• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.93-112
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• 2015

Depending on the change in consumer's consumption pattern, existing retail shop has evolved in hypermarket or convenience store offering grocery and daily products mostly. Therefore, it is important to maintain the inventory levels and proper product configuration for effectively utilize the limited space in the retail store and increasing sales. Accordingly, this study proposed proper product configuration and inventory level strategy based on RFM(Recency, Frequency, Monetary) model and SOM(self-organizing map) for manage the retail shop effectively. RFM model is analytic model to analyze customer behaviors based on the past customer's buying activities. And it can differentiates important customers from large data by three variables. R represents recency, which refers to the last purchase of commodities. The latest consuming customer has bigger R. F represents frequency, which refers to the number of transactions in a particular period and M represents monetary, which refers to consumption money amount in a particular period. Thus, RFM method has been known to be a very effective model for customer segmentation. In this study, using a normalized value of the RFM variables, SOM cluster analysis was performed. SOM is regarded as one of the most distinguished artificial neural network models in the unsupervised learning tool space. It is a popular tool for clustering and visualization of high dimensional data in such a way that similar items are grouped spatially close to one another. In particular, it has been successfully applied in various technical fields for finding patterns. In our research, the procedure tries to find sales patterns by analyzing product sales records with Recency, Frequency and Monetary values. And to suggest a business strategy, we conduct the decision tree based on SOM results. To validate the proposed procedure in this study, we adopted the M-mart data collected between 2014.01.01~2014.12.31. Each product get the value of R, F, M, and they are clustered by 9 using SOM. And we also performed three tests using the weekday data, weekend data, whole data in order to analyze the sales pattern change. In order to propose the strategy of each cluster, we examine the criteria of product clustering. The clusters through the SOM can be explained by the characteristics of these clusters of decision trees. As a result, we can suggest the inventory management strategy of each 9 clusters through the suggested procedures of the study. The highest of all three value(R, F, M) cluster's products need to have high level of the inventory as well as to be disposed in a place where it can be increasing customer's path. In contrast, the lowest of all three value(R, F, M) cluster's products need to have low level of inventory as well as to be disposed in a place where visibility is low. The highest R value cluster's products is usually new releases products, and need to be placed on the front of the store. And, manager should decrease inventory levels gradually in the highest F value cluster's products purchased in the past. Because, we assume that cluster has lower R value and the M value than the average value of good. And it can be deduced that product are sold poorly in recent days and total sales also will be lower than the frequency. The procedure presented in this study is expected to contribute to raising the profitability of the retail store. The paper is organized as follows. The second chapter briefly reviews the literature related to this study. The third chapter suggests procedures for research proposals, and the fourth chapter applied suggested procedure using the actual product sales data. Finally, the fifth chapter described the conclusion of the study and further research.

• Journal of the East Asian Society of Dietary Life
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• v.13 no.1
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• pp.25-38
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• 2003

This study was conducted to establish the microbiological quality standards applying the HACCP system on sushi items of Japanese restaurant in Korea. The study evaluated hygienic conditions of kitchen and workers, pH time-temperature relationship, and microbial assessments during whole process of sushi making in 2001. Overall hygienic conditions were normal for both kitchen and for workers by 3 point scale, but hygienic controls against the cross-contamination were still needed. Each process of sushi making was performed under the risk of microbial contamination, since pH value of most of ingredients was over pH 4.6 and also production time(3.5~6 hrs) were long enough to cause problems. Microorganisms were high enough to cause foodborne illness ranged 8.0$\times$10$^2$~3.3$\times$10$^{6}$ CFU/g of TPC and 1.0$\times$10$^1$~1.6$\times$10$^3$CFU/g of coliforms, although TPC, coliforms and Staphylcoccus aureus were within the standard limits (TPC 10$^2$~10$^{6}$ CFU/g, coliforms 10$^3$CFU/g). However, Salmonella and Vibrio parahaemolyticus were not detected. High populations TPC and coliforms were also found in the cooks' hands and cooking utensils(TPC 10$^2$~10$^{6}$ CFU/100cm$^2$and Coliforms 10$^1$~10$^3$CFU/100cm$^2$). Based on the CCP decision tree analysis, the CCPs were the holding steps far six sushi production line except the tuna and the thawing step for tuna sushi. In conclusion, overall state of sushi production was fairly good but much improvement was still needed.