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101 HỘI THẢO KHOA HỌC QUỐC GIA VỀ LOGISTICS VÀ QUẢN LÝ CHUỖI CUNG ỨNG VIỆT NAM LẦN THỨ 4 (CLSCM-2024) EFFICIENCY EVALUATION 3PL LOGISTICS BASED ON DEA-EBM MODEL: A CASE STUDY IN VIETNAM ĐÁNH GIÁ HIỆU QUẢ CÁC CÔNG TY DỊCH VỤ LOGISTICS BÊN THỨ BA TẠI VIỆT NAM SỬ DỤNG MÔ HÌNH EBM-PHƯƠNG PHÁP PHÂN TÍCH BAO DỮ LIỆU THI HAI - YEN TRAN*, NGOC HIEP - NGUYEN Faculty of Business Management Department, FPT University, FPT Polytechnic Ha Noi *Email: [email protected] Abstract This paper attempts to evaluate efficiency for 3PL Logistics industry with financial indicators. Data on the industry were obtained from Vietnam Market. It analysing the performance of the Third -party Logistics firms during the period of 2023, the DEA-SBM was used to determine that achieved the highest performance in terms of the input and output variables. This study aims to quantify the 3PL Logistics industry’s operational efficiency and to provide an overview of the state of operations so that managers and regulators can enhance their performance. We use Epsilon- Based Measure Efficiency (EBM) to calculate the operating efficiency 10 3PL Logistics companies in this study. This paper can be a beneficial reference to Third - party Logistics firms for the policymakers, investors, development, and Third - party Logistics management. Keywords: EBM, 3PL -Third - party Logistics. 1. Introduction Vietnam's logistics service industry in the period 2017-2023 has made remarkable progress; driven by positive economic growth, the shifting trend of international capital flows, and Vietnam's improved position in the global supply chain. Third-party logistics” (3PL) refers to the use of an external company to manage and carry out one or more logistics processes for a business. These operations can include transportation, warehousing, inventory management, and packaging. 3PL providers offer a range of services to help companies increase efficiency, reduce costs, and improve their supply chain operations. The Third-Party Logistics (3PL) market covers various providers that provide or specialize in the above-mentioned third-party logistics services. The revenue in Vietnam's Third Party Logistics (3PL) market is forecasted to reach US$5.64bn in 2024. In this study, our focus lies on examining the ten firms in the market for their expertise in ensuring secure 3PL logistics. These firms include Cang Sai Gon, Container Vietnam, Van tai va xep do Hai An, Van tai SaFi, Kho van mien Nam, Giao nhan xep do Tan Cang, Transimex, Buu Chinh Viettel, Dinh Vu, and Vitac. Through our research findings, we aim to guide policymakers, experts, and government to help managers assess their business performance and identify areas for improvement.The DEA-EBM model is an effective method for evaluating the performance of firms in the same industry. EBM uses input and output data of firms to assess the performance of each firm relative to others in the group. This study employs EBM to evaluate the performance of ten 3PL logistics companies in Vietnam in 2023. The results of this study can provide useful information for 3PL logistics companies to asses their business performance and identify areas for improvement. Furthermore, by deepening our understanding of the security challenges faced by companies, this research contributes to the adoption and implementation of technology across various industries. 2. Literature review Having been employed in literature for many decades, financial ratios are the simplest tools for evaluating the financial performance of firms. These studies will use quantitative techniques to demonstrate how 3PL logistics companies have benefited the economy. Chia - Nan Wang et al.[1] merged the Epsilon-Based Measure (EBA) and DEA Malmaquist to assess which lithium-ion battery (LIB) manufacturers are the best. Hezekiah et al.[2] evaluated the overall technical efficiency (OTE), pure technical efficiency (PTE), and scale efficiency (SE) of 17 FMCG companies using the Data Envelopment analysis technique. Vo-Thi-Minh- Nhat et al.[3] evaluated the security industry's efficacy for
102 HỘI THẢO KHOA HỌC QUỐC GIA VỀ LOGISTICS VÀ QUẢN LÝ CHUỖI CUNG ỨNG VIỆT NAM LẦN THỨ 4 (CLSCM-2024) government agencies, experts, and decision makers using the DEA-SBM model. Liang.Chen and Guozhu.Jia[4] utilized the SBM and the DEA to analyze China's regional industry's environmental efficiency. The study's findings indicate that, from 2008 to 2012, China's industry's environmental efficiency were generally low, with the exception of a few developed provinces. The report assisted the Chinese government in concentrating on development and low environmental efficiency. O.N. Arunkumar and T. Radha Ramanan.[5] used the data collected from 46 companies and applied the DEA model to calculate performance for the Indian food and beverage industry to find reasons why technical inefficiencies, and then using the OLS regression to identify the determinants of these inefficiencies reveals that the current ratio and financial assets to total assets are contributing significantly to the inefficiencies.Pei Fun Lee et al.[6] employed the DEA model to assess logistics organizations' efficiency. According to the research, there is an operational risk component in every logistics company in Malaysia that applies the data envelopment analysis (DEA) approach. The basic indicator approach (BIA) in the proposed model indicates the operational risk capital need component. Wang, Chia-Nan et al.[7] forecasted and assessed the effectiveness of third-party logistics using the GM(1,1) and the EBM model. The finding demonstrate high that C.H Robinson World -wide (CHRW), Echo Global Logistics (EHCO), and United Parcel Service (UPS) achieve high efficiency and maintain a consistent efficiency score over the course of the term. Between 2013 and 2022, Expeditors International (EXPD) and Kerry Logistics Network (KRRYF) do not reach efficiency. Customers can choose the top TPL providers with the aid of the 10 third-party logistics providers' increasing and decreasing variation index. Jaclyn A-Card, et al.[8] used the EBM model to clarify differences between online travel product shoppers and nonshoppers. The result is to find suggested evidence for the usefulness of the EBM model when determining personal characteristic differences between shoppers and nonshoppers but not for store characteristics.Hsu- Fang-Ming et al.[9] proposed a decision support system known as Intelligent Tourist Attractions Systems (IATS) using the EBM model and a Bayesian network. 3. Methodology 3.1. Epsilon-Based Measure Efficiency In DEA, there are 2 types of technical efficiency measures: radial measures and non-radial measures. The radial measurement takes only the corresponding change in input or output, ignoring any slack. The non-radial measurement, on the other hand, deals directly with slacks and is unconcerned about the proportion of inputs and outputs changing. As a result, in some situations, both can lead to incorrect evaluation. To address this problem, the EBM model was created. Both radial and non-radial features are combined in the model. This framework contains two parameters, one scalar and one vector, which are defined by affinity index in relation to the inputs and outputs. These two factors are used to combine the radial and non-radial models into a single model for evaluating DMU efficiency. By showing that the EBM is input-oriented (EBM I-C) for DMU0 = (x0 , y0), we then calculate it as This is subject to: θx0 − Xλ − s − = 0 Yλ ≥ y0 , λ ≥ 0, s − ≥ 0 Where the weight (relative importance) of input (i) is wi − and ∑ wi s − i=1 = 1 (wi − ≥ 0∀i ) and εx is the parameter that integrates the radial θ and nonradial slacks terms. 3.2 Diversity Index and Affinity Index Pearson's correlation is important in the DEA because it clarifies the association between two variables. It converts the raw data into a correlation estimate. If the Pearson index is high, it indicates that the two variables are related. On the other hand, a low correlation coefficient indicates a skewed input- output relationship. Pearson's correlation coefficient is a number that varies from -1 to +1. Furthermore, one of the most essential aspects in a DEA is weight. The weight determines the effect of the input on the output [51]. If the weight is close to 0, it means that the output is unaffected by changes in the input. Positive weights suggest an inverse relationship between input and output, i.e., if the input increases, the output decreases. The values of εx and wi have a significant impact on determining the efficiency of DMUs in the EBM model. The EBM model, on the other hand, will employ the affinity index between two vectors rather than the Pearson's correlation coefficient as a model. Let a ∈ R+ n and b ∈ R+ n be two non-negative vectors with a dimension n. They show the values that have been checked for a certain input component

104 HỘI THẢO KHOA HỌC QUỐC GIA VỀ LOGISTICS VÀ QUẢN LÝ CHUỖI CUNG ỨNG VIỆT NAM LẦN THỨ 4 (CLSCM-2024) 2. Revenue (RE): Revenue represents the total income generated by a company from its primary operations, typically from sales of goods or services. 4.3. Evaluation of DMUs Performance This part’s financial data from 2023 is shown in Table 2. The 2023 efficiency will be shown in Tables 3 and 4 below. Before evaluating the DMUs’ efficiency using EBM, one of the most crucial consideration was assessing whether a positive value for the data existed. Aside from that, there’s an isotonic relationship between the input and output data. The ecoefficiency of correlation, which ranges from 0 to +1 , define the link between two variable. The two variable were highly correlated if index was close to +1. The Pearson’s correlation of the DMUs is shown in Table for each. The minimal correlation coefficient, as seen in the findings, was 0.3978 higher than 0. This demonstrates that all data variable were correlated in a meaningful sense, making it possible to carry out EBM. The diversity index and affinity index matrices in the EBM model for 2023 are shown in Tables 5 and 6. According to the diversity matrix and affinity matrix results, the values vary from 0 to 0.29 and from 0.419 to 1, respectively.. Therefore, DMU efficienc or inefficiency can be ranked using EBM. Subsequently, the EBM model's weight to input/output and epsilon are computed (Table 7). The efficiency of ten companies will be obtained based on EBM’s factor weight and epsilon. According to the results, Buu Chinh Viettel, Giao Nhan Xep Do Tan Cang, and Van Tai SAFI all demonstrated strong efficiency with a score of 1. After that, Transimex and Cang Sai Gon display a low-efficiency score. In Figure 1, you can see a list of 10 3PL Logistics companies. Their rankings in this ranking are decided by their performance measures. Every company has a place, and the most highly ranked corporation is acknowledged as the most successful and productive. For instance, Transimex and Cang Sai Gon performed ineffectively, while Buu Chinh Viettel, Giao nhan xep Table 2. Data in 2023 Table 3. Statistics on input/output data year 2023 Table 4. Person’s correlation coefficient year 2023 DMU (I) Total Asset (I) Liabilities (I) Owners 'equity (O) Gross profit (O) Revenue Cang Sai Gon 5,345,657 2,504,311 2,841,346 327,369 942,618 Container Viet Nam 5,186,553 1,889,730 3,296,823 654,439 2,180,945 Van tai va xep do Hai An 5,358,949 2,188,204 3,170,746 611,066 2,612,690 Van tai SAFI 888,663 156,188 732,475 176,200 1,017,527 Kho van Mien Nam 2,916,602 626,984 2,289,618 317,134 1,794,859 Giao nhan xep do Tan Cang 942,546 344,426 598,120 238,703 1,528,683 Transimex 7,603,218 2,803,334 4,799,885 418,263 2,446,049 Buu chinh Viettel 6,777,327 5,195,831 1,581,496 875,998 19,589,873 Dinh Vu 1,633,586 259,325 1,374,261 225,395 549,212 Vitaco 1,643,850 499,021 1,144,830 197,826 1,076,622 Total Asset Liabilities OWNERS 'EQUITY Gross profit Revenue Max 7603218 5195831 4799885 875998 19589873 Min 888663 156188 598120 176200 549212 Average 3829695.1 1646735.4 2182960 404239.3 3373907.8 SD 2383813.81 1523852.376 1273524.556 222625.7865 5444625.247 Total Asset Liabilities OWNERS 'EQUITY Gross profit Revenue Total Asset 1 0.878553187 0.820579844 0.764052861 0.483498824 Liabilities 0.87855319 1 0.447933949 0.862713162 0.816856659 OWNERS 'EQUITY 0.82057984 0.447933949 1 0.397881821 0.072395823 Gross profit 0.76405286 0.862713162 0.397881821 1 0.769539781 Revenue 0.48349882 0.816856659 0.072395823 0.769539781 1