Placement of Products and Improvement of Order Picking Process through Association Analysis: A Case Study in Pharmaceutical Warehouse

Year 2018, Volume: 1 Issue: 2, 21 - 27, 30.11.2018

Fatma Dinçer , Alpaslan Fığlalı

https://doi.org/10.34088/kojose.427957

Abstract

While taking strategic decisions for the future
in today's business world, which has a constantly changing and dynamic
structure, several implications can be drawn with the information obtained from
databases and the correct processing and analysis of this information. With
regard to these implications, various decisions can be taken to improve the
processes. Businesses can execute their processes more efficiently and they are
able to make better decisions on the future by processing the data that will benefit
by using the data mining techniques. In this study, the past orders of a
pharmaceutical company were analyzed and an association analysis of the
products in these orders was performed and a methodological framework has been
presented based on the Apriori Algorithm results to ensure that the products
are placed at the optimum level in the warehouse. Therefore, this study
contributes to the improvement of order picking process.

Keywords

Apriori Algorithm, Association Analysis, Data Mining, Order Picking

References

• Van den Berg J. P., Zijm W. H. M., 1999. Models for warehouse management: Classification and examples. Int. J. Production Economics, 519, 519–528.
• Chen M. C., Lin C. P.,2007. A Data Mining Approach to Product Assortment and Shelf Space Allocation. Expert Systems with Applications, 32, 976-986.
• Yang T. C., Lai H., 2006. Comparison of Product Bundling Strategies on Different Online Shopping Behaviors. Electronic Commerce Research and Applications, 5, 295-304.
• Ay D., Çil İ., 2008. Use of Association Rules in Layout Planning AT Migros Türk A.Ş. Journal of Industrial Engineering, 21, 14-29.
• Kılınç Y., 2009. Mining Association Rules For Quality Related Data In An Electronics Company. Master’s Thesis, Middle East Technical University, Industrial Engineering, 1-23.
• Koç M., Karabatak M., 2011. Investigation of the Effect of Social Networks on Students Using Data Mining. 5th International Computer & Instructional Technologies Symposium, Fırat University, Elazığ- Turkey, 22-24 September.
• Güngör E., Yalçın N., Yurtay N., 2013. Technical Elective Course Selection Analysis with Apriori Algorithm. National Distance Education and Technologies Symposium, Selçuk University, Konya,1-3 November, 114-119.
• Doğan B., Erol B., Buldu A., 2014. Usage of Association Rules for Customer Relationship Management in the Insurance Sector. Marmara Journal of Science, 3, 105-114.
• Doğrul G., Akay D., Kurt M., 2015. Analysis of Traffic Accidents by Association Rules. Gazi Journal of Engineering Sciences, 1, 265-284.
• Kaur M., Kang S., 2016. Market Basket Analysis: Identify The Changing Trends of Market Data Using Association Rule Mining. International Conference on Computational Modeling and Security, Procedia Computer Science, 85, 78-85.
• Yener F., Yazgan H., Cömert S., Kır S., Kaya Y., 2016. Solution of Order Batching Problem with Association Rules and Genetic Algorithm: A Case Study in Pharmacy Warehouse. Journal of Transportation and Logistics, 1, 130-142.
• Edelstein H. A., Edelstein H. C., 1997. Building, Using, and Managing the Data Warehouse. In: Data Warehousing Institute, 1st edn., Prentice Hall PTR, Englewood, Cliffs.
• Han J., Kamber M., 2006. Data Mining: Concepts and Techniques, 2rd edn., Morgan Kaufmann Publishers.
• Karaibrahimoğlu A., 2014. Analyzing Breast Cancer Data Using Association Rule Mining. Phd Thesis, Selçuk University, Graduate School of Natural Sciences, 1-126.
• Kotsiantis S., Kanellopoulos D., 2006. Association Rules Mining: A Recent Overwiev. GESTS Int’l Transactions on Computer Science and Engineering, 78, 71-82
• Agrawal R., Srikant R., 1994. Fast algorithms for mining association rules. Proceedings of the 20th VLDB Conferance, Santiago, Chile, 487-499.
• Bothorel G., Serrurier M., Hurter C., 2011. Using Visual Data Mining Tools to Explore a Set of Association Rules. IHM '11 23rd French Speaking Conference on Human-Computer Interaction, Sophia Antipolis, France, October 24-27.