Process Automation Software Research Articles

A Review to Find Elicitation Methods for Business Process Automation Software

Several organizations have invested in business process automation software to improve their processes. Unstandardized processes with high variance and unstructured data encumber the requirements elicitation for business process automation software. This study conducted a systematic literature review to discover methods to understand business processes and elicit requirements for business process automation software. The review revealed many methods used to understand business processes, but only one was employed to elicit requirements for business process automation software. In addition, the review identified some challenges and opportunities. The challenges of developing a business process automation software include dealing with business processes, meeting the needs of the organization, choosing the right approach, and adapting to changes in the process during the development. These challenges open opportunities for proposing specific approaches to elicit requirements in this context.

Automation of Business Process Using RPA (Robotic Process Automation)

Abstract: The automation of robotic processes has increased the interest rate trend in recent times. However, most of the Researches describes only theoretical foundations on RPA or industry outcomes after implementing RPA in specific scenarios, especially in finance and outsourcing. With RPA, software users create software robots, or “bots”, that can read, replicate, and then execute many rules-based business processes. RPA automation enables users to build bots by observing human digital actions. Show your bots what to do, then let them do the work. Robotic Process Automation software bots can interact with any application or system the same way people do—except that RPA bots can operate around the clock, nonstop, much faster, and with 100% reliability and precision.

АВТОМАТИЗАЦІЯ БІЗНЕС-ПРОЦЕСІВ ЯК НЕОБХІДНА УМОВА ЕФЕКТИВНОСТІ ОРГАНІЗАЦІЙ

The use of advanced technologies to complete business processes with minimal human intervention is known as "business process automation". The purpose of automation is to replace manual human labor with machine labor and optimize the entire production process. With the help of modern technologies of machine interaction, automated parameter control systems are implemented at factories, the data of which is transferred to the server and processed. Taking into account the analysis of this data, the machine itself decides on any action. Automation speeds up work by redirecting information to the right person at the right time with user-defined rules and actions, helping managers move to a new, higher level of management. It gives an opportunity to focus on the company's strategic goals. Automation is currently a very relevant and important direction in the field of theory and practice of business process management. Its main goal is to increase the socio-economic efficiency of the enterprise through the purposeful implementation of software and hardware systems that increase the speed and accuracy of the implementation of business processes, while minimizing erroneous actions and decisions arising under the influence of the so-called "human factor". The automation system helps to turn monotonous manual work into automatic, making such work more technological and advanced. Enterprises work in a highly competitive environment, therefore, they need constant development, use of best practices and new technologies. With the increase in the number of management tasks in large systems, the volume of processed information increases significantly and the complexity of management algorithms increases. As a result, there is a mismatch between the complexity of the managed object and the ability of any governing body to receive and process information. Therefore, the automation of business processes is a necessary condition for the company's survival in the modern world. Automation of business processes is one of the fastest growing technologies today. Businesses need to do less with more and ensure their processes are as efficient and effective as possible. To do this, they turn to business process automation software to scale processes, solve unique workflow challenges, and maximize productivity.

Digitalization of the Process Landscape for Proprietary Bond Trading

On the surface, the process landscape for fixed income securities trading within most banks has changed only slightly in the past four decades. The value chain remains divided amongst front-, middle-, and back office, with IT in support. Front office negotiates deals with customers and other banks, middle office manages risks and reporting, and back office ensures that payments are made in exchange for a transfer of the bonds that have been traded. Though these processes have gradually migrated to electronic mediums, much of the work in all functional areas remains manual. But the advent of digital technologies, primarily process automation software and data integration in an open software architecture, allows banks to dramatically change how the processes along the value chain are carried out. Repetitive and predictable tasks can be performed by automated software, allowing humans to concentrate on complex activities that require flexibility and discretion. Those tasks that remain in the hands of humans can also be made more efficient by extracting data from software applications along the entire process chain and providing them to the human user at the right point in time in the right system.

A Protocol-Driven, Digital Conversational Agent at the Hospital Bedside to Support Nurse Teams and to Mitigate Delirium and Falls Risk

Background: Up to 1 million falls per year occur during hospital stays in the US, costing hospitals up to $7 billion per year to treat. Delirium, a major cause of falls, is highly prevalent (29%-64%) among hospitalized elders. Our team is developing a care technology that provides 24x7 proactive patient support to mitigate falls and delirium in a scalable, cost-effective, yet person-centered manner. We were awarded an NIH SBIR Fast-Track grant to further our work. Objective: Prior research showed that digital avatars can build psychosocially supportive relationships with older adults living in the community. We conducted a pilot study of these digital avatars in a community hospital setting, and found that bedside avatars helped reduce the rate of falls among elderly inpatients while mitigating delirium and loneliness. Thus, in our present NIH-funded work, we seek to enhance the clinical robustness of our avatar intervention by implementing a more complete set of protocols derived from the evidence-based Hospital Elder Life Program (HELP), and to validate the benefits of this next-generation intervention in a larger scale, multi-site clinical study. Methods: Avatars are displayed on tablets as virtual dogs or cats, responding to touch. They are able to talk intelligently due to live support from a 24x7 team of trained humans combined with protocol guidance and process automation software. At the bedside, avatars talk with patients and implement protocols including reorientation and bed exercises, based on HELP. English-speaking patients aged 65+ on three medical surgical units were selected in our pilot study based on subjective nursing risk assessment and patient consent. Participants on two intervention units received an avatar for their entire hospital stay. Participants on a control unit received a daily visit from a nursing student. Measures were administered upon study enrollment and discharge. Quarterly fall rates by unit were reported by the hospital. In our present work, we will conduct a larger, multi-site clinical study starting with two hospitals in late 2018 and incorporating electronic medical record integration to increase the objectivity of patient enrollment, ease the patient enrollment process, and apply interventions intelligently tailored to each patient. Additional hospitals may join the two-year study through 2020. Results: In our pilot study, intervention (n=41) and control (n=54) patients were equivalent except for an intervention group bias toward females (P=.02). Mean age was 76.5 years, with 55% female, 46% African-American, and 44% speaking English as a second language. Typical length of stay was 3-6 days. On average, avatars performed 71.3 observational check-ins, 61 minutes of engagement using 11.5 images or audio files, and 6.5 protocol-driven tasks per patient per day. Compared to control patients, intervention patients showed a significantly greater reduction in delirium score (CAM, P=.003) and loneliness (UCLA-LS, P=.008). The primary intervention unit had 0.9 falls per 1000-patient days during the 3-month study period, compared to 6.5 on the control unit (86% reduction). Conclusions: Previous findings support the use and development of avatar technology for hospitalized patients at risk for delirium or falls. Hospitals interested in joining our two-year clinical study may contact the principal investigator.

The use of process simulation models in virtual commissioning of process automation software in drinking water treatment plants

This research deals with the contribution of process simulation models to the factory acceptance test (FAT) of process automation (PA) software of drinking water treatment plants. Two test teams tested the same piece of modified PA-software. One team used an advanced virtual commissioning (AVC) system consisting of PA-emulation and integrated process simulation models. The other team used the same PA-emulation but basic parameter relations instead of the process simulation models, the virtual commissioning (VC) system. Each test team found one (different) error of the 13 errors put into the software prior to the experiment; most of the errors were found prior to the functional test. The team using the AVC-system found three errors, the team using the VC-system found four, but the AVC-team judged 1% of the test items ‘not possible’, the VC-team 17%. It was concluded that the hypothesis that with AVC more errors could be found than with VC could not be accepted. So, for the FAT of PA-software of drinking water treatment plants, the addition of basic parameter relations to PA-emulation was sufficient. It was not the exact process behavior that helped to find errors, but the passing of process thresholds.