• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.2
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• pp.43-50
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• 2008

In this paper we suggest the methods that compute the total inventory cost based on EOQ and the total inventory cost based on OMMIP. The total inventory cost consists of purchasing cost, ordering cost, inventory holding cost, stockout cost and so on. This papers also proposes the method that decides optimum order quantity as the order amount to minimize the total inventory cost with comparison of EOQ total inventory cost and OMMIP total inventory cost according to inventory holding cost as a fraction of unit cost.

• Korean Journal of Agricultural Science
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• v.44 no.2
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• pp.283-295
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• 2017

This study analyzed the effect of the intrinsic and extrinsic attributes of gochujang, Korean red chili paste, on purchasing intention and recommendation intention for consumption. Survey participants were female, married, aged 30 - 39 years, and highly educated with graduation from a university. Most participants purchased gochujang 1 - 2 times per year, most commonly at a shopping mall, and acquired information on the gochujang product from an advertisement or sponsored TV shows. For the factor analysis, five variables for intrinsic quality were considered: namely, healthiness, economics, convenience, diversity, and sense, whereas three variables were considered for extrinsic quality: trust, external appearance, and image. The factor analysis also confirmed the correlation between the validity and the reliability of the purchasing and recommendation intentions. The effect of intrinsic quality of gochujang on purchasing and recommendation intentions was tested through a multiple regression analysis. The purchase intention was most significantly affected by healthiness, cost, and convenience. On the other hand, the recommendation intention was most significantly affected by the diversity and, to a lesser degree, by the healthiness of the product. Among the extrinsic qualities, trust of consumers and the product appearance had a significant effect on purchasing intention. Recommendation intention was significantly affected by the appearance. And trust significantly influenced the recommendation. Therefore, a concrete and systematic marketing approach considering these factors.

• Journal of Korean Society of Forest Science
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• v.102 no.1
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• pp.24-29
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• 2013

This study was carried out for preliminary feasibility review to investigate work efficiency of chestnut harvest using yield net and to find a way to reduce the cost for purchasing and managing yield net. To this end, we conducted a survey of 6 forest farm houses in Cheongyang, Gongju and Buyeo regions where yield nets are being used efficiently. Cost-benefit analysis based on Net Present Value and Benefit Cost Ratio was used to examine the investment profitability. While regions of Cheongyang A, Cheongyang B and Buyeo A are profitable in spite of no subsidy for purchasing yield net from the government, the regions of Gongju A, Gongju B and Buyeo B are not profitable without subsidy. When an 80% of subsidy for purchasing yield nets is provided, the forest farm houses in Cheongyang A, Cheongyang B, Buyeo A and Gongju B regions are found to be profitable while those in Gongju A and Buyeo B regions are still not profitable. We consider that the different results come from the differences in the planting method of chestnut, labor efficiency, labor skill and the orchard conditions such as slop. Finally, several efforts for government and cultivator are suggested to expand the use of yield net; planting chestnut in line, establishing installation and management methods, supporting the cost for purchasing yield net and studying detailed effects besides profitability issue.

• Journal of the Korea Fashion and Costume Design Association
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• v.25 no.3
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• pp.1-16
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• 2023

The purposes of this study were to investigate bag purchasing behaviors and design preferences of male and female adult consumers, and to find the differences depending on sex and age variable. A survey was conducted on 400 male and female adults from 20s to 50s. The questionnaire consisted of bag purchase behaviors, bag design preferences, and the subjects' demographic characteristics. The data were analyzed by Cronbach's α, factor analysis, x² test and t-test using SPSS. The results were as follows. First, as bag selection criteria, four factors (practicality, symbolism, aesthetics, and economics) were derived, and adult consumers considered economics as the most important among the factors. As for purchasing information sources, three factors (media, human resources, and store) were derived, and adult consumers considered human resources and store information sources more important than media. The main motive for purchasing bags was age and damage of the owned products, and Internet shopping malls were the most common purchasing place. The average annual cost of purchasing bags was 100,000 to 300,000 won, and the frequency of purchase was about once a year. Second, as bag preference images, four factors (individual, romantic, active, and classic image) were derived, and adult consumers preferred classic images the most. The shoulder bag was the most preferred as the bag shape, and black was the most preferred bag color. For the material, natural leather was the most preferred, and for the size, medium size was the most preferred. Third, bag purchasing behaviors and design preferences showed many significant differences according to the sex and age of the consumers. Therefore, the results of this study suggests that bag companies need to establish product development and marketing strategies in consideration of differences according to the sex and age group of adult consumers.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.95-108
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• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.

• Journal of the Korean Operations Research and Management Science Society
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• v.35 no.1
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• pp.111-124
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• 2010

In the last decade, traveling purchaser problem (TPP) has received some attention of the researchers in the operational research area. TPP is a generalization of the well-known traveling salesman problem (TSP), which has many real-world applications such as purchasing the required raw materials for the manufacturing factories and the scheduling of a set of jobs over some machines, and many others. In this paper we suggest heterogeneous multiple traveling purchaser problem with budget constraint (HMTPP-B) which looks for several cycles starting at and ending to the depot and visiting a subset at a minimum traveling cost and such that the demand for each product is satisfied and the cost spent for purchasing the products does not exceed a given budget threshold. All the past studies of TPP are restricted on a single purchaser. Therefore we randomly generated some instances. CPLEX is used for getting optimal solutions in these experiments.

• Management Science and Financial Engineering
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• v.18 no.2
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• pp.23-27
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• 2012

The problem of jointly determining a robust optimal bundle of price and order quantity for a retailer in a single-retailer, single supplier, single-product supply chain is considered. Demand is modeled as a decreasing power function of product price, and unit purchasing cost is modeled as a decreasing power function of order quantity and demand. Parameters defining the two power functions are uncertain but their possible values are characterized by ellipsoids. We extend a previous study in two ways; the purchasing cost function is generalized to take into account the economies of scale realized by higher product demand in addition to larger order quantity, and an exact transformation into an equivalent convex optimization program is developed instead of a geometric programming approximation scheme proposed in the previous study.

• Journal of the Korea Institute of Building Construction
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• v.8 no.3
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• pp.51-58
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• 2008

The paradigm shift in housing market has changed consumers' interests to the cost of housing, it is the mos important factor considered in purchasing house recently. In this study, some factors influencing on the cost o: housing and some preference factors considered in purchasing house are analyzed. The AHP(Analytic Hierarchy Process) is used to analyze preference factors. The results of this study can be used as a decision maker in the initial planning stage of construction industry.

• Journal of Digital Convergence
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• v.18 no.10
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• pp.527-535
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• 2020

The purpose of research is to examine mothers with elementary school children in Chungcheong and the convergence effect of recognition of food labeling standards on purchasing behavior. A two-step cluster analysis was performed for group classification according to the purchase behavior of processed foods and the collection was determined by Schwarz's BIC criteria. Three types were determined: "convenience pursuit," "large mart preference," and "high cost reverse purchase". The proportion of college graduates in 'large mart preference' was higher, the proportion of employment mothers in 'high cost reverse purchase' was higher, and the need for food labeling standards was higher in 'large mart preference'. 'Shelf life' was recognized as the most important item. 'Large market preference' scored higher in 'used materials' and 'food additives', 'nutrition labelling'. In order to improve the purchasing behavior of processed foods, above all else, it is necessary to develop customized educational media that can be easily applied to real life.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.449-455
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• 2002

This paper considers that advanced planning and scheduling (APS) in manufacturing and the efficient purchasing where each customer order has its due date and multi-suppliers exit We present a Make-To­Order Supply Chan (MTOSC) model of efficient purchasing process from multi-suppliers and APS with outsourcing in a supply chain, which requires the absolute due date and minimized total cost. Our research has included two states. One is for efficient purchasing from suppliers: (a) selection of suppliers for required parts; (b) optimum part lead­time of selected suppliers. Supplier selection process has received considerable attention in the business­management literature. Determining suitable suppliers in the supply chain has become a key strategic consideration. However, the nature of these decisions usually is complex and unstructured. These influence factors can be divided into quantitative and qualitative factors. In the first level, linguistic values are used to assess the ratings for the qualitative factors such as profitability, relationship closeness and quality. In the second level a MTOSC model determines the solutions (supplier selection and order quantity) by considering quantitative factors such as part unit price, supplier's lead-time, and storage cost, etc. The other is for APS: (a) selection of the best machine for each operation; (b) deciding sequence of operations; (c) picking out the operations to be outsourcing; and (d) minimizing makespan under the due date of each customer's order. To solve the model, a genetic algorithm (GA)-based heuristic approach is developed. From the numerical experiments, GA­based approach could efficiently solve the proposed model, and show the best process plan and schedule for all customers' orders.