APPLICATION OF CONTINUOUS CASH FLOW STREAMS IN THE FIELD OF RELIABILITY ENGINEERING USING LAPLACE TRANSFORM

It is suggested that economic problems with stochastic character can be easily solved if the cost variables, which are stochastic in nature, have as their source the stochastic character of the failure. The economical aspects of reliability and the knowledge of reliability engineering in applied economic are of much more importance than it is devoted in literature. This paper presents some examples of this issue. In the first part, the present value computation technique using transform technique is outlined, which, is rarely applied in economics analysis. The second part shortly summarises the most important theorems of reliability engineering. Finally, in the main part, a cost model is presented which can be used to address stochastic economic problems, where sources of stochastism are failure processes

Revisiting discounted cash flows model as a capital budgeting decision tool

Purpose- In thisstudy, the classical discounted cash flows (DCDF) model is revisited and the input factors of the model are analysed in details. Methodology- A model analysis approach is used in this research. The fundamental assumptions and the input factors (cash flows, time period, risk, discount rate, etc.) of DCF model are questioned. Findings- NPV and IRR are two methods using discounted cash flows and oftenly applied for capital budgeting decisions. The assumptions used in the DCF analysis are very strong and not fitting well in the reality of practical life. Economic life of the project may be much longer or shorter than projected in the analysis. The computation of discount rate bases on subjective interpretations (weights of capital components, cost of debt, opportunity cost of equity). Estimation of cash flows is the most critical input of the analysis but generally the least weighted factor (CF or FCF, inclusion of only relevant cash flows, depreciation and interest expenses, installments, credit sales and purchases, etc.). Risk adjustment can be made either on the discount rate or expected cash flows. Moreover, the analysis of international capital investments makes the issue more complicated. Also, the inclusion of real options adds an economic value to the analysis. Conclusion- DCF is not straight forward a capital budgeting model anyone can easily use. The application of DCF requires expertise and a picky view on details. Nevertheless, the acceptance of capital investments utilizing DCF method can not be independent of overall company strategic goals.Publisher's Versio

ANALISIS PENGARUH TINGKAT EFISIENSI TERHADAP TINGKAT KONSENTRASI INDUSTRI JAMU INDONESIA

This final research is entitled, “An Analysis of the Influence of Efficiency Level on the Herbal Drinking (Jamu) Industry Concentration Level in Indonesia.†This research was intended to identify the jamu industry concentration level during the pre economic crisis and after economic crisis and want to know the influence of efficiency level on the jamu industry concentration level. The secondary data included those collected from publications by Central Statistics Bureau, Jamu Producers Association, and Ministry of Industry and Trading and those of the sources such as research journals, newspapers, magazine and reviews of the literature. Analysis technique is used descriptive analysis qualitative and quantitative. The research results show that before and economic crisis jamu industry was marked by the average concentration level of 77 percent and the average value of concentration level after the crisis, it lowered to 67 percent. The regression computation showed that the efficiency level influenced significantly on that of concentration as shown by the R-Square value of 27,50 pecent. Keywords: Efficiency and Concentration Level

Estimating the Expected Value of Partial Perfect Information in Health Economic Evaluations using Integrated Nested Laplace Approximation

The Expected Value of Perfect Partial Information (EVPPI) is a decision-theoretic measure of the "cost" of parametric uncertainty in decision making used principally in health economic decision making. Despite this decision-theoretic grounding, the uptake of EVPPI calculations in practice has been slow. This is in part due to the prohibitive computational time required to estimate the EVPPI via Monte Carlo simulations. However, recent developments have demonstrated that the EVPPI can be estimated by non-parametric regression methods, which have significantly decreased the computation time required to approximate the EVPPI. Under certain circumstances, high-dimensional Gaussian Process regression is suggested, but this can still be prohibitively expensive. Applying fast computation methods developed in spatial statistics using Integrated Nested Laplace Approximations (INLA) and projecting from a high-dimensional into a low-dimensional input space allows us to decrease the computation time for fitting these high-dimensional Gaussian Processes, often substantially. We demonstrate that the EVPPI calculated using our method for Gaussian Process regression is in line with the standard Gaussian Process regression method and that despite the apparent methodological complexity of this new method, R functions are available in the package BCEA to implement it simply and efficiently

A Review of Methods for the Analysis of the Expected Value of Information

Over recent years Value of Information analysis has become more widespread in health-economic evaluations, specifically as a tool to perform Probabilistic Sensitivity Analysis. This is largely due to methodological advancements allowing for the fast computation of a typical summary known as the Expected Value of Partial Perfect Information (EVPPI). A recent review discussed some estimations method for calculating the EVPPI but as the research has been active over the intervening years this review does not discuss some key estimation methods. Therefore, this paper presents a comprehensive review of these new methods. We begin by providing the technical details of these computation methods. We then present a case study in order to compare the estimation performance of these new methods. We conclude that the most recent development based on non-parametric regression offers the best method for calculating the EVPPI efficiently. This means that the EVPPI can now be used practically in health economic evaluations, especially as all the methods are developed in parallel with

Calculating the Expected Value of Sample Information in Practice: Considerations from Three Case Studies

Investing efficiently in future research to improve policy decisions is an important goal. Expected Value of Sample Information (EVSI) can be used to select the specific design and sample size of a proposed study by assessing the benefit of a range of different studies. Estimating EVSI with the standard nested Monte Carlo algorithm has a notoriously high computational burden, especially when using a complex decision model or when optimizing over study sample sizes and designs. Therefore, a number of more efficient EVSI approximation methods have been developed. However, these approximation methods have not been compared and therefore their relative advantages and disadvantages are not clear. A consortium of EVSI researchers, including the developers of several approximation methods, compared four EVSI methods using three previously published health economic models. The examples were chosen to represent a range of real-world contexts, including situations with multiple study outcomes, missing data, and data from an observational rather than a randomized study. The computational speed and accuracy of each method were compared, and the relative advantages and implementation challenges of the methods were highlighted. In each example, the approximation methods took minutes or hours to achieve reasonably accurate EVSI estimates, whereas the traditional Monte Carlo method took weeks. Specific methods are particularly suited to problems where we wish to compare multiple proposed sample sizes, when the proposed sample size is large, or when the health economic model is computationally expensive. All the evaluated methods gave estimates similar to those given by traditional Monte Carlo, suggesting that EVSI can now be efficiently computed with confidence in realistic examples.Comment: 11 pages, 3 figure

Feasibility of a solar photo-voltaic system as an energy source for lighting in grid-connected residential buildings in Cameroon : case study of Buea : a thesis submitted in partial fulfilment of the requirements for the degree of Master of Environmental Management (without major) at Massey University, Palmerston North (New Zealand)

Cameroon has the second largest hydropower potential in Africa after the Democratic Republic of Congo. However, even with this potential, electricity supply in the country is insufficient and unreliable especially in the midst of the dry season, thus the many residents affected are inconvenienced due to lack of energy for lighting. This and coupled with climate change constraints, necessitates the investigation of measures geared towards effective utilization of the available energy from the grid and the feasibility of an alternative energy source to be employed in the onsite generation of electricity in residential buildings for lighting. In this research, a total of 100 residential dwellings of different classes (T1 to T7) were surveyed in the town of Buea, Cameroon. The survey employed the use of a questionnaire designed to collect data on current lighting technologies used in dwellings and the electricity load for lighting and basic communication appliances (radios and mobile phone chargers) of the dwellings. An economic and environmental analysis for transition towards efficient lighting in the surveyed dwellings was conducted. The load profiles of the dwellings classified from the k-means algorithm in R Statistics were used in the HOMER Pro software for a techno-economic modelling of residential PV systems (stand-alone and grid back-up) to meet the load of the dwellings. The survey had a questionnaire return rate of 92%. Results of the survey revealed that artificial lighting in the dwellings is achieved through the use of the following technologies: incandescent lamps, compact fluorescent lamps (CFL) and fluorescent tubes. The economic assessment of efficient lighting transition in the dwellings for an artificial daily lighting duration of six hours revealed a net present value (NPV) that ranges from $47 (T1 building) to$282.02 (T5 building), a benefit cost ratio (BCR) of 1.84 and a simple payback period (PBP) of 0.17 year (2 months) for the substitution of current incandescent lamps in dwellings with CFL. The substitution of incandescent lamps with light emitting diodes (LED) revealed an NPV of the range $89.14 (T1 building) to$370 (T5 building), a BCR of 3.18 and a PBP of 1.92 years (23 months). The substitution of incandescent lamps with CFL and LED results to a reduction in lighting related greenhouse gas (GHG) emissions from dwellings by 66.6% and 83.3% respectively. Results from the HOMER modelling revealed a levelized cost of electricity (LCOE) of the PV system under the following parameters: 0% annual capacity shortage, 40% minimum battery state if charge (SOC), 25 years PV lifetime, 5% discount rate and 2% inflation rate to be 10 to 13 times more expensive (stand-alone system) and four to eight times more expensive (back-up system) compared to the grid electricity. The PV systems have potentials to save an annual emission of 89.17 to 527.37 kgCO2-e for the stand-alone system. Favourable government policies are necessary to spur the deployment of these low carbon technologies in the residential sector of Cameroon