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The purpose of this paper is to present a methodology to assist enterprise decision makers (DMs) to select from a number of processes during Business Process Reengineering (BPR) according to organizational objectives. Indeed, after the identification and classification of process and illustration of the organizational objectives and criteria, the effect of each process on each objective and criterion is calculated to select the most appropriate processes for reengineering purposes.

The proposed methodology uses fuzzy quality function deployment (QFD) technique to convert the qualitative data (DM’s opinion) to quantitative ones and then calculates the effects of each process on the organizational objectives and criteria. Then, by using the result of fuzzy QFD, the amount of satisfaction of each process according to each criterion is calculated. By combining this data with other effective variables in BPR projects such as “cost” and “time,” a multi-objective goal programming (GP) model is formulated and solved to identify the most appropriate business processes.

In fact, a quantitative model is presented in which fuzzy QFD and GP methods are combined to help DMs to adopt an appropriate strategy for implementing BPR projects successfully by selecting proper processes for reengineering purposes. In addition, the presented model uses both qualitative and quantitative data and turns them into quantitative ones. An example is also provided to show how the model works.

Following this investigation, other researchers could able to complete the model with more dynamic and local variables to enhance the accuracy of the model.

The introduced model will support organizations and managers to select appropriate processes for BPR; so in practice, the mentioned projects will be more efficient and successful.

The paper study is essential for organizations, because the presented decision-making model is based on fuzzy QFD and GP methods that enable the enterprises to select the business processes during the BPR projects easily. In this paper, a GP model is presented to create a balance between organizational satisfaction level and cost and time of implementing BPR projects considering organizational constraints. The proposed model was applied to a real case and the authors showed that it is an easy-to-use, valid, and powerful tool for implementing BPR projects.

The purpose of the present study is to investigate the antecedents of export performance and branding advantage, as a key type of competitive advantage in export markets among entrepreneurs and managers of agri-food exporters.

A sample of entrepreneurs from 182 exporting firms of the agriculture and food industry participated in a cross-sectional survey. The data were collected by a self-reporting questionnaire and partial least squares were used to analyse the data and assess the path model.

Results revealed that experiential resources strongly promote communication capabilities. Also, communication, distribution and product development capabilities contribute to the creation of the branding advantage in export markets. In addition, a positive relationship between the branding advantage and export performance of agri-food products is confirmed.

The study targets exporters of agri-food products. Hence, the results should be interpreted regarding the context of low-technology firms. Further, this paper delineates branding advantage considerations that managers need to account for to achieve effective exporting. Practitioners can efficaciously exploit resources to achieve a competitive advantage, considering that they focus on building capabilities, and in particular, communication capabilities.

The present study highlights the role of the branding advantage as an important type of competitive advantage in international entrepreneurship and export markets. It attempts to examine the combined relationships of resources and capabilities with branding advantage and export performance.

Automated dust monitoring in workplaces helps provide timely alerts to over-exposed workers and effective mitigation measures for proactive dust control. However, the cluttered nature of construction sites poses a practical challenge to obtain enough high-quality images in the real world. The study aims to establish a framework that overcomes the challenges of lacking sufficient imagery data (“data-hungry problem”) for training computer vision algorithms to monitor construction dust.

This study develops a synthetic image generation method that incorporates virtual environments of construction dust for producing training samples. Three state-of-the-art object detection algorithms, including Faster-RCNN, you only look once (YOLO) and single shot detection (SSD), are trained using solely synthetic images. Finally, this research provides a comparative analysis of object detection algorithms for real-world dust monitoring regarding the accuracy and computational efficiency.

This study creates a construction dust emission (CDE) dataset consisting of 3,860 synthetic dust images as the training dataset and 1,015 real-world images as the testing dataset. The YOLO-v3 model achieves the best performance with a 0.93 F1 score and 31.44 fps among all three object detection models. The experimental results indicate that training dust detection algorithms with only synthetic images can achieve acceptable performance on real-world images.

This study provides insights into two questions: (1) how synthetic images could help train dust detection models to overcome data-hungry problems and (2) how well state-of-the-art deep learning algorithms can detect nonrigid construction dust.

Real-time location sensing (RTLS) systems offer a significant potential to advance the management of construction processes by potentially providing real-time access to the locations of workers and equipment. Many location-sensing technologies tend to perform poorly for indoor work environments and generate large data sets that are somewhat difficult to process in a meaningful way. Unfortunately, little is still known regarding the practical benefits of converting raw worker tracking data into meaningful information about construction project progress, effectively impeding widespread adoption in construction.

The presented framework is designed to automate as many steps as possible, aiming to avoid manual procedures that significantly increase the time between progress estimation updates. The authors apply simple location tracking sensor data that does not require personal handling, to ensure continuous data acquisition. They use a generic and non-site-specific knowledge base (KB) created through domain expert interviews. The sensor data and KB are analyzed in an abductive reasoning framework implemented in Answer Set Programming (extended to support spatial and temporal reasoning), a logic programming paradigm developed within the artificial intelligence domain.

This work demonstrates how abductive reasoning can be applied to automatically generate rich and qualitative information about activities that have been carried out on a construction site. These activities are subsequently used for reasoning about the progress of the construction project. Our framework delivers an upper bound on project progress (“optimistic estimates”) within a practical amount of time, in the order of seconds. The target user group is construction management by providing project planning decision support.

The KB developed for this early-stage research does not encapsulate an exhaustive body of domain expert knowledge. Instead, it consists of excerpts of activities in the analyzed construction site. The KB is developed to be non-site-specific, but it is not validated as the performed experiments were carried out on one single construction site.

The presented work enables automated processing of simple location tracking sensor data, which provides construction management with detailed insight into construction site progress without performing labor-intensive procedures common nowadays.

While automated progress estimation and activity recognition in construction have been studied for some time, the authors approach it differently. Instead of expensive equipment, manually acquired, information-rich sensor data, the authors apply simple data, domain knowledge and a logical reasoning system for which the results are promising.