Abstract
In many supply chain systems, inventory control is a key decision-making problem. ABC classification is usually used for inventory items aggregation because the number of inventory items is so large that it is not computationally feasible to set stock and service control guidelines for each individual item. Then different managing methods have been applied to control the inventory level of the items. However, because of the complexity of inter-relationships among items, there is a small amount of research that treats decision making with correlation of inventory items. Therefore, how to treat the correlation is a challenge when developing inventory strategies. This chapter firstly establishes a new algorithm of inventory classification based on the association rules; by using the support-confidence framework the consideration of the cross-selling effect is introduced to generate a new criteria, which is then used to rank inventory items. Then, a numerical example is used to explain the new algorithm and empirical experiments are implemented to evaluate its effectiveness and utility, comparing with traditional ABC classification.
This chapter is organized as follows. Section 12.1 introduces the importance of the consideration of association. Section 12.2 provides an overview of relational research. Section 12.3 outlines our approach and the issues to be addressed, and provides the detailed descriptions of the new algorithm. Section 12.4 and Section 12.5 present a numerical example and the empirical experiments, respectively. Section 12.6 presents conclusions and outlines future research.
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References
Agrawal R, Imilienski T, Swami A (1993) Mining association rules between sets of items in large database. In: Proceedings of SIGMOD, pp 207–216
Agrawal R, Srikant R (1994) Fast algorithm for mining association rules. In: Proceedings of VLDB, pp 487–499
Anand SS, Hughes JG, Bell DA, Patrick AR (1997) Tackling the cross-sales problem using data mining. In: Proceedings of PAKDD’97, pp 331–343
Brijs T, Swinnen G, Vanhoof K, Wets G (1999) Using association rules for product assortment decisions: a case study. In: Proceedings of KDD, pp 254–260
Brijs T, Goethals B, Swinnen G, Vanhoof K, Wets G (2000a) A data mining framework for optimal product selection in retail supermarket data: the generalized PROFSET model. In: Proceedings of ACMSIGKDD, pp 300–304
Brijs T, Vanhoof K, Wets G (2000b) Reducing redundancy in characteristic rule discovery by using integer programming techniques. Intell Data Analys 4:229–240
Chase RB, Aquilano NJ, Jacobs FR (1998) Production and operations management manufacturing and services, 8th edn. McGraw-Hill, Boston, pp 607–608
Cohen MA, Ernst R (1988) Multi-item classification and generic inventory stock control policies. Prod Inventory Manage J 3rd Quarter:6–8
Cooper LG, Giuffrida G (2000) Turning data mining into a management science tool: new algorithms and empirical results. Manage Sci 46(2):249–264
Flores BE, Whybark DC (1987) Implementing multiple criteria ABC analysis. J Oper Manage 7(1/2):79–85
Han J, Micheline K (2001) Data mining: concepts and techniques, chap 6. Morgan Kaufmann, San Fransisco, CA, pp 226–269
Kleinberg JM (1998) Authoritative Sources in a Hyperlink Environment, Proceedings of 9th ACM-SIAM Symposium on Discrete Algorithms, pp 668–677
Kaku I, Xiao Y (2008) A new algorithm of inventory classification based on the association rules. Int J Serv Sci 1(2):148–163
Lee YC, Hong TP, Lin WY (2005) Mining association rules with multiple minimum supports using maximum constraints. Int J Approx Reason 40:44–54
Lenard JD, Roy B (1995) Multi-item inventory control: a multicriteria View. Eur J Oper Res 87:685–692
Liu B, Hsu W, Ma Y (1999) Mining association rules with multiple minimum supports. In: Proceedings of 1999 International Conference on Knowledge Discovery and Data Mining, pp 337–341
Silver EA, Pyke DF, Peterson R (1988) Inventory Management and Production Planning and Scheduling (3rd Edition), pp 30–35
Wang K, Su MT (2002) Item selection by “hub-authority” profit ranking. In: Proceedings of SIGKDD’02, pp 652–657
Wong RC, Fu AW, Wang K (2003) MPIS: Maximal-profit item selection with cross-selling consideration. In: Proceedings of the 3rd IEEE International Conference on Data Mining, pp 371–378
Wong RC, Fu AW, Wang K (2005) Data mining for inventory item selection with cross-selling consideration. Data Minin Know Discov 11(1):81–112
Zhang C, Zhang S (2002) Association Rule Mining: Models and Algorithms, 25–36
Acknowledgements
This research work is a cooperation including Dr. Yiyong Xiao and Professor Ikou Kaku. Their contribution is very much appreciated.
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Yin, Y., Kaku, I., Tang, J., Zhu, J. (2011). Decision Making with Consideration of Association in Supply Chains. In: Data Mining. Decision Engineering. Springer, London. https://doi.org/10.1007/978-1-84996-338-1_12
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DOI: https://doi.org/10.1007/978-1-84996-338-1_12
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