe Container Service 1 (AEC1). For example, the CMA-CGM alliance uses 12 containerships for the French-Asia Line 8 (FAL 8) service with an average containership size of 15,830 TEU. The service includes 12 ports of call over 77 days of which 17 days are spent in the port and 60 days in transit. The ship size range for this voyage is from 13,096 TEU to 18,982 TEU. The alliances use multiple vessels in providing their service. The data collected was for a loop, not the individual vessels. The data set includes a total of 25 loops by the 229 containerships serving 330 ports of call. Each service loop was averaged for: total time of a loop (days), the number of ports of call, time at port and time at sea (days), average vessel size (TEUs) and the number of vessels. Port time is the difference between the shipping company s listing of arrival and departure times, plus the time for ships entering and leaving the port. All the data is in days. 4.1 Total port time: regression analysis A linear cross-section multiple regression analysis was calculated for the impact of several important variables on TPT, the dependent variable. The key independent variables include: the number of vessels (NOV) in a service, the average vessel size (VS) in the service, and the number of ports of call (NOP) in the service. These key variables explain a large portion of TPT. The calculations indicate that all the estimates are significant at 5% or better with a coefficient of determination (R 2 ) of 57.4%. TPT = NOV VS NOP (2) (T-Stat or (4.596) ( or (2.455 or (4.318 or Sig) 1.2%) 2.3%) 0.03%) R 2 = 57.4%, Adjusted R 2 = 51.3%, n= 25, F-Stat = The estimated equation 2 indicates that: NOV indicates that a service loop increase by one vessel reduces the total number of days in the port by 0.50 days or 12 hours (significant at 1.2%) VS indicates that an increase in vessel size by one TEU increases the port time by days or 1.2 minutes (significant at 2.3%). Alternatively, an increase of

7 Time in port (days) Guan, Yahalom and Yu 7 vessel size by 1000 TEU increases port time by 0.49 days or 12 hours NOP indicates that an increase in the number of ports of call by one, increases the total port time by days or about 17 hours (significant at 0.03%). A covariance analysis between the variables of equation 2 indicates that there is very little variance and covariance between the variables. The largest variance is in the number of vessels, which is only followed by a variance in the number of ports of call. The covariance between the variables are extremely small fractions (Table 1). TABLE 1: Variance and Covariance Matrix for Equation 2 Constant Number of Number of Vessel size vessels ports of call (TEU) Constant Number of vessels E E-05 Number of ports of call E E-06 Vessel size (TEU) E E E-08 The statistical significance of each variable in equation 2 explains TPT. Equation 2 s results for each independent variable relation to the dependent variable are illustrated in Figures 1 to 4. The illustration shows the general trend of each pair of variables. Total port time and number of vessels. The trend of the total port time decreases with the increase in the number of vessels in the service as shown in Figure 1. This could be due to a smaller number of containers being discharged and loaded in each port along the liner service Number of vessels FIGURE 1: Port Time vs. Number of Vessels. Total port time and vessel size. The trend of the total port time increases with vessel size as shown in Figure 2. This increase is explained by increase in beam size (14), port difficulties and inefficiencies in accommodating large containerships.

8 Time in port (days) Total port time (days) Guan, Yahalom and Yu ,000 9,000 10,000 11,000 12,000 13,000 14,000 15,000 16,000 Vessel size in TEU FIGURE 2: Total Port Time (Days) vs. Vessel Size. Port time verses number of ports. As the number of ports of call increases, the trend for the time in port (in days) is also to increase (Figure 3). This increase is due to the time inefficiencies associated with calling multiple ports Number of ports of call FIGURE 3: Port time vs. Number of Ports of Call. Number of ports of call and vessel size. The number of ports of call in a loop has gradually increased with the increase of containership size (Figure 4). The bigger ship needs to stop at more ports. A big ship cannot discharge and load all its containers in one port.

9 Number of ports of call Guan, Yahalom and Yu ,000 9,000 10,000 11,000 12,000 13,000 14,000 15,000 16,000 Containership size port time. FIGURE 4: Number of Ports of Call vs. Containership Size. These results are meaningful in indicating the overall negative impact of large vessels on 4.2 The total effect of increase in ship size on port time: sensitivity analysis A sensitivity analysis of the percentage change in port time was regressed (equation 3) against the percent change of the same variables of equation 2 in order to determine their effect on port time. The elasticity results indicate their significant sensitivity to port time with a coefficient of determination (R 2 ) of 52.3%. LogTPT = LogNOV LogVS LogNOP (3) (T-Stat) (-0.973) ( or 8.87%) (1.844 or 7.99%) (3.576 or 0.19%) R 2 = 52.3%, Adjusted R 2 = 45.2%, n= 24, F-Stat = 7.31 The regression results or elasticities provide sensitivity measures of the change in port time due to a 1% change of each of the following: LogNVP indicates that as the number of vessels increase by 1%, total port time decreases by 0.16% (significant at 8.87%) (Figure 5). This is a relative small figure LogVS indicates that as the vessel size (TEU) increases by one percent, port time increases by 2.52% (significant at 8%) (Figure 6). This indicates that port time is very sensitive to vessel size, which is consistent with Yahalom and Guan (14) LogNOP indicates that as the number of ports of call increase by one percent, port time increases by 0.519% (very significant at 0.19%) (Figure 7). Thus, ports are impacted by the liner service s number of ports of call. The total effect of the increase in the independent variables (lognov+logvs+lognop) by one percent on total port time is 2.878%. This is a very significant impact indicating

10 Port time increase (%) Guan, Yahalom and Yu 10 the magnitude of the diseconomies of scale. A covariance analysis between the variables of equation 3 indicates very little variance and covariance between them. The largest variance is in vessel size growth, The covariance between the variables is small as shown in table 2. TABLE 2: Variance and Covariance Matrix for Equation 3 Constant Number of vessels (% change) Number of ports (% change) Vessel Size (% change) Constant Number of vessels (% change) Number of ports (% change) Vessel size (% change) The overall statistical significance of equation 3 is important in explaining changes in TPT. Therefore, each of the variables of equation 3 is also illustrated in figures 5 to 7. The illustration highlights the relationship between independent variable changes and TPT changes. Port time change due to an increase in the number of vessels. The increase in the number of vessel calls has a negative impact on port time (Figure 5 and equation 3). This negative impact is expected when the increase of number of vessels also reduces the number of containers discharged and loaded in each port. 50% 40% 30% 20% 10% 0% -50% -30% -10% -10% 10% 30% 50% 70% 90% 110% 130% 150% -20% -30% -40% Increase in the number of vessels (%) FIGURE 5: Impact of the Increase in the Number of Vessels on Increase of Port Time. Port time increase due to vessel increase in size. Containerships increase in size effects port time. Figure 6 shows that as vessel size increases (%), port time increases as well (%). From equation 3 we see that this sensitivity is very large, at a rate of 2.52%. This sensitivity can be explained in several ways, including (1) larger beam size of the new vessel class, and therefore

11 Port time increase (%) Port time increase (%) Guan, Yahalom and Yu 11 the time it takes to finish a bay (Yahalom and Guan, 14), (2) ports slow acquisition of new technology and (3) other port inefficiencies in handling larger containerships. 50% 40% 30% 20% 10% 0% -1% -10% 1% 3% 5% 7% 9% 11% 13% -20% -30% -40% Vessel increase in size (%) FIGURE 6: Impact of Vessel Increase in Size on Increase in Port Time. Port time increase due to the number of ports of call. The increase in the number of ports of call impacts on port time. Equation 3 demonstrates significant sensitivity of in Figure 7, confirming these impacts. 50% 40% 30% 20% 10% 0% -50% -30% -10% -10% 10% 30% 50% 70% -20% -30% -40% Increase in the number of ports of call (%) FIGURE 7: Impact of Increase in the Number of Ports of Call on Increase on Port time. The statistical analysis demonstrates the relationship between port time and critical variables in the containership industry that impact on port time, such as vessel size, number of ports of call and liner service deployment. 5. CONCLUSION AND RECOMMENDATIONS The paper, using statistical analysis, identifies key variables that contribute to port time due to shipowner/operator strategies, using their fleet in order to provide a viable containership service

12 Guan, Yahalom and Yu 12 at a reasonable port time. The research found that with the increase in containership size, the port time increased. That is, economies of scale that are gained at sea are lost at the port. The contribution to diseconomies of scale is primarily due to ship size, but the large number of ports of call and the large number of vessels per liner service also contribute to the diseconomies of scale at the port. The analysis determined the sensitivity of port time to the changes in containership size, especially to vessel size. As vessel size increases by one percent, port time increases by more than 2.5 percent. This dominating outcome is not a surprise because discharge and load technologies lag behind the demand of the large containerships. Furthermore, the auxiliary effect of vessel size deployment strategy indicates an almost 2.9 percent increase in total port time. Obviously, other auxiliary factors play a role as well, such as port inefficiencies and the amount of equipment available, to name a few. In light of these findings we recommend that ports improve terminal productivity and operation efficiency in order for the liner service to reduce the number of ports of call. These can be achieved with better technology and its timely installation. The analysis recognizes that in order to overcome the diseconomies of scale in a port generated by the increase in vessel size, the liner service needs to call more ports for the large vessels. We recommend further research on these issues in order to determine long-term changes that would increase productivity in line with containership size.

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