• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.2
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• pp.97-109
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• 2014

This paper considers a reverse supply chain with simultaneous recovery of used products and manufacturing of brand-new ones. Recovered products are downgraded and have to be sold in a market different from that of brand-new products at a different price. In case of a shortage of recovered product inventory, a brand-new item, if available, can be offered at the price of a recovered product. In other words, one-way demand substitution is allowed. We address the joint decision of when to manufacture brand-new product, when to recover returned product, and how to control demand substitution to maximize the hybrid production system's profits. To this end, we propose a Markov decision Process model and investigate the structure of the optimal policy. Performance comparison is numerically implemented between the models with and without downward demand substitution option under different operating conditions of the system parameters.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.3
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• pp.312-321
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• 2007

We address a disposal issue of returned products in a product recovery system where a single product is stocked in order to meet a demand from customers who may return products after usage. Product returns occur randomly and can be accepted for remanufacturing or disposed of depending on the state of the system. We examine the structure of the optimal disposal policy for returned product that utilizes the information of the inventory of both serviceable and remanufacturable products. Numerical study indicates that it can be characterized by a monotonic threshold type of the curve. A disposal is allowed only when the remanufacturable inventory level exceeds a threshold which is the function of the inventory level of serviceable product and it is decreasing as the serviceable inventory level increases. Sensitivity analysis also indicates that the optimal disposal policy and the optimal profit have monotonic properties with respect to system parameters.

• Journal of the Korean Society of Clothing and Textiles
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• v.32 no.12
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• pp.1891-1902
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• 2008

With concerns for consumers' return behaviors affecting internet shopping malls' profits and product management in the internet clothing market, this study is designed to investigate determinants affecting return and path models for return behaviors. For an empirical study, questionnaires are prepared and respondents in their 20s and 30s with internet clothing purchase experience are selected using the convenience sampling. A total of 517 questionnaires are used for the final analysis. Data are analyzed by using SPSS 12.0 software and descriptive statistics, $x^2$-test, discriminant analysis, regression analysis, and path analysis is conducted. The results are as follows. First, ones who have returned after purchasing clothing items in internet shopping reached 63.4% of the total consumers. Respondents returned items with price at 50 thousand won or less stood at 67.2%, and the most frequent return shopping malls are open markets with their return rate at 51.1%. Second, variables such as risk perception, information search, impulse buying, buying experience, and age have a positive effect on return experience. Impulse buying and buying experience turn out to have a significant effect on the degree of return, but risk perception, information search, age, and gender to have an insignificant effect. Return intention is significantly affected by risk perception, gender, and age. Third, the analysis of path model for return experience shows that perceived risk has a positively effect, and information search has a direct effect as well as an indirect effect through buying experience or impulse buying. The analysis of path model for the degree of return shows that risk perception does not have effect, but information search has indirect effect through buying experience or impulse buying. This study is thought to find consumers' return behavior characteristics in online shopping, and help businesses operating online shopping malls to efficiently manage returns and set up strategies against returns.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.12
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• pp.1169-1172
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• 2006

The prices of chemical products are fluctuated by several factors. The chemical companies can't predict and be ready to all of these changes, so they are exposed to the risk of a profit fluctuation. But they can reduce this risk by making a well-diversified product portfolio. This problem can be thought as the optimization of the product portfolio. We assume that the profits come from the 'spread' between a naphtha and a chemical product. We calculate a mean and a variation of each spread and develop an automatic module to calculate the optimal portion of each product. The theory is based on the Markowitz portfolio management. It maximizes the expected return while minimizing the volatility. At last we draw an investment selection curve to compare each alternative and to demonstrate the superiority. And we suggest that an investment selection curve can be a decision-making tool.

• Journal of the Korea Society of Computer and Information
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• v.16 no.8
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• pp.165-176
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• 2011

In this paper, we develop the cost minimization model to select two incentives and total order quantity with consideration of remanufacture company's return incentive. Return rate is sensitive to the incentive that the manufacture company offers. Using a EOQ(Economic Order Quantity) model of a cost minimization, we show concavities of the model about two incentives and total order quantity respectively. According to the proposed algorithm using the concavities, we find out the optimized incentive prices and total order quantity. Through numerical study, we examine sensitive analysis of the incentive price and order quantity for each parameter when the return rate is sensitive to incentive. Company lessens incentive to reduce total price. However, this makes the total price increase due to a diminution of return quantity. We expect that domestic or overseas remanufacture businesses are able to decide optimal incentive and total order quantity by this research.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.34 no.2
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• pp.29-40
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• 2018

Strength design wind loads for the wind resistance design of structures shall be evaluated by the product of wind loads calculated based on the basic wind speed with 100 years return period and the wind load factor 1.3 specified in the provisions of load combinations in Korean Building Code (KBC) 2016. It may be sure that the wind load factor 1.3 in KBC(2016) had not been determined by probabilistic method or empirical method using meteorological wind speed data in Korea. In this paper, wind load factors were evaluated by probabilistic method and empirical method. The annual maximum 10 minutes mean wind speed data at 69 meteorological stations during past 40 years from 1973 to 2012 were selected for this evaluation. From the comparison of the results of those two method, it can be found that the mean values of wind load factors calculated both probability based method and empirical based method were similar at all meteorological stations. When target level of reliability index is set up 2.5, the mean value of wind load factors for all regions should be presented about 1.35. When target level of reliability index is set up 3.0, wind load factor should be presented about 1.46. By using the relationship between importance factor(conversion factor for return period) and wind load factor, the return periods for strength design were estimated and expected wind speeds of all regions accounting for strength design were proposed. It can be found that return period to estimate wind loads for strength design should be 500 years and 800 years in according to target level of reliability index 2.5 and 3.0, respectively. The 500 years basic wind speed map for strength design was suggested and it can be used with a wind load factor 1.0.

• Journal of Korean Institute of Industrial Engineers
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• v.34 no.4
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• pp.398-407
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• 2008

We consider a product recovery system that a single product is stocked in order to meet a demand from customers who may return products after usage. This paper addresses a problem of when to release a procurement process to replenish serviceable inventory and how many new products to procure. The structure of the optimal procurement policy is examined and numerically identified as a monotonic threshold curve. A numerical procedure is presented to jointly find the optimal procurement order size, optimal procurement policy, and optimal discounted profit. Sensitivity analysis also indicates that these optimal performance measurements have monotonic properties with respect to system parameters.

• Journal of the Korean Society of Clothing and Textiles
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• v.29 no.5 s.142
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• pp.637-648
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• 2005

The purpose of this study was to investigate the differences among internet and TV home-shopping perceived risk segments in regard to clothing shopping orientations and purchase intention. The subjects used for the study were 290 female consumers aged from 20 to 40 living in Seoul. The study used factor analysis, cluster analysis, ANOVA, Duncan test, and $\chi^2-test$. The results showed that the Internet and TV home-shopping perceived risks consisted of 9 factors: Products uncertainty risk, Internet shopping mall trust risk, account-related risk, delivery risk, social risk, size risk, exchange/return risk, TV watching-related risk, and price risk. The cluster analysis showed that there were five groups segmented: Size risk/TV watching risk group, Social risk/Internet trust risk group, Return risk/TV watching low-risk group, Delivery risk/product trust group, and Product risk group. The clothing shopping orientations were classified by 5 factors: Planned shopping, pleasure shopping, sales/fashion oriented shopping, time saving shopping, and credit card preference/in-store oriented shopping. The results showed that the five segmented perceived risk groups differed in regard to clothing shopping orientations, purchase intention, and demographics. Further group differences and implications of the results were discussed.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.4
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• pp.249-260
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• 2019

The study examines the impact of financial risk, convenience risk, non-delivery risk; return policy risk and product risk on online consumer behavior of Malaysian consumers. The research employed a self-administered survey to collect empirical data from 245 Malaysian online shoppers by using convenience sampling. Cronbach alpha was calculated to confirm the reliability of the data and then normality was assessed. Confirmatory Factor Analysis was then conducted to test the model using the goodness-of-fit tests. And finally, structural equation modeling is used to test the hypotheses and draw conclusions. IBM SPSS AMOS version 22.0 was utilized for data analysis. The research indicates that product risk, convenience risk, and return policy risk have a significant and positive impact on online shopping behavior. Financial risk is found to have insignificant and negative effects on consumer behavior. In addition, the non-delivery risk is found to have a significant and negative impact on online shopping behavior. The findings provide a useful model for measuring and managing perceived risk in online shopping which may result in an increase in participation of Malaysian consumers and reduce their cognitive deficiencies in the e-commerce environment. Several managerial implications are discussed along with the scope for future research.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.893-901
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• 2014

A certain number of products are sold each month and some of them are returned for repair. In this study both return rate and cumulative return rate are shown on the graph to show the general trend of how many products are returned as time goes by. Next this type of summary data can be considered as a conglomeration of both left and right censored data. So reliability analysis is attempted for this type of summary data. Lastly, left censored data can be traced to find the exact time period during which the product has been claimed. In that case the left censored data can be taken as failure data. So similar type of reliability analysis is attempted for the resulting right censored data.