Abstract
Timely identification of recently rising patterns is required in business process. Data mining methods are most appropriate for the characterization, valuable examples extraction, and predications which are essential for business support and decision-making. Some research studies have also expanded the use of this idea in inventory management. However, not very many research analyzes have considered the utilization of the data mining approach for supply chain inventory management. In this chapter, two unique cases for supply chain inventory management dependent on cross-selling effect are presented. First, the cross-selling effect in different clusters is characterized as a basis for deciding the significance of items. Second, the cross-selling in different time periods is considered as a criterion for ranking inventory items. An example is devised to approve the outcomes. It is illustrated that by using this modified approach, the ranking of items may get affected resulting in higher profit.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Agrawal R, Imielinski T, Swami (1993) Mining association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD international conference on management of data, New York, NY, USA, pp. 207–216
Agarwal R (2017) Optimal order quantity and inventory classification using clustering. Int J Appl Manag Sci Eng 4(2):41–52
Agarwal R (2017) Ordering policy and inventory classification using temporal association rule mining. Int J Prod Manag Assess Technol 6(1):37–49
Agarwal R (2017) Decision making with association rule mining and clustering in supply chains. Int J Data Netw Sci 1(1):11–18
Agarwal R (2017) Opportunity cost estimation using temporal association rule mining. Int J Serv Sci 6(3/4):261–272
Agarwal R, Mittal M, Pareek S (2016) Optimal inventory classification using data mining techniques. Optimal inventory control and management techniques, IGI Global Publisher, pp 236–255
Agarwal R, Mittal M, Pareek S (2018) Optimal ordering policy with inventory classification using data mining techniques. Promoting business process improvement through inventory control techniques. IGI Global, pp 305–326
Anand SS, Hughes JG, Bell DA, Patrick AR (1997) Tackling the cross-sales problem using data mining. In: Proceedings of the 2nd Pacific-Asia conference on knowledge discovery and data mining, Hong Kong, 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 the 5th ACM SIGKDD international conference on knowledge discovery & data mining, New York, USA, pp 254–260
Brijs T, Swinnen G, Vanhoof K, Wets G (2000) A data mining framework for optimal product selection in retail supermarket data: The generalized PROFSET model. In: Proceedings of the 6th ACM SIGKDD international conference on knowledge discovery & data mining, New York, USA, pp 300–304
Cohen MA, Ernst R (1988) Multi-item classification and generic inventory stock control Policies. Prod Invent Manag J 29(3):6–8
Chase RB, Aquilano NJ, Jacobs FR (1998) Production and operations management. Irwin/McGraw Hill, New York
Ernst R, Cohen MA (1990) Operations related groups (ORGs): a clustering procedure for production/inventory systems. J Oper Manag 9(4):574–598
Flores BE, Whybark DC (1987) Implementing multiple criteria ABC analysis. J Oper Manag 7(1 & 2):79–85
Han J, Kamber M (2006) Data mining: concepts and techniques. Morgan Kaufmann Publishers, San Francisco
Kaku I (2004) A data mining framework for classification of inventories. In: Proceedings of the 5th Asia Pacific industrial engineering and management systems, Japan, pp 450–455
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 C-H, Chen M-S, Lin C-R (2003) Progressive partition miner: an efficient algorithm for mining general temporal association rules. J IEEE Trans Knowl Data Eng 15(4):1004–1017
Li Y, Ning P, Wang XS, Jajodia S (2001) Discovering calendar-based temporal association rules. In: Proceedings of the 8th international symposium on temporal representation and reasoning, Itlay, pp 111–118
Mittal M, Pareek S, Agarwal R (2015) Loss profit estimation using association rule mining with clustering. Manag Sci Lett 5(2):167–174
Mittal M, Pareek S, Agarwal R (2015) Ordering policy using temporal association rule mining. Int J Data Sci 1(2):157–171
Ng WL (2007) A simple classifier for multiple criteria ABC analysis. Eur J Oper Res 177(1):344–353
Ozan C, Mustafa SC (2008) A web-based decision support system for multi-criteria inventory classification using fuzzy AHP methodology. Exp Syst Appl 35(3):1367–1378
Ramanathan R (2006) ABC inventory classification with multiple-criteria using weighted linear optimization. Comput Oper Res 33(3):695–700
Silver EA, Pyke DF, Peterson R (1998) Inventory management & production planning & scheduling, 3rd edn. Wiley, New York
Tan KC, Kannan VR, Handfield RB (1998) Supply chain management: supplier performance and firm performance. Int J Purch Mater Manag 34(3):2–9
Towill DR (1996) Time compression and supply chain management – a Guided Tour. Logist Inf Manag 9(6):41–53
Wang K, Xu C, Liu B (1999) Clustering transactions using large items. In: Proceedings of the 8th international conference on information and knowledge management, USA, pp 483–490
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, USA, pp 371–378
Wong RC, Fu AW, Wang K (2005) Data mining for inventory item selection with cross-selling consideration. Data Min Knowl Disc 11(1):81–112
Xiao Y, Zhang R, Kaku I (2011) A new approach of inventory classification based on loss profit. Exp Syst Appl 38(8):9382–9391
Yin Y, Kaku I, Tang J, Zhu JM (2011) Data mining concepts, methods and applications in management and engineering design. Springer, London
Zhou P, Fan L (2007) A note on multi-criteria ABC inventory classification using weighted linear optimization. Eur J Oper Res 182(3):1488–1491
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Agarwal, R. (2020). Decision-Making with Temporal Association Rule Mining and Clustering in Supply Chains. In: Shah, N., Mittal, M. (eds) Optimization and Inventory Management. Asset Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-13-9698-4_25
Download citation
DOI: https://doi.org/10.1007/978-981-13-9698-4_25
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9697-7
Online ISBN: 978-981-13-9698-4
eBook Packages: Business and ManagementBusiness and Management (R0)