Hybrid Approach to Automation, RPA and Machine Learning

- By Wiesław Kopec´, Kinga Skorupska, Piotr Gago, Krzysztof Marasek 

Polish-Japanese Academy of Information Technology

Paper Link

Courtesy DZone 

Abstract

One of the more prominent trends within Industry 4.0 is the drive to employ Robotic Process Automation (RPA), especially as one of the elements of the Lean approach.     The full implementation of RPA is riddled with challenges relating both to the reality of everyday business operations, from SMEs to SSCs and beyond, and the social effects of the changing job market. To successfully address these points there is a need to develop a solution that would adjust to the existing business operations and at the same time lower the negative social impact of the automation process. To achieve these goals we propose a hybrid, human-centred approach to the development of software robots. This design and  implementation method combines the Living Lab approach with empowerment through participatory design to kick-start the co-development and co-maintenance of hybrid software robots which, supported by variety of AI methods and tools, including interactive and collaborative ML in the cloud, transform menial job posts into higher-skilled positions, allowing former employees to stay on as robot co-designers and maintainers, i.e. as co-programmers who supervise the machine learning processes with the use of tailored high-level RPA Domain-Specific Languages (DSLs) to adjust the functioning of the robots and maintain operational flexibility

Challenges to the Implementation of Automated Processes

The method of automation itself since its inception has faced multiple challenges which still remain relevant. They are concentrated in three key areas related to technical, organizational and socioeconomic aspects.

Technical:

• Costly and tedious maintenance
• Multiple input data formats
• Keeping paper documentation
• Low quality of existing data

Organizational:

• Lack of clear processes
• Multiple fragmentary solutions
• Unique legacy software

Socioeconomic:

• Loss of job posts
• Lack of awareness of RPA

Proposed Solution

The three problem areas discussed above can be directly addressed by a shift in thinking about Robotic Process Automation from a purely algorithmic IT perspective to the HCI one.  Ultimately, RPA can become a human-centred endeavour as software robots relieve employees of tedious repetitive tasks, allowing them to increase their competences and build value in other areas. Thus, below we discuss the proposed solutions.

Distributed and Crowdsourced Machine Learning Approach

This postulate corresponds with the novel interactive and collaborative approach to machine learning. In particular, we think that the use of neural networks to expand the functionality of software robots can ensure that various input and output data is efficiently handled, including audio and images. This process ought to be supported by empowered employees, who can verify the quality of the input and output data retrieved and analysed by various ML tools and techniques, including correct OCR-tagging, handling exceptions and rare cases. This process should be supported by state-of-the-art technology based on our advanced research on eye-tracking methods and techniques. Finally, the employees from co-maintainers of the solution can become co-designers and co-programmers, as they learn to modify the pseudo-code responsible for the functioning of the software robot they oversee.

Supplementation of Existing Solutions

The use of software robots which directly emulate the jobs of specific human employees allows the companies to continue to use their time-tested business processes and legacy software. Moreover, the central platform for RPA and the high-level language tailored to the crafted RPA Domain-Specific Language are easier to maintain and will remain up to date, including the cloud-based Machine Learning and Neural Network components employed. The one to one mapping of the human tasks and the involvement of current employees as co-programmers ensures the continuity of business operations as exceptions can be handled on the fly by internal staff.

Employee Empowerment through Participation

The implementation of software robots need not be followed by massive layoffs. Based on our previous advances in participatory design and co-design, Living Lab activities and higher-level crowdsourcing solutions coupled with cloud-based collaborative solutions for quality assurance, also in ML, According to the principles of Participatory Design, employees who work in the target capacity of the robots can be the best co-designers and co-maintainers. Through their empowerment via participation in Living Labs and training, they are motivated to increase their competences and learn how to efficiently perform their job of supervisors of machine learning and hybrid RPA high-level DLS programmers. Through this, their jobs are transformed from menial to skilled, and their time is freed to work on more challenging aspects of the business.

Benefits of the Hybrid Approach

1. Flexibility and Participatory Maintenance: AI and ML-Powered design allows for automation is derived from the patterns taught by employees in a Living Lab environment, who in turn learn how to facilitate this process.

2. Adjustment to Existing Business Processes: This ensures a lower entry barrier than classic BPA with process re-engineering. The businesses can retain their current practices, supplementing human work with RPAs where possible, which means that the entry barrier is lower, as this solution is cheaper than BPA with process re-engineering and seamless, allowing for continuity of operation.

3. Empowerment: Some employees who used to perform low-skilled jobs instead of being displaced, become co-creators and co-maintainers of the software robots.

The implementation process of this Hybrid Approach can consist of the following stages:

1. Analysis of RPA penetration and potential within the company
2. Workshops with employees to identify opportunity areas
3. Living Lab approach to process analysis with data-collection workstations
4. Participatory design of specific software robots
5. Supervised Training of AI-based solutions
6. Employee empowerment training sessions
7. Co-programming and co-maintenance of software robots in RPA DSLs

Conclusion

Abstract proposes a design approach that addresses key challenges of RPA. The method relies on participatory design of software robots, facilitated by a Living Lab environment, interactive and collaborative AI solutions, including machine learning and neural networks whereby menial tasks are turned into high-skilled jobs, increasing employee satisfaction and lowering turnover usable in multiple contexts, such as for example automatic tests, code deployment, customer service and machine and device control.