Öz

This study aims to evaluate the logistics efficiency of African countries using Data Envelopment Analysis (DEA), EATWIOS, and MEREC methods. Four input variables (logistics infrastructure, container port traffic, customs, international shipping) and five output variables (gross domestic product (GDP), carbon dioxide (CO2) emission, timeliness, tracking and tracing, competence, and quality of logistics services) were identified based on the literature review for the analysis of countries' efficiency. Among the fifty-four African countries, 18 countries with complete data on the input and output variables were included in the evaluation. In this context, the ranking of African countries according to their logistics performance was obtained using the slacks-based DEA and EATWIOS hybrid method by assuming equal criteria weights and then weighting the criteria with the MEREC method. As a result of the MEREC analysis, it is found that the most important input variables in the evaluation of logistics performance are container port traffic score and logistics infrastructure facilities, while the most important output variables are GDP country share and CO2 emissions from transportation and logistics. According to the final analysis results, the Democratic Republic of the Congo has the highest performance and Benin has the lowest.

Anahtar Kelimeler

• Logistics Efficiency
• African Countries
• Data Envelopment Analysis
• MEREC
• EATWIOS

MEREC, Veri Zarflama Analizi ve EATWIOS Yöntemlerinin Hibrit Kullanımı ile Afrika Ülkelerinin Lojistik Performanslarının Değerlendirilmesi

Öz

Bu çalışmanın amacı, Afrika ülkelerinin lojistik etkinliklerinin Veri Zarflama Analizi (VZA), EATWIOS ve MEREC yöntemleri ile çok kriterli olarak değerlendirilmesidir. Ülkelerin etkinliklerinin analizi için literatür incelemesine dayalı olarak dört girdi (lojistik altyapı, konteyner liman trafiği, gümrükleme sürecinin verimliliği, uluslararası gönderi maliyetleri) ve beş çıktı değişkeni (gayri safi yurtiçi hasıla (GSYH), karbondioksit (CO2) emisyonu, zamanındalık, izleme ve takip, lojistik hizmetlerin yetkinliği ve kalitesi) belirlenmiştir. Elli dört Afrika ülkesi arasından, belirlenen girdi çıktı değişkenlerinde tam veriye sahip olan 18 ülke değerlendirmeye alınmıştır. Bu bağlamda, Afrika ülkelerinin lojistik performanslarına göre sıralamaları önce kriter ağırlıkları eşit varsayılarak daha sonra da kriterler MEREC yöntemi ile ağırlıklandırılarak boşluk tabanlı VZA ve EATWIOS hibrit yöntemi kullanılarak elde edilmiştir. MEREC analizi sonucunda lojistik performansının değerlendirilmesinde en önemli girdi değişkenlerinin konteyner liman trafiği skoru ile lojistik alt yapı olanaklarının olduğu; en önemli çıktı değişkenlerinin ise GSYİH ülke payı ve ulaşım ve lojistik kaynaklı CO2 emisyonu olduğu bulgulanmıştır. Nihai analiz sonuçlarına göre performansı en yüksek ülkenin Demokratik Kongo Cumhuriyeti, en düşük ülkenin Benin olduğu tespit edilmiştir.

Anahtar Kelimeler

• Lojistik Etkinlik
• Afrika Ülkeleri
• Veri Zarflama Analizi
• MEREC
• EATWIOS.

Kaynakça

1. Abdoulkarim, H. T., Fatouma, S. H. ve Hassane, H. T. (2019). Assessment of dry port efficiency in Africa using data envelopment analysis. Journal of Transportation Technologies, 9(02), 193. https://doi.org/10.4236/jtts.2019.92012
2. Acar, M. F. (2021). Lojistik performans indeks: Türkiye-Avrupa Birliği karşılaştırması. International Journal of Advances in Engineering and Pure Sciences, 33(3), 422-428. https://doi.org/10.7240/jeps.845982
3. Acquah, D. A. (2018). Selection of gateway port for west African landlocked countries using data envelopment analysis. International Journal of Novel Research in Marketing Management and Economics, 5(2), 7-17. https://www.noveltyjournals.com/issue/IJNRMME/Issue-2-May-2018-August-2018
4. Adıgüzel Mercangöz, B., Yıldırım, B. F. ve Kuzu Yıldırım, S. (2020). Time period based COPRAS-G method: application on the Logistics Performance Index. Scientific Journal of Logistics, 16 (2), 239-250. http://doi.org/10.17270/J.LOG.2020.432
5. Akyüz, G. ve Aka, S. (2017). Çok kriterli karar verme teknikleriyle tedarikçi performansı değerlendirmede toplamsal bir yaklaşım. Journal of Management & Economics Research, 15(2), 28-46. http://dx.doi.org/10.11611/yead.277893
6. Aldian, A. ve Taylor, M.A.P. (2005). A consistent method to determine flexible criteria weights for multicriteria transport project evaluation in developing countries. J. East. Asia Soc. Transport. Stud, 6, 3948-3963. https://doi.org/10.11175/easts.6.3948
7. Altın, F. G. ve Filiz, T. (2022). Assessment of the performance of logistics villages operated by the Turkish state railways using MCDM and DEA Methods. Ege Academic Review, 22(2), 169-182. https://doi.org/10.21121/eab.983220
8. Avkıran, N. K., Tone, K. ve Tsutsui, M. (2008). Bridging radial and non-radial measures of efficiency in DEA. Annals of Operations Research, 164, 127-138. https://doi.org/10.1007/s10479-008-0356-8

9. Aytekin, A., Ecer, F., Korucuk, S. ve Karamaşa, Ç. (2022). Global innovation efficiency assessment of EU member and candidate countries via DEA-EATWIOS multi-criteria methodology. Technology in Society, 68, 101896. https://doi.org/10.1016/j.techsoc.2022.101896
10. Banker, R. D., Charnes, A. ve Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078-1092. https://doi.org/10.1287/mnsc.30.9.1078
11. Birleşmiş Milletler (2023), Economic Development in Africa Report-2023. https://unctad.org/publication/economic-development-africa-report-2023 Birleşmiş Milletler, (2010), Economic Development in Africa Report -2010. https://unctad.org/publication/economic-development-africa-report-2010
12. Carayannis, E. G., Grigoroudis, E. ve Goletsis, Y. (2016). A multilevel and multistage efficiency evaluation of innovation systems: A multiobjective DEA approach. Expert Systems with Applications, 62, 63-80. https://doi.org/10.1016/j.eswa.2016.06.017
13. Chakamera, C., ve Pisa, N. M. (2021). Associations between logistics and economic growth in Africa. South African Journal of Economics, 89(3), 417-438. https://doi.org/10.1111/saje.12272
14. Chang, Y. T., Park, H. S., Jeong, J. B. ve Lee, J. W. (2014). Evaluating economic and environmental efficiency of global airlines: A SBM-DEA approach. Transportation Research Part D: Transport and Environment, 27, 46-50. https://doi.org/10.1016/j.trd.2013.12.013
15. Charnes, A., Cooper, W. W. ve Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444. https://doi.org/10.1016/0377-2217(78)90138-8
16. Charnes, A., Cooper, W. W., Seiford, L. ve Stutz, J. (1982). A multiplicative model for efficiency analysis. Socio-Economic Planning Sciences, 16(5), 223-224. https://doi.org/10.1016/0038-0121(82)90029-5
17. Charnes,A., Cooper, W.W., Golany, B., Seiford, L.ve Stutz, J. (1985). Foundations of data envelopment analysis and pareto-koopmans empirical production functions, Journal of Econometrics, 30, 91–107. https://doi.org/10.1016/0304-4076(85)90133-2
18. Chiu, Y. H., Chen, Y. C. ve Bai, X. J. (2011). Efficiency and risk in Taiwan banking: SBM super-DEA estimation. Applied economics, 43(5), 587-602. https://doi.org/10.1080/00036840802599750
19. Coelli, T. (1998). A multi-stage methodology for the solution of orientated DEA models. Operations Research Letters, 23(3-5), 143-149. https://doi.org/10.1016/S0167-6377(98)00036-4
20. Çalık, A., Erdebilli, B. ve Özdemir, Y. S. (2023). Novel integrated hybrid multi-criteria decision-making approach for logistics performance index. Transportation Research Record, 2677 (2), 1392-1400. https://doi.org/10.1177/03611981221113314
21. D’Aleo, V. ve Sergi, B. S. (2017). Does logistics influence economic growth? The European experience. Management Decision, 55(8), 1613-1628. https://doi.org/10.1108/MD-10-2016-0670
22. Deprem, Ö. (2008). Veri zarflama analizi ve bir uygulama (Yayımlanmamış Yüksek Lisans Tezi). Yıldız Teknik Üniversitesi Fen Bilimleri Enstitüsü. İstanbul.
23. Dünya Bankası (2018). Connecting to compete 2018 trade logistics in the global economy the logistics performance index and its indicators. https://www.worldbank.org/en/news/infographic/2018/07/24/logistics-performance-index-2018
24. Dünya Bankası (2023a). Lojistik Performans İndeksi 2023, chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://lpi.worldbank.org/sites/default/files/2023-04/LPI_2023_report_with_layout.pdf
25. Dünya Bankası (2023b). Lojistik Performans İndeksi (LPI). https://lpi.worldbank.org/international/aggregated-ranking
26. Dünya Bankası Meta Veri Sözlüğü, https://databank.worldbank.org/metadataglossary/logistics-performance-index-(lpi)/series/LP.LPI.OVRL.XQ
27. Dünya Ekonomik Forumu. (2020). Küresel Rekabetçilik Raporu, World Economic Forum. URL: https://www.weforum.org/publications/the-global-competitiveness-report-2020/.
28. Ekici, Ş. Ö., Kabak, Ö. ve Ülengin, F. (2016). Linking to compete: Logistics and global competitiveness interaction. Transport Policy, 48, 117-128. https://doi.org/10.1016/j.tranpol.2016.01.015
29. Färe, R., Grosskopf, S. ve Whittaker, G. (2007). Network dea. Zhu, J. ve Cook, W.D. (Ed), Modeling data ırregularities and structural complexities in data envelopment analysis içinde (s. 209-240), Springer Link.
30. Farrell, M.J. (1957) The measurement of productive efficiency. J. Roy. Statist. Soc. Set. A, III (1957), 253-290.
31. Gavurova, B., Kocisova, K., Behun, M. ve Tarhanicova, M. (2018). Environmental performance in OECD countries: A non-radial DEA approach. Acta Montanistica Slovaca, 23(2), 206-2015. https://actamont.tuke.sk/ams2018.html
32. Gök Kısa, A. C. ve Ayçin, E. (2019). OECD ülkelerinin lojistik performanslarının SWARA Tabanlı EDAS yöntemi ile değerlendirilmesi. Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Faskültesi Dergisi, 9 (1), 301-325. https://doi.org/10.18074/ckuiibfd.500320
33. Guo, P. ve Tanaka, H. (2001). Fuzzy DEA: a perceptual evaluation method. Fuzzy Sets and Systems, 119(1), 149-160. https://doi.org/10.1016/S0165-0114(99)00106-2
34. Hadžikadunic, A., Stevic, Ž., Badi, I., ve Roso, V. (2023). Evaluating the logistics performance index of European Union Countries: An integrated multi-criteria decision-making approach utilizing the Bonferroni Operator. International Journal of Knowledge and Innovation Studies, 1, 44-59. https://doi.org/10.56578/ijkis010104. Hemmati, M., Dalghandi, S. ve Nazari, H. (2013). Measuring relative performance of banking industry using a DEA and TOPSIS. Management Science Letters, 3(2), 499-504. https://doi.org/10.5267/j.msl.2012.12.025 Hua, Z. ve Bian, Y. (2007). DEA with undesirable factors. Modeling data irregularities and structural complexities in data envelopment analysis içinde (s.103-121), Springer Link.
35. Işık, O., Aydın, Y. ve Kosaroglu, S. M. (2020). The assessment of the logistics performance index of CEE countries with the new combination of SV and MABAC methods. LogForum, 16(4), 549-559. http://doi.org/10.17270/J.LOG.2020.504
36. Jahanshahloo, G. R., Lotfi, F. H., Shoja, N., Tohidi, G. ve Razavyan, S. (2005). Undesirable inputs and outputs in DEA models. Applied Mathematics and Computation, 169 (2), 917-925. https://doi.org/10.1016/j.amc.2004.09.069
37. Kara, M. Tayfur, L. ve Basık, H. (2009). Küresel ticarette lojistik üslerin önemi ve Türkiye. Mustafa Kemal Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, Cilt: 6. Sayı: 11. ss. 69-84. https://dergipark.org.tr/tr/pub/mkusbed/issue/19558/208484
38. Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z. ve Antucheviciene, J. (2021). Determination of objective weights using a new method based on the removal effects of criteria (MEREC). Symmetry, 13(4), 525. https://doi.org/10.3390/sym13040525
39. Kılınç, E., Fidan, O. ve Mutlu, H. M. (2019). Comparison of Turkey, China and Russian Federation according to the Logistics Performance Index. Uluslararası Ekonomik Araştırmalar Dergisi, 5(2), 17-34. https://dergipark.org.tr/tr/pub/ead/issue/50818/662626
40. Knoema (2022). Global GHG and CO2 emissions, 1970 - 2021. https://knoema.com/EDGARED2019/global-ghg-and-co2-emissions-1970-2021?accesskey=onvwcwf
41. Markovits-Somogyi, R. ve Bokor, Z. (2014). Assessing the logistics efficiency of European countries by using the DEA-PC methodology. Transport, 29(2), 137-145. https://doi.org/10.3846/16484142.2014.928787 Marti, L. Martin, J. Puertas, R. (2017). A DEA- Logistics Performance Index. Journal of Applied Economics, 20(1), 169-192. https://doi.org/10.1016/S1514-0326(17)30008-9
42. Martí, L., Puertas, R. ve García, L. (2014). The importance of the Logistics Performance Index in international trade. Applied Economics, 46(24), 2982-2992. https://doi.org/10.1080/00036846.2014.916394
43. Mešić, A., Miškić, S., Stević, Ž. ve Mastilo, Z. (2022). Hybrid MCDM solutions for evaluation of the logistics performance index of the Western Balkan countries. Economics, 10(1), 13-34. https://doi.org/10.2478/eoik-2022-0004
44. Miškić, S., Stević, Ž., Tadić, S., Alkhayyat, A. ve Krstić, M. (2023). Assessment of the LPI of the EU countries using MCDM model with an emphasis on the importance of criteria. World Review of Intermodal Transportation Research, 11(3), 258-279. https://doi.org/10.1504/WRITR.2023.132501
45. Mousavi-Nasab, S. H. ve Sotoudeh-Anvari, A. (2017). A comprehensive MCDM-based approach using TOPSIS, COPRAS and DEA as an auxiliary tool for material selection problems. Materials & Design, 121, 237-253. https://doi.org/10.1016/j.matdes.2017.02.041
46. Munim, Z. H. ve Schramm, H. J. (2018). The impacts of port infrastructure and logistics performance on economic growth: the mediating role of seaborne trade. Journal of Shipping and Trade, 3(1), 1-19. https://doi.org/10.1186/s41072-018-0027-0
47. Ngangaji, M. M. F. (2019). An assessment of container terminal efficiency in East Africa ports using data envelopment analysis (DEA): the case of Dar es Salaam & Mombasa ports (Yayımlanmamış Yüksek Lisans Tezi) World Maritime University, Tanzanya.
48. Nguyen, C. D. T., Luong, B. T. ve Hoang, H. L. T. (2021). The impact of logistics and infrastructure on economic growth: Empirical evidence from Vietnam. The Journal of Asian Finance, Economics and Business, 8(6), 21-28. https://doi.org/10.13106/jafeb.2021.vol8.no6.0021
49. Odu, G. O. (2019). Weighting methods for multi-criteria decision making technique. Journal of Applied Sciences and Environmental Management, 23(8), 1449-1457. https://dx.doi.org/10.4314/jasem.v23i8.7
50. Ojala, L. ve Celebi, D. (2015). The World Bank’s Logistics Performance Index (LPI) and drivers of logistics performance. Proceeding of MAC-EMM, OECD, 3-30. https://www.semanticscholar.org/paper/The-World-Bank%27s-Logistics-Performance-Index-(LPI)-Ojala-%C3%87elebi/e9d3433e9e41914974e8f2f507ecbfcb962e0317
51. Ozmen, M. (2019). Logistics competitiveness of OECD countries using an improved TODIM method. Sādhanā, 44, 1-11. https://doi.org/10.1007/s12046-019-1088-y
52. Peters, M. L. ve Zelewski, S. (2006, April). Efficiency analysis under consideration of satisficing levels for output quantities. Proceedings of the 17th Annual Conference of the Production and Operations Management Society (POMS) içinde, 28 (1.05). https://www.researchgate.net/publication/331155463_Efficiency_Analysis_under_Consideration_of_Satisficing_Levels_for_Output_Quantities
53. Rashidi, K. ve Cullinane, K. (2019). Evaluating the sustainability of national logistics performance using Data Envelopment Analysis. Transport Policy, 74, 35-46. https://doi.org/10.1016/j.tranpol.2018.11.014
54. Rezaei, J., van Roekel, W. S. ve Tavasszy, L. (2018). Measuring the relative importance of the logistics performance index indicators using Best Worst Method. Transport Policy, 68, 158-169. https://doi.org/10.1016/j.tranpol.2018.05.007
55. Rizet, C. Browne, M. Cornelis, E. ve Leonardi, J. (2012). Assessing carbon footprint and energy efficiency in competing supply chains: Review – case studies and benchmarking. Transportation Research Part D: Transport And Environment, 17(4), 293–300. https://doi.org/10.1016/j.trd.2012.01.002
56. Sadjadi, S. J. ve Omrani, H. (2010). A bootstrapped robust data envelopment analysis model for efficiency estimating of telecommunication companies in Iran. Telecommunications Policy, 34(4), 221-232. https://doi.org/10.1016/j.telpol.2009.09.003
57. Sadjadi, S. J., Omrani, H., Abdollahzadeh, S., Alinaghian, M. ve Mohammadi, H. (2011). A robust super-efficiency data envelopment analysis model for ranking of provincial gas companies in Iran. Expert Systems with Applications, 38(9), 10875-10881. https://doi.org/10.1016/j.eswa.2011.02.120
58. Said, M. ve Fatima-Zahra, D. (2018). L’impact de la logistique et du transport sur la performance econmique. Laboratoire de Recherche en Management des Organisations (LАREMO), 2, 1-33. https://doi.org/10.48430/IMIST.PRSM/remac-n2.12344
59. Seiford, L. M. ve Zhu, J. (2002). Modeling undesirable factors in efficiency evaluation. European journal of operational research, 142(1), 16-20. https://doi.org/10.1016/S0377-2217(01)00293-4
60. Selamzade, F., Ersoy, Y., Ozdemir, Y. ve Celik, M. Y. (2023). Health efficiency measurement of OECD countries against the COVID-19 pandemic by using DEA and MCDM methods. Arabian Journal for Science and Engineering, 48(11), 15695-15712. https://doi.org/10.1007/s13369-023-08114-y
61. Senir, G. (2021). Comparison of domestic logistics performances of Turkey and European Union countries in 2018 with an integrated model. LogForum, 17(2), 193-204. http://doi.org/10.17270/J.LOG.2021.576
62. Sezer, S. (2016). Lojistik sektörünün ekonomiye etkisi: OECD ülkeleri üzerine bir uygulama (Yayımlanmamış Doktora Tezi). Anadolu Üniversitesi Sosyal Bilimler Enstitüsü. Eskişehir.
63. Shamsuzzoha, A., Ehrs, M., Addo-Tenkorang, R., Nguyen, D. ve Helo, P. (2013). Performance Evaluation Of Tracking And Tracing For Operations. Int. J. Shipping and Transport Logistics, 5(1), 31-54. https://doi.org/10.1504/IJSTL.2013.050587
64. Shokouhi, A. H., Hatami-Marbini, A., Tavana, M., ve Saati, S. (2010). A robust optimization approach for imprecise data envelopment analysis. Computers & Industrial Engineering, 59(3), 387-397. https://doi.org/10.1016/j.cie.2010.05.011
65. Singh, S. (2023), Logistics Market Research Report Informatiın By Transportation Type (Airways, Waterways, Railways and Roadways), By Logistics Type (First Party, Second Party, and Third Party), By End User (Industrial and Manufacturing, Retail, Healthcare, and Oi l& Gas), And By Region (North America, Europe, Asia-Pacific, and Rest of the World)-Market Forecast Till 2030. https://www.marketresearchfuture.com/reports/logistics-market-5076
66. Sueyoshi, T. (2000). Stochastic DEA for restructure strategy: an application to a Japanese petroleum company. Omega, 28(4), 385-398. https://doi.org/10.1016/S0305-0483(99)00069-9
67. Toloo, M., Tone, K. ve Izadikhah, M. (2023). Selecting slacks-based data envelopment analysis models. European Journal of Operational Research, 308(3), 1302-1318. https://doi.org/10.1016/j.ejor.2022.12.032 Tone, K. (2001). A slacks- based measure of efficiency in data envelopment analysis. European Journal Of Operation Research, 130(3), 498-509. https://doi.org/10.1016/S0377-2217(99)00407-5
68. Tone, K. ve Tsutsui, M. (2009). Application of network DEA model to vertically integrated electric utilities. GRIPS Discussion Papers, pp. 07-03). National Graduate Institute for Policy Studies. https://ideas.repec.org/p/ngi/dpaper/07-03.html
69. Twrdy, E. (2003). Container traffic in European ports. Integrated Transport Review, 16(2), 111-115. https://hrcak.srce.hr/102334
70. Uludağ, A. S., ve Şahin, E. (2023). Verimlilik analizlerinde tatmin seviyesi tayin edilmemiş EATWIOS mu? OCRA mı?: Sağlık turizmi üzerine bir araştırma. Verimlilik Dergisi, 57(2), 289-312. https://doi.org/10.51551/verimlilik.1155635
71. Ulutaş, A. ve Karaköy, Ç. (2019). An analysis of the logistics performance index of EU countries with an integrated MCDM model. Economics and Business Review, 5(4), 49-69. https://doi.org/10.18559/ebr.2019.4.3
72. Ulutaş, A. ve Karaköy, Ç. (2021). Evaluation of LPI values of transition economies countries with a grey MCDM model. Handbook of research on applied aı for ınternational business and marketing applications içinde (s. 499-511). IGI Global.
73. UNCTAD. (2023). Trade and Development Report. United Nations. https://unctad.org/publication/trade-and-development-report-2023
74. Ustalı, N. K. ve Tosun, Ö. (2020). Investigation of logistic performance of G-20 countries using data envelopment analysis and malmquist total factor productivity analysis. Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 7(3), 755-781. https://doi.org/10.30798/makuiibf.792066 van Dyck, G. K. (2015). Assessment of port efficiency in West Africa using data envelopment analysis. American Journal of Industrial and Business Management, 5(04), 208. https://doi.org/10.4236/ajibm.2015.54023
75. Wang, C. N., Dang, T. T. ve Wang, J. W. (2022). A combined data envelopment analysis (DEA) and grey based multiple criteria decision making (G-MCDM) for solar PV power plants site selection: A case study in Vietnam. Energy Reports, 8, 1124-1142. https://doi.org/10.1016/j.egyr.2021.12.045
76. Yıldırım, M. ve Ayvaz, B. (2019). Ülkelerin lojistik performanslarının veri zarflama analizi ile ölçümü. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 18(35), 57-73. https://dergipark.org.tr/tr/pub/ticaretfbd/issue/55970/565306
77. Yıldırım, B. F. ve Adıgüzel Mercangöz, B. (2020). Evaluating the logistics performance of OECD countries by using fuzzy AHP and ARAS-G. Eurasian Economic Review, 10(1), 27-45. https://doi.org/10.1007/s40822-019-00131-3
78. Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X
79. Zheng, Z. (2021). Energy efficiency evaluation model based on DEA-SBM-Malmquist index. Energy Reports, 7, 397-409. https://doi.org/10.1016/j.egyr.2021.10.020