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Prediction of damage parameters of a 3PL company via data mining and neural networks

Author

Listed:
  • Feyza Gürbüz

    (Erciyes University)

  • İkbal Eski

    (Erciyes University)

  • Berrin Denizhan

    (Sakarya University)

  • Cihan Dağlı

    (Missouri University of Science and Technology)

Abstract

This study covers an application in predicting error parameters for logistic service sector (logistic service provider). In the application real data regarding the last 5 years of a 3PL company has been used. The most significant criterion of the success of 3PL companies is the storage and transference of their products to their customers with no damage. It is for this reason that the data related to errors, in particular, have been dwelled upon. Data mining and ANN techniques used in practice are in widespread use. However, examples of their application in providing logistic service are few in number. For this purpose, the classification techniques of data mining were applied to estimate 3PL damage parameters as the class attribute. Find laws, decision tree and decision forest modules (engines) of Polyanalyst are used to discover the similarities and information recovery about the 3PL damage parameters. Moreover, in this study, to compare the prediction results artificial neural networks are used. In order to catch robust and adaptive neural network approach, four types of neural predictors are operated in this research. These neural network predictors are; Back Propagation Neural Network (BBNN), General Regression Neural Network, Radial Basis Neural Network and Adaptive Neuro-Fuzzy Neural Network (ANFIS). The results of four structures have proven that an ANFIS type can be employed to estimate the damage parameters of a 3PL company. This study shows how the 3PL can evaluate and dataof damages to improve their service quality and cost effectiveness to the customers. he analysis of the data related operational errors in 3PL’s is one of the most important contributions of this study in that such analyses can guide this and other similar companies in managing and reducing the number of their future errors. Analysis of these damages with artificial intelligence may help prevent 3PLs for the future process and also predict the effect of cost for the company.

Suggested Citation

  • Feyza Gürbüz & İkbal Eski & Berrin Denizhan & Cihan Dağlı, 2019. "Prediction of damage parameters of a 3PL company via data mining and neural networks," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1437-1449, March.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:3:d:10.1007_s10845-017-1337-z
    DOI: 10.1007/s10845-017-1337-z
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    References listed on IDEAS

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    1. Aguezzoul, Aicha, 2014. "Third-party logistics selection problem: A literature review on criteria and methods," Omega, Elsevier, vol. 49(C), pages 69-78.
    2. Bin Qiao & Shenle Pan & Eric Ballot, 2019. "Dynamic pricing model for less-than-truckload carriers in the Physical Internet," Post-Print hal-01414135, HAL.
    3. Chong Ju Choi & Carla C. J. M. Millar & Caroline Y. L. Wong, 2005. "Knowledge and the State," Palgrave Macmillan Books, in: Knowledge Entanglements, chapter 0, pages 19-38, Palgrave Macmillan.
    4. Schikora, Paul F. & Godfrey, Michael R., 2003. "Efficacy of end-user neural network and data mining software for predicting complex system performance," International Journal of Production Economics, Elsevier, vol. 84(3), pages 231-253, June.
    5. Bin Qiao & Shenle Pan & Eric Ballot, 2016. "Less-than-truckload Dynamic Pricing Model in Physical Internet," Post-Print hal-01326416, HAL.
    6. David J. Hand & Heikki Mannila & Padhraic Smyth, 2001. "Principles of Data Mining," MIT Press Books, The MIT Press, edition 1, volume 1, number 026208290x, April.
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