Analysing Empty Container Movements In Turkey In Relation To Maritime Foreign Trade

One of the major reasons of maritime container transportation is the fact of empty container movements occurring due to sectoral inefficiency. Beyond trade imbalance, empty movements are subject to operational drivers and other reasons arising from voluntary commercial decisions of ship operators, called the market effect. Container transportation inherently consists of both trade imbalance and operational drivers. However commercial decisions create extra costs i.e. operational cost and environmental disaster arising from empty container circulation. Submitting how to reveal the amount of empty movements originating from commercial decisions and submitting solution proposals how to decrease this determine the topic of this dissertation. First of all, this dissertation analyses empty container movements relevant to foreign trade of Turkey in terms of trade imbalance and other than trade imbalance. The amount of additional empty container movements (AECM) refers to the magnitude of empty movements other than trade imbalance. The amount of AECM has been calculated on terminal basis by means of a genuine model. Theoretically and generally in practice, any inbound container that is full or empty is expected to depart the same terminal as an outbound container that is full or empty, including deliveries to inland depots apart from exceptional cases. This fact in Turkish terminals shapes our model, which is called the closed-circuit model (CCM). A generic formula is proposed in this study to analyse the available statistical data on a terminal basis. The AECM calculation in this study covers only datasets of inbound and outbound traffics of Turkey but not the cargo operations under transit and cabotage regulations. The scope of a CCM has been extended to an area including several terminals rendering service for a common hinterland, where applicable. This study reveals that practically AECM is mainly reduced by two methods. These are container transfers between terminals and container interchange between ship operators. Accordingly, the magnitude of container imbalance on terminal basis has been revealed in the analysis. After deducting both from the original AECM called hereafter "true AECM - AECM_T"), the novel concept can be described as "visible AECM (AECM_V)". Namely, true AECM indicates the AECM if no equipment transfers or interchange was implemented. Each equipment interchange between ship operators reduces two units of containers for AECM (one for inbound and one for outbound traffic). Within this scope, the sum of visible AECM, equipment transfers between terminals/hinterlands (ECM_RC), and two-fold equipment interchange (N_IC ) comprises the true AECM. AECM_T= AECM_V+ECM_RC+2N_IC All versions of AECM are on terminal/hinterland basis. Since we do not have data about the amount of equipment interchange, we could not calculate true AECM. Nevertheless, we can calculate the sum of AECM_V and EMC_RC. It is called here in this study as "initial AECM (AECM_I)". AECM_I= AECM_V+ ECM_RC The calculation steps of visible AECM have been detailed herebelow. In case the number of full inbound throughput is more than the number of full outbound throughput in a terminal, the difference between the numbers of full inbound and full outbound reflects the excess number of empty containers to depart (step 1). After extracting this from the number of actual empty outbound containers, we compute the number of departed containers except structural trade imbalance (step 2). Then, we add the number of empty inbound units to reach the total AECM (step 3). The AECM calculated herein is visible AECM because we can calculate it through a dataset including equipment transfers and interchanges already conducted. In case the number of full outbound throughput is more than the number of full inbound throughput, the difference between the numbers of full outbound and full inbound throughput reflects the short-coming of empty containers to arrive. After extracting this amount from the number of actual empty inbound containers, we compute the number of arrived containers except for structural trade imbalance. Then, we add the number of empty outbound units to reach the total number of AECM. Our model, the CCM, states the following: AECM_V= { E_o-(F_i-F_o )+ E_i, if F_i ≥ F_o or, AECM_V= { E_i-(F_o-F_i )+ E_o, if F_o ≥ F_i Subject to F_i+E_i=F_o+E_o Where Full Inbound Container Movement : F_i Full Outbound Container Movement : F_o Empty Inbound Container Movement : E_i Empty Outbound Container Movement : E_o Additional Empty Container Movement (Visible) : AECM_V The proposed methodology determines the true AECM, utilising a CCM, determining the effect of trade imbalance, and considering the effects arising from equipment transfer and interchange. However in this study AECM refers to initial AECM. Trade imbalance is the difference between full container inbound and outbound traffic of similar types of equipment. We can only distinguish 20-foot and 40-foot equipment types regardless of whether they are standard or special. The traffic between 2012 and 2016 for 20-foot equipment in Turkey where the trade imbalance ratio (trade imbalance/full in and out containers) is 0.31, the initial AECM ratio (initial AECM/full in and out containers) is 0.20. The traffic between 2012 and 2016 for 40-foot equipment the trade imbalance ratio is 0.17, the initial AECM ratio is 0.26. The results indicate that trade imbalance is not the sole reason for empty container movements in Turkish terminals. Several terminals in Turkey exhibit container imbalances both for 20-foot and 40-foot equipments. For 20-foot equipment, Ambarli and Kocaeli hinterlands and Antalya terminal have excess equipment, while Gemlik and İzmir-Aliaga hinterlands suffer from lack of equipment. Mersin-Iskenderun hinterland seems self-sufficient. For 40-foot equipment, all hinterlands and Antalya terminal keep the same outlook except Kocaeli hinterland which has a reverse situation. The main approachment in the dissertation is the method of sorting each element out from total number of empty container movement. The first element to sort out is trade imbalance. Initial AECM reflects the remainder of sorting out process. The second element is operational drivers. The prominent driver in Turkey is cargo seasonality. Other operational drivers i.e. abundant container owners, container condition, uncertainties in demand/handling/shipment and blind spots in supply chain have been ignored because appropriate data have not been acquired. However effects of these parameters are limited for the case of Turkey. In terms of cargo seasonality, existence of seasonality in a dataset is investigated by combined seasonality test which comprises of parametric and non-parametric test procedures. If seasonality exists seasonality effect in a dataset of full container traffic is decomposed on monthly basis by means of X-13 ARIMA-SEATS which is one of major decomposition methods. In conclusion, in a number of 33 datasets in total 112 in hand, seasonality effect has been detected. A genuine algorithm to determine whether seasonality exists on terminal or hinterland basis has revealed that a number of 17 terminals/hinterlands consists of cargo seasonality and a genuine interpretation has helped to sort the seasonality effect out from initial AECM. The final dataset on terminal basis in hand reflects empty container movements arising from commercial decisions of ship operators on individual or sectoral basis, i.e. market effect. Since the datasets of seasonality adjusted initial AECM represent market effect it can be described as AECM originating from market effect. The root causes of market effect are described as container imbalance based on ship operator and competition in a route among ship operators. We have two separate datasets to analyse. The first one includes inbound and outbound throughputs between 2006 and 2016 relevant to 18 terminals located in Turkey. The other one comprises full container throughputs on ship operator basis. Since they both are to be exploited one has to test that the same population is measured by two separate measurers. It was performed by Wilcoxon signed rank test. According to the test result it cannot be rejected that data pairs of each year are from the same population. Accordingly, both root causes have been analysed through regression analysis. In the analysis of container imbalance the dependent variable is AECM originating from market effect and the independent variable is total container imbalance based on ship operator. In the analysis of competition the dependent variable is a ratio connected with AECM originating from market effect and the independent variable is the element of competition index that was generated by utilising entropy method. Since it would be misleading to compare hinterlands of which one owns high amount of throughput while other one owns low, utilising the concept of ratio is preferable instead of utilising magnitude of AECM. The elements of the ratio are AECM and the amount of initial empty container movement which means that the amount of container transfer between terminals should be added on sea-going empty movement of foreign trade. Both regression analyses lean on both datasets for each variable. The study is limited to four hinterlands across Turkey. They are Istanbul-Kocaeli, Gemlik, Izmir-Aliaga and Mersin. For both regression analyses 32 observation units belonging to 20' and 40' types of container traffics between 2012 and 2016 were included. The results indicate that 33 percent of AECM originating from market effect arises from container imbalance based on ship operator. Furthermore it runs into 71 percent if the incompatible observation units of Izmir-Aliaga hinterland for 20' containers are removed from the dataset. On the other hand, the level of competition among ship operators feebly leads to AECM originating from market effect in Turkey. 10 percent of AECM originating from market effect can be explained by the effect of competition. As a result, regarding container throughput in Turkey, one might come up with the inference that primarily container imbalance based on ship operator and secondarily competition level among ship operators trigger empty container movements originated from market effect.