Intelligent Process Automation Research Articles

Military Field Camp Installation Improvement Solutions: Lithuania Case

The installation plan, operation and functions of the military field camp are very important for a military unit action. Every army in a NATO country strives to set up a military camp that is as flexible and comfortable as possible. NATO has set standards for the installation of a military field camp, and each country in the organization relies on this standard. This study was focused on how to build a military field camp as quickly as possible and to minimize the loss of various resources. The research developed a focus group survey analysis method to identify weaknesses in the military field camp setting process, accomplished an in-depth interview analysis method with four Lithuanian Grand Duke Vytenis General Support Logistics Battalion Field Camp Installation Experts. Moreover, was accomplished a comparative analysis to compare military field camps plans before and after installation. In addition, an operational process analysis approach was used, which depicts the installation of a military field camp from planning to unit deployment. There was used the Bizagi Modeler v3.7 software to construct the intelligent process automation diagram which showed the military camp installation improvement solutions [18]. Moreover, the in-deep cause analysis method was used, which helped to identify the main problems of the military field camp installation.

A Supervised Machine Learning Approach for Intelligent Process Automation in Container Logistics

Abstract Process control in manufacturing industries usually lacks flexibility and adaptability. The process planning is traditionally pursued within the production scheduling and then remains unchanged until a major overhaul is necessary. Consequently, no process knowledge acquired by the machine operators is used to adapt, and thus improve, the process control. In this paper, a fully automated process control solution for container logistics is proposed, which is based on deep neural networks and has been trained from process steering decisions made by employees. Further, a fully automated framework for the labeling of container images is introduced, making use of inherent properties of the logistic process. This allows to automatically generate data sets without the need for manual labeling by an operator.

Intelligent Process Automation in Audit

Intelligent Process Automation (IPA) achieves flexible and intelligent automation by combining Robotic Process Automation (RPA), Artificial Intelligence (AI), and other emerging technologies. This paper focuses on the utility of IPA for the audit profession. Specifically, this paper provides a framework for implementing IPA in audit engagements using the concept of audit workflow. A simple prototype based on a simulated use case is constructed to illustrate the IPA implementation framework. The potential applications of IPA in pension and inventory audits are provided, and the expected impacts of IPA on audit efficiency and effectiveness are discussed.

A Semantic Web-Based Approach to Knowledge Management for Grid Applications

Knowledge has become increasingly important to support intelligent process automation and collaborative problem solving in large-scale science over the Internet. This paper addresses distributed knowledge management, its approach and methodology, in the context of grid application. We start by analyzing the nature of grid computing and its requirements for knowledge support; then, we discuss knowledge characteristics and the challenges for knowledge management on the grid. A semantic Web-based approach is proposed to tackle the six challenges of the knowledge lifecycle - namely, those of acquiring, modeling, retrieving, reusing, publishing, and maintaining knowledge. To facilitate the application of the approach, a systematic methodology is conceived and designed to provide a general implementation guideline. We use a real-world Grid application, the GEODISE project, as a case study in which the core semantic Web technologies such as ontologies, semantic enrichment, and semantic reasoning are used for knowledge engineering and management. The case study has been fully implemented and deployed through which the evaluation and validation for the approach and methodology have been performed

Ion‐exchange and affinity chromatography costs in α‐galactosidase purification

The purification of alpha-galactosidase from soybean seeds is a five to six-step procedure consisting of cryoprecipitation, acid precipitation and ammonium sulfate fractionation followed by two or three chromatography steps. The procedures, while not optimized, were carried out in a manner that resulted in 414-515-fold purification, as reported previously. The costs of two purification sequences were compared. In the best case, the preparative-scale costs of stationary phase, reagents, and hardware were $790 per million enzyme units, excluding labor. Stationary phase costs predominated over extraction, chromatography reagent, and eluent costs when the stationary phase is replaced after 10-40 cycles of use. However, if stationary phase life exceeds 50-200 cycles, stationary phase costs become similar in magnitude to eluent and reagent costs. Labor costs, which are process-specific and difficult to estimate, exceed all other costs by a factor of 10-50 at a small scale of operation and constitute a major cost, regardless of scale. This case study provides equations and a frame-work for carrying out a first comparison of costs for multistep purification sequences. Column life, throughput, and scale of operation were found to determine not only the magnitude, but also the relative contributions, of the different components that make up purification costs. This analysis shows that there are major opportunities for reducing purification costs through the development of less expensive stationary phases and the implementation of intelligent process control and automation for process scale chromatography.