Quality testing and product rationing by input suppliers

Quality testing by suppliers has significant ramifications for downstream supply chain participants and retail consumers. This article focuses on such implications accounting for the fact that suppliers often enjoy discretion in quality testing and reporting. Under a discretionary testing and reporting environment, we show that a supplier can improve the market's perception of product quality by engaging in self-imposed production cuts. Production cuts dampen supplier incentives to engage in excessive quality testing, putting the supplier and the market on a more equal information footing. This reduces the market's need to skeptically discount product quality to protect itself. The improved market perception, then, reduces quality testing demand, introducing cost savings. The result that costly production cuts can improve quality perceptions indicates that the groundwork for influencing market perceptions may have to be laid upfront, even prior to acquiring private information, providing a contrast to routine signaling models.

Validation of the use of input-output data for embodied energy analysis of the Australian construction industry

This paper evaluates a recently developed hybrid method for the embodied energy analysis of the Australian construction industry. It was found that the truncation associated with process analysis can be up to 80%, whilst the use of input-output analysis alone does not always provide a perfect model for replacing process data. There is also a considerable lack in the quantity and possibly quality of process data currently available. These findings suggest that current best-practice methods are sufficiently accurate for most typical applications, but this is heavily dependant upon data quality and availability. The hybrid method evaluated can be used for the optimisation of embodied energy and for identifying opportunities for improvements in energy efficiency.

Validation of the use of Australian input-output data for building embodied energy simulation

Traditional!y, the simulation of buildings has focused 011 operational energy consumption in an attempt to determine the potential for energy savings. Whilst operational energy of Australian buildings accounts for around 20% of total energy consumption nationally, embodied energy represents 20 to 50 times the annual operational energy of 1110st Australian buildings. Lower values have been shown through a number of studies that have analysed the embodied energy of buildings and their products, however these have now shown to be incomplete in system boundary. Many of these studies have used traditional embodied energy analysis methods, such as process analysis and input-output analysis, Hybrid embodied energy analysis methods have been developed, but these need to be compared and validated. This paper reports on preliminary work on this topic. The findings so far suggest that current best-practice methods are sufficiently accurate for most typical applications, but this is heavily dependant upon data quality and availability.

Analysis of stamping production data with view towards quality management

A quality analysis trial was undertaken at Ford Geelong Stamping Plant on a press line that was fitted with standard press sensors to measure press and binder force over the stamping cycle for each panel. The quality of randomly sampled panels was measured by obtaining the panel thicknesses at five points, for 135 panels in total. These points were chosen such that they exhibited different forming modes. This paper analyses the input force data and the output quality data from the trial to determine any potential relationships. The analysis of the production data was performed using statistical correlation techniques to determine initial potential relationships between input and output variables. An Active Shape Model was used to extract features when identifying the major sources of variation within the input data. However, the initial analysis of the data elicited no direct relationship between the input variables measured and the panel thicknesses. This result is significant as the data collected is from a standard sensor configuration found in many press lines through-out the world. The reason for the lack of a direct relationship is believed to come from the lack of sensitivity in the force measurements which are not able to identify small changes in the process, whereas gross geometric variations have in previous studies shown an obvious relationship with changes in the force press profile. This means that existing force sensors require augmentation by additional sensors if a detailed automatic quality control system for the press lines based on input sensors alone.

Quality of life indexes for national policy: review and agenda for research

A number of governments and public policy institutes have developed ldquoQuality of Life Indexesrdquo – statistics that attempt to measure the quality of life for entire states or regions. We develop 14 criteria for determining the validity and usefulness of such QOL indexes to public policy. We then review 22 of the most-used QOL indexes from around the world. We conclude that many of the indexes are successful in that they are reliable, have established time series measures, and can be disaggregated to study subpopulations. However, many fall short in four areas: (1) indexes vary greatly in their coverage and definitions of domains of QOL, (2) none of the indexes distinguish among the concepts of input, throughput, and output that are used by public policy analysts, (3) they fail to show how QOL outputs are sensitive to public policy inputs, and (4) none have examined convergent validity against each other. We conclude that many of these indexes are potentially very useful for public policy and recommend research to further improve them.

Is the quality of entrepreneurial business plans related to the outcome of a new venture?

The evaluation of new ventures often involves two key aspects of entrepreneurial business plans: how best to write them and how best to rate (evaluate) them. Ultimately the performance of the venture should be the definitive criterion of quality. Surprisingly, the writing, rating and performance effects of entrepreneurial business plans (EBPs) comprise three related but under researched areas. This paper empirically tested principles for writing and rating entrepreneurial business plans to draw inferences on how to improve the private equity investment evaluation process. A simplified perspective of General Systems Theory guided our empirical investigation of the input and outcome of the VC investment decision. Our empirical investigation reveals that entrepreneurial business plans that comport with the writing principles from the literature improve a new venture’s likelihood of success.

Towards development of a quality cost model for automotive stamping

The current work used discrete event simulation techniques to model the economics of quality within an actual automotive stamping plant. Automotive stamping is a complex, capital intensive process requiring part-specific tooling and specialised machinery. Quality control and quality improvement is difficult in the stamping environment due to the general lack of process understanding and the large number to interacting variables. These factors have prevented the widespread use of statistical process control. In this work, a model of the quality control techniques used at the Ford Geelong Stamping plant is developed and indirectly validated against results from production. To date, most discrete event models are of systems where the quality control process is clearly defined by the rules of statistical process control. However, the quality control technique used within the stamping plant is for the operator to perform a 100% visual inspection while unloading the finished panels. In the developed model, control is enacted after a cumulative count of defective items is observed, thereby approximating the operator who allows a number of defective panels to accumulate before resetting the line. Analysis of this model found that the cost sensitivity to inspection error is dependent upon the level of control and that the level of control determines line utilisation. Additional analysis of this model demonstrated that additional inspection processes would lead to more stable cost structures but these structures many not necessarily be lower cost. The model was subsequently applied to investigate the economics of quality improvement. The quality problem of panel blemishes, induced by slivers (small metal fragments), was chosen as a case stuffy. Errors of 20-30% were observed during direct validation of the cost model and it was concluded that the use of discrete event simulation models for applications requiring high accuracy would not be possible unless the production system was of low complexity. However, the model could be used to evaluate the sensitivity of input factors and investigating the effects of a number of potential improvement opportunities. Therefore, the research concluded that it is possible to use discrete event simulation to determine the quality economics of an actual stamping plant. However, limitations imposed by inability of the model to consider a number of external factors, such as continuous improvement, operator working conditions or wear and the lack of reliable quality data, result in low cost accuracy. Despite this, it still can be demonstrated that discrete event simulation has significant benefits over the alternate modelling methods.

Optimal sensor placement and measurement of wind for water quality studies in urban reservoirs

We collaborate with environmental scientists to study the hydrodynamics and water quality in an urban district, where the surface wind distribution is an essential input but undergoes high spatial and temporal variations due to the complex urban landform created by surrounding buildings. In this work, we study an optimal sensor placement scheme to measure the wind distribution over a large urban reservoir with a limited number of wind sensors. Unlike existing sensor placement solutions that assume Gaussian process of target phenomena, this study measures the wind which inherently exhibits strong non-Gaussian yearly distribution. By leveraging the local monsoon characteristics of wind, we segment a year into different monsoon seasons which follow a unique distribution respectively. We also use computational fluid dynamics to learn the spatial correlation of wind in the presence of surrounding buildings. The output of sensor placement is a set of the most informative locations to deploy the wind sensors, based on the readings of which we can accurately predict the wind over the entire reservoir surface in real time. 10 wind sensors are finally deployed around or on the water surface of an urban reservoir. The in-field measurement results of more than 3 months suggest that the proposed sensor placement and spatial prediction approach provides accurate wind measurement which outperforms the state-of-the-art Gaussian model based or interpolation based approaches.

Sensor placement and measurement of wind for water quality studies in urban reservoirs

We study the water quality in an urban district, where the surface wind distribution is an essential input but undergoes high spatial and temporal variations due to the impact of surrounding buildings. In this work, we develop an optimal sensor placement scheme to measure the wind distribution over a large urban reservoir using a limited number of wind sensors. Unlike existing solutions that assume Gaussian process of target phenomena, this study measures the wind that inherently exhibits strong non-Gaussian yearly distribution. By leveraging the local monsoon characteristics of wind, we segment a year into different monsoon seasons that follow a unique distribution respectively. We also use computational fluid dynamics to learn the spatial correlation of wind. The output of sensor placement is a set of the most informative locations to deploy the wind sensors, based on the readings of which we can accurately predict the wind over the entire reservoir in real time. Ten wind sensors are deployed. The in-field measurement results of more than 3 months suggest that the proposed sensor placement and spatial prediction scheme provides accurate wind measurement that outperforms the state-of-the-art Gaussian model based on interpolation-based approaches.

Prosthetic motor imaginary task classification based on EEG quality assessment features

Brain Computer Interface (BCI) plays an important role in the communication between human and machines. This communication is based on the human brain signals. In these systems, users use their brain instead of the limbs or body movements to do tasks. The brain signals are analyzed and translated into commands to control any communication devices, robots or computers. In this paper, the aim was to enhance the performance of a brain computer interface (BCI) systems through better prosthetic motor imaginary tasks classification. The challenging part is to use only a single channel of electroencephalography (EEG). Arm movement imagination is the task of the user, where (s)he was asked to imagine moving his arm up or down. Our system detected the imagination based on the input brain signal. Some EEG quality features were extracted from the brain signal, and the Decision Tree was used to classify the participant's imagination based on the extracted features. Our system is online which means that it can give the decision as soon as the signal is given to the system (takes only 20 ms). Also, only one EEG channel is used for classification which reduces the complexity of the system which leads to fast performance. Hundred signals were used for testing, on average 97.4% of the up-down prosthetic motor imaginary tasks were detected correctly. This method can be used in many different applications such as: moving artificial limbs and wheelchairs due to it's high speed and accuracy.

Investigation on the effect of cutting fluid pressure on surface quality measurement in high speed thread milling of brass alloy (C3600) and aluminium alloy (5083)

The quality of a machined finish plays a major role in the performance of milling operations, good surface quality can significantly improve fatigue strength, corrosion resistance, or creep behaviour as well as surface friction. In this study, the effect of cutting parameters and cutting fluid pressure on the quality measurement of the surface of the crest for threads milled during high speed milling operations has been scrutinised. Cutting fluid pressure, feed rate and spindle speed were the input parameters whilst minimising surface roughness on the crest of the thread was the target. The experimental study was designed using the Taguchi L32 array. Analysing and modelling the effective parameters were carried out using both a multi-layer perceptron (MLP) and radial basis function (RBF) artificial neural networks (ANNs). These were shown to be highly adept for such tasks. In this paper, the analysis of surface roughness at the crest of the thread in high speed thread milling using a high accuracy optical profile-meter is an original contribution to the literature. The experimental results demonstrated that the surface quality in the crest of the thread was improved by increasing cutting speed, feed rate ranging 0.41-0.45 m/min and cutting fluid pressure ranging 2-3.5 bars. These outcomes characterised the ANN as a promising application for surface profile modelling in precision machining.

Exceptional object analysis for finding rare environmental events from water quality datasets

This paper provides a novel Exceptional Object Analysis for Finding Rare Environmental Events (EOAFREE). The major contribution of our EOAFREE method is that it proposes a general Improved Exceptional Object Analysis based on Noises (IEOAN) algorithm to efficiently detect and rank exceptional objects. Our IEOAN algorithm is more general than already known outlier detection algorithms to find exceptional objects that may be not on the border; and experimental study shows that our IEOAN algorithm is far more efficient than directly recursively using already known clustering algorithms that may not force every data instance to belong to a cluster to detect rare events. Another contribution is that it provides an approach to preprocess heterogeneous real world data through exploring domain knowledge, based on which it defines changes instead of the water data value itself as the input of the IEOAN algorithm to remove the geographical differences between any two sites and the temporal differences between any two years. The effectiveness of our EOAFREE method is demonstrated by a real world application - that is, to detect water pollution events from the water quality datasets of 93 sites distributed in 10 river basins in Victoria, Australia between 1975 and 2010. © 2012 Elsevier B.V..