Available online at ScienceDirect. Procedia Engineering 187 (2017 )

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1 Available online at ScienceDirect Procedia Engineering 187 (2017 ) th International Scientific Conference Transbaltica 2017: Transportation Science and Technology Studying Demand for Freight Forwarding Services in Ukraine on the Base of Logistics Portals Data Vitalii Naumov a, *, Oksana Kholeva b a Cracow University of Technology, Poland b Kharkiv National Automobile and Highway University, Ukraine Abstract A method of studying a flow of requests on freight forwarding services with the use of the information logistics portal tools has been proposed. On the base of experimental studies results for the Ukrainian road transport market, distributions of the request flow parameters have been grounded and numerical characteristics of demand for services of Ukrainian freight forwarding companies have been determined The The Authors. Published Published by Elsevier by Elsevier Ltd. This Ltd. is an open access article under the CC BY-NC-ND license ( Peer-review under responsibility of the organizing committee of the 10th International Scientific Conference Transbaltica 2017: Peer-review Transportation under Science responsibility and Technology. of the organizing committee of the 10th International Scientific Conference Transbaltica 2017 Keywords: freight forwarding, demand for transport services, requests flow, information logistics portal 1. Introduction Development of information technologies over the past years has provided a high level of informatization and virtualization of technological processes at contemporary transport markets. This led to the changes in a role of freight forwarders as of companies providing intermediary services. Contemporary forwarders are the architects of supply chains that provide the most efficient way of interaction between the transport market participants. * Corresponding author. address: vnaumov@pk.edu.pl The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( Peer-review under responsibility of the organizing committee of the 10th International Scientific Conference Transbaltica 2017 doi: /j.proeng

2 318 Vitalii Naumov and Oksana Kholeva / Procedia Engineering 187 ( 2017 ) Therefore, the efficiency of the forwarding companies technological processes nowadays is one of the key factors which determines an efficiency of freight transport systems [1]. Evaluation of demand for production of enterprises (or their services) is a necessary stage, which is usually implemented before making decisions on improving the quality of production (or clients servicing), in increasing the efficiency of technological processes, on improving the company s competitiveness, etc. Incorrect information about the parameters of the demand is often the cause of wrong conclusions and wrong recommendations. Therefore, the problem of the demand estimating is particularly relevant in solving scientific and practical problems in the field of transportation and freight forwarding, as the obtained results provide the correctness at the stage of assessing the state of the research object [2]. In this paper we aim to work out such a method for estimation of demand for freight forwarding services, which allows researchers and transport managers to obtain adequate data for simulations of freight forwarding processes. In order to achieve this goal, we discuss possible data sources, propose a sequence of operations for measurements of demand parameters, and use the proposed methodology on the example of Ukrainian freight forwarding market. 2. Literature review Demand for cargo deliveries is described in the transportation market models on the basis of a model of the requests flow, which generates appropriate informational, material and financial flows in the macro-logistics system of the transport services market [3, 4]. Such an abstraction corresponds to the actually used principles of communication between the transportation market entities: the freight owners and carriers servicing by freight forwarders is carried by means of informational logistics portals. In the modern market of the road freight transportation, ensuring the needs of freight owners for cargo deliveries and the needs of carriers for vehicles loading in most cases is provided under the mediation of a freight forwarding company as an operator servicing the flow of information about clients requests. The conducted literature review, based on [3 8], leads to the conclusion, that demand evaluation task is commonly considered as a task of numerical estimation of a single parameter, which is used for description of demand. In most approaches the evaluation of a transport demand refers to the determination of its predictive value [3, 5, 7]. The need for preliminary studies of demand parameters, which are the stochastic values, as a rule, is not considered. Usually, the definition of predictive value is implemented on the basis of statistical data on the demand values for previous periods, which does not allow to study the process of demand formation. Such an approach causes incorrect results of the evaluation process. In accordance with [2], the basic unit, forming the demand, is a request for transportation services a need of a client for services, supported by its purchasing abilities and presented at the market in order to be satisfied. The request for services is the basis and reason for interaction between the transport market entities. A set of potential and actual requests for the company services forms a demand for its services; respectively, a set of requests for services of all companies in a region represents the demand for transportation services in this region, etc. Each request could be quantified by a set of indicators, the most significant of which are the shipment volume, the distance of delivery and time interval between requests. Since a set of consecutive requests for services of transport companies characterizes the demand, the demand estimation problem could be transformed into the problem of the request flow parameters estimation. An information about the requests flows for the particular forwarding company could be obtained on the base of data from this company, and for the transportation market as a whole on the base of data from specialized information portals [9]. In this paper, as the studying of demand for forwarding services, we understand the determination of random variables of the demand numerical parameters [2]. These demand characteristics are initial data for solving a number of problems in improving the efficiency of freight forwarding processes [10]. 3. Choosing the information source The most popular informational resources at the Ukrainian transport market are della.ua and lardi-trans.com. Freight owners and carriers have an opportunity to apply their need for the cargo deliveries or the reverse load of vehicles. Dispatchers of forwarding companies have an access to the relevant database of requests (the access is

3 Vitalii Naumov and Oksana Kholeva / Procedia Engineering 187 ( 2017 ) subscribed by forwarders under the terms of fee for the use of the portal services). The main task of the dispatcher, as a manager of the transportation processes, is to form the requests into such delivery routes, which provide a costeffective load of vehicles. In this paper tools of the lardi-trans.com portal were used in order to obtain data for analysis of the demand for freight forwarding services. The choice of this informational resource was driven by the provided tools for downloading of data for a set of requests. Since the processing of a large number of requests is required, such a function is a prerequisite for effective data analysis. Unlike lardi-trans.com, the della.ua informational portal does not provide opportunities to work with a group of requests to those users, who are not players of the transportation services market. Another significant advantage of the lardi-trans.com tools use for demand studies is a considerably lower rate of the requests duplication in a database of the informational system: for a sample of 500 requests (the maximum sample size provided by the system) about 3% of requests are duplicated. In a set of 500 recent requests provided by della.ua, as a rule, about 70% of requests are duplicated. In addition, a sample of requests obtained with the help of the della.ua search engine could contain stale requests; there are no such requests in the lardi-trans.com database because of high intensity of the informational flow. 4. Method of processing the requests for services of forwarding companies At the first stage of the studies of demand for transportation services, we propose to estimate an intensity of the requests flow. The intensity value determines an expediency of further studies of the request interval as a stochastic variable: if mean value of the requests interval is significantly lower than mean duration of servicing procedure, the value of the time interval between consecutive requests does not affect the servicing technology; for mean interval value comparable with the mean servicing duration, the random variable distribution has significant influence on the choice of delivery technological schemes [2]. For modeling the demand for transport services, the convenient way to present geographical characteristics of requests is to combine them in the origin-destination matrix (O-D matrix) mapping requests distribution according to delivery directions. The O-D matrix is a square matrix, its size N is equal to the number of geographical regions in the servicing area. An element c ij of the O-D matrix is the number of requests for cargo delivery from the i-th to the j-th region during the specified time period. As origin and destination regions in the O-D matrix at the territory of Ukraine is convenient to use the administrative districts (N = 24); we assume that such a level of detail would be sufficient to characterize the demand for long-distance deliveries across Ukraine. Since intensity of the demand for freight transportation varies for the different regions of Ukraine, the conventional division of the territory of Ukraine into zones is assumed in a practice of freight forwarding (Table 1). Table 1. Distribution of the regions of Ukraine among zones. Zone Regions included into the zone 1 Cherkasy, Dnipropetrovsk, Kyiv, Kirovohrad, Poltava, Vinnytsia, Zaporizhia, Zhytomyr 2 Kherson, Mykolaiv, Odessa 3 Chernihiv, Donetsk, Kharkiv, Luhansk, Sumy 4 Ivano-Frankivsk, Khmelnitskyi, Lviv, Rivne, Ternopil, Volyn 5 Chernivtsi, Crimea, Zakarpattia Each zone is characterized by a certain value of a probability of the return vehicle s load, which in turn affects a value of the tariff for cargo delivery in a corresponding direction. So, since the probability of the return load for the central and eastern regions (zones 1 and 3) is higher than for southern and western regions (zones 2 and 4), then the tariff for road cargo deliveries in the directions of central and eastern regions is lower than in the southern and western directions. The probability of return load is significantly lower for Chernivtsi region, Crimea and Zakarpattia, so they are allocated into the separate zone. The O-D matrix calculated for the presented geographical

4 320 Vitalii Naumov and Oksana Kholeva / Procedia Engineering 187 ( 2017 ) zones characterizes the demand for long-distance road deliveries in terms of their attractiveness relative to the possibility of return loading. Technologically, processes of delivery of various cargo types differ significantly, in the first place by the method of loading and unloading and by the vehicle s body. Therefore, while processing data on the requests flow, it is expedient to conduct a segmentation according to the vehicle s body type and the shipment volume. In this case, the demand for freight forwarding services could be presented as a set of requests flows for the respective demand segments. In order to obtain the representative data on the flow of requests we propose to study the demand in the following sequence: obtain a sample with the maximum size from all available requests for delivery of goods in Ukraine; on the base of this sample, study the structure of demand according to vehicle s body type, calculate first variant of the O-D matrix, form hypotheses on the distribution of stochastic variables of the shipment volume and delivery term (delivery time expected by the client); obtain a sample with the maximum size from all available requests in the second day of studies; on the base of the sample check previously obtained hypotheses on the demand structure and distributions of the numerical demand parameters; form several samples with the maximum size with the regular time interval between sampling moments on the third day of observations in order to study the demand dynamics an intensity of new requests appearance in the database of the informational portal; obtained sets of requests are combined in a single sample, which is used for checking of the requests duplication level, and for checking of the demand structure and hypotheses on the demand parameters distribution, obtained as a result of the studies at previous stages; form a set of samples with the regular time interval between sampling moments on the fourth day of observations in order to confirm the demand dynamics, obtained at the previous stage; the samples are also combined in a single sample for double checking of the requests duplication level, the demand structure and distribution of the demand parameters; all the data, obtained at the previous stages, is combined in a single sample, which is used in order to formulate final conclusions about the demand structure, its dynamics, and the distribution of the numerical parameters. 5. Results of the requests flow processing Studies of the request flow were conducted in accordance with the described method in the period from 29 th of May to 1 st of June To obtain data on the flow of requests for forwarding services, the primary processing of the information was carried out in order to convert the data, received from the lardi-trans.com portal, into the data set containing the following fields: identification number of the request, required body type of the vehicle, the shipment description, origin and destination regions, the shipment volume, required delivery term. The sample obtained in the first day of observation (formed at 12:00) contained 499 requests, of which 49 requests were duplicate entries. For the demand analysis, duplicate requests were screened out with the use of appropriate MS Excel functions. In order to hypothesize the distribution of the stochastic values of the shipment volume and the delivery term the histograms for the obtained data were made (Fig. 1). Primary analysis of the histogram in the Fig. 1a allows us to suggest that the requests flow should be divided into two parts: requests for delivery with the shipment volume up to 18 tons and requests with the shipment volume over 18 tons. The validity of such an assumption could be explained by the presence of two peaks at the histogram in the beginning of the range (the shipment volumes up to 1 t) and for the shipment volume of about 22 t. If a set of requests is divided into two mentioned groups, the sample of requests with the volume of shipment up to 18 t has a distribution close to exponential, and the sample of requests with the shipment volume over 18 t could be characterized by lognormal or gamma distribution. A shape of the histogram for the stochastic variable of the delivery term (Fig. 1b) allows us to hypothesize its exponential distribution.

5 Vitalii Naumov and Oksana Kholeva / Procedia Engineering 187 ( 2017 ) Fig. 1. Distribution of the requests flow parameters: (a) the shipment volume; (b) delivery time. To check the hypotheses on distribution of the demand parameters we used the software that implements in R the method of statistical hypothesis testing according to the Pearson criterion [11]. This software allows researchers to test the hypothesis for the most common distributions of random variables, the check may be carried out for the distribution of the histogram variants with different number of bins. Such an approach allows eliminating possible cases, when the hypothesis on a random variable distribution is rejected for a variant of the histogram with a number of bins obtained on the base of the Sturges rule, but is not rejected for another number of bins. As a result of use of the described software while processing data on the request flow parameters, obtained on the first day of studies ( ), zero-hypotheses on exponential distribution of the shipment volume up to 18 t, lognormal distribution of the shipment volume over 18 t and exponential distribution of the delivery term were not rejected (for the probability value of 0.05). On the second day of studies these results were confirmed. On the third day of studies ( ) the samples were formed in the lardi-trans.com system with an interval of 20 min. between 11:40 and 13:20. As a result, 6 samples of 3000 requests in total were obtained. To determine the intensity of appearance of new requests in the database of the information portal, for each of the samples a share of new requests (excluding duplicate requests) was estimated. The obtained results allow us to suggest that the share of new requests that appear in the system during the minute, when the sample was selected, is more than 89%, while on average the system receives 97.93% of the new requests. In the fourth stage of the research demand, held in between 14:50 and 15:35 of , samples with the size of 350 requests were formed in the lardi-trans.com system with an interval of 5 minutes. As a result, we obtained 10 samples of 3500 requests in total. Analysis of the dynamics of demand suggests that a share of new requests that appear in the system during a minute, is more than 85%, and the average share of new requests equals to 93.94%. For the defined zones of the territory of Ukraine, the obtained consolidated matrix of demand distribution contains the values shown in Table 2. Table 2. Consolidated matrix of the demand distribution by zones of Ukraine for the demand studies conducted between and Zone Total % 12.48% 6.29% 4.67% 0.66% 47.60% % 3.73% 0.88% 0.73% 0.25% 8.16% % 2.39% 4.43% 0.57% 0.14% 17.21% % 4.40% 1.45% 9.29% 2.42% 26.10% % 0.14% 0.01% 0.18% 0.07% 0.92% Total 44.82% 23.14% 13.06% 15.44% 3.54% % On the basis of the results of the demand studies, we estimated the intensity of a flow of requests for freight forwarding services as an average value, weighted by the number of the obtained samples. At the first and second

6 322 Vitalii Naumov and Oksana Kholeva / Procedia Engineering 187 ( 2017 ) stages of studies, one sample per a day was analyzed; the corresponding value of intensity for each of the obtained samples was 450 req./min. At the third stage, 6 samples were analyzed, for which an average value of the requests flow intensity was 490 req./min. At the last stage of the demand studies 10 samples were analyzed, as a result, an average intensity value amounted to 490 req./min. as well. On the grounds of these results, we conclude that the weighted average value of the flow intensity for requests for road cargo deliveries in Ukraine equals to 474 req./min. Other numerical parameters of the demand, also estimated as average values, are presented in Table 3. Table 3. Parameters of demand for forwarding services on road transport deliveries in Ukraine. Parameter Share of requests on vehicles with covered body [%] Share of requests on dump trucks [%] Share of requests on vehicles with other type of body [%] 7.66 Share of requests with shipment volume less than 18 tons [%] Share of requests with shipment volume more than 18 tons [%] Scale parameter for the stochastic variable of shipment volume (up to 18 tons) [ton 1 ] Scale parameter for the stochastic variable of shipment volume (over 18 tons) Shape parameter for the stochastic variable of shipment volume (over 18 tons) Scale parameter for the stochastic variable of delivery term [days 1 ] Average weighted value The derived numerical parameters are main characteristics, on which basis a model of demand for freight forwarding services could be implemented. Such a model could be used as a part of some complex simulation models for solving of different problems in the field of freight forwarding service, for example: justification of strategies for transport market participants, development of the effective technological processes of freight forwarding, improvement of the vehicles fleet structure, etc. 6. Conclusions The proposed method for estimation of the demand for freight forwarding services allows on the basis of publicly available data to determine requests flow parameters as random variables of the flow characteristics, as well of the flow intensity and the requests distribution across geographical regions. The obtained in this study numerical parameters of the demand could be used as the basis for simulations of the requests flow in solving the problems of increasing the efficiency of freight forwarding services. The further direction of our studies is the development of specialized software for modeling of demand for freight forwarding services on the basis of the derived distributions and numerical characteristics of the demand parameters. References [1] J.-P. Rodrigue, The geography of transport systems, Routledge, New York [2] V. Naumov, Freight forwarding in logistics systems [in Russian], KhNAHU, Kharkiv [3] J. Chow, C. H. Yang, A. C. Regan, State-of-the art of freight forecast modeling: lessons learned and the road ahead, Transportation 37 (2010) [4] V. Barone, F. Crocco, D. W. E. Mongelli, Freight transport demand models for applications in urban areas, Applied Mechanics and Materials 442 (2014) [5] W. B. Allen, The demand for freight transportation: A micro approach, Transportation Research 11(1) (1977) [6] C. Winston, The demand for freight transportation: models and applications, Transportation Research Part A: General 17(6) (1983) [7] A. C. Regan, A. G. Rodrigo, Modeling freight demand and shipper behavior: State of the art, future directions, Institute of Transport Studies, University of California [8] A. Albert, A. Schaefer, Demand for freight transportation in the U.S.: A high-level view, 54th Annual Transportation Research Forum (2013)

7 Vitalii Naumov and Oksana Kholeva / Procedia Engineering 187 ( 2017 ) [9] J. H. Wan, Y. W. Zhang, Design and implementation of the freight forwarding management information system based on ROR, Proc. of the Int. Conf. on Control Engineering and Information System (2015) [10] W. Zhou, J. Zhang, H. Chen, Service quality evaluation for international freight forwarder, Proc. of the 7th Int. Conf. on Service Systems and Service Management (2010) [11] V. Naumov, R code for checking if the distribution of a stochastic variable fits well. Availavle from Internet: R_code_for_checking_if_the_distribution_of_a_stochastic_variable_fits_well