Search results

Despite the potential of Big Data analytics, the analysis of Micro Data represents the main way of forecasting the expected values of recorded amounts and/or ratios for small auditing firms and certified public accountants dealing with analytical procedures. This study aims to examine how effective Micro Data analytics are by testing the forecast accuracy of the ratio of the allowance for doubtful accounts to the trade accounts receivable and the natural logarithm of the net sales of goods and services, the first exposed to a greater uncertainty than the second.

Micro Data are low in volume, variety, velocity and variability, but high in veracity. Given the over-fitting problems affecting Micro Data analytics, the in-sample and out-of-sample forecasts were made for both tests. Multiple regression and neural network models were performed using a sample of 35 Italian industrial listed companies.

The accuracy level of the forecasting models was found in terms of mean absolute percentage error and other accuracy measures. The neural network model provided more accurate forecasts than multiple regression in both tests, showing a higher accuracy level for the amounts exposed to less uncertainty. Moreover, no generalized conclusions on predictors included in the models could be drawn.

The examination of forecast accuracy helps auditors to evaluate whether analytical procedures can be successfully applied to detect misstatements when Micro Data are used and which model gives the most accurate forecasts.

This is the first study to measure the forecast accuracy of the multiple regression and neural network models performed using a Micro Data set. Forecast accuracy is crucial for evaluating the effectiveness of analytical procedures.

Automated flow line manufacturing systems are becoming more and more relevant in industry, especially in the food and beverage sector. Improving the efficiency of automated flow line manufacturing systems is the core objectives of all companies as measured by the overall equipment effectiveness (OEE) index. The purpose of this paper is to carry out an innovative micro downtime data collection and statistical analysis in the food and beverage sector; it introduces a numerical indicator called “Cost Performance Indicator-CPI” to estimate the performance improvement of investment activities. Moreover this analysis will be used as a basis to carry out a new simulative model to study micro downtime of automatic production lines. In addition, the presented micro downtime data collection and statistical analysis will be used to construct a new simulative model to support improvement activities.

Descriptive and statistical analyses are carried out about OEE, time to repair (TTR) and time to failure (TTF) data. The least efficient production line is identified and principal causes of inefficiency are investigated. Micro downtime (downtime lower than 15 minutes) covers 57 percent of inefficiency. Investigations are carried out into the three principal machines affected by this inefficiency. The study then investigates the causes of micro downtime of these machines using ad hoc data collection and analysis. The probability distributions of TTF and TTR are evaluated and an analysis of micro downtime causes and a cause-effect is carried out. The most attractive investment in terms of recoverable OEE (1.44 percent) and costs is analyzed through the calculation of a CPI. One of the conclusions is to recommend the introduction of a payback period with a variable contribution margin.

This study get the basis for the construction of a new simulative model based on ad hoc micro downtime probability distributions, applied in automated flow line manufacturing systems. It gives an effort to downtime analysis in automated production lines and a guideline for future analysis. Results of this study can be generalized and extended to other similar cases, in order to study similar micro downtime inefficiency of other production lines. The statistical analysis developed could also potentially be used to further investigate the relationship between the reliability of specific machines and that of the entire line.

The case study presents a new detailed micro downtime data collection and statistical analysis in the beverage sector with the application of a numerical indicator, the CPI, in order to drive future actions. In addition, the presented micro downtime data collection and statistical analysis will be used to construct a new simulative model to support improvement activities. Moreover, results can be generalized and used as a basis for other micro downtime analyses involving the main causes of inefficiency in automated production lines.

The purpose of this paper is to anonymize web server log files used in e‐commerce web mining processes.

The paper has applied statistical disclosure control (SDC) techniques to achieve its goal. More precisely, it has introduced the micro‐aggregation of web access logs.

The experiments show that the proposed technique provides good results in general, but it is especially outstanding when dealing with relatively small websites.

As in all SDC techniques there is always a trade‐off between privacy and utility or, in other words, between disclosure risk and information loss. In this proposal, it has borne this issue in mind, providing k‐anonymity, while preserving acceptable information accuracy.

Web server logs are valuable information used nowadays for user profiling and general data‐mining analysis of a website in e‐commerce and e‐services. This proposal allows anonymizing such logs, so they can be safely outsourced to other companies for marketing purposes, stored for further analysis, or made publicly available, without risking customer privacy.

Current solutions to the problem presented here are very poor and scarce. They are normally reduced to the elimination of sensitive information from query strings of URLs in general. Moreover, to its knowledge, the use of SDC techniques has never been applied to the anonymization of web logs.

Micro-video platforms have gained attention in recent years and have also become an important new channel for merchants to advertise their products. Since little research has studied micro-video advertising, this paper aims to fill the research gap by exploring the determinants of micro-video advertising clicks. We form a micro-video advertising click prediction model and demonstrate the effectiveness of the multimodal information extracted from the advertisement producers, commodities being sold and micro-video contents in the prediction task.

A multimodal analysis framework was conducted based on real-world micro-video advertisement datasets. To better capture the relations between different modalities, we adopt a cooperative learning model to predict the advertising clicks.

The experimental results show that the features extracted from different data sources can improve the prediction performance. Furthermore, the combination of different modal features (visual, acoustic, textual and numerical) is also worth studying. Compared to classical baseline models, the proposed cooperative learning model significantly outperforms the prediction results, which demonstrates that the relations between modalities are also important in advertising micro-video generation.

To the best of our knowledge, this is the first study analysing micro-video advertising effects. With the help of our advertising click prediction model, advertisement producers (merchants or their partners) can benefit from generating more effective micro-video advertisements. Furthermore, micro-video platforms can apply our prediction results to optimise their advertisement allocation algorithm and better manage network traffic. This research can be of great help for more effective development of the micro-video advertisement industry.

Of critical concern to the world is the need to reduce consumption and waste of natural resources. This study provides a multi-level exploration of the ways situational and transformational links between levels and challenges are related to the adoption and utilization of material flow cost accounting in Vietnam, to encourage green productivity.

Based on triangulation of public documents at different institutional levels and a set of semi-structured interviews, situational and transformational links and challenges for material flow cost accounting in Vietnam are examined using purposive and snowball sampling of key actors.

Using a multi-level framework the research identifies six situational and transformational barriers to implementation of material flow cost accounting and suggests opportunities to overcome these. The weakest links identified involve macro-to meso-situational and micro-to macro-transformational links. The paper highlights the dominance of meso-level institutions and lack of focus on micro transformation to cut waste and enable improvements in green productivity.

The paper identifies ways for companies in Vietnam to reduce unsustainability and enable transformation towards sustainable management and waste reduction.

The paper is the first to develop and use a multi-level/multi-time period framework to examine the take-up of material flow cost accounting to encourage transformation towards green productivity. Consideration of the Vietnamese case builds understanding of the challenges for achieving United Nations Sustainable Development Goal number 12, to help enable sustainable production and consumption patterns.

The purpose of this study is to demonstrate and characterise a soft-tooled micro-injection moulding process through in-line measurements and surface metrology using a data-intensive approach.

A soft tool for a demonstrator product that mimics the main features of miniature components in medical devices and microsystem components has been designed and fabricated using material jetting technique. The soft tool was then integrated into a mould assembly on the micro-injection moulding machine, and mouldings were made. Sensor and data acquisition devices including thermal imaging and injection pressure sensing have been set up to collect data for each of the prototypes. Off-line dimensional characterisation of the parts and the soft tool have also been carried out to quantify the prototype quality and dimensional changes on the soft tool after the manufacturing cycles.

The data collection and analysis methods presented here enable the evaluation of the quality of the moulded parts in real-time from in-line measurements. Importantly, it is demonstrated that soft-tool surface temperature difference values can be used as reliable indicators for moulding quality. Reduction in the total volume of the soft-tool moulding cavity was detected and quantified up to 100 cycles. Data collected from in-line monitoring was also used for filling assessment of the soft-tool moulding cavity, providing about 90% accuracy in filling prediction with relatively modest sensors and monitoring technologies.

This work presents a data-intensive approach for the characterisation of soft-tooled micro-injection moulding processes for the first time. The overall results of this study show that the product-focussed data-rich approach presented here proved to be an essential and useful way of exploiting additive manufacturing technologies for soft-tooled rapid prototyping and new product introduction.

Sport participation is not exclusively determined by individual socio‐demographic factors (micro level) since infrastructure factors such as the availability of sport facilities and sport programmes (macro level) can also play a role in this regard. The purpose of this paper is to provide evidence for these determinants of sport participation using multi‐level analyses.

A survey among the resident population in the city of Munich was carried out in 2008 (n=11,715). Furthermore, secondary data on the available sport infrastructure in every urban district of Munich (n=25) were collected. Multi‐level analyses were conducted to find the micro and macro level determinants of sport participation.

The results show that aside from micro level factors, the availability of swimming pools and parks is especially important for residents’ sport activity. Moreover, sport activity in non‐profit sport clubs can be enhanced by both a good supply of sport programmes offered by sport clubs as well as a poor supply of programmes from commercial sport providers and the municipality.

Multi‐level analyses can be recommended for future research on sport participation. The use of GIS data would be fruitful in this regard.

It can be recommended that municipalities invest in the construction of swimming pools and parks.

The paper shows that multi‐level analyses are a relatively new method of analysis for research on sport participation and that they represent the most suitable approach for analysing multi‐level data.

Purpose – This chapter examines the central and potent role of ‘micro-moments’ in relation to the development and construction of corporately responsible cultures and environments.

Methodology/approach – The chapter engages a participant observational method set within an interpretivist methodology. The data generated take the form of vignettes which are used to explore the issues.

Findings – The discussion and argument demonstrate that while much worthwhile attention has been paid to the macro aspects and dimensions of corporate social responsibility, less scrutiny has been focused on the myriad micro-moments that operate to ultimately create macro-settings. The chapter illustrates the nature of micro-moments and shows their interactive nature combined with their consequences and implications for building corporately social irresponsible or corporately social responsible environments.

Research limitations/implications – The chapter underlines the vital role of micro-moments for corporate social responsibility. The data consist of a number of vignettes which illustrate a particular circumscribed setting. As is commonly the case with inductive research, further work, mindful of on-going reliability and validity measures, will be required to assess the generalisability of the findings across other sectors and organisations.

Practical implications – The chapter affords people working in organisations the opportunity to reflect on their actions in the micro-moment and scale them towards corporately social responsible outcomes.

Social implications – Improvement of micro-moment interactions should work to improve corporate social responsibility across a range of organisational settings.

Originality/value – The chapter constructs a novel argument in relation to micro-moments and demonstrates through original vignette data the impact and interplay of micro-moments for corporate social responsibility/irresponsibility.