Effect of component interdependency on inventory allocation

  • Yohanes Kristianto NugrohoEmail author
  • AHM Shamsuzzoha
  • Petri T. Helo
Conference paper


The objective of this research approach is to improve the responsiveness and agility of the supply chain network by considering the allocation of inventory control, especially for the allocation of safety stock. This is achieved through considering the effect of components interdependencies and offering guaranteed lead times. An analytical model is presented in this paper, which is supported by discrete event simulation model in order to investigate the effect of material interdependency on the reduction to safety stock allocation. A case example from lead acid battery manufacturing supply chain network is used to demonstrate the applicability of the models. The results, by applying design structure matrix (DSM), showed that less material interdependency reduces the safety stock allocation significantly. The material interdependencies are reduced through clustering operation. The results also showed that reduction of material interdependency reduces the unnecessary investment in inventory management. The difference between the presented analytical model and the discrete event simulation is not significant, which also validate the proposed modeling approach.


inventory allocation component interdependency design structure matrix (DSM) supply chain management safety stock 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Box G., Jenkins G, Reinsel G (1994) Time series analysis: forecasting and control, 3rd Edition. Holden-Day San FranciscoGoogle Scholar
  2. Bucklin, L.P.: Postponement, speculation and the structure of distribution channels. Journal of Marketing Research (JMR) 2(1), 26–31 (1965)CrossRefGoogle Scholar
  3. Collier D (1980) Justifying component part standardization. In: Proceedings fo the 12th National AIDS Meeting, American Institute for Decision Sciences, Las Vegas, NV, p 405Google Scholar
  4. Collier, D.: The measurement and operating benefits of component part commonality. Decision Sciences 12(1), 85–96 (1981)CrossRefGoogle Scholar
  5. Evans, D.: A note on modular design - a special case in nonlinear programming. Operations Research 18(3), 562–564 (1970)CrossRefGoogle Scholar
  6. Evans, D.H.: Modular design - A special case in nonlinear programming. Operations Research 11(4), 637–647 (1963)zbMATHCrossRefGoogle Scholar
  7. Graves, S.C.: A single-item inventory model for a nonstationary demand process. Manufacturing & Service Operations Management 1(1), 50 (1999)CrossRefGoogle Scholar
  8. Graves, S.C., Willems, S.P.: Strategic inventory placement in supply chains: Nonstationary demand. Manufacturing & Service Operations Management 10(2), 278–287 (2008)CrossRefGoogle Scholar
  9. Graves, S.C., Willems, S.P., Zipkin, P.: Optimizing strategic safety stock placement in supply chains. Manufacturing & Service Operations Management 2(1), 68 (2000)CrossRefGoogle Scholar
  10. Jiao, J., Tseng, M.M.: Understanding product family for mass customization by developing commonality indices. Journal of Engineering Design 11(3), 225–243 (2000)CrossRefGoogle Scholar
  11. Kristianto, Y., Helo, P.: Strategic thinking in supply and innovation in dual sourcing procurement. International Journal of Applied Management Science 1(4), 401–419 (2009)CrossRefGoogle Scholar
  12. Kristianto, Y., Helo, P.: Built-to-order supply chain: response analysis with control model. International Journal of Procurement Management 3(2), 181–198 (2010)CrossRefGoogle Scholar
  13. Lee, H.L.: Effective inventory and service management through product and process redesign. Operations Research 44(1), 151 (1996)zbMATHCrossRefGoogle Scholar
  14. Lee, H.L., Padmanabhan, V.: Information distortion in a supply chain: The bullwhip effect. Management Science 43(4), 546 (1997)zbMATHCrossRefGoogle Scholar
  15. Levén E, Segerstedt A (2004) Inventory control with a modified croston procedure and erlang distribution. International Journal of Production Economics 90(3):361 – 367, production Control and SchedulingGoogle Scholar
  16. Martin M, Ishii K (1996) Design for variety: a methodology for understanding the costs of product proliferation. In: Wood K (ed) Design Theory and Methodology - DTM’96, ASME, Irivine, CA, 96-DETC/DTM-1610Google Scholar
  17. Mikkola, J.H.: Management of product architecture modularity for mass customization: Modeling and theoretical considerations. IEEE Transactions on Engineering Management 54(1), 57–69 (2007)CrossRefGoogle Scholar
  18. Neale, J.J., Willems, S.P.: Managing inventory in supply chains with nonstationary demand. Interfaces 39(5), 388–399 (2009)CrossRefGoogle Scholar
  19. Pagh, J.D., Cooper, M.C.: Supply chain postponement and speculation strategies: How to choose the right strategy. Journal of Business Logistics 19(2), 13–33 (1998)Google Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Yohanes Kristianto Nugroho
    • 1
    Email author
  • AHM Shamsuzzoha
    • 1
  • Petri T. Helo
    • 1
  1. 1.Department of ProductionUniversity of VaasaVaasaFinland

Personalised recommendations