An improvement selection methodology for key performance indicators

Key performance indicators (KPIs) are critical measures for determining the health of a manufacturing plant in relationship to the plant’s goals. In today’s competitive environment, manufacturers cannot be careless about their business; in fact, they must ensure that their KPIs are effective and use them to make improvements when necessary. This paper describes a method for suggesting improvements to a manufacturer’s KPIs, based on the results achieved from a workshop to score the KPI on a number of predefined criteria. The approach uses a prospect theory approach to weight the scoring. Different problem formulations were derived that allow for both recommendations for improvements and the recommendations for disinvestments to over-performing KPIs. The authors applied the developed approach to two workshop outputs, each from independent manufacturers, and the results highlighted the significant difference between the two manufacturers in terms of improvement priorities and KPI assessment. The optimal improvement suggestions were compared to those found through a fast heuristic. It was determined that given the underlying assumptions of the approach that the heuristic solutions were just as adequate as the optimal ones.     

Heat wave vulnerability and exposure mapping for Osaka City,Japan

Heat waves and heat-related stresses are increasing environmental concerns in urban areas. The impact of heat waves is dependent on the intensity and duration of each event and on underlying environmental and socio-demographic factors which influence population vulnerability. In order to develop effective adaptation strategies, it is important to develop a method to clearly identify the most vulnerable areas based on these factors. The purpose of this study is to develop and map a heat wave vulnerability index combined with heat exposure analysis to identify areas where interventions can be targeted. The vulnerability index was derived from a principle component analysis of eight key variables that influence heat wave vulnerability. Eight proxy measures of vulnerability were obtained from 2010 census and land-use data for the 1904 census districts of Osaka City. Three principle components explained >77 % of the variance (age, employment and education; social isolation; density and lack of green space). The components were combined and weighted to produce a vulnerability score for each census district. The vulnerability scores ranged from 0 to 106, were categorised into eight vulnerability levels and were overlaid with fine-scale air temperature observations. The resulting output identified the distribution of population vulnerability and exposure. This assessment of vulnerability, combining exposure and sensitivity components, can provide precedent for efficient, targeted action to be taken to reduce the impact of heat waves at present and under climate change.     

Carbon emission reduction and cost–benefit of methane digester systems on hog farms in China

Three different sizes of hog farms were selected to analyze the carbon emissions reduction and the cost–benefit of three methane digester systems. The sizes of the digesters are 2,200, 2,200 and 800 m³, respectively. The sales of slaughter hogs from them are 50,000, 35,000 and 10,000 head, respectively. The carbon emissions reductions were 5,237, 4,017, and 1,334 tons, respectively. The results show that while the methane digester systems have a significant effect on carbon emissions reduction, it is difficult to operate the systems sustainably. If the carbon emissions reduction can be traded at high enough prices in the carbon offset markets, then the systems will be profitable and sustainable. Newly established China's domestic carbon offset market could provide this possibility, but more government support is needed. In addition, this study shows that scale economies make the digester adoption relatively more profitable for larger farms than smaller ones.     

Assessing local planning capacity to promote environmentally sustainable residential development

Smart growth and sustainability planning have, in recent years, become central issues in planning discourse. Scholars have argued that planning capacity at the local government level is critical for smart growth planning, and that planners have a fundamental role to play in advancing local and regional sustainability. In this paper, we investigate the extent to which local planning capacity enables communities to promote more sustainable, smart growth residential development. Based on a 2013 survey of 38 county and 53 municipal governments in the state of Wisconsin, USA, this study finds that the majority of the sample communities have permitted residential developments characterized as transit-oriented, New Urbanist, mixed use, infill developments, or conservation subdivisions as alternatives to low-density, automobile-dependent conventional developments. The study also finds that jurisdictions with higher planning capacities are more likely to overcome significant barriers to more sustainable residential development.     

A conceptual approach to model sand–gravel extraction from rivers based on a game theory perspective

Sand–gravel mining is a significant parameter of economic development and social welfare function in modern societies. As demand for aggregate increases in construction industry, conflicts for the availability of the resource and environmental impacts become more intense. The present paper describes the contested status quo in riverbed sand–gravel mining activities with an example from Greece, as a case study. The scope is to propose a methodology about good governance of the mining sector that promotes a sustainable sharing of aggregate resource by securing environment and safekeeping revenues in the mining trade market.     

Benchmarking Optical/Thermal Satellite Imagery for Estimating Evapotranspiration and Soil Moisture in Decision Support Tools

Generally, one expects evapotranspiration (ET) maps derived from optical/thermal Landsat and MODIS satellite imagery to improve decision support tools and lead to superior decisions regarding water resources management. However, there is lack of supportive evidence to accept or reject this expectation. We “benchmark” three existing hydrologic decision support tools with the following benchmarks: annual ET for the ET Toolbox developed by the United States Bureau of Reclamation, predicted rainfall‐runoff hydrographs for the Gridded Surface/Subsurface Hydrologic Analysis model developed by the U.S. Army Corps of Engineers, and the average annual groundwater recharge for the Distributed Parameter Watershed Model used by Daniel B. Stephens & Associates. The conclusion of this benchmark study is that the use of NASA/USGS optical/thermal satellite imagery can considerably improve hydrologic decision support tools compared to their traditional implementations. The benefits of improved decision making, resulting from more accurate results of hydrologic support systems using optical/thermal satellite imagery, should substantially exceed the costs for acquiring such imagery and implementing the remote sensing algorithms. In fact, the value of reduced error in estimating average annual groundwater recharge in the San Gabriel Mountains, California alone, in terms of value of water, may be as large as $1 billion, more than sufficient to pay for one new Landsat satellite.