Skip to main content
SpringerLink
Log in

Joint replenishment of associated spare parts using clustering approach

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Most often, we observe that certain spare parts are used together for various maintenance activities of equipment. If spare parts are used frequently in a group, then there exists some hidden dependency among these parts. Sometimes the entire maintenance work cannot be accomplished due to the shortage of a single spare in the group. These parts are termed here as associated spare items. These parts can be determined by frequent itemsets mining. In this paper, the groups of spare parts are found out through hierarchical clustering method using support and weighted support of the data mining measure. A methodology is proposed to incorporate both support and weighted support values for computing an overall cost of replenishment for joint replenishment policy for the associated spares. Finally, a case study is presented to illustrate the validation of our model and compared the results among the group with normal support and group with weighted support.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Atkins DR, Iyogun PO (1988) Periodic versus ‘can-order’ policies for coordinated multi-item inventory system. Manag Sci 34(6):791–796

    Article  MathSciNet  Google Scholar 

  2. Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. In: Proc. 1993 ACM SIGMOD International Conference on Management of Data, Washington, D.C., 207–216

  3. Agarwal RC, Aggarwal CC, Prasad VVV (2001) A tree projection algorithm for generation of frequent itemsets. J Parallel Distrib Comput 61:350–361

  4. Agarwal R, Mittal M, Pareek S (2015) EOQ estimation for imperfect quality items using association rule mining with clustering. Decis Sci Lett 4(4):497–508

    Article  Google Scholar 

  5. Bala, P. K. (2008). Exploring various forms of purchase dependency in retail sale, Proceedings of the World Congress on Engineering and Computer Science, Lecture Notes in Engineering and Computer Science, WCECS 2008, 1101–1104

  6. Bala PK, Sural S, Banerjee RN (2010) Association rule for purchase dependence in multi-item inventory. Prod Plan Control 21(3):274–285

    Article  Google Scholar 

  7. Balintfy JL (1964) On a basic class of multi-item inventory problems. Manag Sci 10(2):287–297

    Article  Google Scholar 

  8. Brijs, T., Swinnen, G., Vanhoof, K., & Wets, G. (1999). Using association rules for product assortment decisions: a case study, In: Proc. of ACM SIGKDD, 254–260

  9. Brijs, T., Goethals, B., Swinnen, G., Vanhoof, K., & Wets G. (2000). A data mining framework for optimal product selection in retail supermarket data: the generalized PROFSET model, In: Proc. of ACM SIGKDD, 300–304

  10. Curry GL, Skeith RW, Harper RG (1970) A multiproduct dependent inventory model. AIIE Trans 2(3):263–267

    Article  Google Scholar 

  11. Fung RYK, Ma X, Lau HCW (2001) (T, S) policy for coordinated inventory replenishment systems under compound Poisson demands. Prod Plan Control 12(6):575–583

    Article  Google Scholar 

  12. Harding JA, Shahbaz M, Srinivas, Kusiak A (2006) Data mining in manufacturing: a review. J Manuf Sci Eng 128:969–976

    Article  Google Scholar 

  13. Han, J., & Kamber, M. (2006) Data mining: concepts and techniques, Morgan Kaufmann Publishers, 227–284

  14. Ilgin MA, Tunali S (2007) Joint optimization of spare parts inventory and maintenance policies using genetic algorithms. Int J Adv Manuf Technol Vol. 34(5):594–604

    Article  Google Scholar 

  15. Khouja M, Goyal S (2008) A review of the joint replenishment problem literature. Eur J Oper Res 186(1):1–16

    Article  MathSciNet  MATH  Google Scholar 

  16. Liu L, Yuan X-M (2000) Coordinated replenishment in inventory systems with correlated demands. Eur J Oper Res 123(3):490–503

    Article  MathSciNet  MATH  Google Scholar 

  17. Moharana UC, Sarmah SP (2015) Determination of optimal kit for spare parts using association rule mining. Int J Syst Assur Eng Manag 3(6):238–247

  18. Moharana UC, Sarmah SP (2016) Determination of optimal order-up to level quantities for dependent spare parts using data mining. Comput Ind Eng 95:27–40

    Article  Google Scholar 

  19. Panagiotidou S (2014) Joint optimization of spare parts ordering and maintenance policies for multiple identical items subject to silent failures. Eur J Oper Res 235(2):300–314

    Article  MathSciNet  MATH  Google Scholar 

  20. Park C, Seo J (2013) Consideration of purchase dependence in inventory management. Comput Ind Eng 66(2):274–285

    Article  Google Scholar 

  21. Sadoyan H, Zakarian A, Mohanty P (2006) Data mining algorithm for manufacturing process control. Int J Adv Manuf Technol 28(3):342–350

    Article  Google Scholar 

  22. Srikant, R. & Agrawal R. (1996). Mining quantitative association rules in large relational tables, In: Proc. 1996 ACM SIGMOD Internet Conf. Management of Data, 1–12

  23. Silver EA (1974) A control system for coordinated inventory replenishment. Int J Prod Res 12(6):647–671

    Article  Google Scholar 

  24. Tsai CY, Tsai CY, Huang PW (2009) An association clustering algorithm for can-order policies in the joint replenishment problem. Int J Prod Econ 117(1):30–41

    Article  MathSciNet  Google Scholar 

  25. Van Eijs MJG (1993) A note on the joint replenishment problem under constant demand. J Oper Res Soc 44(2):185–191

  26. Viswanathan S (1997) Periodic review (s, S) policies for joint replenishment inventory systems. Manag Sci 43(10):1447–1454

    Article  Google Scholar 

  27. Wong W, Fu AW, Wang K (2005) Data Mining for Inventory Item Selection with cross-selling considerations. Data Min Knowl Disc 11(1):81–112

    Article  MathSciNet  Google Scholar 

  28. Yin, Y., Kaku, I., Tang, J., & Zhu, J. (2011). Association rules mining in inventory database, Data mining: concepts methods and applications in management and engineering, Springer, 9–23

  29. Yun, U., & Leggett, J. (2005).Weighted frequent itemset mining with a weight range and a minimum weight. In: Proceeding of the 2005 SIAM international conference on data mining (SDM’05), Newport Beach, CA, 636–640

  30. Zhang R-Q (2012) An extension of partial backordering EOQ with correlated demand caused by cross-selling considering multiple minor items. Eur J Oper Res 220(3):876–881

    Article  MathSciNet  MATH  Google Scholar 

  31. Zhang R, Kaku I, Xiao Y, (2011) Deterministic EOQ with partial backordering and correlated demand caused by cross-selling. Eur J Oper Res 210(3):537–551

  32. Zhang R, Kaku I, Xiao Y (2012) Model and heuristic algorithm of the joint replenishment problem with complete backordering and correlated demand. Int J Prod Econ 139(1):33–41

Download references

Author information

Authors and Affiliations

  1. Department of Industrial and Systems Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, 721302, India

    U. C. Moharana & S. P. Sarmah

Authors
  1. U. C. Moharana
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. P. Sarmah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to U. C. Moharana.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Moharana, U.C., Sarmah, S.P. Joint replenishment of associated spare parts using clustering approach. Int J Adv Manuf Technol 94, 2535–2549 (2018). https://doi.org/10.1007/s00170-017-0909-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-017-0909-6

Keywords

Search

Navigation