Network externalities are essentially dynamic in that the value consumers feel about a product is affected by the size of the existing customer base that uses that product. However, existing studies on network externalities analyzed the effects of network externalities in a static way, not dynamic. In this study, unlike previous studies, the impact of network externalities on price competition in a vertically differentiated market is dynamically analyzed. To this end, a two-period duopoly game model was used to reflect the dynamic aspects of network externalities. Based on the game model, the Nash equilibria for price, sales volume, and revenue were derived and numerically analyzed. The results can be summarized as follows. First, if high-end product has strong market power, the high-end product vendor takes almost all benefits of the network externality. Second, when high-end product has strong market power, the low-end product will take over most of the initial sales volume increase. Third, when market power of high-end product is not strong, it can be seen that the effects of network externalities on the high and low-end products are generally proportional to the difference in quality. Lastly, if there exists a strong network externality, it is shown that the presence of low-end product can be more profitable for high-end product vendor. In other words, high-end product vendor has incentive to disclose some technologies for the market entrance of low-end product, even if it has exclusive rights to the technologies. In that case, however, it is shown that the difference in quality should be maintained significantly.
In order to deal with high uncertainty and variability in emergency medical centers, many researchers have developed various models for their operational planning and scheduling. However, most of the models just provide static plans without any risk measures as their results, and thus the users often lose the opportunity to analyze how much risk the patients have, whether the plan is still implementable or how the plan should be changed when an unexpected event happens. In this study, we construct a simulation model combined with a risk-based planning and scheduling module designed by Simio LLC. In addition to static schedules, it provides possibility of treatment delay for each patient as a risk measure, and updates the schedule to avoid the risk when it is needed. By using the simulation model, the users can experiment various scenarios in operations quickly, and also can make a decision not based on their past experience or intuition but based on scientific estimation of risks even in urgent situations. An example of such an operational decision making process is demonstrated for a real mid-size emergency medical center located in Seoul, Republic of Korea. The model is designed for temporal short-term planning especially, but it can be expanded for long-term planning also with some appropriate adjustments.
The process control methods based on the statistical analysis apply the analysis method or mathematical model under the assumption that the process characteristic is normally distributed. However, the distribution of data collected by the automatic measurement system in real time is often not followed by normal distribution. As the statistical analysis tools, the process capability index (PCI) has been used a lot as a measure of process capability analysis in the production site. However, PCI has been usually used without checking the normality test for the process data. Even though the normality assumption is violated, if the analysis method under the assumption of the normal distribution is performed, this will be an incorrect result and take a wrong action. When the normality assumption is violated, we can transform the non-normal data into the normal data by using an appropriate normal transformation method. There are various methods of the normal transformation. In this paper, we consider the Box-Cox transformation among them. Hence, the purpose of the study is to expand the analysis method for the multivariate process capability index using Box-Cox transformation. This study proposes the multivariate process capability index to be able to use according to both methodologies whether data is normally distributed or not. Through the computational examples, we compare and discuss the multivariate process capability index between before and after Box-Cox transformation when the process data is not normally distributed.
Since guided missiles with the characteristics of the one-shot system remain stored throughout their entire life cycle, it is important to maintain their storage reliability until the launch. As part of maintaining storage reliability, period of preventive test is set up to perform preventive periodic test, in this case failure detection rate has a great effect on setting up period of preventive test to maintain storage reliability. The proposed method utilizes failure rate predicted by the software on the basis of MIL-HDBK-217F and failure mode analyzed through FMEA (Failure Mode and Effect Analysis) using data generated from the actual field. The failure detection rate of using the proposed method is applied to set periodic test of the actual guided missile. The proposed method in this paper has advantages in accuracy and objectivity because it utilizes a large amount of data generated in the actual field.
As the importance of Knowledge Management System (KMS) in the military increases, Republic of Korea Army (ROK Army) developed Army Knowledge Portal. Although the members in the military are encouraged to use the portal, few members currently use it. This study was conducted to find variables to predict the user’s intention to use the portal, which contributes to activating the use of Army Knowledge Portal in the army. On the basis of Technology Acceptance Model (TAM), ten variables such as perceived ease of use, general information security awareness, information security awareness, expectation for external rewards, expectation for relationships, sense of self-worth, attitude toward compliance with security policy, attitude toward knowledge sharing, intention of non-combat knowledge sharing, and intention of combat knowledge sharing were considered as independent variables. 105 participants on active duty who currently use or have experience to use the portal participated in this study. The results indicated that general information security awareness and information security awareness increases compliance with the information security policy. In addition, the attitude toward knowledge sharing is enhanced by expectations for relationship and sense of self-worth. Based on the results, the authors propose the need for policy alternatives to reinforce the reward system and security policy, which activates the use of Knowledge Portal Service for ROK Army.
As the technological gap amongst manufacturers decreases, the life cycle of products has shortened, and competition accelerates due to the development of technology, incumbent manufacturing companies face growth limitations. In order to overcome such business issues, manufacturing companies are increasingly interested in changes in business models and innovations, especially in the direction of providing services where they can maintain the competitive advantage of their products. In such context, this empirical study examines managerial leadership, differentiation strategies, and products and services pricing as ‘servitization factors’, which can be driving forces for moving into a new era of growth for Korean SMEs, focusing on the mediating effects of servitization competency. The results are as follows : First, it was confirmed that executive leadership, differentiation strategy, and information & communication technology capability have a direct effect on service sales. Second, the process competency, partnership competency, and ICT competency, which are presented as the service competence of SMEs, were found to play an important role in inducing service sales in managerial leadership, differentiation strategy, product and services pricing. It also emphasized the role of the public policy such as helping to foster SMEs as key partners in the expansion of social facilities and establishing platforms through ICT and data utilization for the convergence of manufacturing services.
The growth and employment effects of R&D investment were analyzed according to business size, export value and manufacturing sectors so as to suggest improvement directions for effective industry policies. The effect of R&D investment was considered simultaneously from the two perspectives of growth and employment effect, and the causality analysis was carried out by using a path analysis. The result of the path analysis confirmed significant differences in the growth effect of R&D investment depending on business size. However, the effect of increasing employment was difficult to obtain statistically significant results for any various combinations of business size and export value. This is a mixture of directions for the effects of R&D investment on employment, which could be due to the failure to consider appropriate time lags between investment and effect. Efficiency analysis by industry sectors confirmed significant differences in efficiency depending on business size, but differences depending on export value were difficult to identify. In order to derive improvement policy by industry sector according to business size and export value, the direction of selective support policy and universal support policy was derived for six industry groups by combining the return to scale in the efficiency analysis and R&D concentration. Hirschman-Herfindahl index is used for calculating R&D concentration.
As information technology advances, the penetration of smart devices connected to the Internet, such as smart phone and tablet PC, has rapidly expanded, and as sensor prices have fallen the Internet of Things has begun to be introduced in the industry. Today's industry is rapidly changing and evolving, requiring companies to respond to the new paradigm of business. In this situation, companies need to actively manage and maintain customer relationships in order to acquire loyal customers who bring them a high return. The purpose of this study is to suggest a method to manage customer relationship using real time IoT data including IoT product usage data, customer characteristics and transaction data. This study proposes a method of segmenting customers through RFM analysis and transition index analysis. In addition, a real-time monitoring through control charts is used to identify abnormalities in product use and suggest ways of differentiating marketing for each group. In the study, 44 samples were classified as 9 churn customers, 10 potential customers, and 25 active customers. This study suggested ways to induce active customers by providing after-sales benefit for product reuse to a group of churn customers and to promote the advantages or necessity of using the product by setting the goal of increasing the frequency of use to a group of potential customers. Finally, since the active customer group is a loyal customer, this study proposed an one-on-one marketing to improve product satisfaction.
High variance observed in the measurement system can cause high process variation that can affect process capability badly. Therefore, measurement system analysis is closely related to process capability analysis. Generally, the evaluation for measurement system and process variance is performed separately in the industry. That is, the measurement system analysis is implemented before process monitoring, process capability and process performance analysis even though these analyses are closely related. This paper presents the effective concurrent evaluation procedure for measurement system analysis and process capability analysis using the table that contains Process Performance (Pp), Gage Repeatability & Reproducibility (%R&R) and Number of Distinct Categories (NDC). Furthermore, the long-term process capability index (Pp), which takes into account both gage variance and process variance, is used instead of the short-term process capability (Cp) considering only process variance. The long-term capability index can reflect well the relationship between the measurement system and process capability. The quality measurement and improvement guidelines by region scale are also described in detail. In conclusion, this research proposes the procedure that can execute the measurement system analysis and process capability analysis at the same time. The proposed procedure can contribute to reduction of the measurement staff’s effort and to improvement of accurate evaluation.
The population of domestic companion animals is estimated to be about 10 million now. In recent years, the domestic pet market has been launching a wide range of products and services for high quality, smart and well-being. As a result, the market size will increase from 900 billion won in 2012 to 2.3 trillion won in 2016, which has more than doubled in five years. The industry expects to reach 6 trillion won by 2020, expecting 3 trillion won this year. In particular, domestic dogs and cats market is estimated at 275.5 billion won, accounting for 19% of the domestic animal market and 1.425 billion won for the world market.
However, despite the growing market for companion animals products, unfortunately the import dependence on related industrial goods is still high and the quality of service is very low. Unlike Europe and the United States, 90% of companion animals are housed in apartments, often causing problems in the health and safety of companions and companions.
The purpose of this study is to develop a smart house for companion animals with environmental friendliness and AI function that can be won in competition with products of developed countries. The results of this study are expected to contribute to the creation of a new value - added base for the related industries through the strengthening of the competitiveness of the related SMEs and further the effect of employment increase and import substitution.
Self Organizing Map (SOM) is a neural network that is effective in classifying patterns that form the feature map by extracting characteristics of the input data. In this study, we propose an algorithm to determine the cell formation and the machine layout within the cell for the cell formation problem with operation sequence using the SOM. In the proposed algorithm, the output layer of the SOM is a one-dimensional structure, and the SOM is applied to the parts and the machine in two steps. The initial cell is formed when the formed clusters is grouped largely by the utilization of the machine within the cell. At this stage, machine cell are formed. The next step is to create a flow matrix of the all machine that calculates the frequency of consecutive forward movement for the machine. The machine layout order in each machine cell is determined based on this flow matrix so that the machine operation sequence is most reflected. The final step is to optimize the overall machine and parts to increase machine layout efficiency. As a result, the final cell is formed and the machine layout within the cell is determined. The proposed algorithm was tested on well-known cell formation problems with operation sequence shown in previous papers. The proposed algorithm has better performance than the other algorithms.