Performance of cumulative sum schemes for monitoring low count-level processes

In many instances attributes data must be used to monitor a manufacturing (or other) process that, in normal conditions, should operate at very low count levels for defects. Lucas (1989) has directed attention to this problem, and has investigated a new control scheme for low count-level processes. An alternative scheme is proposed, based on a Cumulative Sum (CUSUM) of the number (termed Run-Length) of successive samples having zero count-levels between samples having at least one count. Using the criterion of Average Run Length (the average number of samples until a signal is generated) comparisons of the Lucas scheme and the Run-Length CUSUM scheme indicate that ARL values for the Run-Length CUSUM can be up to 50% lower.     

Developing an integrated approach for the strategic monitoring of regional spatial plans

Monitoring is a challenging and fundamental issue within planning processes, yet there is little understanding as to whether plans achieved their goals in guaranteeing sustainable development on a territorial level. Follow-up of spatial planning is rather difficult due to insufficient methodologies, deficiencies in plans’ contents and resource limitations. The main goal of this research is to develop and test a conceptual framework to support the development of effective indicator based regional spatial plans’ (RSPs) monitoring systems. The conceptual framework includes indicators with distinct purposes and has the following features: (i) it establishes links between RSPs, their strategic environmental assessment and monitoring indicators; (ii) it incorporates the contribution of other existing monitoring systems in the regions; and (iii) it defines specific indicator selection criteria. In order to inform the development of the conceptual framework we: (i) review international practice of RSP monitoring; (ii) analyse the features of current Portuguese RSPs; (iii) test the conceptual framework using the RSP and the regional sustainability indicator system of the Algarve region (Portugal) as a case study. Two types of indicators were developed - output/implementation and results/effects - for improving the identification of cause-effect relationships between RSP implementation and associated territorial effects; although this is a continuing challenge, given the strategic nature of RSPs. The usefulness of the conceptual framework was demonstrated in the definition of monitoring indicators for the Algarve's RSP, as well as in the integration of an existing regional sustainability monitoring system.     

Combining forecasts for universally optimal performance

There are two potential directions of forecast combination: combining for adaptation and combining for improvement. The former direction targets the performance of the best forecaster, while the latter attempts to combine forecasts to improve on the best forecaster. It is often useful to infer which goal is more appropriate so that a suitable combination method may be used. This paper proposes an AI-AFTER approach that can not only determine the appropriate goal of forecast combination but also intelligently combine the forecasts to automatically achieve the proper goal. As a result of this approach, the combined forecasts from AI-AFTER perform well universally in both adaptation and improvement scenarios. The proposed forecasting approach is implemented in our R package AIafter, which is available at https://github.com/weiqian1/AIafter.     

Performance management 40 years on: a review. Some key decisions and consequences

This paper reviews the past four decades of experience of performance management (PM). Beginning with a brief history of its international spread and development, a short section on conceptual issues is followed by a strategic analysis of the research thus far. From this analysis is extracted a set of key decisions which must be made for any PM system. Each decision has significant consequences, and none are purely technical (though they may occasionally be represented as such). In many cases difficult trade-offs are inevitable.     

Application of bootstrap re-sampling method in statistical measurement of sustainability

Sustainability is of essential interest for many organizations and is defined as the ability to maintain existing resources at a certain rate or level when encountered with barriers. Factors affecting sustainability are categorized as enablers (capacities) and barriers (challenges) that have positive and negative effects on sustainability, respectively. To evaluate the status of sustainability, organizations need a measurement method to account for all the aspects of the sustainability classified into social, economic, and environmental tiers. Previously many researchers have provided indexes and measure specific to the studied field, which is not applicable to other areas. Furthermore, the proposed methods fail to cover all the aspects of sustainability. This study investigates a statistical method to measure the sustainability and the application of the bootstrap re-sampling method in order to overcome the problem with normality assumption when the sample size is not large enough and thus develop a more realistic stochastic model. The Bootstrap re-sampling method enables the unbiased estimation of population parameters such as mean and standard deviation. The proposed method is evaluated by comparing its results with those found in the literature.     

Analytically comparing disaster resilience across multiple dimensions

It is important to compare the resilience of complex human systems to different types of disasters, in order to assess their inherent vulnerabilities and take appropriate actions to strengthen them. Resilient behavior can be complicated and multi-dimensional, however, and one must be able to characterize the different ways in which that resilience actually exhibits itself in practice. With this in mind, this paper discusses creating a multi-dimensional indicator for the resilience of a complex human system, and it explores an approach for visualizing and analyzing the relationships between each of the individual resilience dimensions. Because decision makers may differ on the relative contribution of the different dimensions to overall resilience, the paper further discusses the issue of weighting the different dimension values and the impacts that such a weighting scheme can have on the relative ranking of different scenarios. We illustrate the ability to characterize the complexity of multi-dimensional resilience by analyzing an empirical data set that measures the relative resilience of the New York metropolitan area to seven different natural disasters between 2010 and 2012.     

Optimizing a portfolio of mean-reverting assets with transaction costs via a feedforward neural network

Optimizing a portfolio of mean-reverting assets under transaction costs and a finite horizon is severely constrained by the curse of high dimensionality. To overcome the exponential barrier, we develop an efficient, scalable algorithm by employing a feedforward neural network. A novel concept is to apply HJB equations as an advanced start for the neural network. Empirical tests with several practical examples, including a portfolio of 48 correlated pair trades over 50 time steps, show the advantages of the approach in a high-dimensional setting. We conjecture that other financial optimization problems are amenable to similar approaches.     

Analysing the role of sustainable development indicators in accounting for and constructing a Sustainable Scotland

The main objective of this paper was to analyse how sustainable development indicators impacted upon the integration of sustainable development into the governing of Scotland. A major concern was whether an accounting technology could represent this complex multi-dimensional and interdisciplinary concept. We analysed the relationship between the official sustainable development strategy of the Scottish Executive and the associated indicator set using an analytics of government framework (Dean, M. (1999). Governmentality: Power and rule in modern society. London: Sage Publications. Dean, M. (2007). Governing societies. Berkshire: Open University Press). We observed a lack of alignment between these sustainable development indicators and the visions, fields of visibilities, forms of knowledge and techniques of government contained in this strategy. Critical aspects of this strategy were omitted from the indicator set and we argue that these indicators did not to effectively measure progress towards a Sustainable Scotland but that they could calculatively capture and distort the sustainable development governing process. The analytical framework used allowed us to problematise these indicators and contribute to a wider discourse on the composition and nature of sustainable development indicators.     

Performance evaluation of production systems monitored by statistical process control and off-line inspections

Quality and productivity performance measures are very often considered in separate phases of the production system design process. However, the production system architecture affects the efficiency of the quality control system as well as the quality control configuration has an impact on the performance of the production system.The paper proposes a new analytical method for evaluating the performance of production systems in which statistical process control (SPC) techniques are implemented. Machines behaviour is monitored by measuring quality characteristics of the produced parts through off-line inspection devices and sampling inspections. The numerical results show the good accuracy of the proposed method, provide new insight in the relations among the two areas and pave the way to the joint design of production logistics and quality control systems.