Advertisement

An Approach for Designing Order Size Dependent Lead Time Models for Use in Inventory and Supply Chain Management

  • Peter NielsenEmail author
  • Zbigniew Michna
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 56)

Abstract

This paper addresses the issue of lead time behavior in supply chains. In supply chains without information sharing a supply chain member can only use the information they observe; orders/demand and their lead times. Using this information four different scenarios of lead time behavior are suggested and discussed. Based on this discussion an analytical approach is proposed that investigates the link between order quantities and lead times. This approach is then demonstrated on data from a company. In the particular case it is determined that there seems to be a link between order quantities and lead times, indicating that a complex lead time model may be necessary. It is also concluded that current state of supply chain management does not offer any methods to address this link between order quantities and lead times and that therefore further research is warranted.

Keywords

Supply chain management Lead times Bullwhip effect Stochastics 

References

  1. 1.
    Bischak, D.P., Robb, D.J., Silver, E.A., Blackburn, J.D.: Analysis and management of periodic review order-up-to level inventory systems with order crossover. Prod. Oper. Manag. 23(5), 762–772 (2014)CrossRefGoogle Scholar
  2. 2.
    Bradley, J.R., Robinson, L.W.: Improved base-stock approximations for independent stochastic lead times with order crossover. Manuf. Serv. Oper. Manag. 7(4), 319–329 (2005)Google Scholar
  3. 3.
    Chen, F., Drezner, Z., Ryan, J.K., Simchi-Levi, D.: Quantifying the bullwhip effect in a simple supply chain: the impact of forecasting, lead times, and information. Manag. Sci. 46(3), 436–443 (2000)CrossRefzbMATHGoogle Scholar
  4. 4.
    Disney, S.M., Maltz, A., Wang, X., Warburton, R.D.H.: Inventory management for stochastic lead times with order crossovers. Eur. J. Oper. Res. 248, 473–486 (2016)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Do, N.A.D., Nielsen, P., Michna, Z., Nielsen, I.E.: Quantifying the bullwhip effect of multi-echelon system with stochastic dependent lead time, advances in production management systems. In: Proceedings International Conference Innovative and Knowledge-Based Production Management in a Global-Local World: IFIP WG 5.7, APMS 2014, Part I, 20–24 Sept 2014, vol. 438, pp. 419–426. Springer, Ajaccio, France (2014)Google Scholar
  6. 6.
    Duc, T.T.H., Luong, H.T., Kim, Y.D.: A measure of the bullwhip effect in supply chains with stochastic lead time. Int. J. Adv. Manuf. Technol. 38(11–12), 1201–1212 (2008)CrossRefGoogle Scholar
  7. 7.
    Joe, H.: Multivariate Models and Dependence Concepts. Chapman & Hall, London (1997)CrossRefzbMATHGoogle Scholar
  8. 8.
    Kim, J.G., Chatfield, D., Harrison, T.P., Hayya, J.C.: Quantifying the bullwhip effect in a supply chain with stochastic lead time. Eur. J. Oper. Res. 173(2), 617–636 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Lee, H.L., Padmanabhan, V., Whang, S.: The bullwhip effect in supply chains. Sloan Manag. Rev. 38(3), 93–102 (1997)zbMATHGoogle Scholar
  10. 10.
    Michna, Z., Nielsen I.E., Nielsen P.: The bullwhip effect in supply chains with stochastic lead times. Math. Econ. 9(16) (2013)Google Scholar
  11. 11.
    Nelsen, R.: An Introduction to Copulas. Springer, New York (1999)CrossRefzbMATHGoogle Scholar
  12. 12.
    Nielsen P., Michna Z., Do, N.A.D., Sørensen B.B.: Lead times and order sizes—a not so simple relationship. In: 36th International Conference Information Systems Architecture and Technology 2015, Karpacz, Part II, pp. 65–75 (2016). doi:  10.1007/978-3-319-28555-9_6
  13. 13.
    Nielsen, P., Michna, Z., Do, N.A.D.: An empirical investigation of lead time distributions, advances in production management systems. In: Proceedings International Conference Innovative and Knowledge-Based Production Management in a Global-Local World: IFIP WG 5.7, APMS 2014, Part I, Sept 20–24 2014, vol. 438. Springer, Ajaccio, France, pp. 435–442 (2014)Google Scholar
  14. 14.
    Pahl, J., Voß, S., Woodruff, D.L.: Production planning with load dependent lead times. 4OR 3(4), 257–302 (2005)Google Scholar
  15. 15.
    Relich, M., Witkowski, K., Saniuk, S., Šujanová, J.: Material demand forecasting: an ERP system perspective. Appl. Mech. Mater. 527, 311–314 (2014)Google Scholar
  16. 16.
    R-Project.org: www.r-project.org (2016)
  17. 17.
    Scott, D.W.: Multivariate Density Estimation. Theory Practice Vision Wiley, New York (1992)Google Scholar
  18. 18.
    Sitek P.: A hybrid CP/MP approach to supply chain modelling, optimization and analysis. In: Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 1345–1352 (2014). doi:10.15439/2014F89Google Scholar
  19. 19.
    Wang, X., Disney, S.M.: Mitigating variance amplifications under stochastic lead-time: the proportional control approach. Eur. J. Oper. Res. (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Mechanical and Manufacturing EngineeringAalborg UniversityAalborg OestDenmark
  2. 2.Department of Mathematics and CyberneticsWroclaw University of EconomicsWroclawPoland

Personalised recommendations