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Assessing the performance of intermodal city logistics terminals in Thessaloniki
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Available online at www.sciencedirect.com Transportation Transportation Research Research Procedia Procedia 00 00 (2016) (2016) 000–000 000–000
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Transportation Research Procedia 24C (2017) 17–24 www.elsevier.com/locate/procedia
3rd Conference on Sustainable Urban Mobility, 3rd CSUM 2016, 26 – 27 May 2016, Volos, Greece
Assessing the performance of intermodal city logistics terminals in Thessaloniki a*, Giannis Adamosaa, Eftihia Nathanailaa Michael Gogasa* aa Traffic
Traffic Transportation Transportation and and Logistics Logistics Laboratory Laboratory –– TTLog, TTLog, Faculty Faculty of of Civil Civil Engineering, Engineering, University University of of Thessaly, Thessaly, Volos, Volos, Greece Greece
Abstract Abstract This This paper paper presents presents aa comparative comparative analysis analysis of of two two urban urban intermodal intermodal freight freight transport transport terminals terminals focusing focusing on on last last mile mile distribution: distribution: the the port port of of Thessaloniki Thessaloniki (ThPA) (ThPA) and and Kuehne Kuehne ++ Nagel Nagel (K+N) (K+N) distribution distribution center center in in Thessaloniki. Thessaloniki. Through Through the the pairwise pairwise comparison comparison of of the the two two different different supply supply chain chain interchanges, interchanges, aa decision decision support support tool tool is is provided provided to to the the terminals’ terminals’ operators operators and and their their customers customers and and partners, partners, namely namely shippers, shippers, forwarders, forwarders, transport transport companies, companies, users users or or customers. customers. The The evaluation evaluation of of the the terminals’ terminals’ performance performance is is elaborated elaborated based based on on aa tailored tailored multi multi criteria criteria Key Key Performance Performance Indicator Indicator (KPI)-based (KPI)-based assessment assessment framework, framework, while while the the selection selection and and significance significance (weight) (weight) of of the the incorporated incorporated criteria criteria and and respective respective KPIs KPIs is is predetermined predetermined by by the the involved involved stakeholders stakeholders through through aa multi multi stakeholder stakeholder participation participation scheme scheme using using the the pairwise pairwise comparison comparison according according to to the the Analytic Analytic Hierarchy Hierarchy Process Process (AHP) (AHP) approach. approach. ThPA ThPA terminal terminal is is ranked ranked first first according according to to its its performance performance pertaining pertaining to to the the role role of of an an intermodal intermodal interchange interchange for for the the wider geographical area of Thessaloniki, however K+N terminal’s performance index was slightly lower, while in several wider geographical area of Thessaloniki, however K+N terminal’s performance index was slightly lower, while in several KPIs KPIs and and criteria criteria itit seems seems to to perform perform better. better. © © 2016 2016 The The Authors. Authors. Published Published by by Elsevier Elsevier B.V. B.V. © 2017 The under Authors. Published by Elsevier B.V. committee of the 3rd CSUM 2016. Peer-review responsibility of the organizing Peer-review under responsibility of the organizing committee of of the 3rd CSUM CSUM 2016. 2016. Peer-review under responsibility of the organizing committee the 3rd Keywords: Keywords: city city logistics, logistics, multi-stakeholder, multi-stakeholder, multi-criteria multi-criteria evaluation evaluation framework, framework, key key performance performance indicators, indicators, comparative comparative analysis, analysis, performance performance indices, indices, sensitivity sensitivity analysis, analysis, AHP AHP *Corresponding *Corresponding author. author. Tel.: Tel.: +30 +30 2421074164; 2421074164; fax: fax: +30 +30 2421074131. 2421074131. E-mail E-mail address: address: [email protected] [email protected]
1. Introduction Urban economies are evolving rapidly towards a higher level of material intensiveness. Moving freight within urban areas is a common urban transportation challenge that impacts many large metropolises. The means over which freight distribution can take place in urban areas as well as the strategies that can improve its overall efficiency while mitigating congestion and environmental externalities. It includes the provision of services contributing to efficiently managing the movements of goods in cities and providing innovative responses to customer demands (Rodrigue et al., 2013). Taniguchi et al. (1999) define city logistics as “the process for totally optimizing the logistics and transport activities by private companies in urban areas while considering the traffic environment, the traffic congestion and energy consumption within the framework of a market economy”. Freight terminals can be described as nodes, where goods are 1reight1pped 1reight1pped between between two two or or more more transport transport modes, modes, and and facilitate facilitate logistics logistics operations operations that that cover cover the the needs needs and and services services of of the the whole whole transportation transportation chain. chain. These These terminals terminals
2352-1465 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the organizing committee of the 3rd CSUM 2016. 10.1016/j.trpro.2017.05.061
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are fully geographically defined areas, managed by public or private actors, and where all activities, including transport, handling and distribution of cargo are operated by several enterprises, such as transport and logistics providers or users, established within the terminals (Gogas & Nathanail, 2014). In literature, several approaches for defining a typology for freight terminals, are met. For example, based on the geographical coverage, volume and capacity, Weigmans et al. (1998) identified five characteristic types, namely: a) mainport terminals, which provide deep-sea, rail, truck and barge connections worldwide, b) International European terminals, which also provide deep-sea, rail, truck and barge connections, but on a more continental level, c) national terminals that operate on country level, and provide rail, barge and truck connections, d) regional terminals, operating mainly as regional distribution centers, offering low cost budget solutions, and e) local terminals, which provide a rather simple connection with rail or barge. Rodrigue and Hatch (2009) defined three types of terminals, including port terminals, rail terminals and distribution centers. The European project REFORM, identified four categories of transport and logistics terminals, i.e. city terminals, freight villages, industrial and logistic parks and special logistic areas (Nathanail, 2007). Lastly, the European project CLOSER, proposed a simpler approach of the REFORM typology, using fewer characteristics and adding one more category thus, rural terminals. A summary of the characteristics of this typology follows (Andersen & Eidhammer, 2011): Special logistics areas, are usually ports or airports having a national or international orientation, and operated by airport or maritime port authorities. Industrial and logistic parks, usually covering a large area with big industrial and transport companies. The majority of them is located in the outskirts of the cities or in old industrial areas. Their orientation is regional or national. Freight terminals, receiving usually public influence on their operation, are mainly located in the outskirts of cities, and they have regional or national orientation. City terminals, are usually located in or in close vicinities to the cities. These terminals are typically operated by forwarders and retailers. Rural terminals, having a similar role with city terminals, are sometimes controlled by smaller local companies. The performance of freight terminals relies on the performance of multiple processes that are undertaken within these areas. The role and performance of terminals that are located in the outskirts of the cities, such as industrial and logistic parks, affect the performance of urban distribution to a large extent, and consequently determine the structure of city logistics. The aim of this paper is to develop and demonstrate the assessment of the performance of two intermodal freight and logistics terminals in the wider Thessaloniki area, ranking them as per their role as interchanges and interconnectors amongst the intercity freight flows and urban deliveries. The multi-criteria evaluation framework takes into account as parameters both quantitative and qualitative indicators associated with the wider supply chain operational activities and attributes, aiming at the facilitation of the decision-making process concerning the optimum terminal selection. The evaluation methodology is implemented in two terminals in Greece, a privately operated railroad freight terminal (city terminal), and the Port of Thessaloniki (special logistics area). A short profile of the two terminals is provided below: 1) The private terminal is an inland intermodal freight terminal, managed and operated by a logistics service provider and forwarding company (Kuehne+Nagel), which imports and exports goods to/from Greece also including last mile distribution in greater Thessaloniki area using the road and railway network. 2) The port of Thessaloniki is managed by Thessaloniki Port Authority S.A. The port provides handling services for various types of cargo, shipping services, passenger maritime services and customs services. Apart from the trucks, accessibility to the freight terminal is provided by rail underpinning intermodality. This paper reviews and implements methodologies, transport and logistics related network models and Key Performance Indicator (KPI) – based methods for the comparison of the two terminals, in terms of size, handling equipment, hours of operation, throughput (e.g. containers’ arrivals) and other components. In addition, it examines the ownership and operational characteristics of the terminals and highlights the efficiencies and the reasons for customer and freight forwarder choice of a particular terminal, which is of great interest to the overall supply chain considerations in the context of decision making from the side of the terminal users. For the evaluation, a Multi-
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Criteria Assessment (MCA) method, based on Analytic Hierarchy Process (AHP), is used. The expected outcomes include a comparison of the performance of these terminals indicating the most effective one with respect to the performance criteria that are set and a case-specific discussion about the most efficient type of intermodality in order to support the last-mile distribution. The evaluation framework is based on criteria and their KPIs. Both criteria and respective KPIs, as well as their significance (weight) in the evaluation process are selected by the stakeholders involved in the operation of the two terminals, within the context of a Multi Stakeholder Multi Criteria Assessment Framework. Pairwise comparison is used to assess the two terminals’ performance against the selected criteria and indicators. Presentation of the above is done in the succeeding four sections, which incorporate the following: 1st section (Introductory): State of the art on the city logistics concept and the intermodal freight transport interchanges’ attributes, operations and activities associated to last mile distribution. 2nd section: Development of the methodology, concerning the terminals’ analysis and pairwise comparison. In this section the description of the structuring of the AHP utilized for the identification of the criteria, selection of the KPIs and allocation of weights to criteria and KPIs is also provided. 3rd section: Presentation of terminal comparative analysis results based on numerical values produced by the quantification of KPIs taking into consideration the criteria and indicators weights as determined within the previous section. A sensitivity analysis is conducted in order for the two terminals’ pairwise comparison results to be further validated. 4th section: Elaboration of important conclusions for decision making. 2. Methodology Pertaining to the pairwise comparison of the two terminals concerning their efficiency and attributes, but also in light of their impacts on the urban distribution, the methodology is shaped as follows: 1. Definition of criteria and performance indicators The criteria which were used regarding the assessment of the performance of these terminals are: management policy, supply side performance, organizational and institutional structure, terminal properties and level of service (Järvi and Nagel, 2013). 2. Weight allocation to criteria and indicators This was done by a pairwise comparison, and finally they resulted in eigen values, which comprise the weights assigned to each criterion and KPI. 3. Quantification of the performance indicators Each performance indicator was quantified, based on data collected by the managing companies and provided to the authors for this analysis. 4. Prioritization of the terminals The collected data was combined and the prioritization of the terminals was resulted, based on the integrated and individual evaluation scores. The assessment of the two terminals was elaborated through a “multi criteria evaluation framework” based on criteria and their respective indicators – KPIs (Key Performance Indicators) selected through the Delphi Method (Criteria Assess and Measure Evaluation process) by a panel of experts constituted of the terminals’ representatives as well as their users, customers and partners (freight forwarders, shippers, logistics service providers, receivers, infrastructure and equipment providers and service, system and software operators), also considering the availability of respective data. Towards a more holistic approach, both quantitative and qualitative criteria and indicators are incorporated in the analysis. In particular, based on the analysis of intermodal interchanges elaborated within the European Research project CLOSER (EU FP7) (Christiansen et al, 2012), several KPIs were selected and grouped under five criteria. Some additional indicators were also incorporated based on the authors’ previous experience on terminal performance assessment from the project STRAIGHTSOL (EU FP7) (Andersen et al, 2014) and the INTERREG III B CADSES project IMONODE (Nathanail et al, 2005; Nathanail, 2007). The multi-criteria assessment framework is presented in Table 1. In particular: Within the first column, the evaluation criteria are listed, while their weights attributed are depicted in the second column.
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The indicators (KPIs) are included within the third column and their respective weights are presented in the fourth column. The fifth column is auxiliary and it incorporates the description of each indicator (KPI), under the prism of what information or extra explanation has been indicated to the involved stakeholders in order to come up with attributing the respective significance per indicator and per criterion. The indicators included in each criterion have their weights summing up to 100%. The same applies for the sum of all criteria weights, following the principles of the pairwise comparison and AHP process.
Table 1. Multi criteria assessment framework Criterion weight
Criterion
Indicator (KPI) Multimodality rate Environmental burden
Management policy
Organisational and institutional structure
Supply side performance
Terminal properties
Level service
15%
15%
20%
Infrastructure and equipment safety and security Independence of terminal or interchange management
25%
20% 20% 40%
Description Percentage of multimodal shipments over total GHG emissions, noise nuisance and traffic (low/medium/high) Likelihood of human losses, i.e. annual number of human injuries / fatalities per respective vehicle kilometres
20%
Likelihood of accidents, i.e. annual number of accidents per respective vehicle kilometres
50%
Independence from transport operators and local actors (yes/no/partial) Whether all companies have access to a terminal/interchange on equal conditions (yes/no/partial) Number of institutional levels involved in the interchange planning
Fair and equal access
40%
Institutional complexity
10%
Employee productivity
50%
Ratio between flows and inputs, TEU transhipped per employee and year
50%
Total number of TEUs lifted per year and crane
Equipment productivity Saturation ratio (TEUs) Saturation ratio (total cargo tonnage)
25%
of
Human safety and security
Indicator weight
10% 10%
Ratio between actual volumes and maximum capacity (daily average,%) Ratio between actual volumes and maximum capacity (daily average,%) Potential for expandability (% increase compared to today’s capacity) Number of kilometres from city centre to interchange/terminal
Expandability
10%
Distance from city centre
10%
Distance from commercial areas
10%
Number of kilometres from terminal to nearest commercial centre
Distance from industrial zones
10%
Number of kilometres from interchange/terminal to nearest industrial zone
Transshipment time Connection and distance to primary motor-way network Connection and distance to primary railway network
10%
Time needed for loading / unloading per TEU
10%
Direct, indirect or no access to nearest highway and proximity
10%
Direct, indirect or no access and proximity
Connection to ports
5%
Direct, indirect or no access and proximity
Connection to airports
5%
Direct, indirect or no access and proximity
Handling cost
20%
Punctuality
20%
Average price paid per TEU transhipped (€) Percentage of arrivals / departures within defined
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Criterion
Criterion weight
Indicator (KPI)
Indicator weight
Description tolerance for delay
Origin-destination time
10%
Average time for last mile roundtrip in city centre
Loss and damage
15%
Percentage of shipments with loss or damage
Supply chain visibility Information availability Terminal integration level
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15% 10% 10%
Percentage of terminal coverage with GPS, RFID, CCTV, e-PoDs, camera surveillance systems etc Existence of real time information and alerts inside the terminal Proximity and access of terminal to auxiliary services (e.g. customs)
The numerical values of the KPIs were either accumulated as raw data through the terminals’ annual reports or estimated based on information acquired by the terminals’ representatives in the context of individual interviews. After the quantification of each KPI, their respective grades were determined based on the grading scale used in the aforementioned projects, always in communication with the terminals’ representatives, adjusting the final grading scale taking into account their personal experience and expertise in this field adopting the DELPHI method. In addition, the significance of each criterion and respective KPI was investigated through the elaboration of a pairwise comparison in the context of the Analytic Hierarchy Process (AHP), in order to come up with their individual weights applied in the multi criteria analysis. All the stakeholders involved in freight assignments elaborated through those terminals participated in the establishment of weights through the AHP. Their viewpoints were recorded through a questionnaire survey organized and implemented by the authors of this paper during the last half of 2014, in order to gain a holistic multi stakeholder multi criteria approach. The AHP was selected as it is one of the Fuzzy Multiple Criteria Decision Making methods, providing not only a simple and very flexible model for a given problem, but also an easy applicable decision making methodology that assist the decision maker to precisely decide the judgments (Saaty, 1977; Chen and Wang, 2010; Li and Li, 2009; Nathanail et al., 2014). Based on the prioritization and weighting of criteria and respective KPIs, the pairwise comparison of the two terminals is elaborated to indicate which of the two is more efficient regarding its services and performance, in order to provide a valid decision making tool for terminal selection. The prioritization of the two terminals is then tested through a sensitivity analysis which constitutes a technique used to determine how different values of an independent variable will affect a particular dependent variable under a given set of assumptions. 3. Terminal comparative analysis The implementation of the methodology on the two terminals, leads to results which constitute a handy decision making auxiliary tool for freight assignment employing maritime and road transport modes or rail and road transport modes using the supply chain destined to Thessaloniki city for last mile delivery. The multi criteria assessment framework is based on five criteria: Management policy, Organizational and institutional structure, Supply side performance, Terminal properties and Level of service (Nathanail et al, 2016). The terminal comparative analysis is accomplished through the elaboration of a multi stakeholder multi criteria assessment framework. This framework was based on the values of those criteria and respective indicators, also taking into account their weights as determined by the involved stakeholders through the AHP. The grades of the KPIs were produced based on their numerical value and the grading scale determined based on literature review (Nathanail et al, 2005; Nathanail, 2007), also taking into account the involved stakeholders’ point of view on that through the Delphi method. In particular, based on the review of available sources and the expert group’s opinion, the grading scales were determined through a range of numerical values decided by the expert group after brainstorming, taking into consideration all the special characteristics and conditions in the area of study. The partial and total performance indices of the two terminals (Port of Thessaloniki and Kuehne+Nagel) are presented within Table 2.
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Table 2. Partial and total performance indices of Port of Thessaloniki (ThPA) and Kuehne + Nagel (K+N) terminals (Nathanail et al, 2016) Performance index Criterion Thessaloniki Port Authority Kuehne+Nagel Management policy 2.6 5.6 Organisational and institutional structure 8.5 7.9 Supply side performance 7 4 Terminal properties 7.1 6.45 Level of service 7.9 7.2 All criteria (Total Performance Index – TPI) 6.815 6.2375
It was observed that Kuehne+Nagel’s terminal performs better concerning the first criterion on “Management policy” due to the high multimodality rate and the higher performance on environmental burden, as well as safety and security issues. On the other hand, the Port of Thessaloniki terminal prevails when it comes to all the other criteria due to higher productivity of both personnel and equipment, while also being a little better performing in “terminal properties” and the provided “level of service” to partners and customers. Overall, the Port of Thessaloniki terminal outmaches Kuehne+Nagel’s terminal by 6.815 to 6.2375. In order to further validate the results, a sensitivity analysis was elaborated as well. Through the increase or decrease of each criterion’s weight by 10%, an effort is made to investigate any modification in the prioritization of the terminals concerning their partial and total performance indices. In particular, each time the emphasis (+10%) or the demotion (-10%) is set on one criterion, according to the common methodology adopted in similar cases based on the literature review (Nathanail, 2007). Thus, each criterion’s weight is firstly increased and then decreased (one at a time) counterbalancing accordingly the rest of the criteria weights (respectively increased or diminished in order for all criteria weights to sum up to 100%). The respective performance indices are depicted within Table 3. Table 3. Partial and total performance indices increasing / decreasing criteria weights (Nathanail et al, 2016) Total Performance Index (TPI) Criterion Management policy (+10%) Management policy (-10%) Organisational and institutional structure (+10%) Organisational and institutional structure (-10%) Supply side performance (+10%)
ThPA 6,3125 7,3175 7,05 6,58 6,8625
K+N 6,15875 6,31625 6,44625 6,02875 5,95875
Supply side performance (-10%) Terminal properties (+10%) Terminal properties (-10%) Level of service (+10%) Level of service (-10%)
6,7675 6,875 6,755 6,975 6,655
6,51625 6,265 6,21 6,35875 6,11625
The modification of each of the criteria weights by 10% does not have any impact in the prioritization of the terminals, as the ThPA’s terminal still has a higher total performance index compared to K+N’s terminal in all of the cases. This outcome proves the stability of the prioritization results for a weight fluctuation of ±10%. This is good and validates the correctness of the terminal analysis conducted through the proposed evaluation framework as the produced results are independent from attributing subjective weights to the evaluation criteria and indicators. In other words, the superiority of ThPA terminal over the one of K+N constitutes an objective result, close to reality. 4. Conclusions In the framework of this paper, two terminals, potentially involved in Thessaloniki’s supply chain and last mile (urban) distribution, were prioritized through a pairwise comparison, being evaluated as per their attributes and general performance (facts and figures) according to a Key Performance Indicator (KPI)-based multi stakeholder multi criteria assessment framework. In order to estimate the significance of each criterion and KPI in the analysis, all involved stakeholders imposed their point of view through the elaboration of Analytic Hierarchy Process (AHP).
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Based on the results and findings, the port of Thessaloniki (ThPA) terminal is ranked first according to its performance pertaining to the role of an intermodal interchange. The ThPA’s terminal predominance over the one of K+N is further validated through the elaboration of sensitivity analysis in the context of which the weights of criteria are increased and diminished accordingly by 10% in order to avoid objectivity. Nevertheless, Kuehne + Nagel (K+N) terminal’s performance index is only 8,5% lower than ThPA’s, while in several KPIs and criteria it seems to perform better. The prevailing ranking of ThPA versus K+N is validated through a sensitivity analysis. Although simpler processes could have been used to derive the same conclusions, the selection of a Multi-Stakeholder Multi-Critetia evaluation is justified as it is the most objective, integrated and holistic approach. Concluding, this study developed and validated an integrated evaluation framework, which can be used by decision makers, who are involved mainly in the last mile goods distribution. Applying the evaluation framework, stakeholders may support future decisions for strategic planning purposes, addressed by various criteria, trends and trade-offs. Acknowledgements Part of the research of this paper was done within the framework of the European Commission’s project NOVELOG (http://novelog.eu/). References Andersen J., Eidhammer O., Osland, O., Parra L., Adamos G. (2010). Interconnections between short and long-distance transport networks: Structure of interface and existing indicators. Deliverable 3.1. CLOSER – Connecting Long and Short-distance networks for Efficient tRansport. Andersen, J. & Eidhammer, O. (2011). Core indicators for the interconnection between short and long-distance transport networks. Deliverable 3.2. CLOSER – Connecting Long and Short-distance networks for Efficient tRansport. Andersen J., Eidhammer O., Gogas M., Papoutsis K., Nathanail E. (2014). Demonstration assessments. Deliverable 5.1. STRAIGHTSOL – STRAtegies and measures for smarter urban 7reight SOLutions. Best Urban Freight Solutions (2014). http://www.bestufs.net/ Accessed on 14 Jan 2015. Chen, M. K., WANG, S. (2010). The critical factors of success for information service industry in developing international market: Using analytic hierarchy process (AHP) approach. Expert Systems with Applications, Vol. 37 2010, pp. 694-704. Christiansen, P., Johansen, B.G., Andersen, J., Eidhammer, O. (2012). Case studies: Results and synthesis. Deliverable 5.2. CLOSER – Connecting Long and Short-distance networks for Efficient tRansport. Dan Li., Weixin L., Pian F. (2013). The Efficiency Measurement of Coastal Container Terminals in China. J Transpn Sys Eng & IT, 2013, 13(5), 10−15. Eckhardt, J., Hietajärvi, A-M., Rönty, J., Andersen, J., Eidhammer, O. (2012). Guidance and recommendations for interconnection between long distance and local/regional freight transport. Deliverable 6.2. CLOSER – Connecting Long and Short-distance networks for Efficient tRansport. Gogas M., Nathanail E. (2014). Multilevel multicriteria design of intermodal transport Freight Center networks. International Conference on Engineering and Applied Sciences Optimization (OPTi 2014), Kos Island, Greece, 4-6 June 2014. Järvi, T., Nagel, I. (2013). Guidance and recommendations for interconnection between long distance and local/regional passenger transport. Deliverable 6.1. CLOSER – Connecting Long and Short-distance networks for Efficient tRansport. Kapros S., Panou K., Tsamboulas D. (2005). Multicriteria approach to the evaluation of intermodal freight villages. Transportation Research Record: Journal of the Transportation Research Board, 1906, 56-63. Li, S., Li, J.Z. (2009). Hybridizing human judgment, AHP, simulation and a fuzzy expert system for strategy formulation under uncertainty. Expert Systems with Applications, Vol. 36 2009, 5557-5564. Nathanail E., Gogas M. (2005). Spatial planning – Development of nodal points and terminals. Deliverable 4. IMONODE – Efficient Integration of cargo transport Modes and NODEs in CADSES area. Nathanail E., (2007). Developing an integrated logistics terminal network in the CADSES area. Transition Studies Review, May 2007, Volume 14, Issue 1, pp 125-146. Nathanail, E. G., Gogas, M. A., Papoutsis K. N. (2014). Investigation of Stakeholders’ View towards the introduction of ICT in Supply Chain using Analytic Hierarchy Process. Journal of Traffic and Logistics Engineering, Vol. 2 No. 2, pp. 113-119. Nathanail, E. G., Gogas, M. A., Adamos G. T. (2016). Urban freight terminals: A sustainability cross-case analysis. The second International Conference Green Cities 2016 – Green Logistics for Greener Cities. Raicua R., Raicu S., Popa M. Costescu D. (2012). On the evaluation of urban logistics intermodal terminal projects. Procedia – Social and Behavioral Sciences, 39, 726 – 738. Rodrigue J-P., Hatch A. (2009). North American Intermodal Transportation: Infrastructure, Capital and Financing Issues, The Equipment Leasing and Finance Foundation, Washington, DC. Rodrigue JP., Comtois C., Slack B. (2013). The geography of transport systems. 3 rd edition, New York: Routledge. ISBN 978-0-415-82254-1
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Saaty, T. (1972). An eigenvalue allocation model for prioritization and planning. In: working paper Energy Management and Policy Center, University of Pennsylvania, US. Saaty, T. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, Vol. 15 1977, pp. 234-281. Taniguchi, E., Thompson, R.G., Yamada, T. (1999). Modeling city logistics. In: City Logistics I (E. Taniguchi and R.G. Thompson, eds.), Institute of Systems Science Research, Kyoto, pp. 3-37. Wiegmans B.W., Masurel E., Nijkamp P. (1999). “Intermodal Freight Terminals: An Analysis of the Freight Terminal Market,” Transportation Planning and Technology, Vol. 63, No. 2, pp. 105-168.
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Available online at www.sciencedirect.com Transportation Transportation Research Research Procedia Procedia 00 00 (2016) (2016) 000–000 000–000
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Transportation Research Procedia 24C (2017) 17–24 www.elsevier.com/locate/procedia
3rd Conference on Sustainable Urban Mobility, 3rd CSUM 2016, 26 – 27 May 2016, Volos, Greece
Assessing the performance of intermodal city logistics terminals in Thessaloniki a*, Giannis Adamosaa, Eftihia Nathanailaa Michael Gogasa* aa Traffic
Traffic Transportation Transportation and and Logistics Logistics Laboratory Laboratory –– TTLog, TTLog, Faculty Faculty of of Civil Civil Engineering, Engineering, University University of of Thessaly, Thessaly, Volos, Volos, Greece Greece
Abstract Abstract This This paper paper presents presents aa comparative comparative analysis analysis of of two two urban urban intermodal intermodal freight freight transport transport terminals terminals focusing focusing on on last last mile mile distribution: distribution: the the port port of of Thessaloniki Thessaloniki (ThPA) (ThPA) and and Kuehne Kuehne ++ Nagel Nagel (K+N) (K+N) distribution distribution center center in in Thessaloniki. Thessaloniki. Through Through the the pairwise pairwise comparison comparison of of the the two two different different supply supply chain chain interchanges, interchanges, aa decision decision support support tool tool is is provided provided to to the the terminals’ terminals’ operators operators and and their their customers customers and and partners, partners, namely namely shippers, shippers, forwarders, forwarders, transport transport companies, companies, users users or or customers. customers. The The evaluation evaluation of of the the terminals’ terminals’ performance performance is is elaborated elaborated based based on on aa tailored tailored multi multi criteria criteria Key Key Performance Performance Indicator Indicator (KPI)-based (KPI)-based assessment assessment framework, framework, while while the the selection selection and and significance significance (weight) (weight) of of the the incorporated incorporated criteria criteria and and respective respective KPIs KPIs is is predetermined predetermined by by the the involved involved stakeholders stakeholders through through aa multi multi stakeholder stakeholder participation participation scheme scheme using using the the pairwise pairwise comparison comparison according according to to the the Analytic Analytic Hierarchy Hierarchy Process Process (AHP) (AHP) approach. approach. ThPA ThPA terminal terminal is is ranked ranked first first according according to to its its performance performance pertaining pertaining to to the the role role of of an an intermodal intermodal interchange interchange for for the the wider geographical area of Thessaloniki, however K+N terminal’s performance index was slightly lower, while in several wider geographical area of Thessaloniki, however K+N terminal’s performance index was slightly lower, while in several KPIs KPIs and and criteria criteria itit seems seems to to perform perform better. better. © © 2016 2016 The The Authors. Authors. Published Published by by Elsevier Elsevier B.V. B.V. © 2017 The under Authors. Published by Elsevier B.V. committee of the 3rd CSUM 2016. Peer-review responsibility of the organizing Peer-review under responsibility of the organizing committee of of the 3rd CSUM CSUM 2016. 2016. Peer-review under responsibility of the organizing committee the 3rd Keywords: Keywords: city city logistics, logistics, multi-stakeholder, multi-stakeholder, multi-criteria multi-criteria evaluation evaluation framework, framework, key key performance performance indicators, indicators, comparative comparative analysis, analysis, performance performance indices, indices, sensitivity sensitivity analysis, analysis, AHP AHP *Corresponding *Corresponding author. author. Tel.: Tel.: +30 +30 2421074164; 2421074164; fax: fax: +30 +30 2421074131. 2421074131. E-mail E-mail address: address: [email protected] [email protected]
1. Introduction Urban economies are evolving rapidly towards a higher level of material intensiveness. Moving freight within urban areas is a common urban transportation challenge that impacts many large metropolises. The means over which freight distribution can take place in urban areas as well as the strategies that can improve its overall efficiency while mitigating congestion and environmental externalities. It includes the provision of services contributing to efficiently managing the movements of goods in cities and providing innovative responses to customer demands (Rodrigue et al., 2013). Taniguchi et al. (1999) define city logistics as “the process for totally optimizing the logistics and transport activities by private companies in urban areas while considering the traffic environment, the traffic congestion and energy consumption within the framework of a market economy”. Freight terminals can be described as nodes, where goods are 1reight1pped 1reight1pped between between two two or or more more transport transport modes, modes, and and facilitate facilitate logistics logistics operations operations that that cover cover the the needs needs and and services services of of the the whole whole transportation transportation chain. chain. These These terminals terminals
2352-1465 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the organizing committee of the 3rd CSUM 2016. 10.1016/j.trpro.2017.05.061
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are fully geographically defined areas, managed by public or private actors, and where all activities, including transport, handling and distribution of cargo are operated by several enterprises, such as transport and logistics providers or users, established within the terminals (Gogas & Nathanail, 2014). In literature, several approaches for defining a typology for freight terminals, are met. For example, based on the geographical coverage, volume and capacity, Weigmans et al. (1998) identified five characteristic types, namely: a) mainport terminals, which provide deep-sea, rail, truck and barge connections worldwide, b) International European terminals, which also provide deep-sea, rail, truck and barge connections, but on a more continental level, c) national terminals that operate on country level, and provide rail, barge and truck connections, d) regional terminals, operating mainly as regional distribution centers, offering low cost budget solutions, and e) local terminals, which provide a rather simple connection with rail or barge. Rodrigue and Hatch (2009) defined three types of terminals, including port terminals, rail terminals and distribution centers. The European project REFORM, identified four categories of transport and logistics terminals, i.e. city terminals, freight villages, industrial and logistic parks and special logistic areas (Nathanail, 2007). Lastly, the European project CLOSER, proposed a simpler approach of the REFORM typology, using fewer characteristics and adding one more category thus, rural terminals. A summary of the characteristics of this typology follows (Andersen & Eidhammer, 2011): Special logistics areas, are usually ports or airports having a national or international orientation, and operated by airport or maritime port authorities. Industrial and logistic parks, usually covering a large area with big industrial and transport companies. The majority of them is located in the outskirts of the cities or in old industrial areas. Their orientation is regional or national. Freight terminals, receiving usually public influence on their operation, are mainly located in the outskirts of cities, and they have regional or national orientation. City terminals, are usually located in or in close vicinities to the cities. These terminals are typically operated by forwarders and retailers. Rural terminals, having a similar role with city terminals, are sometimes controlled by smaller local companies. The performance of freight terminals relies on the performance of multiple processes that are undertaken within these areas. The role and performance of terminals that are located in the outskirts of the cities, such as industrial and logistic parks, affect the performance of urban distribution to a large extent, and consequently determine the structure of city logistics. The aim of this paper is to develop and demonstrate the assessment of the performance of two intermodal freight and logistics terminals in the wider Thessaloniki area, ranking them as per their role as interchanges and interconnectors amongst the intercity freight flows and urban deliveries. The multi-criteria evaluation framework takes into account as parameters both quantitative and qualitative indicators associated with the wider supply chain operational activities and attributes, aiming at the facilitation of the decision-making process concerning the optimum terminal selection. The evaluation methodology is implemented in two terminals in Greece, a privately operated railroad freight terminal (city terminal), and the Port of Thessaloniki (special logistics area). A short profile of the two terminals is provided below: 1) The private terminal is an inland intermodal freight terminal, managed and operated by a logistics service provider and forwarding company (Kuehne+Nagel), which imports and exports goods to/from Greece also including last mile distribution in greater Thessaloniki area using the road and railway network. 2) The port of Thessaloniki is managed by Thessaloniki Port Authority S.A. The port provides handling services for various types of cargo, shipping services, passenger maritime services and customs services. Apart from the trucks, accessibility to the freight terminal is provided by rail underpinning intermodality. This paper reviews and implements methodologies, transport and logistics related network models and Key Performance Indicator (KPI) – based methods for the comparison of the two terminals, in terms of size, handling equipment, hours of operation, throughput (e.g. containers’ arrivals) and other components. In addition, it examines the ownership and operational characteristics of the terminals and highlights the efficiencies and the reasons for customer and freight forwarder choice of a particular terminal, which is of great interest to the overall supply chain considerations in the context of decision making from the side of the terminal users. For the evaluation, a Multi-
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Criteria Assessment (MCA) method, based on Analytic Hierarchy Process (AHP), is used. The expected outcomes include a comparison of the performance of these terminals indicating the most effective one with respect to the performance criteria that are set and a case-specific discussion about the most efficient type of intermodality in order to support the last-mile distribution. The evaluation framework is based on criteria and their KPIs. Both criteria and respective KPIs, as well as their significance (weight) in the evaluation process are selected by the stakeholders involved in the operation of the two terminals, within the context of a Multi Stakeholder Multi Criteria Assessment Framework. Pairwise comparison is used to assess the two terminals’ performance against the selected criteria and indicators. Presentation of the above is done in the succeeding four sections, which incorporate the following: 1st section (Introductory): State of the art on the city logistics concept and the intermodal freight transport interchanges’ attributes, operations and activities associated to last mile distribution. 2nd section: Development of the methodology, concerning the terminals’ analysis and pairwise comparison. In this section the description of the structuring of the AHP utilized for the identification of the criteria, selection of the KPIs and allocation of weights to criteria and KPIs is also provided. 3rd section: Presentation of terminal comparative analysis results based on numerical values produced by the quantification of KPIs taking into consideration the criteria and indicators weights as determined within the previous section. A sensitivity analysis is conducted in order for the two terminals’ pairwise comparison results to be further validated. 4th section: Elaboration of important conclusions for decision making. 2. Methodology Pertaining to the pairwise comparison of the two terminals concerning their efficiency and attributes, but also in light of their impacts on the urban distribution, the methodology is shaped as follows: 1. Definition of criteria and performance indicators The criteria which were used regarding the assessment of the performance of these terminals are: management policy, supply side performance, organizational and institutional structure, terminal properties and level of service (Järvi and Nagel, 2013). 2. Weight allocation to criteria and indicators This was done by a pairwise comparison, and finally they resulted in eigen values, which comprise the weights assigned to each criterion and KPI. 3. Quantification of the performance indicators Each performance indicator was quantified, based on data collected by the managing companies and provided to the authors for this analysis. 4. Prioritization of the terminals The collected data was combined and the prioritization of the terminals was resulted, based on the integrated and individual evaluation scores. The assessment of the two terminals was elaborated through a “multi criteria evaluation framework” based on criteria and their respective indicators – KPIs (Key Performance Indicators) selected through the Delphi Method (Criteria Assess and Measure Evaluation process) by a panel of experts constituted of the terminals’ representatives as well as their users, customers and partners (freight forwarders, shippers, logistics service providers, receivers, infrastructure and equipment providers and service, system and software operators), also considering the availability of respective data. Towards a more holistic approach, both quantitative and qualitative criteria and indicators are incorporated in the analysis. In particular, based on the analysis of intermodal interchanges elaborated within the European Research project CLOSER (EU FP7) (Christiansen et al, 2012), several KPIs were selected and grouped under five criteria. Some additional indicators were also incorporated based on the authors’ previous experience on terminal performance assessment from the project STRAIGHTSOL (EU FP7) (Andersen et al, 2014) and the INTERREG III B CADSES project IMONODE (Nathanail et al, 2005; Nathanail, 2007). The multi-criteria assessment framework is presented in Table 1. In particular: Within the first column, the evaluation criteria are listed, while their weights attributed are depicted in the second column.
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The indicators (KPIs) are included within the third column and their respective weights are presented in the fourth column. The fifth column is auxiliary and it incorporates the description of each indicator (KPI), under the prism of what information or extra explanation has been indicated to the involved stakeholders in order to come up with attributing the respective significance per indicator and per criterion. The indicators included in each criterion have their weights summing up to 100%. The same applies for the sum of all criteria weights, following the principles of the pairwise comparison and AHP process.
Table 1. Multi criteria assessment framework Criterion weight
Criterion
Indicator (KPI) Multimodality rate Environmental burden
Management policy
Organisational and institutional structure
Supply side performance
Terminal properties
Level service
15%
15%
20%
Infrastructure and equipment safety and security Independence of terminal or interchange management
25%
20% 20% 40%
Description Percentage of multimodal shipments over total GHG emissions, noise nuisance and traffic (low/medium/high) Likelihood of human losses, i.e. annual number of human injuries / fatalities per respective vehicle kilometres
20%
Likelihood of accidents, i.e. annual number of accidents per respective vehicle kilometres
50%
Independence from transport operators and local actors (yes/no/partial) Whether all companies have access to a terminal/interchange on equal conditions (yes/no/partial) Number of institutional levels involved in the interchange planning
Fair and equal access
40%
Institutional complexity
10%
Employee productivity
50%
Ratio between flows and inputs, TEU transhipped per employee and year
50%
Total number of TEUs lifted per year and crane
Equipment productivity Saturation ratio (TEUs) Saturation ratio (total cargo tonnage)
25%
of
Human safety and security
Indicator weight
10% 10%
Ratio between actual volumes and maximum capacity (daily average,%) Ratio between actual volumes and maximum capacity (daily average,%) Potential for expandability (% increase compared to today’s capacity) Number of kilometres from city centre to interchange/terminal
Expandability
10%
Distance from city centre
10%
Distance from commercial areas
10%
Number of kilometres from terminal to nearest commercial centre
Distance from industrial zones
10%
Number of kilometres from interchange/terminal to nearest industrial zone
Transshipment time Connection and distance to primary motor-way network Connection and distance to primary railway network
10%
Time needed for loading / unloading per TEU
10%
Direct, indirect or no access to nearest highway and proximity
10%
Direct, indirect or no access and proximity
Connection to ports
5%
Direct, indirect or no access and proximity
Connection to airports
5%
Direct, indirect or no access and proximity
Handling cost
20%
Punctuality
20%
Average price paid per TEU transhipped (€) Percentage of arrivals / departures within defined
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Criterion
Criterion weight
Indicator (KPI)
Indicator weight
Description tolerance for delay
Origin-destination time
10%
Average time for last mile roundtrip in city centre
Loss and damage
15%
Percentage of shipments with loss or damage
Supply chain visibility Information availability Terminal integration level
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15% 10% 10%
Percentage of terminal coverage with GPS, RFID, CCTV, e-PoDs, camera surveillance systems etc Existence of real time information and alerts inside the terminal Proximity and access of terminal to auxiliary services (e.g. customs)
The numerical values of the KPIs were either accumulated as raw data through the terminals’ annual reports or estimated based on information acquired by the terminals’ representatives in the context of individual interviews. After the quantification of each KPI, their respective grades were determined based on the grading scale used in the aforementioned projects, always in communication with the terminals’ representatives, adjusting the final grading scale taking into account their personal experience and expertise in this field adopting the DELPHI method. In addition, the significance of each criterion and respective KPI was investigated through the elaboration of a pairwise comparison in the context of the Analytic Hierarchy Process (AHP), in order to come up with their individual weights applied in the multi criteria analysis. All the stakeholders involved in freight assignments elaborated through those terminals participated in the establishment of weights through the AHP. Their viewpoints were recorded through a questionnaire survey organized and implemented by the authors of this paper during the last half of 2014, in order to gain a holistic multi stakeholder multi criteria approach. The AHP was selected as it is one of the Fuzzy Multiple Criteria Decision Making methods, providing not only a simple and very flexible model for a given problem, but also an easy applicable decision making methodology that assist the decision maker to precisely decide the judgments (Saaty, 1977; Chen and Wang, 2010; Li and Li, 2009; Nathanail et al., 2014). Based on the prioritization and weighting of criteria and respective KPIs, the pairwise comparison of the two terminals is elaborated to indicate which of the two is more efficient regarding its services and performance, in order to provide a valid decision making tool for terminal selection. The prioritization of the two terminals is then tested through a sensitivity analysis which constitutes a technique used to determine how different values of an independent variable will affect a particular dependent variable under a given set of assumptions. 3. Terminal comparative analysis The implementation of the methodology on the two terminals, leads to results which constitute a handy decision making auxiliary tool for freight assignment employing maritime and road transport modes or rail and road transport modes using the supply chain destined to Thessaloniki city for last mile delivery. The multi criteria assessment framework is based on five criteria: Management policy, Organizational and institutional structure, Supply side performance, Terminal properties and Level of service (Nathanail et al, 2016). The terminal comparative analysis is accomplished through the elaboration of a multi stakeholder multi criteria assessment framework. This framework was based on the values of those criteria and respective indicators, also taking into account their weights as determined by the involved stakeholders through the AHP. The grades of the KPIs were produced based on their numerical value and the grading scale determined based on literature review (Nathanail et al, 2005; Nathanail, 2007), also taking into account the involved stakeholders’ point of view on that through the Delphi method. In particular, based on the review of available sources and the expert group’s opinion, the grading scales were determined through a range of numerical values decided by the expert group after brainstorming, taking into consideration all the special characteristics and conditions in the area of study. The partial and total performance indices of the two terminals (Port of Thessaloniki and Kuehne+Nagel) are presented within Table 2.
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Table 2. Partial and total performance indices of Port of Thessaloniki (ThPA) and Kuehne + Nagel (K+N) terminals (Nathanail et al, 2016) Performance index Criterion Thessaloniki Port Authority Kuehne+Nagel Management policy 2.6 5.6 Organisational and institutional structure 8.5 7.9 Supply side performance 7 4 Terminal properties 7.1 6.45 Level of service 7.9 7.2 All criteria (Total Performance Index – TPI) 6.815 6.2375
It was observed that Kuehne+Nagel’s terminal performs better concerning the first criterion on “Management policy” due to the high multimodality rate and the higher performance on environmental burden, as well as safety and security issues. On the other hand, the Port of Thessaloniki terminal prevails when it comes to all the other criteria due to higher productivity of both personnel and equipment, while also being a little better performing in “terminal properties” and the provided “level of service” to partners and customers. Overall, the Port of Thessaloniki terminal outmaches Kuehne+Nagel’s terminal by 6.815 to 6.2375. In order to further validate the results, a sensitivity analysis was elaborated as well. Through the increase or decrease of each criterion’s weight by 10%, an effort is made to investigate any modification in the prioritization of the terminals concerning their partial and total performance indices. In particular, each time the emphasis (+10%) or the demotion (-10%) is set on one criterion, according to the common methodology adopted in similar cases based on the literature review (Nathanail, 2007). Thus, each criterion’s weight is firstly increased and then decreased (one at a time) counterbalancing accordingly the rest of the criteria weights (respectively increased or diminished in order for all criteria weights to sum up to 100%). The respective performance indices are depicted within Table 3. Table 3. Partial and total performance indices increasing / decreasing criteria weights (Nathanail et al, 2016) Total Performance Index (TPI) Criterion Management policy (+10%) Management policy (-10%) Organisational and institutional structure (+10%) Organisational and institutional structure (-10%) Supply side performance (+10%)
ThPA 6,3125 7,3175 7,05 6,58 6,8625
K+N 6,15875 6,31625 6,44625 6,02875 5,95875
Supply side performance (-10%) Terminal properties (+10%) Terminal properties (-10%) Level of service (+10%) Level of service (-10%)
6,7675 6,875 6,755 6,975 6,655
6,51625 6,265 6,21 6,35875 6,11625
The modification of each of the criteria weights by 10% does not have any impact in the prioritization of the terminals, as the ThPA’s terminal still has a higher total performance index compared to K+N’s terminal in all of the cases. This outcome proves the stability of the prioritization results for a weight fluctuation of ±10%. This is good and validates the correctness of the terminal analysis conducted through the proposed evaluation framework as the produced results are independent from attributing subjective weights to the evaluation criteria and indicators. In other words, the superiority of ThPA terminal over the one of K+N constitutes an objective result, close to reality. 4. Conclusions In the framework of this paper, two terminals, potentially involved in Thessaloniki’s supply chain and last mile (urban) distribution, were prioritized through a pairwise comparison, being evaluated as per their attributes and general performance (facts and figures) according to a Key Performance Indicator (KPI)-based multi stakeholder multi criteria assessment framework. In order to estimate the significance of each criterion and KPI in the analysis, all involved stakeholders imposed their point of view through the elaboration of Analytic Hierarchy Process (AHP).
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Based on the results and findings, the port of Thessaloniki (ThPA) terminal is ranked first according to its performance pertaining to the role of an intermodal interchange. The ThPA’s terminal predominance over the one of K+N is further validated through the elaboration of sensitivity analysis in the context of which the weights of criteria are increased and diminished accordingly by 10% in order to avoid objectivity. Nevertheless, Kuehne + Nagel (K+N) terminal’s performance index is only 8,5% lower than ThPA’s, while in several KPIs and criteria it seems to perform better. The prevailing ranking of ThPA versus K+N is validated through a sensitivity analysis. Although simpler processes could have been used to derive the same conclusions, the selection of a Multi-Stakeholder Multi-Critetia evaluation is justified as it is the most objective, integrated and holistic approach. Concluding, this study developed and validated an integrated evaluation framework, which can be used by decision makers, who are involved mainly in the last mile goods distribution. Applying the evaluation framework, stakeholders may support future decisions for strategic planning purposes, addressed by various criteria, trends and trade-offs. Acknowledgements Part of the research of this paper was done within the framework of the European Commission’s project NOVELOG (http://novelog.eu/). References Andersen J., Eidhammer O., Osland, O., Parra L., Adamos G. (2010). Interconnections between short and long-distance transport networks: Structure of interface and existing indicators. Deliverable 3.1. CLOSER – Connecting Long and Short-distance networks for Efficient tRansport. Andersen, J. & Eidhammer, O. (2011). Core indicators for the interconnection between short and long-distance transport networks. Deliverable 3.2. CLOSER – Connecting Long and Short-distance networks for Efficient tRansport. Andersen J., Eidhammer O., Gogas M., Papoutsis K., Nathanail E. (2014). Demonstration assessments. Deliverable 5.1. STRAIGHTSOL – STRAtegies and measures for smarter urban 7reight SOLutions. Best Urban Freight Solutions (2014). http://www.bestufs.net/ Accessed on 14 Jan 2015. Chen, M. K., WANG, S. (2010). The critical factors of success for information service industry in developing international market: Using analytic hierarchy process (AHP) approach. Expert Systems with Applications, Vol. 37 2010, pp. 694-704. Christiansen, P., Johansen, B.G., Andersen, J., Eidhammer, O. (2012). Case studies: Results and synthesis. Deliverable 5.2. CLOSER – Connecting Long and Short-distance networks for Efficient tRansport. Dan Li., Weixin L., Pian F. (2013). The Efficiency Measurement of Coastal Container Terminals in China. J Transpn Sys Eng & IT, 2013, 13(5), 10−15. Eckhardt, J., Hietajärvi, A-M., Rönty, J., Andersen, J., Eidhammer, O. (2012). Guidance and recommendations for interconnection between long distance and local/regional freight transport. Deliverable 6.2. CLOSER – Connecting Long and Short-distance networks for Efficient tRansport. Gogas M., Nathanail E. (2014). Multilevel multicriteria design of intermodal transport Freight Center networks. International Conference on Engineering and Applied Sciences Optimization (OPTi 2014), Kos Island, Greece, 4-6 June 2014. Järvi, T., Nagel, I. (2013). Guidance and recommendations for interconnection between long distance and local/regional passenger transport. Deliverable 6.1. CLOSER – Connecting Long and Short-distance networks for Efficient tRansport. Kapros S., Panou K., Tsamboulas D. (2005). Multicriteria approach to the evaluation of intermodal freight villages. Transportation Research Record: Journal of the Transportation Research Board, 1906, 56-63. Li, S., Li, J.Z. (2009). Hybridizing human judgment, AHP, simulation and a fuzzy expert system for strategy formulation under uncertainty. Expert Systems with Applications, Vol. 36 2009, 5557-5564. Nathanail E., Gogas M. (2005). Spatial planning – Development of nodal points and terminals. Deliverable 4. IMONODE – Efficient Integration of cargo transport Modes and NODEs in CADSES area. Nathanail E., (2007). Developing an integrated logistics terminal network in the CADSES area. Transition Studies Review, May 2007, Volume 14, Issue 1, pp 125-146. Nathanail, E. G., Gogas, M. A., Papoutsis K. N. (2014). Investigation of Stakeholders’ View towards the introduction of ICT in Supply Chain using Analytic Hierarchy Process. Journal of Traffic and Logistics Engineering, Vol. 2 No. 2, pp. 113-119. Nathanail, E. G., Gogas, M. A., Adamos G. T. (2016). Urban freight terminals: A sustainability cross-case analysis. The second International Conference Green Cities 2016 – Green Logistics for Greener Cities. 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