Risk Identification Model Research Articles

Financial risks from three dimensions and risk identification model of enterprise

Financial risk is a critical variable in forming enterprise’s financial crisis, the cyclical showing of which is an objective law undecided by the will of people. This paper divided financial risks into three types: financial environment-adaptation risk (FER), financial resource-allocation risk (FRR) and financial stakeholder-cooperation risk (FSR), what constitute the three-dimensional financial risk system of enterprise. Based on the literature review, it analyses the formation and expansion mechanism of threedimensional financial risks in order to give a powerful support for the financial risk identifying research work. Next, it builds a matrix identification model for identifying three-dimensional financial risks of enterprise including an index system and algorithm. Furthermore, a case study is also given. All these results of this paper can provide a theoretical and technical support for the enterprise’s financial risk identifying system.

1303 技術移転行為の根拠に基づいた技術移転プロジェクトの特徴付けとリスク同定(OS1-3 生産システムの国際化・情報共有)

In a previous paper by the authors, the Two-Factor Continuum Model for Risk Identification was proposed. The Two Factors in the model are the Resource Basis of the Technology and the Level of Integration of the Technology Transfer Project (TTP) with other projects. It was suggested by the authors that the Two Factor Continuum Model be used by participants of technology transfer projects as a preliminary guide in order to foreknow the risks to become better prepared, thus resulting to more effective and efficient technology transfers. This paper investigates fifty samples of TTPs that were started within the last 5 years. The paper finds out that there is one essential factor that could be a good predictor of the typical risks and success factors for technology transfer. This essential factor is the rationale or reason behind the technology transfer. Ultimately, this paper proposes a model for arriving at action items to ensure TTP success based on the rationale for the technology transfer.

Formalised risk identification and evaluation models application in the conditions of agricultural enterprises

Recently we can mention in the CR a step by step enterprise number increase in the area of production, transport services, services and financial organisations, which practically apply internal departments of controlling or audit into their managerial structures. That is why it is required to dedicate a wider publication space to the information on the role and principles of managerial instruments, which are provided by controlling and Internal Audit for business administration and financial management. The goal is to ensure a higher quality of preparation or response on the still emerging forms of our enterprises co-operation with foreign business subjects and to match step at the level of building and in particularly in effectiveness of business informational systems usage for managerial process (especially for planning and decision making procedures control) in all areas of business functions. Internal controlling instruments application and using results of risks evaluations by internal audit simultaneously contributes to the improvement of level and effectiveness of intra-enterprise monitoring as a modern conception of control usage in business management. Management level increase and increase of management competency are the conditions of ensuring our enterprises competitiveness.

Project Selection using the PRISMTM Model

This paper describes the overall Project Risk Identification, Selection, and Management (PRISMTM) model, consisting of three components: PRISM l, PRISM 11 and PRISM 111. The paper also presents the research and development of PRISM 1, which was designed to help companies prioritize projects as well as capture critical information for key decision-makers to make go/no go decisions prior to committing significant resources to create a full-scale estimate and bid on the project with the highest profit potential. PRISM 1 was designed not only to assess both qualititative and quantitative project data, but also prioritize project risks and house the historical data collected in order to reduce the number of unknown-unknowns on future projects. PRISMs 11 and 111 will build on PRISM 1 data, but will focus on projects with significantly fewer knowns, more known-unknowns, or unique projects with mostly unknown-unknown risks.

A knowledge-based system for identifying potential project risks

This paper presents an integrated knowledge-based system (KBS) that assists project managers in identifying potential risk factors and the corresponding project risks. The knowledge acquired from previous experience is captured by the proposed project risk identification model which incorporates the work breakdown structure (WBS). The knowledge is then translated into rules for the KBS. A prototype was developed based on the proposed KBS to determine potential risk factors along with their risk effects and the related work packages for an extra high-voltage (EHV) transmission line construction project. The results obtained clearly demonstrate the feasibility and the applicability of this system in a real engineering project environment.

Early versus late identification of infants at high risk of developing moderate to severe bronchopulmonary dysplasia.

We developed a simple method to identify neonates at high risk of bronchopulmonary dysplasia (BPD) and determined whether early (8 hours) and late (14 days) risk assessment is equally useful. A retrospective cohort design was utilized of subjects enrolled in multi-dose surfactant trials to develop each risk identification model. Prospective testing of the late 14-day model was done to determine accuracy. The primary outcome variable (moderate to severe BPD) was defined as the need for oxygen and mechanical ventilation beyond 28 days of life and significant chest X-ray changes. Variables were screened for inclusion in the models by univariate and multiple regression analysis of data available at 14 days or 8 hours of life, converted to yes-no variables by the use of receiver-operator curves; the final model was based on those variables that gave the highest sensitivity and specificity for identifying BPD risk. Thirty-eight out of 116 of the 14-day model subjects developed BPD. The 14-day model (F1O2 > or = 0.30 and ventilation index (defined as 10,000/peak pressure x rate x PCO2) < 0.510 (or < 0.800 if previously septic)] had a sensitivity of 82% and specificity of 89%. It accurately identified 83% of cases (51/61) during at 1-year prospective test. The positive predictive value was 81% and negative predictive value 88%. Forty-four of the 698 early 8-hour model subjects developed BPD. The 8-hour model [gestational age < 31 weeks, 5-minute Apgar < 9, ventilator rate > 23 breaths/min, and ventilation index < 0.895] had a sensitivity of 73%, specificity of 83%, negative predictive value of 98% but positive predictive value of only 22%. These observation indicated that clinical data can create an accurate and simple model to classify infants into high- or low-risk groups for BPD. Using such models very early in life (e.g., at 8 hours) may lead to a high number of false-positive identifications.