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BUSINESS PROCESS AUTOMATION: ENHANCING EFFICIENCY AND COMPETITIVENESS IN MODERN ORGANIZATIONS
(2024)
The first section of the thesis provides a historical overview of automation, spanning from the first industrial revolution to the current era of highly advanced AI-driven technologies. It emphasizes how important Business Process Automation (BPA) is in today's hectic corporate climate when productivity and competitiveness are key factors. The main focus is on Robotic Process Automation (RPA), which is especially useful in situations with legacy systems since it effectively automates repetitive processes. This study explores the differences between terms and concepts related to automation, including business process automation (BPA), robotic process automation (RPA), artificial intelligence (AI), machine learning (ML), and business process management (BPM). Companies looking to optimize their processes face a changing landscape due to the involvement of various technologies. Using real-world case studies and industry best practices, the thesis provides a thorough examination of the effects of BPA, emphasizing the primary drivers, challenges, and benefits of BPA adoption. A mixed-methods approach integrating quantitative and qualitative research was used as the methodology. Surveys, case studies, and documentation from different organizations are included in the study, based on those who have implemented RPA at their work. This method enables a thorough analysis of BPA's effects on efficacy, productivity, and affordability. Case studies from prominent firms like Capgemini Consulting, PwC, and Deloitte are reviewed to gain insights regarding their BPA journey. Significant gains in customer satisfaction, cost savings, error avoidance, and operational efficiency are shown by this research. They also draw attention to difficulties like opposition from employees, problems with integration, and the requirement for upskilling. The thesis indicates that although big firms have similar motives for adopting BPA, the process of adopting BPA varies depending on the specific circumstances of each firm. Stakeholder engagement and change management are critical components of successful BPA programs, according to key results. The study highlights a balanced, strategic, and context-sensitive approach, offering a useful insight for companies in establishing their BPA strategy. It provides a comprehensive examination of the role that business process automation plays in modern companies, highlighting the ways in which it can radically alter corporate strategy and operations in the digital age. It gives a thorough examination of the challenges involved in putting BPA into practice and offers tactical advice to businesses hoping to use these tools to boost productivity and competitiveness.
As machine learning becomes increasingly pervasive, its resource demands and financial implications escalate, necessitating energy and cost optimisations to meet stakeholder demands. Quality metrics for predictive machine learning models are abundant, but efficiency metrics remain rare. We propose a framework for efficiency metrics, that enables the comparison of distinct efficiency types. A quality-focused efficiency metric is introduced that considers resource consumption, computational effort, and runtime in addition to prediction quality. The metric has been successfully tested for usability, plausibility, and compensation for dataset size and host performance. This framework enables informed decisions to be made about the use and design of machine learning in an environmentally responsible and cost-effective manner.
The importance of machine learning (ML) has been increasing dramatically for years. From assistance systems to production optimisation to healthcare support, almost every area of daily life and industry is coming into contact with machine learning. Besides all the benefits ML brings, the lack of transparency and difficulty in creating traceability pose major risks. While solutions exist to make the training of machine learning models more transparent, traceability is still a major challenge. Ensuring the identity of a model is another challenge, as unnoticed modification of a model is also a danger when using ML. This paper proposes to create an ML Birth Certificate and ML Family Tree secured by blockchain technology. Important information about training and changes to the model through retraining can be stored in a blockchain and accessed by any user to create more security and traceability about an ML model.