• IE interfaces
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• v.22 no.3
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• pp.234-242
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• 2009

It is most important for distribution center in retail business to delivery commodities in a timely manner. Accordingly, many companies try to make distribution center effective using the Warehouse Management System(WMS) integrated legacy system. Also, the Customer Relationship Management(CRM) is the most typical paradigm in management lately. Even though the WMS and CRM are independent system of each other, WMS, coupled with CRM makes customer satisfied more effectively. In this paper, we proposed the methodology for inventory location after analyzing and applying customer buying pattern data in the CRM through the MBA(Market Basket Analysis), which is part of data mining. We used an example modeling a real distribution center in retail through a 3D simulation tool and examined correlation between commodities using customer buying pattern. After that, we applied it to the inventory location system through the MBA in an example. Finally, we identified decrease in the time for picking, which is the majority of distribution center. Besides, we proposed a simulation methodology before applying new methodology. Consequently, it removes potential errors in advance and makes a optimized inventory location system.

• Journal of information and communication convergence engineering
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• v.16 no.1
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• pp.52-59
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• 2018

Odoo is an enterprise resource planning information system providing modules to support the basic business function in companies. This research will look into the development of an additional module at Odoo. This module is a data mining module using Market Basket Analysis (MBA) using FP-Growth algorithm in managing OLTP of sales transaction to be useful information for users to improve the analysis of company business strategy. The FP-Growth algorithm used in the application was able to produce multidimensional association rules. The company will know more about their sales and customers' buying habits. Performing sales trend analysis will give a valuable insight into the inner-workings of the business. The testing of the module is using the data from X Supermarket. The final result of this module is generated from a data mining process in the form of association rule. The rule is presented in narrative and graphical form to be understood easier.

• Agribusiness and Information Management
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• v.4 no.2
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• pp.14-21
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• 2012

Since the Free Trade Agreements (FTAs) with Chile, the EU, and the U.S., Korean agricultural produce markets have turned into a fierce competition landscape. Under these competitive circumstances, marketing is critical. The objective of the research presented herein is to understand the characteristics of customer preferences after locating trends of purchased items. So This research establishes sustainable strategies for Korean agricultural produce. This investigation used market-basket analysis techniques and panel data for its research. Market-basket analysis is a technique which attempts to find groups of items that are commonly found together. The results show that, for one year, processed food using wheat, processed marine products, and pork are commonly bought together and that yogurt and milk also are bought together. The characteristics of customers buying these items are 44 years old and live in a four-person household with two children. These customers do not live with their parents.

• The Journal of Bigdata
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• v.7 no.1
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• pp.213-224
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• 2022

In logistics and distribution, Market Basket Analysis (MBA) is used as an important means to analyze the correlation between major sales products and to increase internal operational efficiency. In particular, the results of market basket analysis are used as important reference data for decision-making processes such as product purchase prediction, product recommendation, and product display structure in stores. With the recent development of e-commerce, the number of items handled by a single distribution and logistics company has rapidly increased, And the existing analytical methods such as Apriori and FP-Growth have slowed down due to the exponential increase in the amount of calculation and applied to actual business. There is a limit to examining important association rules to overcome this limitation, In this study, at the Main-Category level, which is the highest classification system of products, the utility item set mining technique that can consider the sales volume of products together was used to first select a group of products mainly sold together. Then, at the sub-category level, the types of products sold together were identified using FP-Growth. By using this sequential layer filtering technique, it may be possible to reduce the unnecessary calculations and to find practically usable rules for enhancing the effectiveness and profitability.