Experiments toward a practical implementation of an intelligent kanban system

This paper presents laboratory experiments to test a bottom up approach to production control and supply chain management. Built upon the successful traditional kanban (Card) system, the new intelligent system associates a kanban agent to each physical kanban. Instead of relying on demand forecast and planning, kanban agents reason about their own movements to adapt to changing demands. After previous simulations results of the intelligent system showed significant performance improvements over the traditional system, we further use the Auto-ID Laboratory at Cambridge University to test the feasibility of the idea in a realistic manufacturing environment. The results from the experiments demonstrated the superiority on several performance measures of the intelligent system compared to the traditional system used as a benchmark. Moreover, the implementation of the experiments exposed several real world constraints not shown in the simulation study and practical solutions were adopted to address these.

MRP-production planning in agile manufacturing

The research studied a production planning problem in a manufacturing firm. Some production planning software was developed that enabled the efficient and rapid reproduction of an optimum production forecast. Not only was the production cost reduced but a significant length of planning time cn be saved using the software.

Aid, debt relief and new sources of finance for meeting the millennium development goals

This article focuses on aid, debt relief and new sources of finance for meeting the Millennium Development Goals (MDGs). It was said that MDGs provide a clear set of objectives for mobilizing the international development community, especially in the area of development finance. The call for increased aid as well as for more debt relief in the creation of new sources of development finance has increased since the United Nations Financing for Development Summit and the subsequent report of the panel chaired by then President Ernesto Zedillo of Mexico on development finance. The goal of reducing the proportion of people living in extreme poverty by 2015 cannot be achieved in Sub-Saharan Africa (SSA). Such optimistic forecast suggests that MDG income poverty target will not be achieved in SSA until 2147.

Measuring public infrastructure construction impacts on economic growth

The results identify the long-run equilibrium relationships among economic variables in Australia. This study can provide policy makers with information on a one standard error shock to each variable and insights into what percentages of the forecast error variance of a variable are explained by the innovations of each variable.

Comparing different economic linkages among commodity futures

Gasoline (GA) and kerosene (KO) are extracted from crude oil (CO), such that the three fuel commodities share a chemical link. On the other hand, GA also shares an industrial link with natural rubber (NR) and palladium (PA) as complementary commodities that are heavily consumed by the automobile industry. We contrast the information content embedded in the two economic linkages. Focusing on TOCOM futures contracts written on the five commodities and centering on GA, we confirm that incremental information provided by either CO, KO or NR, PA over a buy-and-hold strategy and a naive forecast, are both statistically and economically significant. While the chemical link forecast is more profitable, a double-link forecast generated from a VECM with two cointegrating vectors (KO-GA and GANR prices) outperforms both single-link forecasts based on risk-adjusted profit net of transaction costs. Further comparisons against the profitability of commodity-based momentum strategies documented in Erb and Harvey (2006) and Miffre and Rallis (2007) show that the double-link forecast holds its own against the most profitable of the 75 momentum strategies considered. This strongly suggests that not only are there incremental profits to be gained from harnessing and combining economic links among commodity futures, the resultant incremental profits are economically significant against other proven commodity-based trading strategies in the existing literature.

The accuracy of housing forecasting in Australia

Forecasting is an integral part of all business planning, and forecasting the outlook for housing is of interest to many firms in the housing construction sector. This research measures the performance of a number of industry forecasting bodies; this is done to provide users with an indicator of the value of housing forecasting undertaken in Australia. The accuracy of housing commencement forecasts of three Australian organisations – the Housing Industry Association (HIA), the Indicative Planning Council for the Housing Industry (IPC) and BIS-Shrapnel – is examined through the empirical analysis of their published forecasts supplemented by qualitative data in the form of opinions elicited from several industry “experts” employed in these organisations. Forecasting performance was determined by comparing the housing commencement forecast with the actual data collected by the Australian Bureau of Statistics on an ex-post basis. Although the forecasts cover different time periods, the level of accuracy is similar, at around 11-13 per cent for four-quarter-ahead forecasts. In addition, national forecasts are more accurate than forecasts for individual states. This is the first research that has investigated the accuracy of both private and public sector forecasting of housing construction in Australia. This allows users of the information to better understand the performance of various forecasting organisations.

Forecasting the onset of the grass pollen season in Melbourne (Australia)

In Melbourne, a southern hemisphere city with a cool temperate climate, the grass pollen season has been monitored using a Burkard spore trap for 12 years (11 pollen seasons, which extend from October through January). The onset of the grass pollen season (OGPS) has been defined in various ways using both arbitrary cumulative scores (Sum 75, Sum 100) and percentages (10% Pollen Fly). OGPS, based on the forecast model of pollen season devised by Lejoly-Gabriel (Acta Geogr. Lovan., 13 (1978) 1–260) has been most widely used in efforts to forecast the beginning of the pollen season. OGPS occurred in Melbourne between 20 October to 24 November (average 6 November), a difference of 35 days. Duration of the pollen season ranged from 46 to 81 days, with a mean of 55 days, one of the longest reported. The relationships between onset and various weather parameters for July have enabled us to modify a model, using linear regression analysis, to predict onset. The prediction model is based on a negative correlation between date of onset and the sum of rainfall for July (a winter month). The error of prediction (Ep) is 24% and predicted day of OGPS was precisely predicted on 2 occasions, and on others with a range of accuracy of 3 to 14 days.

House price bubble estimations in Australia's capital cities with market fundamentals

This paper investigates the existence of house price bubbles in Australia's eight capital cities in recent years by using quantitative analyses including Johansen cointegration test, Granger causality test, impulse response and Chow forecast test. While interactions between house prices and market fundamentals are discussed in long-run and causal estimations, shocks from the market fundamentals to house prices are investigated in generalized impulse response analyses. Findings from estimating house price bubbles for eight capital cities suggest that there was an obvious house price bubble in Perth, while a slight house price bubble occurred in Sydney. In contrast, house prices in Adelaide and Darwin can be explained very well by market fundamentals, while house prices in Melbourne, Brisbane, Hobart and Canberra were undervalued in the study period.

A phase II multicentered, single-blind, randomized, controlled trial of the stroke self-management program

Background and Purpose—The benefits of chronic disease self-management programs for stroke survivors are uncertain because individuals with severe impairments have been excluded from previous research. We undertook a phase II randomized controlled trial to determine whether a self-management program designed for survivors (SSMP; 8 weeks) was safe and feasible compared to standard care (control) or a generic self-management program (generic; 6 weeks).
Methods—Stroke survivors were recruited from 7 South Australian hospitals via a letter or indirectly (eg, newspapers). Eligible participants were randomized at a 1:1:1 ratio of 50 per group. Primary outcomes were recruitment, participation, and participant safety. Secondary outcomes were positive and active engagement in life using the Health Education Impact Questionnaire and characteristics of quality of life and mood at 6 months from program completion.
Results—Of 315 people screened, 149 were eligible and 143 were randomized (48 SSMP, 47 generic, 48 control); mean age was 69 years (SD, 11) and 59% were female. Demographic features were similar between groups and 41% had severe cognitive impairment; 57% accessed the interventions, with 52% SSMP and 38% generic completing >50% of sessions (P=0.18). Thirty-two participants reported adverse events (7 control, 12 generic, 13 SSMP; P=0.3; 34% severe); however, none was attributable to the interventions. Potential benefits for improved mood were found.
Conclusions—SSMP was safe and feasible. Benefits of the stroke-specific program over the generic program included greater participation and completion rates. An efficacy trial is warranted given the forecast growth in the stroke population and improved survival trends.

Hybrid neural-evolutionary model for electricity price forecasting

Evolving artificial neural networks has attracted much attention among researchers recently, especially in the fields where plenty of data exist but explanatory theories and models are lacking or based upon too many simplifying assumptions. Financial time series forecasting is one of them. A hybrid model is used to forecast the hourly electricity price from the California Power Exchange. A collaborative approach is adopted to combine ANN and evolutionary algorithm. The main contributions of this thesis include: Investigated the effect of changing values of several important parameters on the performance of the model, and selected the best combination of these parameters; good forecasting results have been obtained with the implemented hybrid model when the best combination of parameters is used. The lowest MAPE through a single run is 5. 28134%. And the lowest averaged MAPE over 10 runs is 6.088%, over 30 runs is 6.786%; through the investigation of the parameter period, it is found that by including future values of the homogenous moments of the instant being forecasted into the input vector, forecasting accuracy is greatly enhanced. A comparison of results with other works reported in the literature shows that the proposed model gives superior performance on the same data set.

Cues and the optimal timing of activities under environmental changes

Organisms time activities by using environmental cues to forecast the future availability of important resources. Presently, there is limited understanding of the relationships between cues and optimal timing, and especially about how this relationship will be affected by environmental changes. We develop a general model to explore the relation between a cue and the optimal timing of an important life history activity. The model quantifies the fitness loss for organisms failing to time behaviours optimally. We decompose the immediate change in fitness resulting from environmental changes into a component that is due to changes in the predictive power of the cue and a component that derives from the mismatch of the old response to the cue to the new environmental conditions. Our results show that consequences may range from negative, neutral to positive and are highly dependent on how cue and optimal timing and their relation are specifically affected by environmental changes.

Using clinical trial data and linked administrative health data to reduce the risk of adverse events associated with the uptake of newly released drugs by older Australians : a model process

Background
The study was undertaken to evaluate the contribution of a process which uses clinical trial data plus linked de-identified administrative health data to forecast potential risk of adverse events associated with the use of newly released drugs by older Australian patients.

Methods
The study uses publicly available data from the clinical trials of a newly released drug to ascertain which patient age groups, gender, comorbidities and co-medications were excluded in the trials. It then uses linked de-identified hospital morbidity and medications dispensing data to investigate the comorbidities and co-medications of patients who suffer from the target morbidity of the new drug and who are the likely target population for the drug. The clinical trial information and the linked morbidity and medication data are compared to assess which patient groups could potentially be at risk of an adverse event associated with use of the new drug.

Results
Applying the model in a retrospective real-world scenario identified that the majority of the sample group of Australian patients aged 65 years and over with the target morbidity of the newly released COX-2-selective NSAID rofecoxib also suffered from a major morbidity excluded in the trials of that drug, indicating a substantial potential risk of adverse events amongst those patients. This risk was borne out in post-release morbidity and mortality associated with use of that drug.

Conclusions
Clinical trial data and linked administrative health data can together support a prospective assessment of patient groups who could be at risk of an adverse event if they are prescribed a newly released drug in the context of their age, gender, comorbidities and/or co-medications. Communication of this independent risk information to prescribers has the potential to reduce adverse events in the period after the release of the new drug, which is when the risk is greatest.

Note: The terms 'adverse drug reaction' and 'adverse drug event' have come to be used interchangeably in the current literature. For consistency, the authors have chosen to use the wider term 'adverse drug event' (ADE).

Does the choice of estimator matter when forecasting returns?

While the literature concerned with the predictability of stock returns is huge, surprisingly little is known when it comes to role of the choice of estimator of the predictive regression. Ideally, the choice of estimator should be rooted in the salient features of the data. In case of predictive regressions of returns there are at least three such features; (i) returns are heteroskedastic, (ii) predictors are persistent, and (iii) regression errors are correlated with predictor innovations. In this paper we examine if the accounting of these features in the estimation process has any bearing on our ability to forecast future returns. The results suggest that it does.

Intelligent energy management for plug-in hybrid electric vehicles : the role of ITS infrastructure in vehicle electrification

The desire to reduce carbon emissions due to transportation sources has led over the past decade to the development of new propulsion technologies, focused on vehicle electrification (including hybrid, plug-in hybrid and battery electric vehicles). These propulsion technologies, along with advances in telecommunication and computing power, have the potential of making passenger and commercial vehicles more energy efficient and environment friendly. In particular, energy management algorithms are an integral part of plug-in vehicles and are very important for achieving the performance benefits. The optimal performance of energy management algorithms depends strongly on the ability to forecast energy demand from the vehicle. Information available about environment (temperature, humidity, wind, road grade, etc.) and traffic (traffic density, traffic lights, etc.), is very important in operating a vehicle at optimal efficiency. This article outlines some current technologies that can help achieving this optimum efficiency goal. In addition to information available from telematic and geographical information systems, knowledge of projected vehicle charging demand on the power grid is necessary to build an intelligent energy management controller for future plug-in hybrid and electric vehicles. The impact of charging millions of vehicles from the power grid could be significant, in the form of increased loading of power plants, transmission and distribution lines, emissions and economics (information are given and discussed for the US case). Therefore, this effect should be considered in an intelligent way by controlling/scheduling the charging through a communication based distributed control.