In this paper, an alternative inventory policy that trades off the bullwhip effect at an upstream facility with cost minimization at a current facility, with the goal of reducing system wide total expected inventory costs, when external demand distributjon is autocorrelated, is considered. The alternative inventory policy has a form that is somewhere between one that completely neglects the autocorrleation and one that actively utilizes the autocorrelation. For this purpose, a mathematical model that allows us to evaluate system wide total expected inventory costs for a periodic review system is developed. This model enables us to identify an optimal inventory policy at a current facility that minimizes system wide total expected inventory costs by the best tradeoff of the bullwhip effect at an upstream facility with cost minimization at a current facility. From numerical experiments, it has been found that (i) when the autocorrelation is negative, the optimal policy is one that actively utilizes the autocorrelation, (ii) when the autocorrelation is small and positive, the optimal policy is one that neglects the autocorrelation, and (iii) when the autocorrelation is large and positive, the optimal policy is somewhere between one that actively utilizes the autocorrelation and one that neglect the autocorrelation.

Investors aim to maximize the return rate for their own investment, utilizing various information as possible as they can access. However those investors, especially individual investors, have limitations of interpretation of the domain-specific information or even the acquisition of the information itself. Thus, individual investors tend to make decision affectively and frequently, which may cause a loss in returns. This study aims to analyze analysts’ target price and to suggest the strategy that could maximize individual’s return rate. Most previous literature revealed that the optimistic bias exists in the analysts’ target price and it is also confirmed in this study. In this context, this study suggests the upper limit of target rate of returns and the optimal value named ‘alpha(α)’ which performs the adjustment of proposed target rate to maximize excess earning returns eventually. To achieve this goal, this study developed an optimization problem using linear programming. Specifically, when the analysts’ proposed target rate exceeds 30%, it could be adjusted to the extent of 59% of its own target rate. As apply this strategy, the investors could achieve 1.2% of excess earning rate on average. The result of this study has significance in that the individual investors could utilize analysts’ target price practically.

This study aims to objectively measure the efficiency of nanotechnology R&D programs by systematically evaluating the inputs and outputs of nanotechnology R&D activities and to find implications for improving the efficiency of nanotechnology R&D programs. Data on input factors such as R&D investment, R&D manpower, R&D period, and output factors such as paper, patent, and commercialization for R&D projects which started from 2008 or afterwards and ended by 2011 are gathered through National Science and Technology Knowledge Information Service, which are used for efficiency evaluation. In this study, we analyzed R&D efficiency in detailed technology units in depth. The process taken in this study is as follows. First, the basic statistics of input and output factors to compare and analyze R&D investment, R&D manpower, R&D period, paper, patent, and commercialization status by technology unit are analyzed. Next, DEA models are utilized to derive the overall efficiency, pure technology efficiency, and scale efficiency by conducting the efficiency evaluation for each technology unit, from which implications for strategic budget allocation are derived. In addition, partial efficiency evaluation is conducted to identify advantages and disadvantages of each technology unit. In turn, cluster analysis is performed to identify similar technology units, from which implications for efficiency improvement are derived.

When evaluating effectiveness of a R&D program, there is a tendency to simply compare the performances of the beneficiaries before and after the program or to compare the differences in the performances of the beneficiaries and the non-beneficiaries before-after the program. However, these ways of evaluating effectiveness of a program have some problems because they can not differentiate between complement effect, which facilitates corporate R&D investment, and substitute effect, which, literally, substitutes corporate R&D investment. Therefore, these problems could bring about wrong policies concerning R&D programs.In this paper, a new approach using path analysis is suggested as a means to overcome these problems and it is utilized, as an application, to evaluate the effectiveness of Plant Engineering Program conducted by Ministry of Trade, Industry & Energy, Korea. First, the direct impact of government R&D investment on corporate R&D investment is analyzed, through which it is identified which of crowding-in effect (complement effect) and crowding-out effect (substitute effect) is dominant. Next, the direct effect of government R&D investment on corporate performance and the direct effect of corporate R&D investment on corporate performance is analyzed respectively. Finally, by combining the two previous analysis, the total effect of government R&D investment on corporate performance is identified.As a result, it turns out that, in Plant Engineering Program, crowding-in effect is more dominant than crowding-out effect and that Plant Engineering Program has definitely positive effect on the beneficiary in terms of corporate performance indirectly and directly.

In this paper, we compared the efficiencies of national R&D investments between NT (Nanotechnology) areas in terms of papers, patents, and commercializations, and found ways to improve the efficiencies of national R&D investments for each NT area. This is in response to huge R&D investments government has made recently in NT areas.Here, we collected data on investments, papers, patents, and commercializations for the R&D projects in NT areas through National Science & Technology Information Service. Based on the data, we analyzed the investment and performances (papers, patents, and commercializations) for each NT area, calculated the efficiency for each NT area, and compared the efficiencies between NT areas. Next, using cluster analysis, we identified several NT areas with similar characteristics in terms of paper efficiency, patent efficiency and commercialization efficiency. Finally, we derived implications for the efficiency enhancement for each grouping.The cluster analysis showed that there could be two groups, one being low in terms of technological outcome (papers and patents) efficiencies and high in terms of commercialization efficiencies, while the other being high in terms of technological outcome (papers and patents) efficiencies and low in terms of commercialization efficiencies. Therefore, the strategy for one group calls for support for technology transfer or technology introduction from other R&D performers and grant of guidance for improving R&D performers’ commercialization ability to other R&D performers while the strategy for the other group calls for R&D support for transfer of technology to other R&D performers, activation of technology transfer and support for commercialization of R&D performers.

In this paper, scale efficiencies and relative efficiencies of R&D projects in Industrial Technology Program, sponsored by Ministry of Trade, Industry and Energy, Korea, are calculated and compared. For the process, various DEA (Data Envelopment Analysis) models are adopted as major techniques. For DEA, two stage input oriented models are utilized for calculating the efficiencies. Next, the calculated efficiencies are grouped according to their subprograms (Industrial Material, IT Fusion, Nano Fusion, Energy Resources, and Resources Technology) and recipient types (Public Enterprise, Large Enterprise, Medium Enterprise, Small Enterprise, Lab., Univ., and etc.) respectively. Then various subprograms and recipient types are compared in terms of scale efficiencies (CCR models) and relative efficiencies (BCC models). In addition, the correlation between the 1st stage relative efficiencies and the 2nd stage relative efficiencies is calculated, from which the causal relationship between them can be inferred. Statistical analysis shows that the amount of input, in general, should increase in order to be scale efficient (CCR models) regardless of the subprograms and recipient types, that the 1st and 2nd stage relative efficiencies are different in terms of the programs and recipient types (BCC models), and that there is no significant correlation between the 1st stage relative efficiencies and the 2nd stage relative efficiencies. However, the results should be used only as reference because the goal each and every subprogram has is different and the situation each and every recipient type faces is different. In addition, the causal link between the 1st stage relative efficiencies and the 2nd relative efficiencies is not considered, which, in turn, is the limitation of this paper.

When evaluating effectiveness of a program, there is a tendency to simply compare the performances of the treated before and after the program or to compare the differences in the performances of the treated and the untreated before-after the program. However, these ways of evaluating effectiveness have problems because they can’t account for environmental changes affecting the treated and/or effects coming from the differences between the treated and the untreated. Therefore, in this paper, panel data analysis (fixed effects model) is suggested as a means to overcome these problems and is utilized to evaluate the effectiveness of fusion technology program conducted by Ministry of Trade, Industry and Energy, Korea. As a result, it turns out that the program has definitely positive impacts on the beneficiary in terms of sales, R&D expenditure, and employment.

In this paper, efficiencies of core technology development projects, conducted by Ministry of Trade, Industry and Energy, are compared. In the process, DEA (Data Envelopment Analysis) is utilized as a main technique for comparing efficiencies. For DEA, input oriented BCC Model is adopted with government grant, recipient expenditure, the number of participating institutions, and project duration as input factors, and the number of patents, the number of papers, and occurred sales as output factors. As a result, next generation mobile communication project turns out to be the most efficient project of all. Therefore, next generation mobile communication project should be benchmarked for the other projects to follow. However, these results should be used only for reference data since every project has a different objective and, of course, is run under a different environment.

이 논문에서는 작업의 처리시간이 임의의 확률분포를 따르고 작업의 납기일이 작업마다 별개인 상황에서의 단일기계 스케줄링문제에 관하여 살펴본다. 이 때 충분히 이른 작업의 수를 최대화시키는 데에 관심을 둔다. 이러한 스케줄링문제를 풀기 위한 두 가지 알고리즘, 즉 이진정수계획모형과 스케줄링 규칙을 제안한다. 여기서 제안하는 스케줄링 규칙은 처리시간과 납기일이 확정적인 경우에 지연작업의 수를 최소화 시켜주는 스케줄링을 제공하는 기존 알고리즘을 처리시간과 납기

이 논문에서는 지진이나 해일과 같은 자연재해가 발생했을 때 생존자의 수를 최대화하기 위한 응급차량의 배차문제에 대하여 살펴본다. 이 경우에는 도로 네트워크상에서 최단거리에 있으리라 예상되는 환자부터 실어 나르는 스케줄링 규칙을 많이 이용한다. 이 스케줄링 규칙을 SEPT(Shortest Expected Processing Time)라고 한다. 이 논문에서는 SEPT보다 효율적이라 생각되는 새로운 스케줄링 규칙을 제안한다. 이 스케줄링 규칙은 처리시간과

  기업의 수요에서 자기상관을 인식하지 못하게 되면, 이 기업은 수요가 서로 독립이면서 동일한 분포를 따른다는 가정 하에 개발된 전통적인 재고주문방법을 시행하게 된다. 따라서 이 경우에 안전재고량, 재고부족 확률, 재고부족량 등과 같은 성과지표의 관점에서 전통적인 재고주문방법이 정확한 재고주무방법에 비하여 얼마만큼 비효율적인가를 알아보는 것은 기업경영의 측면에서 의미 있는 일이다. 이 논문에서는 정기주문시스템 하에서의 두 가지 재고 관리방법을 비교하기

  In this paper, an approach for analyzing brand loyalty and brand switching behavior in car insurance market is presented. A two-choice model by Blumen, et al. that uses Markov chain is adopted as a main technique for estimating brand parameters. Survey

Purpose – Naver has emerged as a new leader in the open market. While existing open markets such as Gmarket, 11th Street, and so on are suffering from profitability deterioration, Naver is attracting sellers based on low commission and powerful search engine. We would like to analyze the impact of Naver shopping on the national economy, especially on employment, in a situation where the market reaction to Naver's strength as a leader in online shopping is mixed. Research Design, Data, and Methodology – Through the demand inducing inter-industry analysis, we estimate the employment inducement effect by Naver shopping from its shopping transaction. In turn, through the supply inducing inter-industry analysis, we estimate the employment inducement effect by Naver shopping from its low commission and powerful search engine. For the purpose of inter-industry analysis, as of 2018, the most recently announced 2014 inter-industry table (extension table) from the Bank of Korea is used. Results – The results of this study are as follows. First, Naver Shopping is expected to generate 7.8 trillion won’s trade in 2018, resulting in 244,225 of job inducement, and 158,598 of employment inducement. In addition, Naver Shopping is estimated to benefit KRW 213 billion to its sellers due to low commission and powerful search function, resulting in 8,667 of job inducement, and 5,655 of employment inducement. Second, in terms of job inducement and employment inducement due to Naver Shopping’s trade, transportation, business support service, information and communication, broadcasting, restaurants and lodging were ranked. Third, in terms of job inducement and employment inducement due to Naver Shopping's low commission and powerful search function, restaurants and hospitality, f/b and cigarette manufacturing, construction, and transportation equipment manufacturing were ranked. Conclusions – The number of job inducement resulting from low commission and powerful search engine of Naver shopping in 2018 was 8,667 (3.7% of 244,225, which was caused by transaction in Naver shopping in 2018), and employment inducement was 5,655 (3.7% of 158,598, which was caused by transaction in Naver shopping in 2018), which can be considered as additional employment impacts of Naver Shopping compared to the other online shopping operators.