Leveraging artificial intelligence for business process management (extended abstract) : A contribution to reference model mining, predictive process monitoring, and process discovery

Artificial intelligence for business process managmen

Chapter 3 How is production changing?

The unprecedented Covid-19 crisis revealed the scale and scope of a new type of economy taking shape in front of our very eyes: the digital economy. This book presents a concise theoretical and conceptual framework for a more nuanced analysis of the economic and sociological impacts of the technological disruption that is taking place in the markets of goods and services, labour markets, and the global economy more generally. This interdisciplinary work is a must for researchers and students from economics, business, and other social science majors who seek an overview of the main digital economy concepts and research. Its down-to-earth approach and communicative style will also speak to businesses practitioners who want to understand the ongoing digital disruption of the market rules and emergence of the new digital business models. The book refers to academic insights from economics and sociology while giving numerous empirical examples drawn from basic and applied research and business. It addresses several burning issues: how are digital processes transforming traditional business models? Does intelligent automation threaten our jobs? Are we reaching the end of globalisation as we know it? How can we best prepare ourselves and our children for the digitally transformed world? The book will help the reader gain a better understanding of the mechanisms behind the digital transformation, something that is essential in order to not only reap the plentiful opportunities being created by the digital economy but also to avoid its many pitfalls

Modeling 4.0: Conceptual Modeling in a Digital Era

Digitization provides entirely new affordances for our economies and societies. This leads to previously unseen design opportunities and complexities as systems and their boundaries are re-defined, creating a demand for appropriate methods to support design that caters to these new demands. Conceptual modeling is an established means for this, but it needs to be advanced to adequately depict the requirements of digitization. However, unlike the actual deployment of digital technologies in various industries, the domain of conceptual modeling itself has not yet undergone a comprehensive renewal in light of digitization. Therefore, inspired by the notion of Industry 4.0, an overarching concept for digital manufacturing, in this commentary paper, we propose Modeling 4.0 as the notion for conceptual modeling mechanisms in a digital environment. In total, 12 mechanisms of conceptual modeling are distinguished, providing ample guidance for academics and professionals interested in ensuring that modeling techniques and methods continue to fit contemporary and emerging requirements

A case study of improving a non-technical losses detection system through explainability

Detecting and reacting to non-technical losses (NTL) is a fundamental activity that energy providers need to face in their daily routines. This is known to be challenging since the phenomenon of NTL is multi-factored, dynamic and extremely contextual, which makes artificial intelligence (AI) and, in particular, machine learning, natural areas to bring effective and tailored solutions. If the human factor is disregarded in the process of detecting NTL, there is a high risk of performance degradation since typical problems like dataset shift and biases cannot be easily identified by an algorithm. This paper presents a case study on incorporating explainable AI (XAI) in a mature NTL detection system that has been in production in the last years both in electricity and gas. The experience shows that incorporating this capability brings interesting improvements to the initial system and especially serves as a common ground where domain experts, data scientists, and business analysts can meet.Peer ReviewedPostprint (published version

Exploring Interpretability for Predictive Process Analytics

Modern predictive analytics underpinned by machine learning techniques has become a key enabler to the automation of data-driven decision making. In the context of business process management, predictive analytics has been applied to making predictions about the future state of an ongoing business process instance, for example, when will the process instance complete and what will be the outcome upon completion. Machine learning models can be trained on event log data recording historical process execution to build the underlying predictive models. Multiple techniques have been proposed so far which encode the information available in an event log and construct input features required to train a predictive model. While accuracy has been a dominant criterion in the choice of various techniques, they are often applied as a black-box in building predictive models. In this paper, we derive explanations using interpretable machine learning techniques to compare and contrast the suitability of multiple predictive models of high accuracy. The explanations allow us to gain an understanding of the underlying reasons for a prediction and highlight scenarios where accuracy alone may not be sufficient in assessing the suitability of techniques used to encode event log data to features used by a predictive model. Findings from this study motivate the need and importance to incorporate interpretability in predictive process analytics.Comment: 15 pages, 7 figure

Organising the Implementation of Industry 4.0 in a High Value German Manufacturing Firm: A Complex Adaptive Systems Approach.

Ph. D. ThesisThis thesis addresses an important research gap in empirical qualitative evidence regarding the organisational aspects of the implementation of Industry 4.0. Whereas there is a basic understanding of the technical implementation in the factory plant, the understanding of the implementation from an organisational perspective is limited. A holistic single case study with 35 semi-structured expert interviews enabled a deep exploration of an implementation in a real-world context at the firm level. The findings demonstrate how a high value German manufacturing company has implemented Industry 4.0, as well as why this firm implemented as it did. Several elements are thematically analysed, representing important examples of how manufacturing firms can organise the implementation of Industry 4.0 in praxis. Covering the three areas of actions, influences and relationships, the implications of the analysed elements are discussed in relation to six theoretical themes, namely centralisation vs. decentralisation, diffusion of new ideas, working in teams, trust, open innovation and path dependence. This thesis represents the first existing study that understands the implementation of Industry 4.0 as a Complex Adaptive System of interrelated system elements which continuously evolve over time. In this sense, a newly developed system model acknowledges important relationship characteristics that lead to a more comprehensive perspective on the complex implementation of Industry 4.0. This thesis contributes to the research field by being the first study to suggest a “dual approach” encompassing important decentralised as well as centralised implementation patterns for a successful process. It furthermore demonstrates how workforce concerns regarding job security significantly influence the emergence of system elements regarding change management during the implementation of Industry 4.0. The thesis offers academic contributions to the Industry 4.0 implementation literature, as well as organisational elements recommended for practitioners when organising the implementation of Industry 4.0

A Conceptual Framework to Support Digital Transformation in Manufacturing Using an Integrated Business Process Management Approach

Digital transformation is no longer a future trend, as it has become a necessity for businesses to grow and remain competitive in the market. The fourth industrial revolution, called Industry 4.0, is at the heart of this transformation, and is supporting organizations in achieving benefits that were unthinkable a few years ago. The impact of Industry 4.0 enabling technologies in the manufacturing sector is undeniable, and their correct use offers benefits such as improved productivity and asset performance, reduced inefficiencies, lower production and maintenance costs, while enhancing system agility and flexibility. However, organizations have found the move towards digital transformation extremely challenging for several reasons, including a lack of standardized implementation protocols, emphasis on the introduction of new technologies without assessing their role within the business, the compartmentalization of digital initiatives from the rest of the business, and the large-scale implementation of digitalization without a realistic view of return on investment. To instill confidence and reduce the anxiety surrounding Industry 4.0 implementation in the manufacturing sector, this paper presents a conceptual framework based on business process management (BPM). The framework is informed by a content-centric literature review of Industry 4.0 technologies, its design principles, and BPM method. This integrated framework incorporates the factors that are often overlooked during digital transformation and presents a structured methodology that can be employed by manufacturing organizations to facilitate their transition towards Industry 4.0