In evaluating health and safety improvements for performance improvement, it is necessary to account for both the contributions of a healthy workforce and the resources required supporting it.
The Economic Assessment of the Work Environment (EAWE) is a financial framework that helps management forecast the financial benefits of health and safety implementations. The five-step process comprises (1) a health assessment to identify critical elements in the work environment, (2) an action plan to address gaps, (3) performance targets based on internal goals and external benchmarks, (4) transformation of the expected improvements in health and safety into expected performance outcomes, and (5) implementation in stages, starting from individual jobs to entire organisation.
EAWE offers a dynamic framework for corporate decision-makers when evaluating health and safety programmes. Further research should explore the bounds of EAWE across different types of organisations and the evolution of performance over time. 相似文献
This article presents the practices of Australian and German financial service providers regarding the implementation of shop-floor control within different types of service systems. The results delivered in this article should serve as a guideline for future research to develop and adapt methods for shop-floor control in financial service systems. Interviews with 25 experts from the Australian and German financial services industry reveal novel insights into the practice of shop-floor control, suggesting that methods and concepts from manufacturing are only used to a limited extent for shop-floor control. Shop-floor control is mostly used to react quickly to unexpected deviations due to a low usage of forecasts and information systems. Thus, there seems to be improvement potential in the financial services industry in comparison with in the manufacturing industry in terms of shop-floor control. Further research within the production research area should use the empirical insights to test and adapt existing methods and to develop new ones, taking cultural differences into account. 相似文献
Managerial decision-making processes often involve data of the time nature and need to understand complex temporal associations among events. Extending classical association rule mining approaches in consideration of time in order to obtain temporal information/knowledge is deemed important for decision support, which is nowadays one of the key issues in business intelligence. This paper presents the notion of multi-temporal patterns with four different temporal predicates, namely before, during, equal and overlap, and discusses a number of related properties, based on which a mining algorithm is designed. This enables us to effectively discover multi-temporal patterns in large-scale temporal databases by reducing the database scan in the generation of candidate patterns. The proposed approach is then applied to stock markets, aimed at exploring possible associative movements between the stock markets of Chinese mainland and Hong Kong so as to provide helpful knowledge for investment decisions. 相似文献
This research joins the growing body of literature that advocates for the use of information and communication technology (ICT) in local governance more particularly in public financial management. Using a case study in Bohol, a province in the Philippines, this paper discusses the impact of ICT on local revenue generation by analyzing both quantitative and qualitative data from 15 municipalities which used e-taxation. This paper argues that the use of ICT can make possible more transparent and accountable revenue generation systems to benefit both government and taxpayers. However, these results are differentiated depending on the level of political leadership, the nature of articulation of the demand for ICT use, the ratio of benefit against cost, and the availability of technical skills and resources at the sub-national level. It is within this context that an eco-system analysis is argued to be useful in analyzing how ICT can be adopted, scaled, and used by sub-national governments to achieve better governance. 相似文献
Financial time series prediction is regarded as one of the most challenging job because of its inherent complexity, and the hybrid forecasting model incorporating autoregressive integrated moving average and support vector machine (SVM) has been implemented widely to deal with the both linear and nonlinear patterns in time series data. However, the SVM model does not take into consideration the time correlation knowledge between different data points in time series, which impacts the learning efficiency of the SVM in real application. To overcome this restriction, this paper proposes the Taylor Expansion Forecasting model as an alternative to the SVM and develops a novel hybrid methodology via combining autoregressive integrated moving average and Taylor Expansion Forecasting to exploit the comprehensive forecasting capacity to the financial time series data with noise. Both theoretical proof and empirical results obtained on several commodity future prices demonstrate that the proposed hybrid model improves greatly the forecasting accuracy. 相似文献