0703f2b0-23d7-4a8f-8b8c-4ec7eaae351120240404072552914wseas:wseasmdt@crossref.orgMDT DepositWSEAS TRANSACTIONS ON COMPUTERS2224-28721109-275010.37394/23205http://wseas.org/wseas/cms.action?id=40264220244120242310.37394/23205.2024.23https://wseas.com/journals/computers/2024.phpYunus EmreYeti̇şDepartment of Industrial Engineering, Sakarya University, 54187, Esentepe Campus Serdivan-Sakarya, TURKEYSafiyeTurgayDepartment of Industrial Engineering, Sakarya University, 54187, Esentepe Campus Serdivan-Sakarya, TURKEYBi̇lalErdemi̇rDepartment of Industrial Engineering, Sakarya University, 54187, Esentepe Campus Serdivan-Sakarya, TURKEYIn the third-party logistics (3PL) environment, it is very important to reduce damage parameters, increase operational efficiency and reduce costs. This study aims to develop strategies for reshaping 3P operations by analyzing the parameters involved in damage control with machine learning. The logistics sector is gradually growing in the world and the potential of the sector is better understood over time. Damage to products in the logistics sector, especially during transportation and storage, not only causes financial losses but also affects customer productivity and operational efficiency. With the use of artificial intelligence techniques, it is possible to determine consumer expectations, predict damage losses, and develop innovative strategies by applying machine learning algorithms. At the same time, options such as driverless vehicles, robots used in storage and shelves, and the easy use of big data within the system, which have emerged with artificial intelligence, minimize errors in the logistics sector. Thanks to the use of artificial intelligence in the logistics sector, businesses are more efficient. This study includes an estimation study in the field of error parameters for the logistics service sector with machine learning methods. In the application, real data of a 3PL company for the last 5 years is used. For the success of 3PL companies, warehousing and undamaged delivery of products are of great importance. The fewer damaged products they send, the more they increase their value. The company examined in the study kept its damage data and wanted it to be analyzed so that it could take precautions accordingly and follow a more profitable path. For this reason, the study focuses on data on errors and damages. This study shows what kind of problems can occur in such a company and how the 3PL company can evaluate the problems to increase customer service quality and cost efficiency.44202444202412232https://wseas.com/journals/computers/2024/a045105-002(2024).pdf10.37394/23205.2024.23.2https://wseas.com/journals/computers/2024/a045105-002(2024).pdf10.1016/j.ijpe.2021.108340Duong, Q.H., Zhou, L., Meng, M., Nguyen, T.V., Ieromonachou, P., Nguyen, D.T., Understanding product returns: A systematic literature review using machine learning and bibliometric analysis, International Journal of Production Economics, Vol. 243, January 2022, 108340. 10.1016/j.tre.2021.102302Sun, Y., Kirtonia, S., Chen, Z.L., A survey of finished vehicle distribution and related problems from an optimization perspective, Transportation Research Part E: Logistics and Transportation Review, Vol. 149, May 2021, 102302 10.1016/s0968-090x(00)00042-5Golob, T.F., Regan, A.C., Impacts of information technology on personal travel and commercial vehicle operations: research challenges and opportunities, Transportation Research Part C: Emerging Technologies, Vol. 9, Issue 2, April 2001, Pages 87-121. 10.1016/j.tre.2020.102217Kam-Fung Cheung, K.F., Bell, M.G.H., Bhattacharjya, J., Cybersecurity in logistics and supply chain management: An overview and future research directions, Transportation Research Part E: Logistics and Transportation Review, Vol. 146, February 2021, 102217. 10.1016/j.jbusres.2022.01.037Raj, A., Mukherjee, A.A., Jabbour, A.B.L.S., Srivastava, S.K., Supply chain management during and post-COVID-19 pandemic: Mitigation strategies and practical lessons learned, Journal of Business Research, Vol. 142, March 2022, Pages 1125-1139 10.1016/j.tre.2010.09.008Rajesh, R., Pugazhendhi, S., Ganesh, K., Muralidharan, C., Sathiamoorthy, R. Influence of 3PL service offerings on client performance in India, Transportation Research Part E: Logistics and Transportation Review, Vol. 47, Issue 2, March 2011, Pages 149-165 10.1016/j.jtrangeo.2011.12.001Lehtinen, J., Bask, A.H., Analysis of business models for potential 3Mode transport corridor, Journal of Transport Geography, Vol. 22, May 2012, Pages 96-108 10.1016/j.ejor.2003.08.016Gunasekaran, A., Ngai, E.W.T., Information systems in supply chain integration and management, European Journal of Operational Research, Vol. 159, Issue 2, 1 December 2004, Pages 269-295. 10.23977/datake.2023.010101Kayali, S., Turgay, S., Predictive Analytics for Stock and Demand Balance Using Deep Q-Learning Algorithm. Data and Knowledge Engineering, (2023) Vol. 1: 1-10. 10.1016/j.ijpe.2022.108454Ashraf,M.H., Yuwen, C., Yalcin, M.G., Minding Braess Paradox amid third-party logistics hub capacity expansion triggered by demand surge, International Journal of Production Economics, Vol. 248, June 2022, 108454 10.23977/socmhm.2023.040406Turgay, S., Güneş, A., Torkul, Y.E., Torkul, B., Designing and Implementing Effective Hospital Capacity Management Systems with SEM Analysis. Social Medicine and Health Management, (2023) Vol. 4: 37-46. 10.1016/j.tre.2020.102217Cheng, K.F., Bell, M.G.H., Bhattacharjya, J., Cybersecurity in logistics and supply chain management: An overview and future research directions, Transportation Research Part E: Logistics and Transportation Review, Volume 146, February 2021, 102217. 10.1080/19427867.2021.1923305Amiri, A.M., Ferguson, M.R., Razavi, S., Adoption patterns of autonomous technologies in Logistics: evidence for Niagara Region, Transportation Letters, Volume 14, Issue 7, September 2022, Pages 685-696. 10.1016/j.jii.2023.100486Varriale, V., Cammarano, A., Michelino, F., Caputo, M., Integrating blockchain, RFID and IoT within a cheese supply chain: A cost analysis, Journal of Industrial Information Integration, Volume 34, August 2023, 100486 10.1016/j.tele.2018.11.006Casino, F., Dasaklis, T.K., Patsakis, C., A systematic literature review of blockchainbased applications: Current status, classification and open issues, Telematics and Informatics, Vol. 36, March 2019, pp. 55-81. İlter, İ., Turgay, S., Privacy Enhancement with Perturbation Method for Multidimensional Grid, Journal of Artificial Intelligence Practice (2023), Vol. 6 Num. 4. 10.1080/19427867.2021.1923305Amiri, A.M., Ferguson, M.R., Razavi, S., Adoption patterns of autonomous technologies in Logistics: evidence for Niagara Region, Transportation Letters, Vol. 14, Issue 7, September 2022, pp.685-696 10.37394/23209.2023.20.21Bhaktiar, P., Mursitama, T.N., So, I.G., Abdinagoro, S.B., Dwidienawati, D., Networking Capability and Learning Capability as Determinants of Firm Performance Mediated by Business Model Innovation, WSEAS Transactions on Information Science and Applications, vol. 20, pp. 178-188, 2023, https://doi.org/10.37394/23209.2023.20.21. 10.1080/17450101.2022.2158041Gregson, N., Work, labour and mobility: opening up a dialogue between mobilities and political economy through mobile work, Mobilities, 18:6, 888-902, 2023. 10.37394/23205.2021.20.19Agrawal, V., Goswami, P.K., Sarma, K.K., Week-ahead Forecasting of Household Energy Consumption Using CNN and Multivariate Data", WSEAS Transactions on Computers, vol. 20, pp. 182-188, 2021, https://doi.org/10.37394/23205.2021.20.19. 10.1016/j.rtbm.2022.100868Paridaens, H., Notteboom, T., Logistics integration strategies in container shipping: A multiple case-study on Maersk Line, MSC and CMA CGM, Research in Transportation Business & Management, Vol. 45, Part C, December 2022, 100868. 10.1016/j.jbusres.2022.01.037Raj, A., Mukherjee, A.A., Jabbour, A.B.L.S., Srivastava, S.K., Supply chain management during and post-COVID-19 pandemic: Mitigation strategies and practical lessons learned, Journal of Business Research, Volume 142, March 2022, Pages 1125-1139. 10.1016/j.trip.2023.100788Chatziioannou, I., Nikitas, A., G. Tzouras, P.G., Bakogiannis, E., AlvarezIcaza, L., Chias-Becerril, L., Karolemeas, C., Tsigdinos, S., Wallgren, P., Rexfelt, O., Ranking sustainable urban mobility indicators and their matching transport policies to support liveable city Futures: A MICMAC approach, Transportation Research Interdisciplinary Perspectives, Volume 18, March 2023, 100788. 10.37394/23209.2023.20.46Kumar, S., Bhupati, R.R., Sharma, H.D., Misra, Y., Failure Log Analytics for Reducing Electrical Machine Downtime using Deep Learning", WSEAS Transactions on Information Science and Applications, vol. 20, pp. 444-452, 2023, https://doi.org/10.37394/23209.2023.20.46. 10.37394/23209.2023.20.38Doutoum, A.S., Eryigit, R., Tugrul, B., Classification of Guava Leaf Disease using Deep Learning", WSEAS Transactions on Information Science and Applications, vol. 20, pp. 356-363, 2023, https://doi.org/10.37394/23209.2023.20.38. 10.1016/j.tre.2023.103299Vlachos, I., Singh, R.K., Triad structure impact on the triad resources-firm performance relationship: Theory and empirical evidence, Transportation Research Part E: Logistics and Transportation Review, Volume 179, November 2023, 103299 10.37394/23202.2021.20.38Jianhong, W., Ricardo A. Ramirez-Mendoza, R.A., Application of Interval Predictor Model Into Model Predictive Control, WSEAS Transactions on Systems, vol. 20, pp. 331- 343, 2021, https://doi.org/10.37394/23202.2021.20.38. 10.37394/23205.2021.20.7Siavash H. Sohrab, Some Implications of a Scale Invariant Model of Statistical Mechanics to Boltzmann versus Shannon Entropy in Thermodynamics and Information Theory, WSEAS Transactions on Computers, vol. 20, pp. 56-65, 2021, https://doi.org/10.37394/23205.2021.20.7. 10.37394/232018.2024.12.15Xhabafti, M., Vika, B., Sinaj, V., A Comparative Study of Statistical and Deep Learning Model-Base weather Prediction in Albania", WSEAS Transactions on Computer Research, vol. 12, pp. 151-160, 2024, https://doi.org/10.37394/232018.2024.12.15. 10.1016/j.jtrangeo.2020.102928Yang, D., Notteboom, T., Zhou, X., Spatial, temporal and institutional characteristics of entry strategies in inland container terminals: A comparison between Yangtze River and Rhine River, Journal of Transport Geography, Vol. 90, January 2021, 102928 10.1016/j.pursup.2009.12.007Selviaridis, K., Spring, M., The dynamics of business service exchanges: Insights from logistics outsourcing, Journal of Purchasing and Supply Management, Vol. 16, Issue 3, September 2010, pp. 171-184 10.1016/j.tre.2021.102302Sun, Y., Kirtonia, S., Chen, Z.L., A survey of finished vehicle distribution and related problems from an optimization perspective, Transportation Research Part E: Logistics and Transportation Review, Volume 149, May 2021, 102302. 10.5267/j.uscm.2023.9.013Shubailat, O., Al-Zaqeba, M., Madi, A & Ababneh, A. (2024). Customs intelligence and risk management in sustainable supply chain for general customs department logistics. Uncertain Supply Chain Management, 12(1), 387-398. 10.1016/j.ijpe.2011.03.006Li, L., Assessing the relational benefits of logistics services perceived by manufacturers in supply chain, International Journal of Production Economics, Vol. 132, Issue 1, July 2011, Pages 58-67