Power generation construction projects involving large amounts of capital can affect the survival of a company along with huge economic losses in the event of a business failure. In general, private companies are organizations with challenging risk taking tendencies while public companies have a risk averse tendency to avoid risk, so these differences in organizational tendencies make it difficult to respond to risk. In particular, public companies are more likely to fail than private companies because they choose the contradiction of risk picking to enter overseas markets with high uncertainty despite their tendency to risk averse due to the nature of the organization. Therefore, these organizations need risk management techniques that reflect a risk-averse strategy. Accordingly, this paper analyzes the risk management research papers of the existing overseas development EPC business in order to find the risk management techniques related to the organizational tendencies of public companies and proposes “establishing a performance audit system for risk management of the organizational tendencies of public companies” as a way to extract the risk factors through the examples of overseas development projects of public companies and to manage the organizational tendencies of public companies that affect them.

The market size of plant projects in overseas is so large that domestic EPC project contractors are actively seeking the overseas projects and then trying to meet completion plans since successful fulfillment of these projects can provide great opportunities for them to expand into new foreign markets. International EPC projects involve all of the uncertainties common to domestic projects as well as uncertainties specific to foreign projects including marine transportation, customs, regulations, nationality, culture and so on. When overseas project gets off-schedule, the resulting uncertainty may trigger unexpected exceptions and then critical effects to the project performance. It usually require much more time and costs to encounter these exceptions in foreign sites compared to domestic project sites. Therefore, an exception handling approach is required to manage exceptions effectively for successful project progress in foreign project sites.In this research, we proposed a methodology for prediction and evaluation of exceptions caused by risks in international EPC projects based on sensitivity analysis and Bayesian Networks. First, we identified project schedule risks and related exceptions, which may meet during the fulfillment of foreign EPC projects that is performed in a sequence of engineering, procurement, preparatory manufacture, foreign shipping, construction, inspection and modification activities, and affect project performance, using literature review and expert interviews. The impact of exceptions to the schedule delay were also identified. Second, we proposed a methodology to predict the occurrence of exceptions caused by project risks and evaluate them. Using sensitivity analysis, we can identify activities that critically affect schedule delay and need to focus by priority. Then, we use Bayesian Networks to predict and evaluate exceptions. Third, we applied the proposed methodology to an international EPC project example to validate the proposed approach. Finally, we concluded the research with the further research topics. We expect that the proposed approach can be extended to apply in exception management in project management.

In this study, we develop a method for analyzing business process based on the event-driven process chain (EPC) model. The method consists of five stages such as identifying abnormal events, finding causes for the abnormal events and problems caused by the abnormal events, making cause-and-effect chains, drawing root-cause map, and defining improvement areas. We illustrate how to apply the method with some examples for the domestic registered mail delivery process.

In this study, we develop a methodology for business process improvement (BPI) based on the event-driven process chain (EPC) model. The methodology consists of six stages for BPI such as identifying abnormal events, finding causes for the abnormal events and problems caused by the abnormal events, making cause and effect chains, drawing root-cause map, scoping business processes for improvement, and defining process improvement projects. We illustrate how to apply the methodology with some examples for the domestic registered mail delivery process.

This paper seeks to Combination for Efficient Application of SPC/EPC minimize variability by transferring the output variable to a related process controllable variable, while SPC seeks to reduce variability by detecting and eliminating assignable causes of variation. In the case of product control, a very reasonable objective is to try to minimize the variance of the output deviations from the target or set point. We consider an alternative EPC model with autoregressed disturbance. We compare three control systems; EPC, Cp, SPC combined system with EWMA, CUSUM and Shewhart. This paper shows through simulation that the performance of the integrated model of EPC and SPC, Cp is more preferable than that of EPC.

By analysis of the processes before and after applying RFID in warehouse management and the improvement the RFID system brings to warehouse management based on model of warehouse management in manufacture enterprises, a set of standard process after applying RFID system in SCM members at different phases is designed in this paper.

Multi-criteria decision making is deducing the relative importance in the criterion of decision making and each alternative which is able to making a variety of choices measures the preferred degree in the series of low-raking criterions. Moreover, this is possible by synthesizing them systematically. In general, a fundamental problem decision maker solve for multi-criteria decision making is evaluating a set of activities which are considered as the target logically, and this kind of work is evaluated and synthesized by various criterions of the value which a chain of activities usually hold in common. In this paper, we are the eigenvector methods in weights calculating. For the purpose of making optimal decision, the data of five different car models are used. For computing, we used Visual Numerica Version 1.0 software package.

EPC seeks to minimize variability by transferring the output variable to a related process input(controllable) variable, while SPC seeks to reduce variability by detecting and eliminating assignable causes of variation. In the case of product control, a very reasonable objective is to try to minimize the variance of the output deviations from the target or set point. We consider an alternative EPC model with second-order autoregressive disturbance. We compare three control systems; EPC, EPC combined with EWMA. This paper shows through simulation that tlhe performance of the integrated model of EPC and EWMA is more preferable than that of EPC.

SPC와 EPC의 통합은 연속공정산업에서 성공적으로 사용된다. 그러나, 이산적인 부품 제조공정의 모니터링과 연속 생산공정 모니터링 양쪽 모두를 포함하는 산업에서의 통합기법을 적용하기 위해서는 실시간 보정과 공정조절 등에 관한 연구가 필요하다. 본 연구에서는 SPC와 EPC의 차이를 비교분석하고, 혼성 산업에서 SPC와 EPC를 성공적으로 통합하여 효율적인 공정관리의 적용 사례를 제시하고자 한다.

EPC seeks to minimize variability by transferring the output variable to a related process input(controllable) variable, while SPC seeks to reduce variability by detecting and eliminating assignable causes of variation. Tn the case of product control, a very reasonable objective is to try to minimize the variance of the output deviations from the target or set point. We consider an alternative EPC model with second-order autoregressed disturbance. We compare three control systems; EPC, EPC combined system with EWMA, CUSUM and Shewhart. This paper shows through simulation that the performance of the integrated model of EPC and SPC is more preferable than that of EPC.

Statistical process control(SPC) and engineering process control(EPC) are two strategies for quality improvement that have developed independently. In general, both techniques have reduction of variability as their objective, although they seek to accomplish these objective in different ways. Integration of SPC and EPC is finding wide recognition and is successfully used in continuous process industries. A simple case of an integral controller is presented here to show the application of MMSE controller to stochastic process. In order to keep the system on target, the actual adjustment in terms of MMSE using integral control can be implemented. This paper demonstrates that SPC, already used for process monitoring in the parts industries, and EPC, now used in process industries, may be integrated in various type industry for more effective control of processes.

There are two approaches to process control. The one is engineering process control(EPC) which is one of the techniques very widely used in the process industry and based on control theory which aims at keeping the process on target using manipulating variable. The other is statistical process control(SPC) whose main purpose is to look for assignable causes(variability) in the process. To design an integrated or combined scheme of SPC and EPC is gaining recognition in the process experiences for hybrid industry. This paper aims to investigate recent study concerned on the integration of SPC and EPC. First, we consider the difference between SPC and EPC in simple terms and review various models of EPC for integration including evaluation of previous study. Finally, we suggest some prospective research area concerned on the integration of SPC and EPC.

EPC seeks to minimize variability by transferring the output variable to a related process input(controllable) variable, while SPC seeks to reduce variability by detecting and eliminating assignable causes of variation. In the case of product control, a very reasonable objective is to try to minimize the variance of the output deviations from the target or set point. We consider an alternative EPC model with second-order autoregressive disturbance. We compare three control systems； EPC, EPC combined with EWMA, and Shewhart. This paper shows through simulation that the performance of the integrated model of EPC and SPC is more preferable than that of EPC.

In this paper, we Investigate rationales for feedback adjustment using some techniques associated with automatic process control based on some nonstatioary disturbance models. Feedback-control schemes are often operated so that the nature of the disturbance that is being compensated is concealed and unusual deviations from the target cannot be taken account of. In this connection feedback control schemes is useful to extend the idea of common causes and special causes to such systems. Minimum-cost feedback schemes are discussed for some simple ,but practically interesting ,models.

Engineering process control (EPC) is one of the techniques very widely used in process. EPC is based on control theory which aims at keeping the process on target. Statistical process control (SPC), also known as statistical process monitoring. The main purpose of SPC is to look for assignable causes (variability) in the process data. The combined SPC/EPC scheme is gaining recognition in the process industries where the process frequently experiences a drifting mean. This paper aims to study the difference between SPC and EPC in simple terms and presents a case study that demonstrates successful integration of SPC and EPC for a product in drifting industry. Statistical process control (SPC) monitoring of the special causes of a process, along with engineering feedback control such as proportional-integral-derivative (PID) control, is a major tool for on-line quality improvement.