Skip to main content
Log in

Lead time performance in a internet product delivery supply chain with automatic consolidation

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Internet sales have increased exponentially in the last decade. Much of the internet sales are of physical products in urban areas that require product delivery transportation with a tight delivery lead time. With this challenge, a new type of transportation services has been developed aiming to cope with a strict control of transportation lead time. In this paper, an internet product delivery service with customer orders that are multi-item as well as single item is simulated. We address specifically the mismatch between supply and demand when retailers for any reason are unable to estimate the configuration of multi-item orders. Three scenarios of demand patterns are simulated (demand as forecasted, lower than forecasted and higher than forecasted) using discrete-event simulation to look at the effect on transportation lead time. Results show the positive effect on the mismatch between demand and resource capacity which is expressed in higher number delayed delivery orders. The excess of capacity in the product delivery supply chain has not a positive impact on delivery time of orders as technically orders are not delivered before the multi-item components are not available. This leads to think that the excess of resources are not an element that add value to customers waiting for their orders.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Ala-Risku T, Karkkaainen M, Holmstrom J (2003) Evaluating the applicability of merge-in-transit: a step by step process for supply chain managers. Int J Logist Manag 14(2):67–81

    Article  Google Scholar 

  • Amiribesheli M, Benmansour A, Bouchachia A (2015) A review of smart homes in healthcare. J Ambient Intell Human Comput 6:495. doi:10.1007/s12652-015-0270-2

  • Avila-Torres P, Caballero R, Litvinchev I, et al (2017) The urban transport planning with uncertainty in demand and travel time: a comparison of two defuzzification methods. J Ambient Intell Human Comput. doi:10.1007/s12652-017-0545-x

  • Banks J, Carson JS, Nelson BL, Nicol DM (1995) Discrete-event system simulation. Prentice Hall, Upper Saddle River

    Google Scholar 

  • Bruccoleri M, Cannella S, La Porta G (2014) Inventory record inaccuracy in supply chains: the role of workers’ behavior. Int J Phys Distrib Logist Manag 44(10):796–819

    Article  Google Scholar 

  • Camacho-Vallejo JF, Muñoz-Sánchez R, González-Velarde JL (2015) A heuristic algorithm for a supply chain׳ s production-distribution planning. Comput Oper Res 61:110–121

    Article  MathSciNet  MATH  Google Scholar 

  • Cannella S, Bruccoleri M, Framinan JM (2016) Closed-loop supply chains: what reverse logistics factors influence performance? Int J Product Econ 175:35–49

    Article  Google Scholar 

  • Choi T (2016) Inventory service target in quick response fashion retail supply chains. Serv Sci 8(4):406–419

    Article  Google Scholar 

  • Cigolini R, Pero M, Rossi T, Sianess A (2014) Linking supply chain configuration to supply chain perfrmance: a discrete event simulation model. Simul Modell Pract Theory 40:1–11

    Article  Google Scholar 

  • Cole MH, Parthasarathy M (1998) Optimal design of merge-in-transit distribution networks. Research report. Mack-Blackwell National Rural Transportation Study Centre, University of Arkansas, Fayetteville

    Google Scholar 

  • Cronin JJ, Brady MK, Hult G T M (2000) Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environments. J Retail 76:2

    Article  Google Scholar 

  • Croxton KL, Gendron B, Magnanti TL (2003) Models and methods for merge in transit operations. Transp Sci 37(1):1–22

    Article  Google Scholar 

  • De Treville S, Schuroff N, Trigeorgis L, Avanzi B (2014) Optimal sourcing and lead-time reduction under evolutionary demand risk. Prod Oper Manag 23(12):2103–2117

    Article  Google Scholar 

  • Fisher M, Raman A (2010) The new science of retailing. Harvard Business Press, Boston

    Google Scholar 

  • Grewala D, Rggenveen AL, Norfalt J (2017) The future of retaling. 93(1):1–6

  • Karkkainen M, Ala-Risku T, Holmstrom J (2003) Increasing customer value and decreasing distribution costs with merge-in-transit. Int J Phys Distrib Logist Manag 33(2):132–148

    Article  Google Scholar 

  • Kibert CJ (2016) Sustainable construction hoboken. Wiley, New Jersey

    Google Scholar 

  • Kopczak LR (1995) Logistics partnership and supply chain restructuring. Ph.D. thesis. Stanford University, Palo Alto, California, USA

  • Kvanli AH, Pavur RJ, Guynes CS (2000) Introduction to business statistics: a computer integrated, data analysis approach, 5th edn. South-Western College Publishing, Cincinnati

    Google Scholar 

  • Law AM, Kelton WD (2000) Simulation modeling and analysis, 3rd edn. McGraw-Hill, New York

    MATH  Google Scholar 

  • Monsreal Barrea MM, Cruz-Mejia O (2014) Reverse logistics of recovery and recycling of non-returnable beverage containers in the brewery industry. Int J Phys Distrib Logist Manag. doi.10.1108/IJPDLM-08-2012-0258

    Google Scholar 

  • O’Leary DE (2000) Reengineering assembly, warehousing and billing processes. Info Syst Front 1:379–387

    Article  Google Scholar 

  • Pidd M (1998) Computer simulation in management science. Wiley, West Sussex

    Google Scholar 

  • Rabinovich E, Evers PT (2003) Product fulfillment in supply chains supporting internet-retailing operations. J Bus Logist 24(2):205–236

    Article  Google Scholar 

  • Robinson S (2004). Simulation: the practice of model development and use. Wiley Chichester

    Google Scholar 

  • Su KW, Huang PH, Chen PH et al. (2016) J Ambient Intell Human Comput 7:817. doi:10.1007/s12652-016-0343-x

    Google Scholar 

  • Waller MA, Fawcett SE (2013) Data science, predictive analytics and big data: a revolution that will transform supply chain design and management. J Bus Logist 34(2):77–84

    Article  Google Scholar 

  • Yinan Q, Tang M, Zhang M (2016) Mass customization in flat organization: the mediating role of supply chain planning and corporation coordination. J Appl Res Technol 12(2):171–181

    Article  Google Scholar 

  • Zhang XZ (1997) Demand fulfillment rates in an assable-to-order system with multiple products and dependent demands. Prod Oper Manag 6(3):309–324

    Article  Google Scholar 

  • Zhang J, Wang F, Wang K, Lin W, Xu X, Chen C (2011) Data-driven intelligent transportation systems: a survey. IEEE Trans Intell Transp Syst 12(4):1624–1639

    Article  Google Scholar 

Download references

Acknowledgements

First EAI International Conference on Computer Science and Engineering, November 11–12, 2016, Penang, Malaysia.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to J. A. Marmolejo.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cruz-Mejia, O., Marmolejo, J.A. & Vasant, P. Lead time performance in a internet product delivery supply chain with automatic consolidation. J Ambient Intell Human Comput 9, 867–874 (2018). https://doi.org/10.1007/s12652-017-0577-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-017-0577-2

Keywords

Navigation