A framework for life cycle cost estimation of a product family at the early stage of product development

A cost estimation method is required to estimate the life cycle cost of a product family at the early stage of product development in order to evaluate the product family design. There are difficulties with existing cost estimation techniques in estimating the life cycle cost for a product family at the early stage of product development. This paper proposes a framework that combines a knowledge based system and an activity based costing techniques in estimating the life cycle cost of a product family at the early stage of product development. The inputs of the framework are the product family structure and its sub function. The output of the framework is the life cycle cost of a product family that consists of all costs at each product family level and the costs of each product life cycle stage. The proposed framework provides a life cycle cost estimation tool for a product family at the early stage of product development using high level information as its input. The framework makes it possible to estimate the life cycle cost of various product family that use any types of product structure. It provides detailed information related to the activity and resource costs of both parts and products that can assist the designer in analyzing the cost of the product family design. In addition, it can reduce the required amount of information and time to construct the cost estimation system.

The importance of environmental issues and costs in Life Cycle Cost Analysis (LCCA) for highway projects

Sustainability has been increasingly recognised as an integral part of highway infrastructure development. In practice however, the fact that financial return is still a project’s top priority for many, environmental aspects tend to be overlooked or considered as a burden, as they add to project costs. Sustainability and its implications have a far-reaching effect on each project over time. Therefore, with highway infrastructure’s long-term life span and huge capital demand, the consideration of environmental cost/ benefit issues is more crucial in life-cycle cost analysis (LCCA). To date, there is little in existing literature studies on viable estimation methods for environmental costs. This situation presents the potential for focused studies on environmental costs and issues in the context of life-cycle cost analysis. This paper discusses a research project which aims to integrate the environmental cost elements and issues into a conceptual framework for life cycle costing analysis for highway projects. Cost elements and issues concerning the environment were first identified through literature. Through questionnaires, these environmental cost elements will be validated by practitioners before their consolidation into the extension of existing and worked models of life-cycle costing analysis (LCCA). A holistic decision support framework is being developed to assist highway infrastructure stakeholders to evaluate their investment decision. This will generate financial returns while maximising environmental benefits and sustainability outcome.

Minimizing the effects of overfitting and collinearity in construction cost estimation : a new hybird approach

Research problem: Overfitting and collinearity problems commonly exist in current construction cost estimation applications and obstruct researchers and practitioners in achieving better modelling results. Research objective and method: A hybrid approach of Akaike information criterion (AIC) stepwise regression and principal component regression (PCR) is proposed to help solve overfitting and collinearity problems. Utilization of this approach in linear regression is validated by comparing it with other commonly used approaches. The mean square error obtained by leave-one-out cross validation (MSELOOCV) is used in model selection in deciding predictive variables.

Cost-effectiveness of an intervention to reduce emergency re-admissions to hospital among older patients

Background. The objective is to estimate the cost-effectiveness of an intervention that reduces hospital readmission among older people at high risk. A cost-effectiveness model to estimate the costs and health benefits of the intervention was implemented. Methodology/Principal Findings. The model used data from a randomised controlled trial conducted in an Australian tertiary metropolitan hospital. Participants were acute medical admissions aged >65 years with at least one risk factor for readmission: multiple comorbidities, impaired functionality, aged >75 years, 30 recent multiple admissions, poor social support, history of depression. The intervention was a comprehensive nursing and physiotherapy assessment and an individually tailored program of exercise strategies and nurse home visits with telephone follow-up; commencing in hospital and continuing following discharge for 24 weeks. The change to cost outcomes, including the costs of implementing the intervention and all subsequent use of health care services, and, the change to health benefits, represented by quality adjusted life years, were estimated for the intervention as compared to existing practice. The mean change to total costs and quality 38 adjusted life years for an average individual over 24 weeks participating in the intervention were: cost savings of $333 (95% Bayesian credible interval $-1,932:1,282) and 0.118 extra quality adjusted life years (95% Bayesian credible interval 0.1:0.136). The mean net41 monetary-benefit per individual for the intervention group compared to the usual care condition was $7,907 (95% Bayesian credible interval $5,959:$9,995) for the 24 week period. Conclusions/Significance. The estimation model that describes this intervention predicts cost savings and improved health outcomes. A decision to remain with existing practices causes unnecessary costs and reduced health. Decision makers should consider adopting this 46 program for elderly hospitalised patients.

Online estimation of distorted power system signal parameters

An algorithm based on the concept of Kalman filtering is proposed in this paper for the estimation of power system signal attributes, like amplitude, frequency and phase angle. This technique can be used in protection relays, digital AVRs, DSTATCOMs, FACTS and other power electronics applications. Furthermore this algorithm is particularly suitable for the integration of distributed generation sources to power grids when fast and accurate detection of small variations of signal attributes are needed. Practical considerations such as the effect of noise, higher order harmonics, and computational issues of the algorithm are considered and tested in the paper. Several computer simulations are presented to highlight the usefulness of the proposed approach. Simulation results show that the proposed technique can simultaneously estimate the signal attributes, even if it is highly distorted due to the presence of non-linear loads and noise.

Greenhouse Gas Fluxes in Tropical and Temperae Agriculture: The Need for a Full-Cost Accounting of Global Warming Potentials

Agriculture's contribution to radiative forcing is principally through its historical release of carbon in soil and vegetation to the atmosphere and through its contemporary release of nitrous oxide (N2O) and methane (CHM4). The sequestration of soil carbon in soils now depleted in soil organic matter is a well-known strategy for mitigating the buildup of CO2 in the atmosphere. Less well-recognized are other mitigation potentials. A full-cost accounting of the effects of agriculture on greenhouse gas emissions is necessary to quantify the relative importance of all mitigation options. Such an analysis shows nitrogen fertilizer, agricultural liming, fuel use, N2O emissions, and CH4 fluxes to have additional significant potential for mitigation. By evaluating all sources in terms of their global warming potential it becomes possible to directly evaluate greenhouse policy options for agriculture. A comparison of temperate and tropical systems illustrates some of these options.

The right to be green: using human rights law to achieve environmental protection

The relationship between the environment and human rights has long been recognised. It is now largely accepted that a ‘good’ environment is a necessary precondition for the enjoyment of a wide range of human rights, including the right to health, the right to an adequate standard of living, and even the right to life. It has even been suggested that as humans we all possess a right to live in an environment of a certain standard, based on the intrinsic value of the natural world to all human beings. In this context much has been written regarding the important role that the environment plays in human lives. This paper looks at the flip-side of this discussion, and examines what human rights can do for the environment. It is argued that, while there are valid criticisms for linking environmental protection too strongly to human needs, there is nonetheless much to be gained from using human rights law as a framework to achieve environmental protection.

An estimation of crash risks using a log-linear model and the induced exposure technique

Prevention and safety promotion programmes. Traditionally, in-depth investigations of crash risks are conducted using exposure controlled study or case-control methodology. However, these studies need either observational data for control cases or exogenous exposure data like vehicle-kilometres travel, entry flow or product of conflicting flow for a particular traffic location, or a traffic site. These data are not readily available and often require extensive data collection effort on a system-wide basis. Aim: The objective of this research is to propose an alternative methodology to investigate crash risks of a road user group in different circumstances using readily available traffic police crash data. Methods: This study employs a combination of a log-linear model and the quasi-induced exposure technique to estimate crash risks of a road user group. While the log-linear model reveals the significant interactions and thus the prevalence of crashes of a road user group under various sets of traffic, environmental and roadway factors, the quasi-induced exposure technique estimates relative exposure of that road user in the same set of explanatory variables. Therefore, the combination of these two techniques provides relative measures of crash risks under various influences of roadway, environmental and traffic conditions. The proposed methodology has been illustrated using Brisbane motorcycle crash data of five years. Results: Interpretations of results on different combination of interactive factors show that the poor conspicuity of motorcycles is a predominant cause of motorcycle crashes. Inability of other drivers to correctly judge the speed and distance of an oncoming motorcyclist is also evident in right-of-way violation motorcycle crashes at intersections. Discussion and Conclusions: The combination of a log-linear model and the induced exposure technique is a promising methodology and can be applied to better estimate crash risks of other road users. This study also highlights the importance of considering interaction effects to better understand hazardous situations. A further study on the comparison between the proposed methodology and case-control method would be useful.