1,721,007 research outputs found
Using the Hofstede-Gray Framework to Argue Normatively for an Extension of Islamic Corporate Reports
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Modelling 'hard-to-measure' costs in environmental management accounting
No full textThis paper reviews measurement issues that have arisen in the environmental management accounting literature and provides a statistical approach to quantifying the financial results associated with vaguely defined outcomes from physical processes. Among the latter are outcomes relating to safety and pollution both of which also impact on political visibility. A classification of 'hard-to-measure' costs is given with illustrations of how mathematical modelling allows these to be estimated and their implications for decision-making better understood. Our approach provides an integrated analysis of return and risk
Using Automated Equilibrium Correction Modelling in Analytic Review
No full textPurpose – The purpose of this paper is to show how dynamic regression models based on equilibrium correction principles can be used to form auditor expectations of account balances as part of the analytic review.
Design/methodology/approach – The design and method are empirical, using the automated econometric software of PcGets and annual data of the Toyota Company over the period 1950-2004 to generate forecasts of sales and earnings.
Findings – Automated equilibrium correction models (AECMs) are shown to possess stable parameters and provide reliable one year ahead forecasts of sales based on macro-economic data. AECMs are then used to generate indicative earnings forecasts conditional upon sales as an expectation generating tool for directing auditors' attention to possible sources of error in financial statements.
Research limitations/implications – Analysis is illustrative of a general method and does not
provide exhaustive treatment of the full range of potential application of AECMs.
Practical implications – Until recently, econometric problems have made the use of dynamic regression models in auditing difficult for non-specialists to implement. Developments in automated software packages such as PcGets now make the use of such procedures by audit practitioners possible.
Originality/value – Relatively little is known about dynamic regression models in the accounting and auditing literature. This paper introduces the basic concepts underpinning AECMs and demonstrates their potential to contribute to the analytic review toolkit of the auditor
Utilising Reliability and Condition Monitoring Data for Asset Health Prognosis
No full textThe ability to forecast machinery health is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models which attempt to forecast machinery health based on condition data such as vibration measurements. This paper demonstrates how the population characteristics and condition monitoring data (both complete and suspended) of historical items can be integrated for training an intelligent agent to predict asset health multiple steps ahead. The model consists of a feed-forward neural network whose training targets are asset survival probabilities estimated using a variation of the Kaplan–Meier estimator and a degradation-based failure probability density function estimator. The trained network is capable of estimating the future survival probabilities when a series of asset condition readings are inputted. The output survival probabilities collectively form an estimated survival curve. Pump data from a pulp and paper mill were used for model validation and comparison. The results indicate that the proposed model can predict more accurately as well as further ahead than similar models which neglect population characteristics and suspended data. This work presents a compelling concept for longer-range fault prognosis utilising available information more fully and accurately
Optimising preventive maintenance strategy for production Lines
No full textPreventive Maintenance (PM) is often applied to improve the reliability of production lines. A Split System Approach (SSA) based methodology is presented to assist in making optimal PM decisions for serial production lines. The methodology treats a production line as a complex series system with multiple (imperfect) PM actions over multiple intervals. The conditional and overall reliability of the entire production line over these multiple PM intervals are hierarchically calculated using SSA, and provide a foundation for cost analysis. Both risk-related cost and maintenance-related cost are factored into the methodology as either deterministic or random variables. This SSA based methodology enables Asset Management (AM) decisions to be optimised considering a variety of factors including failure probability, failure cost, maintenance cost, PM performance, and the type of PM strategy. The application of this new methodology and an evaluation of the effects of these factors on PM decisions are demonstrated using an example. The results of this work show that the performance of a PM strategy can be measured by its Total Expected Cost Index (TECI). The optimal PM interval is dependent on TECI, PM performance and types of PM strategies. These factors are interrelated. Generally, it was found that a trade-off between reliability and the number of PM actions needs to be made so that one can minimise Total Expected Cost (TEC) for asset maintenance.\u
Improving asset management process modelling and integration\ud
No full textAsset management (AM) processes play an important role in assisting enterprises to manage their assets more efficiently. To visualise and improve AM processes, the processes need to be modelled using certain process modelling methodologies. Understanding the requirements for AM process modelling is essential for selecting or developing effective AM process modelling methodologies. However, little research has been done on analysing the requirements. This paper attempts to fill this gap by investigating the features of AM processes. It is concluded that AM process modelling requires intuitive representation of its processes, ‘fast’ implementation of the process modelling, effective evaluation of the processes and sound system integration.\u
Machine prognostics based on health state estimation using SVM\ud
No full textThe ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation, and can also improve productivity and enhance system safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and an assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. All machine components are subjected to degradation processes in real environments and they have certain failure characteristics which can be related to the operating conditions. This paper describes a technique for accurate assessment of the remnant life of machines based on health state probability estimation and involving historical knowledge embedded in the closed loop diagnostics and prognostics systems. The technique uses a Support Vector Machine (SVM) classifier as a tool for estimating health state probability of machine degradation, which can affect the accuracy of prediction. To validate the feasibility of the proposed model, real life historical data from bearings of High Pressure Liquefied Natural Gas (HP-LNG) pumps were analysed and used to obtain the optimal prediction of remaining useful life. The results obtained were very encouraging and showed that the proposed prognostic system based on health state probability estimation has the potential to be used as an estimation tool for remnant life prediction in industrial machinery.\u
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