A Case Study for Business Integration as a Service

This paper presents Business Integration as a Service (BIaaS) to allow two services to work together in the Cloud to achieve a streamline process. We illustrate this integration using two services; Return on Investment (ROI) Measurement as a Service (RMaaS) and Risk Analysis as a Service (RAaaS) in the case study at the University of Southampton. The case study demonstrates the cost-savings and the risk analysis achieved, so two services can work as a single service. Advanced techniques are used to demonstrate statistical services and 3D Visualisation services under the remit of RMaaS and Monte Carlo Simulation as a Service behind the design of RAaaS. Computational results are presented with their implications discussed. Different types of risks associated with Cloud adoption can be calculated easily, rapidly and accurately with the use of BIaaS. This case study confirms the benefits of BIaaS adoption, including cost reduction and improvements in efficiency and risk analysis. Implementation of BIaaS in other organisations is also discussed. Important data arising from the integration of RMaaS and RAaaS are useful for management and stakeholders of University of Southampton

The financial clouds review

This paper demonstrates financial enterprise portability, which involves moving entire application services from desktops to clouds and between different clouds, and is transparent to users who can work as if on their familiar systems. To demonstrate portability, reviews for several financial models are studied, where Monte Carlo Methods (MCM) and Black Scholes Model (BSM) are chosen. A special technique in MCM, Least Square Methods, is used to reduce errors while performing accurate calculations. The coding algorithm for MCM written in MATLAB is explained. Simulations for MCM are performed on different types of Clouds. Benchmark and experimental results are presented for discussion. 3D Black Scholes are used to explain the impacts and added values for risk analysis, and three different scenarios with 3D risk analysis are explained. We also discuss implications for banking and ways to track risks in order to improve accuracy. We have used a conceptual Cloud platform to explain our contributions in Financial Software as a Service (FSaaS) and the IBM Fined Grained Security Framework. Our objective is to demonstrate portability, speed, accuracy and reliability of applications in the clouds, while demonstrating portability for FSaaS and the Cloud Computing Business Framework (CCBF), which is proposed to deal with cloud portability

Pomiar ryzyka w kalkulacji opłacalności inwestycji rzeczowych

In the paper a model of non-financial investment profitability calculation is presented. It is based on the concept of quantile risk measures and a real option valuation. Application of Monte Carlo simulation allows to receive probability distribution of Net Present Value (NPV) and implement risk measures like Cash Flow at Risk (CFaR), Net Present Value at Risk (NPVaR) or Expected Shortfall (ES) in relation to NPV (ES (NPV)). The main contribution of the article is implementation of ES (NPV) that shows the average of worst losses regarding NPV. ES (NPV) informs the investors what the worst result of the project may be.W artykule przedstawiono model kalkulacji opłacalności inwestycji rzeczowych. Jest on oparty na koncepcji kwantylowych miar ryzyka i wycenie opcji realnych. Zastosowanie symulacji Monte Carlo pozwala otrzymać rozkład prawdopodobieństwa wartości zaktualizowanej netto (Net Present Value – NPV) i wdrożyć miary ryzyka, takie jak przepływy pieniężne narażone na ryzyko (Cash Flow at Risk – CFaR), wartość zaktualizowana netto narażona na ryzyko (Net Present Value at Risk – NPVaR) czy oczekiwana strata (Expected Shortfall – ES) w stosunku do NPV – ES (NPV). Głównym wkładem artykułu jest implementacja ES (NPV), która pokazuje średnią najgorszych strat względem NPV. ES (NPV) informuje inwestorów, jaki może być najgorszy wynik projektu

Risk and Income Risk Management Issues for Organic Crops in Greece

Drawing upon a comparative case study of organic and conventional farming in Western Greece, the aim of this study is threefold: firstly, to explore the organic and conventional farmers’ profile through a factor analysis. Secondly, to assess the economic viability of organic cultivation with respect to profitability and risk behaviour, through a Monte Carlo stochastic simulation model. Thirdly, to discuss the necessity for additional income insurance schemes. Research findings indicate that the organic cropping system currently stands out as the most economically viable alternative under the assumption of the existing payments; without payments, however, conventional agriculture would be preferred by all farmers, regardless of their degree of risk aversionorganic crops, income variability, risk analysis, SERF, agricultural policy,

Capturing Risk in Capital Budgeting

NPS NRP Technical ReportThis proposed research has the goal of proposing novel, reusable, extensible, adaptable, and comprehensive advanced analytical process and Integrated Risk Management to help the (DOD) with risk-based capital budgeting, Monte Carlo risk-simulation, predictive analytics, and stochastic optimization of acquisitions and programs portfolios with multiple competing stakeholders while subject to budgetary, risk, schedule, and strategic constraints. The research covers topics of traditional capital budgeting methodologies used in industry, including the market, cost, and income approaches, and explains how some of these traditional methods can be applied in the DOD by using DOD-centric non-economic, logistic, readiness, capabilities, and requirements variables. Stochastic portfolio optimization with dynamic simulations and investment efficient frontiers will be run for the purposes of selecting the best combination of programs and capabilities is also addressed, as are other alternative methods such as average ranking, risk metrics, lexicographic methods, PROMETHEE, ELECTRE, and others. The results include actionable intelligence developed from an analytically robust case study that senior leadership at the DOD may utilize to make optimal decisions. The main deliverables will be a detailed written research report and presentation brief on the approach of capturing risk and uncertainty in capital budgeting analysis. The report will detail the proposed methodology and applications, as well as a summary case study and examples of how the methodology can be applied.N8 - Integration of Capabilities & ResourcesThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

An Economic Impact and Investment Analysis of Armillaria Root Rot in the United States Peach Industry

Peach production in the United States has decreased over the last decade due to increased disease prevalence. Armillaria root rot (ARR) is a lethal root fungus that affects many stone fruits, including peaches, often leading to rapid decline/death of trees and abandonment of orchards. This thesis is divided into four chapters which focus on answering four key questions that, to the best of my knowledge, have not been addressed in previous industry research. The second chapter determines the magnitude of ARR disease prevalence in the United States and producers’ maximum willingness to pay (WTP) for a theoretical ARR-resistant rootstock. Results from a nationwide survey of peach producers indicate that 100% of participants reported having crop loss due to ARR over the past two years. Producers also have an average premium WTP of $2.16 per tree for a rootstock that shows high disease resistance. With an assumed tree price of$6.00 per tree, this implied that producers were willing to pay 36% more for the large increase in ARR resistance. The third chapter examines the economic impact of ARR on the national peach industry and an investment analysis of implementing the root collar excavation (RCE) method in peach orchards. Data was obtained from California, Florida, and South Carolina enterprise budgets and analyzed using a net present value (NPV) method paired with stochastic variables of disease impact year and impact rate. Findings suggest that ARR can decrease the national profitability of growing peaches by an average of $3,740 per acre. Additional findings indicate that implementing the RCE method on ARR-infected sites could increase profits per acre by an average of$657 nationwide

A Real Option Dynamic Decision (rodd) Framework For Operational Innovations

Changing the business operations and adopting new operational innovations, have become key features for a business solution approach. However, there are challenges for developing innovative operations due to a lack of the proper decision analysis tools, lack of understanding the impacts transition will have on operational models, and the time limits of the innovation life cycle. The cases of business failure in operational innovation (i.e. Eastman Kodak Company and Borders Group Inc.,) support the need for an investment decision framework. This research aims to develop a Real Option Dynamic Decision (RODD) framework for decision making, to support decision makers for operational innovation investments. This development will help the business/organization to recognize the need for change in operations, and quickly respond to market threats and customer needs. The RODD framework is developed by integrating a strategic investment method (Real Options Analysis), management transition evaluation (Matrix of Change), competitiveness evaluation (Lotka-Volterra), and dynamic behavior modeling (System Dynamics Modeling) to analyze the feasibility of the transformation, and to assess return on investment of new operation schemes. Two case studies are used: United Parcel Service of America, Inc., and Firefighting Operations to validate the RODD framework. The results show that the benefits of this decisionmaking framework are (1) to provide increased flexibility, improved predictions, and more information to decision makers; (2) to assess the value alternative option with regards to uncertainty and competitiveness; (3) to reduce complexity; and (4) to gain a new understanding of operational innovations