• The Journal of Society for e-Business Studies
• /
• v.23 no.1
• /
• pp.1-22
• /
• 2018

The value chain has been utilized as a strategic tool to improve competitive advantage, mainly at the enterprise level and at the industrial level. However, in order to conduct value chain analysis at the enterprise level, the client companies of the parent company should be classified according to whether they belong to it's value chain. The establishment of a value chain for a single company can be performed smoothly by experts, but it takes a lot of cost and time to build one which consists of multiple companies. Thus, this study proposes a model that automatically classifies the companies that form a value chain based on actual transaction data. A total of 19 transaction attribute variables were extracted from the transaction data and processed into the form of input data for machine learning method. The proposed model was constructed using the Random Forest algorithm. The experiment was conducted on a automobile parts company. The experimental results demonstrate that the proposed model can classify the client companies of the parent company automatically with 92% of accuracy, 76% of F1-score and 94% of AUC. Also, the empirical study confirm that a few transaction attributes such as transaction concentration, transaction amount and total sales per customer are the main characteristics representing the companies that form a value chain.

• Journal of Korea Port Economic Association
• /
• v.25 no.3
• /
• pp.139-164
• /
• 2009

The purpose of this paper is to investigate the value chain efficiency of Korean port investment by using the newly developed multi-year and multi-stage value chain efficiency model of DEA(Data Envelopment Analysis). Inputs[port investment amount, cargo handling capacity, and berthing capacity], and outputs[cargo handling amount, number of ship calls, revenue, and score of customer service satisfaction] are used during 14 years(1994-2007) for 20 Korean seaports by using two kinds of DEA models. Empirical main results are as follows: First, Model 1 shows that the ranking order of multi-stage value chain efficiency is Stage 2, Stage 3-1, Stage 1, and Stage 3-2. And according to the value chain average efficiency scores, ranking order is stages 2, 1, 3-1, and 3-2. In Model 2, 3(Incheon, Mogpo, and Jeju) out of 9 ports show the ranking order of Stages 2, 3-2, 3-1, and 1. And value chain average efficiency scores rank in order of Stages 2, 3-2, 3-1, and 1. Second, the difference among the value chain efficiency scores of each stage comes from the efficiency deterioration of all ports except Stages 2 and 1 in Model 1. In Model 2, value chain efficiency scores among the Stages 3-1, 3-2 compared to Stage 1 were deteriorated. The main policy implication based on the findings of this study is that the manager of port investment and management of Ministry of Land, Transport and Maritime Affairs in Korea should introduce the multi-year, multi-stage value chain efficiency method for deciding the port investment amount and evaluating the effect of port investment after considering the empirical results of this paper carefully.

• Korean Journal of Construction Engineering and Management
• /
• v.23 no.2
• /
• pp.3-15
• /
• 2022

In this study, we develop a simulation model for Supply Chain Management (SCM) of modular construction based steel bridge. To this end, first, Factory Production/Site Construction system data for the steel bridge construction were collected, and supply chain, entities, resources, processes were defined based on the collected data. After that, a steel bridge supply chain simulation model was developed by creating data, flowchart, and animation modules using Arena software. Finally, verification and validation of the model were performed by using animation check, extreme condition check, average value test, Little' s law test, and actual case value test. As a result, the developed simulation model appropriately expressed the processes and characteristics of the steel bridge supply chain without any logical errors, and provided accurate performance evaluation values for the target system. In the future, we expect that the model will faithfully play a role as a performance evaluation platform in developing management techniques for optimally operating the steel bridge supply chain.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.4
• /
• pp.1887-1898
• /
• 2018

In this paper, a method to classify insider threat activity is introduced. The internal threats help detecting anomalous activity in the procedure performed by the user in an organization. When an anomalous value deviating from the overall behavior is displayed, we consider it as an inside threat for classification as an inside intimidator. To solve the situation, Markov Chain Model is employed. The Markov Chain Model shows the next state value through an arbitrary variable affected by the previous event. Similarly, the current activity can also be predicted based on the previous activity for the insider threat activity. A method was studied where the change items for such state are defined by a transition probability, and classified as detection of anomaly of the inside threat through values for a probability variable. We use the properties of the Markov chains to list the behavior of the user over time and to classify which state they belong to. Sequential data sets were generated according to the influence of n occurrences of Markov attribute and classified by machine learning algorithm. In the experiment, only 15% of the Cert: insider threat dataset was applied, and the result was 97% accuracy except for NaiveBayes. As a result of our research, it was confirmed that the Markov Chain Model can classify insider threats and can be fully utilized for user behavior classification.

• Journal of Korean Institute of Industrial Engineers
• /
• v.41 no.5
• /
• pp.511-520
• /
• 2015

Inventory, cost, and the level of service are three interrelated key metrics that most supply chain organizations are striving to optimize. One way to achieve this goal is to create a simulation model to conduct sensitivity analysis and optimization on several different supply chain policies that can be implemented in actual operation. In this paper, a case of Xerox global supply chain modeling and analysis to assess several "what if" scenarios for the consumable policy safety stock is presented. The simulation model, combined with analytical cost model and optimization module, is used to optimize the policy safety stock level to achieve the lowest total value chain cost. It was shown quantitatively that the policy safety stock can be reduced, but it is offset by the inbound premium transportation cost to expedite supplies in shortage, and the outbound premium transportation cost to send supplies to customers via express shipment, requiring fine balance.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2010.11a
• /
• pp.291-292
• /
• 2010

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.24 no.65
• /
• pp.11-22
• /
• 2001

Recently, many studies on the supply chain management have been published due to increasing attention placed on the design and performance analysis of the supply chain as a whole. Using the Beer distribution game introduced in Sterman[1995], we develop a simple order-up-to-R inventory model to minimize sum of the inventory holding cost and shortage cost under probabilistic demand. We show that performance of the model is robust through extensive simulation experiment. Applying the model to serially connected supply chain, we observe that, if the unit shortage cost is relatively high, R value computed independently is an optimal solution.

• Proceedings of the Korea Society for Simulation Conference
• /
• 2001.10a
• /
• pp.405-424
• /
• 2001

Many simulation models have been built to facilitate simulation technique in designing, evaluating, and optimizing supply chains. Simulation is preferred to deal with stochastic natures existing in the supply chain. Moreover simulation has a capability to find local optimum value within each component through entire supply chain. Most of supply chain simulation models have been developed on the basis of discrete-event simulation. Since supply chain systems are neither completely discrete nor continuous, the need of constructing a model with aspects of both discrete-event and continuous simulation is provoked, resulting in a combined discrete-continuous simulation. In this paper, an architecture of combined modeling for supply chain simulation is proposed, which includes the equation of continuous portion in supply chain and how these equations are used in the supply chain simulation models. A simple example of supply chain model dealing with the strategic level of supply chain presented in this paper shows the possibility and the prospect of this approach.

• Journal of the Korea Society for Simulation
• /
• v.10 no.2
• /
• pp.75-89
• /
• 2001

Many simulation models have been built to facilitate simulation technique in designing, evaluating, and optimizing supply chains. Simulation is preferred to deal with stochastic natures existing in the supply chain. Moreover simulation has a capability to find local optimum value within each component through entire supply chain. Most of supply chain simulation models have been developed on the basis of discrete-event simulation. Since supply chain systems are neither completely discrete nor continuous, the need of constructing a model with aspects of both discrete-event and continuous simulation is provoked, resulting in a combined discrete-continuous simulation. In this paper, an architecture of combined modeling for supply chain simulation is proposed, which includes the equation of continuous portion in supply chain and how these equations are used in the supply chain simulation models. A simple example of supply chain model dealing with the strategic level of supply chain presented in this paper shows the possibility and the prospect of this approach.

• The Journal of Industrial Distribution & Business
• /
• v.4 no.2
• /
• pp.31-40
• /
• 2013

Purpose - This study attempted to identify the value promotion clues that may operate as a consumer's motive, by shedding new light on consumer value and by reconstructing each variable analyzed through the means-end chain (MEC) theory. Research design, data, and methodology - In this study, 202 copies of effective questionnaires using the data of Yang and Ju (2012) were subjected to correlation, regression, and SEM. Results - All store selection attributes were verified as having a positive influence on the relationship quality. Although the store selection attributes were verified as exerting a positive influence on the relationship quality, according to the verification result of the mediating effect, consumer value was verified to be influenced only by the relationship quality instead of by the store selection attributes. Conclusion - As a result of path analysis on the proposed model after modification, it was verified that only product factor had a statistically significant positive influence and that social value was completely mediating between relationship quality and emotional value. It may be highlighted that the MEC theory concept would be applicable to the cause-and-effect relationship model.