Strategy for Store Management Using SOM Based on RFM (RFM 기반 SOM을 이용한 매장관리 전략 도출)
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- 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.
Seo Byeong-o (徐丙五, 1862-1936) played a central role in the formation of the Daegu artistic community-which advocated artistic styles combining poetry, calligraphy and painting-during the Japanese colonial period, when the introduction of the Western concept of 'art' led to the adoption of Japanese and Western styles of painting in Korea. Seo first entered the world of calligraphy and painting after meeting Lee Ha-eung (李昰應, 1820-1898) in 1879, but his career as a scholar-artist only began in earnest after Korea was annexed by Japan in 1910. Seo's oeuvre can be broadly divided into three periods. In his initial period of learning, from 1879 to 1897, his artistic activity was largely confined to copying works from Chinese painting albums and painting works in the "Four Gentlemen" genre, influenced by the work of Lee Ha-eung, in his spare time. This may have been because Seo's principal aim at this time was to further his career as a government official. His subsequent period of development, which lasted from 1898 until 1920, saw him play a leading social role in such areas as the patriotic enlightenment movement until 1910, after which he reoriented his life to become a scholar-artist. During this period, Seo explored new styles based on the orchid paintings of Min Yeong-ik (閔泳翊, 1860-1914), whom he met during his second trip to Shanghai, and on the bamboo paintings of Chinese artist Pu Hua (蒲華, 1830-1911). At the same time, he painted in various genres including landscapes, flowers, and gimyeong jeolji (器皿折枝; still life with vessels and flowers). In his final mature period, from 1921 to 1936, Seo divided his time between Daegu and Seoul, becoming a highly active calligrapher and painter in Korea's modern art community. By this time his unique personal style, characterized by broad brush strokes and the use of abundant ink in orchid and bamboo paintings, was fully formed. Records on, and extant works from, Seo's early period are particularly rare, thus confining knowledge of his artistic activities and painting style largely to the realm of speculation. In this respect, eleven recently revealed nanjukseok (蘭竹石圖; orchid, bamboo and rock) paintings, produced by Seo in 1889, provide important clues about the origins and standards of his early-period painting style. This study uses a comparative analysis to confirm that Seo's orchid paintings show the influence of the early gunran (群蘭圖; orchid) and seongnan (石蘭圖; rock and orchid) paintings produced by Lee Ha-eung before his arrest by Qing troops in July 1882. Seo's bamboo paintings appear to show both that he adopted the style of Zheng Xie (鄭燮, 1693-1765) of the Yangzhou School (揚州畵派), a style widely known in Seoul from the late eighteenth century onward, and of Heo Ryeon (許鍊, 1809-1892), a student of Joseon artist Kim Jeong-hui (金正喜,1786-1856), and that he attempted to apply a modified version of Lee Ha-eung's seongnan painting technique. It was not possible to find other works by Seo evincing a direct relationship with the curious rocks depicted in his 1889 paintings, but I contend that they show the influence of both the late-nineteenth-century-Qing rock painter Zhou Tang (周棠, 1806-1876) and the curious rock paintings of the middle-class Joseon artist Jeong Hak-gyo (丁學敎, 1832-1914). In conclusion, this study asserts that, for his 1889 nanjukseok paintings, Seo Byeong-o adopted the styles of contemporary painters such as Heo Ryeon and Jeong Hak-gyo, whom he met during his early period at the Unhyeongung through his connection with its occupant, Lee Ha-eung, and those of artists such as Zheng Xie and Zhou Tang, whose works he was able to directly observe in Korea.
The duty shifts of hospital nurses not only affect nurses' physical and mental health but also present various personnel management problems which often result in high turnover rates. In this context a study was carried out from October to November 1995 for a period of two months to find out the status of hospital nurses' duty shift patterns, and preferred duty hours and fixed duty shifts. The study population was 867 RNs working in five general hospitals located in Seoul and its vicinity. The questionnaire developed by the writer was used for data collection. The response rate was 85.9 percent or 745 returns. The SAS program was used for data analysis with the computation of frequencies, percentages and Chi square test. The findings of the study are as follows: 1. General characteristics of the study population: 56 percent of respondents was (25 years group and 76.5 percent were "single": the predominant proportion of respondents was junior nursing college graduates(92.2%) and have less than 5 years nursing experience in hospitals(65.5%). For their future working plan in nursing profession, nearly 50% responded as uncertain The reasons given for their career plan was predominantly 'personal growth and development' rather than financial reasons. 2. The interval for rotations of duty stations was found to be mostly irregular(56.4%) while others reported as weekly(16.1%), monthly(12.9%), and fixed terms(4.6%). 3. The main problems related to duty shifts particularly the evening and night duty nurses reported were "not enough time for the family, " "afraid of security problems after the work when returning home late at night." and "lack of leisure time". "problems in physical and physiological adjustment." "problems in family life." "lack of time for interactions with fellow nurses" etc. 4. The forty percent of respondents reported to have '1-2 times' of duty shift rotations while all others reported that '0 time'. '2-3 times'. 'more than 3 times' etc. which suggest the irregularity in duty shift rotations. 5. The majority(62.8%) of study population found to favor the rotating system of duty stations. The reasons for favoring the rotation system were: the opportunity for "learning new things and personal development." "better human relations are possible. "better understanding in various duty stations." "changes in monotonous routine job" etc. The proportion of those disfavor the rotating 'system was 34.7 percent. giving the reasons of"it impedes development of specialization." "poor job performances." "stress factors" etc. Furthermore. respondents made the following comments in relation to the rotation of duty stations: the nurses should be given the opportunity to participate in the. decision making process: personal interest and aptitudes should be considered: regular intervals for the rotations or it should be planned in advance. etc. 6. For the future career plan. the older. married group with longer nursing experiences appeared to think the nursing as their lifetime career more likely than the younger. single group with shorter nursing experiences (
Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.