Effects of container ship speed on supply chains CO2 emission

Transcription

1 Effects of container ship speed on supply chains CO2 emission Dr. Nguyen Khoi Tran Maritime Institute, Nanyang Technological University, Singapore. Prof. Dr. Jasmine Siu Lee Lam Corresponding author School of Civil and Environmental Engineering, Nanyang Technological University, Singapore. Abstract This paper studies ship speed beyond the conventional sea scope to explore how it influences cargo lead time and CO 2 emission in supply chains. A model is proposed to simulate thousands of container flows on the Trans-Atlantic trade via a shipping service. According to the simulation results, sailing speed determines half of the total transit time and more than 70% of the carbon footprint. On the one hand, slow steaming brings about fuel saving as well as less CO 2 emission. On the other hand, it extends the transit time of goods and raises inventory burden. An optimal speed is fundamentally a trade-off between fuel cost of ship operators and inventory carrying cost of shippers. 1

2 1. Introduction Container liner shipping plays an important role in international trade. In 2014, it transported 1.63b tonnes (171m TEUs), equivalent to 15% of world seaborne trade (UNCTAD, 2015). Its share in the total value was approximately 60% (Statista, 2016). Shipping operation is carried out on route systems, so route design influences greatly on the efficiency and effectiveness of this mode as well as of intercontinental supply chains. This research aims to measure effects of sailing speed on cargo lead time and CO 2 emission. It is conducted by simulating the movement of container flows between Europe and the USA through a Trans-Atlantic service under different speed levels. The first innovative point is that ship speed is investigated not only in the context of seaside, but also in a wider scope of global supply chains. This approach helps to examine its effects comprehensively. The second point is that the simulation is done with thousands of container shipments, so it is suitable with the practice that a liner service often carries containers for many customers between different points, unlike tramp or industrial shipping with merely a few customers. The rest of this paper is organised as follows. Previous studies are reviewed in Section 2. Section 3 describes the research framework. Simulation results and analyses are demonstrated in Section 4. Some conclusions and future perspectives are mentioned in the final section. 2. Literature review In terms of operation, ship speed regulates time at sea of goods. This time indicator is a decisive factor of a service s competitive advantage and related to freight movements from loading to unloading ports. Seaside transit time contributes a significant portion to global logistics pipelines. It takes around 3 weeks to carry containers between Hong Kong and Rotterdam, 2 weeks between Hong Kong and Los Angeles or between Rotterdam and New York. Saldanha et al. (2009) test the importance of transit time in logistics chains and suggest that it should be a priority of shippers in choosing appropriate carriers. Fast transit time could be seen as a valuable marketing asset for service providers (Pearson and Fossey, 1983). Some studies have indicated market segmentations of small, urgent or high-value freight, for which carriers can provide sprint and reliable services in exchange of premium charges (Alderton, 2008; De Langen, 1999; Stopford, 2001; Visser and Braam, 2001). 2

3 In terms of finance, ship speed determines bunker cost, one of the major shipping expenses. According to a model of Drewry (2009) with a 10,000 TEU vessel and fuel price of $500 per tonne, increasing speed from 20 to 25 knots will lower Europe/Asia round voyage time by 8.8 days but raise a cost of $0.71m. Notteboom and Vernimmen (2009) point out that high bunker price gives carriers more incentive to lower steaming or to add more ports in loops. Being one of the most important transport modes of international trade, container shipping has generated a great amount of greenhouse gases. Cutting ship emission through speed reduction has become a major concern in the industry. Corbett et al. (2009) explore fuel tax levels to incentivize operators to reduce speed and CO 2 emission. Lindstad et al. (2011) investigate the decrease of gas emission and shipping expense at lower speeds. Orsic and Faltinsen (2012) estimate speed loss and expected CO 2 emission on a North Atlantic route. Khan et al. (2012) measure the decline of greenhouse gases and criteria pollutants when speed goes down on the trips between the ports of San Pedro and Oakland. Doudnikoff and Lacoste (2014) discuss the impact of low-sulphur fuel requirement on sailing speed. Based on ship call data, Zis et al. (2014) evaluate how speed reduction and cold-ironing influence on ship emission near and at ports. Since the second half of the 2000s, slow steaming (SS) has grown in popularity to confront the surge of fuel price and over-supply of shipping capacity. Economic and environmental aspects of this strategy have been examined by Wo and Moon (2013), Yin et al. (2014) and Psaraftis and Kontovas (2010). Tai and Lin (2013) validate its effects on the Europe/Far East route. Maloni et al. (2013) clarify cost and benefit of SS by simulating container flows to/from Asia through the port of Los Angeles. Notteboom and Cariou (2013) study the impact of SS on fuel surcharge. Lee et al. (2015) study its effect not only on fuel consumption but also on delivery reliability. Chang and Wang (2014) explore the dependence of speed reduction on fuel cost and charter rate. Ferrari et al. (2015) take the link between SS and service patterns into consideration. Maritime transportation has been growingly integrated into global supply chains. Ports have been no longer stops in end-to-end transport chains, but intermediaries between production and consumption. In the light of such trend, some researchers have attempted to quantify the impact of shipping operation on supply chains. Vernimmen et al. (2007) measure how schedule unreliability affects safety stock levels of shippers. Saldanha et al. (2009) estimate the relationships between transit time and transit time variability, and logistics costs. Tran (2011) and Tran et al. (2016) analyse the role of logistics factors on route design. Harrison and Fichtinger (2013) simulate the effects of transit time, time variability and service frequency on inventory and service levels. Zhang and Lam (2014, 2015) examine 3

4 Daily Maersk s impact on shippers supply chain inventory. Hassel et al. (2016) take accounts of the link between ship scale and generalised chain cost. The issue of ship speed in container shipping has been handled in many works, but is often restricted in the context of maritime transport. As shipping operation has been viewed as a non-detachable part of global supply chains, ship speed is necessarily studied in a broader scope. Our research is influenced by this trend. It approaches the literature gap to study different aspects of ship speed from the whole supply chain perspective. 3. Research framework 3.1. Description The key research question is how transit speed affects supply chain performance. To answer it, we simulate the movement of container flows between the USA and Europe (France, Germany, Belgium, Netherland and the UK) through a shipping service. A monthly seaborne trade data, provided by Port Import Export Reporting Service (PIERS), has been classified into 3,920 freight flows from Europe to the USA and 2,594 vice versa (Table 1). Each flow determines the number of boxes, TEUs and cargo value between two hinterland areas. Table 1: Cargo flows Number of flows Total TEUs Total boxes Total value Europe to US 3,920 97,578 59,154 $5,517,965,376 US to Europe 2,594 74,348 42,318 $4,013,047,458 The simulation process is carried out in six steps (Table 2). Based on the simulation, total lead time and carbon footprint of goods can be estimated. Container movement on the supply chain is divided into five stages: (i) transporting from the original point to the loading port, (ii) dwelling in the loading port, (iii) shipping between loading and unloading ports, (iv) dwelling in the unloading port, and (v) transporting to the destination. CO 2 emission is investigated during inland transportation, port operation and sea transportation. Table 2: Simulation process Step 1: Inputting data - Freight (origin/destination, number of TEUs and containers, total value) - Route (ports of call, order of ports) - Ship (capacity, speed) - Port-to-port nautical distance - Inland distance between hinterlands and ports Step 2: Establishing operational models - Fuel consumption of main, auxiliary and boiler engines Step 3: Simulating flow movement 4

5 - Selecting loading/unloading ports for each cargo flow - Combining flows through ports, and port-to-port connections Step 4: Calculating time indicators - Ship time at sea/in ports - Door-to-door time of each shipment Step 5: Calculating CO 2 emission - Landside operation - Handling operation in ports - Ship operation in ports and at sea 3.2. Input data Freight, : the number of TEUs from hinterland to hinterland per month;, =, : the number of TEUs transported per trip, : the number of trips per month, : the number of containers from hinterland to hinterland per month;, =, : the number of containers transported per trip, : total cargo value from hinterland to hinterland per month;, =, : total cargo value per trip (USD) =(,): set of container flows from Europe to the US =(,): set of container flows from the US to Europe Ship : Ship capacity (TEU)! ": Speed (knot) : the number of trips per month. It is calculated by dividing the monthly shipping demand on the main haul by ship capacity. =max (,) (), ; (,)./, *+, *+, Route List of visited ports: 0, 1, 2 4 (5: the number of visited ports) The sequence of port calls on round voyages: 0, 1, 2 4, 460 = 0, 460 = 1, 462 = 2 5

6 "7!,8,, 9 : voyage distance between ports : and ; (mile) "7!,8,, 8<= is directly extracted from the port-to-port distances of Dataloy website ( 46;C0 ;C0 > >7+1: "7!,8,, 9 = DE: "7!,B,, B<= ; > <7: "7!,8,, 9 = DE: "7!,B,, B<= G,8,, 9 : set of visited ports between ports : and ; > >7: G,8,, 9 = H :, :60 ; I; > <7: G,8,, 9 = G,8,, 9<J 3.3. Operational models Landside operation K",,LK", : the loading and unloading ports of containers from hinterland to hinterland. The closest ports to original and final points of each flow will be chosen as loading and discharging ports. K"7!,M : inland distance between hinterland and port N (km), measured through the ROUTENPLANER function of Google maps ( Inland speed is assumed to be 50 km per hour. C0 2 emission of landside transport between hinterlands and ports are adapted from Tran et al. (2016a) with a unit amount 0.54 kg per TEU.km in Europe and 0.45 kg in the USA Port operation Dwell time before/after ship operation of import/export containers: 3 days. This indicator is adapted from practical surveys of Rankine (2003). CO 2 emission in port operation: kg per TEU (based on APM Terminals, 2010). Handling productivity: 180 boxes per hour (it is assumed that all ports deploy 6 gantry cranes with an hourly productivity of 30 moves) Ship operation Ship operation is classified into three modes: transit at sea, manoeuvring, and hoteling at berths. Fuel is burned for the working of main engines (MEs), auxiliary engines (AEs) and boiler engines (BEs). AEs are used in all operational modes, MEs in transit and manoeuvring, and BEs in two latter modes. MEs, AEs and BEs are assumed to use both heavy fuel oil (HFO) and marine diesel oil (MDO). Their usage 6

7 portions (87% and 13%) are determined using the global sale data of marine bunkers between 2007 and 2011 (published in IMO, 2014). The fuel consumption of AEs and BEs is calculated by multiplying their engine power (Kwh) and specific fuel oil consumption (SFOC g/kwh) (Table 3), whereas that of MEs needs considering sailing speed as the following formula: OLPK Q!LR S7Q = ]+:D:^], ` PT7P QUPV XYZ[ \ `_]:^ ], `a2. Table 3: Specific fuel oil consumptions (SFOC g/kwh) HFO (87%) MDO (13%) Average Main engine Auxiliary engine Boiler Source: SFOCs and the shares of HFO (87%) and MDO (13%) are extracted from IMO (2014). O c b, O b b : fuel consumption per hour of main engines (ME) in transit and manoeuvring (tonne) O c b = cap R 1 =0.98 O b b = cap R 1 =0.96 Specifications of ME power (Kwh) and design speed (knot) of various ship sizes are provided by Man Diesel and Turbo (2013). The consumption is calculated at an operational speed of 20 knots and SFOC of 213 g/kwh. O c +,O b +,O q + : fuel consumption per hour of auxiliary engines (AE) in transit, manoeuvring and hoteling (tonne) O c + = cap r.st12 R 1 =0.7 O b + = cap r.1u2s R 1 =0.41 O q + = cap r.21tv R 1 =0.51 O b w,o q w : fuel consumption per hour of boiler engines (BE) in manoeuvring and hoteling (tonne) O b w = cap r.21v1 R 1 =0.83 O q w = cap r.21v1 R 1 =0.83 The consumptions of AEs and BEs are estimated using the statistics of the Port of Los Angeles (Starcrest, 2016) recording vessel capacity and requested power (Kw) of AEs and BEs during the operating modes, and SFOCs of 226 g/kwh and 304 g/kwh, correspondingly (Table 3) Estimation of performance indicators Time indicators D Inland transport time between area and port N S,M = D`:],x (hour) vr 7

8 Ship hoteling time in port N: the quotient of total loaded/unloaded boxes in the port and handling productivity (180 moves per hour) S M q = w,y 0zr,:(K", =N) (LK", =N) (hour) Total ship time in port N: manoeuvring time (3 hours per arrival/departure) and hoteling time S M, =3 +S M q +3 (hour) Voyage time from port N to port }: cruising time at sea and total ship time in ports S M,~ = `:] x, ],` + Sq x, (hour) Total transit time of freight from area to area : (i) transport time to the loading port; (ii) dwelling time before ship operation; (iii) voyage time from the loading to unloading ports; (iv) dwelling time after ship operation; (v) transport time to the final point. c S, =S,D`, +72+S D`,,ƒD`, +72+S,ƒD`, (hour) Total ship time at sea X = `:] =, J 6`:] J, = ],` (hour) Total ship time in manoeuvring =6 5 (hour) Total ship time at berths = :E0 S,8 (hour) 4, CO 2 emission CO 2 emission rate: tonnes per tonne of fuel (based on emission rates of for HFO and for MDO published by IMO, 2014). In transit time (ME and AE): P ] c = X (O b c +O + c ) (tonne) In manoeuvring time (all engines): P ] b = (O b b +O + b +O w b ) (tonne) In berth time (AE and BE): P ] q = (O + q +O w q ) (tonne) In port operation (17.29 kg per TEU): P, =(, ) 17.29/ (tonne) Landside operation: P D = (,) ˆ, (7K",D`, K",ƒD`, 0.54)/1000+ (,) Š, (7K",D`, K",ƒD`, 0.45)/1000 (tonne) 4. Results and analyses 8

9 4.1. Shipping operation in the supply chain At a speed of 20 knots, a 6,000 TEU ship spends 593 hours at sea and 141 hours in ports to carry 6,239 containers (10,572 TEUs) between Europe and the USA. It follows an end-to-end journey embracing 12 ports (Le Havre -> Felixstowe -> Bremerhaven -> Hamburg -> Rotterdam -> Antwerp -> New York -> Norfolk -> Baltimore -> Charleston -> Savannah -> Houston -> Le Havre). On each trip, 3,414 tonnes of fuel are consumed, 97.4% of which is burnt at sea. Especially, the majority of bunker (3,135 tonnes) is used by main engines for ship propulsion (Figure 1). Figure 1: Breakdown of fuel consumption. Source: visualized by the authors To transport a 20-foot container, it takes hours from the origin to destination, generates 1,280 kg CO 2 (Table 4). Inventory carrying cost is often missed in models of route design (Tran and Haasis, 2015b). Nevertheless, a large volume of goods staying in the logistics pipeline means that shippers must pay a lot for this hidden cost. Table 4: Breakdown of CO2 emission and lead time in the supply chain Landside In ports Seaside Total CO2 emission (kg per TEU) Inland transport Handling operation Ship operation Time (hour) Source: calculated by the authors Sailing speed determines seaside operation, which represents a significant part of the total supply chain cost. Moreover, over half of containers lead time is at sea (281.5 hours). Consequently, 52.3% of inventory carrying cost, and 77% of CO 2 emission (980 kg) arise in this stage. Our research is done 9

10 on the Trans-Atlantic corridor, which is much shorter than Trans-Pacific and Europe/Asia ones. If it is put in the two latter cases, ship speed s effect on supply chains may be much more considerable Speed variation and the viability of slow steaming On the one hand, slower speed results in longer supply chain pipeline. This leads to higher capital and operational ship costs, and especially more inventory volume. These factors go up at the same decreasing rate of ship speed. On the other hand, fuel consumption and CO 2 emission substantially go down. It is generally accepted that unit fuel consumption (per day or hour) decreases in cube with the pace of speed change. However, extra voyage time also incurs with lower speed. Therefore, the relationship between total fuel consumption in a voyage and speed actually follows a square function. In the latter 2000s, the shipping industry was hit by the financial crisis, upswing of oil price and overcapacity. Liner carriers have applied slow steaming to face with such a poor market. Fuel is one of the key attentions of operators. Following our base scenario, at a price of $274 per tonne, fuel expense will be $86.22 per TEU. Additionally, its variation with speed is also larger than the variations of capital and operating costs. If the speed changes from 20 to 15 knots, unit fuel cost will move down by 44.75% ($33.89 per TEU). Fuel saving of 1,308 tonnes per trip simultaneously converts into less CO 2 emission from 13,514 to 9,427 tonnes (Figure 2). Cutting carbon footprint is a striking advantage of slow steaming, which has become a key argument of liner operators to stick with this strategy even when fuel price is cheaper. Extended voyage is an outcome of slow steaming. Average voyage time of Trans-Atlantic routes increased from 28 days in 2005 to 32 days in 2011, of Trans-Pacific routes from 34 to 37 days, of Far East/Europe from 57 to 70 days (Tran and Haasis, 2016b). Consequently, additional 1 or 2 vessels needed deploying per loop. In 2010, 47 vessels, equivalent to 2.4% of global fleet, were absorbed by slow steaming (Bonney and Leach, 2010), which narrowed down the supply/demand gap. Although carriers must pay extra capital and operating costs, the benefit from slow steaming are more overwhelming. Slow steaming seems to bring positive effects for operators as well as environment. In exchange, shippers must keep more inventory to feed longer supply chain. From 20 to 15 knots, average lead time extends by 94 hours, equivalent to an additional inventory cost of $ per TEU, which is far from the saving of fuel cost. 10

11 Average lead time (hour) - Left axis Co2 emission per TEU (kg) - Right axis Ship speed (knot) Figure 2: Speed effects on lead time and CO2 emission. Source: visualized by the authors 0 5. Conclusions and future outlooks In this article, ship speed is examined in a whole supply chain based on the simulation model and data retrieved from practical and reliable sources. The effect of ship speed may be not big in terms of supply chain cost, but considerable in terms of lead time and carbon footprint. It has different impacts on operators and shippers. Optimal speed should be a balance between the costs of operators and shippers. Slow steaming benefits operators and environment, but it is necessary to examine customers losses concerning extended lead time and higher inventory burden. This research also raises some further issues. Firstly, sailing speed is fixed in the whole shipping route, but different levels may be taken into account in a later work. Secondly, the next simulation can be done on the Europe/Asia and Trans-Pacific corridors, which are longer than the Trans-Atlantic one. In these cases, the role of sailing speed can be more considerable. Acknowledgement This research is supported by the Singapore Maritime Institute under the project SMI-2015-MA-16. Reference Alderton, P., Port Management and Operations. London: Informa. 11

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