Managing risk of delay in logistics deliveries using expected value method
DOI:
https://doi.org/10.5585/exactaep.2021.8636Keywords:
Recycling, Wind blades, Wind power, Life cycle.Abstract
This paper develops a decision-making assistant tool for managing the risk of delay in commercial outbound deliveries based on expected value (EV) and Statistical Learning (EL). This tool allows decision takers to prioritize deliveries based both on their quantities and probability of delaying. In this model prioritizing deliveries based on their EV results in minimizing impact on On-Time Delivery (OTD) KPI. The probabilities used on this model stem from a logistic regression model. The coefficients were used to evaluate which variables most impact on the chance of delaying. A simulation was executed on the historical data of multinational electronics company to test the applicability of this model. The quality of the predictions was tested using standard methodology for testing statistical learning models of the literature. Lastly the prioritization based on VE was tested confronting the predicted delay against real delay in each of five risk groups. The results show that the calculated probabilities were a reliable input and that the EV prioritization model allowed to find the high-risk group.Downloads
References
Beamon, B. M. (1999). Measuring supply chain performance. International Journal of Operations & Production Management, 19(3), 275–292. https://doi.org/10.1108/01443579910249714.
Berssaneti, F. T., & Carvalho, M. M. (2015). Identification of variables that impact project success in Brazilian companies. International Journal of Project Management, 33(3), 638–649. https://doi.org/10.1016/j.ijproman.2014.07.002.
Bertrand, J. W. M., & Fransoo, J. (2002). Modelling and Simulation: Operations management research methodologies using quantitative modelling. International Journal of Operations & Production Management, 22(2), 241–264. https://doi.org/10.1108/01443570210414338.
Cai, J., Liu, X., Xiao, Z., & Liu, J. (2009). Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment. Decision Support Systems, 46(2), 512–521. https://doi.org/10.1016/j.dss.2008.09.004.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. In Bayesian Forecasting and Dynamic Models (Second Edi, Vol. 1). https://doi.org/10.1007/b94608.
Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied Logistic Regression. In John Wiley & Sons (3rd Editio). https://doi.org/10.1002/9781118548387.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2017). An Introduction to Statistical Learning: With Applications in R (8th Editio; G. Casella, S. Fienberg, & I. Olkin, Eds.). https://doi.org/10.1007/978-7-4614-7138-7.
Kuhn, N., & Jamadagni, N. (2017). Application of Machine Learning Algorithms to Predict Flight Arrival Delays. 1–6.
Lee, H. L., Padmanabhan, V., & Whang, S. (1997). Information Distortion in a Supply Chain: The Bullwhip Effect. Management Science, 43(4), 546–558. https://doi.org/10.1287/mnsc.43.4.546.
Lessan, J., Fu, L., & Wen, C. (2018). A hybrid Bayesian network model for predicting delays in train operations. Computers and Industrial Engineering, (March). https://doi.org/10.1016/j.cie.2018.03.017.
Long, J. S. (1997). Regression models for categorical and limited dependent variables. American Journal of Sociology, Vol. 103, p. 328. https://doi.org/10.1086/231290.
Long, W. J., Griffith, J. L., Spelker, H. P., & D’Agostino, R. B. (1993). A Comparison of Logistic Regression to Decision-Tree Induction in Medical Domain. Computers and Biomedical Research, (26), 74–97.
Mukherjee, A., Grabbe, S. R., & Sridhar, B. (2014). Predicting Ground Delay Program At An Airport Based On Meteorological Conditions. 14th AIAA Aviation Technology, Integration, and Operations Conference, (June), 1–18. https://doi.org/10.2514/6.2014-2713.
Neely, A., Gregory, M., & Platts, K. (1995). Performance measurament system design: A literature review and research agenda. International Journal of Operations & Production Management, 15(4), 80–116. https://doi.org/10.1108/01443579510083622.
Ni, J., Wang, X., & Li, Z. (2017). Flight Delay Prediction using Temporal and Geographical Information. 1–4. Retrieved from https://cseweb.ucsd.edu/classes/wi17/cse258-a/reports/a032.pdf.
Patah, L. A., & Vargas Neto, D. M. (2016). Avaliação Da Relação Entre a Virtualidade De Equipes E O Desempenho Operacional De Projetos: Uma Análise Quantitativa. Revista de Gestão e Projetos - GeP, 7(2). https://doi.org/10.1017/CBO9781107415324.004.
Raschka, S., & Mirjalili, V. (2019). Python Machine Learning (3rd ed.). Retrieved from https://www.packtpub.com/product/python-machine-learning-third-edition/9781789955750.
Shepherd, C., & Günter, H. (2006). Measuring supply chain performance: current research and future directions. International Journal of Productivity and Performance Management, 55(3/4), 242–258. https://doi.org/10.1108/17410400610653219.
Sterman, J. D. (1989). Modeling Managerial Behavior: Misperceptions of Feedback in a Dynamic Decision Making Experiment Author(s): MODELING MANAGERIAL BEHAVIOR: MISPERCEPTIONS OF FEEDBACK IN A DYNAMIC DECISION MAKING EXPERIMENT*. Source: Management Science MANAGEMENT SCIENCE, 35(3), 321–339. https://doi.org/10.1287/mnsc.35.3.321.
Takagi, Y., Mizuno, O., & Kikuno, T. (2005). An Empirical Approach to Characterizing Risky Software Projects Based on Logistic Regression Analysis. Empirical Software Engineering, 10(4), 495–515. https://doi.org/10.1007/s10664-005-3864-z.
Tibshirani, R. (1996). Regression Shrinkage and Selection Via the Lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x.
Vieira, J. G. V., Yoshizak, H. T. Y., & Ho, L. L. (2015). The effects of collaboration on logistical performance and transaction costs. International Journal of Business Science & Applied Management (IJBSAM), 10(1), 1–14.
Wang, Z., Liang, M., & Delahaye, D. (2018). A hybrid machine learning model for short-term estimated time of arrival prediction in terminal manoeuvring area. Transportation Research Part C: Emerging Technologies, 95(January), 280–294. https://doi.org/10.1016/j.trc.2018.07.019.
Yaghini, M., Khoshraftar, M. M., & Seyedabadi, M. (2013). Railway passenger train delay prediction via neural network model. Journal of Advanced Transportation, 47(3), 355–368. https://doi.org/10.1002/atr.193.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 Exacta
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.