Abstract
Industry 4.0 is having machines working connected as a collaborative community, both inside and outside the walls of the manufacturing sites. Manufacturing, sourcing, and delivery supply chains are now connected, making synchronization possible. Physical product delivery has changed significantly. Smart deliveries are now possible by directing end customer location in dynamic conditions. The capabilities of the delivery system can be simulated using discrete event simulation to compromise on-time delivery. Big data analytics are now a fundamental tool for product delivery analysis of optimal vehicle routing conditions and resource allocation. As companies have improved product delivery capabilities, more complex supply chains have been created. Analytic tools can tackle this complexity in estimating delivery time and product delivery windows under different workload scenarios.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Change history
09 August 2019
In the original version of the book, the following belated corrections are to be incorporated.
References
Cruz-Mejia O, Marmolejo J, Vasant P (2018) Ambient Intell Human Comput 9:867. https://doi.org/10.1007/s12652-017-0577-2
Petersen CG, Aase G (2004) A comparison of picking, storage, and routing policies in manual order picking. Int J Prod Econ 92:11–19. https://doi.org/10.1016/j.ijpe.2003.09.006
Kung SH, Huang C, Kuo W (2011) Order picking algorithms for a two carousel-single-crane automated storage and retrieval system. J Inf Optim Sci 32(3):180–196
Barreto L, Amaral A, Pereira T (2017) Industry 4.0 implications in logistics: an overview. Procedia Manuf 13:1245–1252
Gobbo JA, Busso CM, Gobbo SC, Carreao H (2018) Making the links among environmental protection, process safety and industry 4.0. Process Saf Environ Prot 117:372–382. https://doi.org/10.1016/j.psep.2018.05.017
Smidfelt-Roskqvist L, Winslott-Hiselius L (2016) Online shopping habits and the potential for reductions in carbon dioxide emissions from passenger transport. J Clean Prod 131:163–169. https://doi.org/10.1016/j.jclepro.2016.05.054
Fu Y, Ding J, Wang H, Wang J (2018) Two-objective stochastic flow-shop scheduling with deteriorating and learning effect in Industry 4.0 based manufacturing system. Appl Soft Comput 68:847–855
Luque A, Estela Peralta M, De las Heras A, Cordoba A (2017) State of the industry 4.0 in the andalusian food sector. Procedia Manuf 13:1199–1205. https://doi.org/10.1016/j.promfg.2017.09.195
Miyatake K, Nemoto T, Nakahari S, Hayashi K (2016) Reduction in consumers’ purchasing cost by online shopping. Tenerife, Elsevier, Canary Islands, Spain, vol. 12, pp. 656–666
Liu S, Zhang G, Want L (2018) IoT-enabled dynamic optimization for sustaniable reverse logistics. Proc CIRP 69:662–667. https://doi.org/10.1016/j.procir.2017.11.088 (Copenhagen, Denmark, Eslevier)
Calatayud A (2017) The connected supply chain: enhancing risk management in a changing world. Institutions for development sector connectivity, markets, and finance division. InterAmerican Development Bank
Whitmore A, Agarwal A, Xu LD (2015) The internet of things—A survey of topics and trends. Inf Syst Front 17:261–274
Abazi B (2016) An approach to the impact of transformation from the traditional use of ICT to the internet of things: how smart solutions can transform SMEs. IFAC PapersOnLine 49(29):148–151
Yu J, Subramanian N, Ning K et al (2015) Product delivery service provider selection and customer satisfaction in the era of internet of things: a Chinese e-retailers’ perspective. Int J Prod Econ 159:104–116
Sarac A, Absi N, Dauzère-Pérès S (2010) A literature review on the impact of RFID technologies on supply chain management. Int J Prod Econ 128(1):77–95
Bozarth CC, Warsing DP, Flynn BB et al (2009) The impact of supply chain complexity on manufacturing plant performance. J Oper Manag 27:78–93
Vachon S, Klassen RD (2002) An exploratory investigation of the effects of supply chain complexity on delivery performance. IEEE T Eng Manage 49:218–230
Birkinshaw J, Heywood S (2010) Putting organizational complexity in its place. McKinsey & Company. https://www.mckinsey.com/business-functions/organization/our-insights/putting-organizational-complexity-in-its-place
Christopher M, Holweg M (2017) Supply chain 2.0 revisited: a framework for managing volatility-induced risk in the supply chain. Int J Phys Distr Log 47:2–17
Pawlus W, Liland F, Nilsen N (2017) Modeling and simulation of a cylinder hoisting system for Real-Time Hardware-in-the-Loop testing. SPE drilling and completion 32:69–78
Venables M (2006) Better by design. Digital manufacturing. IET manufacturing engineer, 85(3):24–27
Boussier JM, Cucu T, Ion L et al (2011) Simulation of goods delivery process. Int J Phys Distr Log 41:913–930
Garcia ML, Centeno MA, Peiialoza, G (1999) A simulation of the product distribution in the newspaper industry. In: Proceedings of the 1999 winter simulation conference, vol. 2, pp 1268–1271
Golfarelli M, Rizzi S (2009) What-if Simulation Modeling in Business Intelligence. Int J Data Warehousing 5:24–43
Bonabeau E (2002) Agent-based modeling: methods and techniques for simulating human systems. Proc Nat Acad Sci 99:7280–7287
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Cruz-Mejía, O., Márquez, A., Monsreal-Barrera, M.M. (2019). Product Delivery and Simulation for Industry 4.0. In: Gunal, M. (eds) Simulation for Industry 4.0. Springer Series in Advanced Manufacturing. Springer, Cham. https://doi.org/10.1007/978-3-030-04137-3_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-04137-3_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04136-6
Online ISBN: 978-3-030-04137-3
eBook Packages: EngineeringEngineering (R0)