• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.17-35
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• 2015

Demand forecasting is the activity of estimating the quantity of a product or service that consumers will purchase for a certain period of time. Developing precise forecasting models are considered important since corporates can make strategic decisions on new markets based on future demand estimated by the models. Many studies have developed market growth curve models, such as Bass, Logistic, Gompertz models, which estimate future demand when a market is in its early stage. Among the models, Bass model, which explains the demand from two types of adopters, innovators and imitators, has been widely used in forecasting. Such models require sufficient demand observations to ensure qualified results. In the beginning of a new market, however, observations are not sufficient for the models to precisely estimate the market's future demand. For this reason, as an alternative, demands guessed from those of most adjacent markets are often used as references in such cases. Reference markets can be those whose products are developed with the same categorical technologies. A market's demand may be expected to have the similar pattern with that of a reference market in case the adoption pattern of a product in the market is determined mainly by the technology related to the product. However, such processes may not always ensure pleasing results because the similarity between markets depends on intuition and/or experience. There are two major drawbacks that human experts cannot effectively handle in this approach. One is the abundance of candidate reference markets to consider, and the other is the difficulty in calculating the similarity between markets. First, there can be too many markets to consider in selecting reference markets. Mostly, markets in the same category in an industrial hierarchy can be reference markets because they are usually based on the similar technologies. However, markets can be classified into different categories even if they are based on the same generic technologies. Therefore, markets in other categories also need to be considered as potential candidates. Next, even domain experts cannot consistently calculate the similarity between markets with their own qualitative standards. The inconsistency implies missing adjacent reference markets, which may lead to the imprecise estimation of future demand. Even though there are no missing reference markets, the new market's parameters can be hardly estimated from the reference markets without quantitative standards. For this reason, this study proposes a case-based expert system that helps experts overcome the drawbacks in discovering referential markets. First, this study proposes the use of Euclidean distance measure to calculate the similarity between markets. Based on their similarities, markets are grouped into clusters. Then, missing markets with the characteristics of the cluster are searched for. Potential candidate reference markets are extracted and recommended to users. After the iteration of these steps, definite reference markets are determined according to the user's selection among those candidates. Then, finally, the new market's parameters are estimated from the reference markets. For this procedure, two techniques are used in the model. One is clustering data mining technique, and the other content-based filtering of recommender systems. The proposed system implemented with those techniques can determine the most adjacent markets based on whether a user accepts candidate markets. Experiments were conducted to validate the usefulness of the system with five ICT experts involved. In the experiments, the experts were given the list of 16 ICT markets whose parameters to be estimated. For each of the markets, the experts estimated its parameters of growth curve models with intuition at first, and then with the system. The comparison of the experiments results show that the estimated parameters are closer when they use the system in comparison with the results when they guessed them without the system.

• Journal of Preventive Medicine and Public Health
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• v.22 no.1 s.25
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• pp.29-44
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• 1989

A household survey was conducted to determine the utilization rate of medical facilities and to identify the factors related with the utilization in the South District of Taegu from July 3 to July 15, 1988. Study population included 1,723 family members of 431 households which were selected by one-stage simple cluster random sampling. Well trained medical college students interviewed mainly housewives with a structurized questionnaire. Morbidity rate of acute illness during the 2-week period was 101 per 1,000 persons and it was highest in the age group of 9 years below. The rate for chronic illness was 77 per 1,000 persons, increasing with age, low income and medicaid benefit. During the 2-week period, 689 of 1,000 persons utilized the medical facilities. Of the facilities, most number, 294, used hospital and clinic, and the order ran as pharmacy, health center, and herb medical clinic. The utilization rate was higher in the female, 70-year and older group, medicaid group, the lowest income class and self-employed group than other groups. The average number of visits among users of medical facilities during the 2-week period was 3.25. those who visited medical facilities most frequently were females, the 70-year and older group, the lowest income class and blue collar worker group. During one-year period, admission rate of 1,000 persons was 27.6 and that of female was 38.9, higher than that of male. the eldest group had the highest admission rate. Admission rate of medical insurance beneficiaries was twice or higher than non-beneficiaries. The higher the family monthly income, the more frequently they admitted. During one-year period, average admission days of the persons hospitalized were 22.5 days and males were hospitalized longer than females. The groups which were hospitalized longest were those between the ages of 40 and 49, medical insurance beneficiaries, the lowest income group and unemployed group. During one-year period, average admission days of 1,000 persons were 560 days and those of female were 661 days, more than those of male. The guoups which had the longest admission days were those above 70 years of age, the lowest income and unemployed groups. The medical insurance beneficiaries were three times or longer than non-beneficiaries. In logistic regression analysis of utilization of physician significant independent variables were the 9-year and younger group(+), the 70-year and older group(+), acute illness episode(+), chronic illness episode(+), medical insurance beneficiary(+) and white collar workers(-). Acute and chronic illness episode(+), and medical insurance for government employees and private school teacher(-) were significant variables in analysis of utilization of pharmacy. In multiple regression analysis of the number of physician visits, siginificant variables were acute illnes episode(+), chronic illness episode(+), industrial, occupational and regional medical insurance beneficiary(+), white collar workers(-). Acute and chronic illness episode(+), and medical insurance beneficiary(-) were significant variables in analysis of the number of pharmacy visits. In logistic regression analysis of admission event, significant independent variables were the 9-year and younger group(+), the 70-year and older group(+) , chronic illness episode(+), and medical insurance beneficiary(+).