Abstract
In an OEM milk-run pickup operation over a road network, the manufacturing of components by suppliers is subject to varying tardiness levels on order release dates. Such faults are traditionally diagnosed and treated with a “fail and fix” strategy (FAF), when a failure is recognized as a sudden disruption problem. In practice, quite often a degradation phase occurs in the manufacturing process before a disruption happens. But, within the Industry 4.0 paradigm, it is necessary to prevent faults that may occur at some time in the future, changing the traditional FAF response to a robust predicting and preventing strategy. In such a context, faults must be forecasted in a dynamic way, over a Big Data basis, and the resulting forecasts must be released at once to the logistics agent to allow him to review his milk-run collecting program in due time, thus leading to a better integrated performance. An approximate method to forecast tardiness levels in supplier’s production, intended to help the related logistic operators to reschedule their services in due time, is proposed and illustrated with a case study.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Hopp, W.J., Iravani, S.M., Xu, W.L.: Vertical flexibility in supply chains. Manag. Sci. 56(3), 495–502 (2010)
Hajji, A., Gharbi, A., Kenne, J.P., Pellerin, R.: Production control and replenishment strategy with multiple suppliers. Eur. J. Oper. Res. 208, 67–74 (2011)
DeMatta, R., Miller, T.: Production and inter-facility transportation scheduling for a process industry. Eur. J. Oper. Res. 158, 72–88 (2004)
Selvarajah, E., Steiner, G.: Approximation algorithms for the supplier’s supply chain scheduling problem to minimize delivery and inventory holding costs. Oper. Res. 57(2), 426–438 (2009)
Chen, Z.L.: Integrated production and outbound distribution scheduling: review and extensions. Oper. Res. 58(1), 130–148 (2010)
Larsen, A.: The dynamic vehicle routing problem. Ph.D. Dissertation, Technical University of Denmark (2000)
Yang, J., Jaillet, P., Mahmassani, H.: Real-time multivehicle truckload pickup and delivery problems. Transportation Science 38(2), 135–148 (2004)
Güner, A.R., Murat, A., Chinnam, R.B.: Dynamic routing for milk-run tours with time windows in stochastic time-dependent networks. Transp. Res. Part E 97, 251–267 (2017)
Novaes, A.G., Lima Jr., O.F., Luna, M., Bez, E.T.: Mitigating supply chain tardiness risks in OEM milk-run operations. In: Freitag, M., Kotzab, H., Pannek, J. (eds.) Dynamics in Logistics: Proceedings of the 5th Conference LDIC 2016, vol. 1, pp. 141–150. Springer, Cham (2016)
Novaes, A.G., Lima, O.F., Montoya, G.M.: Forecasting manufacturing tardiness in OEM milk-run operations within the industry 4.0 framework. In: Freitag, M., Kotzab, H., Pannek, J. (eds.) Dynamics in Logistics – Proceedings of the 6th International Conference LDIC 2018, pp. 305–309. Springer, Heidelberg (2018)
Ouelhadj, D., Petrovic, S.: A survey of dynamic scheduling in manufacturing systems. J. Sched. 12, 417–431 (2009)
Chuah, K.H.: Optimization and simulation of just-in-time supply pickup and delivery systems. Ph. D. Dissertation, University of Kentucky (2004)
Brar, G.S., Saini, G.: Milk run logistics: literature review and directions. In: Proceedings of the World Congress of Engineering 2011. London (2011)
Novaes, A.G., Bez, E.T., Burin, P.J., Aragão, D.P.: Dynamic milk-run OEM operations in over-congested traffic conditions. Comput. Ind. Eng. 88, 326–340 (2015)
Basseville, M., Nikiforov, I.V.: Detection of Abrupt Changes: Theory and applications. Prentice-Hall, New Jersey (1993)
Muenchhof, M., Beck, M., Isermann, R.: Fault-tolerant actuators and drive: structures, fault detection principles and applications. Ann. Rev. Control 33, 136–148 (2009)
Xu, Y., Sun, Y., Wan, J., Liu, X., Song, Z.: Industrial big data for fault diagnosis: taxonomy, review, and applications. IEEE Access 5, 17368–17380 (2017)
Tran, V.T., Pham, H.T., Yang, B.S., Nguyen, T.T.: Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine. Mech. Syst. Signal Process. 32, 320–330 (2012)
Branke, J., Mattfeld, D.C.: Anticipation and flexibility in dynamic scheduling. Int. J. Prod. Res. 43(15), 3103–3129 (2005)
Koscianski, A., Souza de Cursi, J.E.: Physically constrained neural networks and regularization of inverse problems. In: 6th World Congress of Structural and Multidisciplinary Optimization. Rio de Janeiro (2005)
Acknowledgements
This research has been supported by the Brazilian CNPq Foundation, Projects 302412/2016-6 and 470899/2013-1.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Novaes, A.G.N., Lima, O.F., De Cursi, J.E.S., Arias, J.A.C., Santos, J.B.S. (2020). Predictive Manufacturing Tardiness Inference in OEM Milk-Run Operations. In: Freitag, M., Haasis, HD., Kotzab, H., Pannek, J. (eds) Dynamics in Logistics. LDIC 2020. Lecture Notes in Logistics. Springer, Cham. https://doi.org/10.1007/978-3-030-44783-0_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-44783-0_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-44782-3
Online ISBN: 978-3-030-44783-0
eBook Packages: EngineeringEngineering (R0)