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Robotic process automation is a disruptive technology to automate already digital yet manual tasks and subprocesses as well as whole business processes rapidly. In contrast to other process automation technologies, robotic process automation is lightweight and only accesses the presentation layer of IT systems to mimic human behavior. Due to the novelty of robotic process automation and the varying approaches when implementing the technology, there are reports that up to 50% of robotic process automation projects fail. To tackle this issue, we use a design science research approach to develop a framework for the implementation of robotic process automation projects. We analyzed 35 reports on real-life projects to derive a preliminary sequential model. Then, we performed multiple expert interviews and workshops to validate and refine our model. The result is a framework with variable stages that offers guidelines with enough flexibility to be applicable in complex and heterogeneous corporate environments as well as for small and medium-sized companies. It is structured by the three phases of initialization, implementation, and scaling. They comprise eleven stages relevant during a project and as a continuous cycle spanning individual projects. Together they structure how to manage knowledge and support processes for the execution of robotic process automation implementation projects.
Plattform für das integrierte Management von Kollaborationen in Wertschöpfungsnetzwerken (PIMKoWe)
(2022)
Das Verbundprojekt „Plattform für das integrierte Management von Kollaborationen in Wertschöpfungsnetzwerken“ (PIMKoWe – Förderkennzeichen „02P17D160“) ist ein Forschungsvorhaben im Rahmen des Forschungsprogramms „Innovationen für die Produktion, Dienstleistung und Arbeit von morgen“ der Bekanntmachung „Industrie 4.0 – Intelligente Kollaborationen in dynamischen Wertschöpfungs-netzwerken“ (InKoWe). Das Forschungsvorhaben wurde mit Mitteln des Bundesministeriums für Bildung und Forschung (BMBF) gefördert und durch den Projektträger des Karlsruher Instituts für Technologie (PTKA) betreut.
Ziel des Forschungsprojekts PIMKoWe ist die Entwicklung und Bereitstellung einer Plattformlösung zur Flexibilisierung, Automatisierung und Absicherung von Kooperationen in Wertschöpfungsnetzwerken des industriellen Sektors.
Contemporary decision support systems are increasingly relying on artificial intelligence technology such as machine learning algorithms to form intelligent systems. These systems have human-like decision capacity for selected applications based on a decision rationale which cannot be looked-up conveniently and constitutes a black box. As a consequence, acceptance by end-users remains somewhat hesitant. While lacking transparency has been said to hinder trust and enforce aversion towards these systems, studies that connect user trust to transparency and subsequently acceptance are scarce. In response, our research is concerned with the development of a theoretical model that explains end-user acceptance of intelligent systems. We utilize the unified theory of acceptance and use in information technology as well as explanation theory and related theories on initial trust and user trust in information systems. The proposed model is tested in an industrial maintenance workplace scenario using maintenance experts as participants to represent the user group. Results show that acceptance is performance-driven at first sight. However, transparency plays an important indirect role in regulating trust and the perception of performance.
Due to computational advances in the past decades, so-called intelligent systems can learn from increasingly complex data, analyze situations, and support users in their decision-making to address them. However, in practice, the complexity of these intelligent systems renders the user hardly able to comprehend the inherent decision logic of the underlying machine learning model. As a result, the adoption of this technology, especially for high-stake scenarios, is hampered. In this context, explainable artificial intelligence offers numerous starting points for making the inherent logic explainable to people. While research manifests the necessity for incorporating explainable artificial intelligence into intelligent systems, there is still a lack of knowledge about how to socio-technically design these systems to address acceptance barriers among different user groups. In response, we have derived and evaluated a nascent design theory for explainable intelligent systems based on a structured literature review, two qualitative expert studies, a real-world use case application, and quantitative research. Our design theory includes design requirements, design principles, and design features covering the topics of global explainability, local explainability, personalized interface design, as well as psychological/emotional factors.
Künstliche Intelligenz (KI) dringt vermehrt in sensible Bereiche des alltäglichen menschlichen Lebens ein. Es werden nicht mehr nur noch einfache Entscheidungen durch intelligente Systeme getroffen, sondern zunehmend auch komplexe Entscheidungen. So entscheiden z. B. intelligente Systeme, ob Bewerber in ein Unternehmen eingestellt werden sollen oder nicht. Oftmals kann die zugrundeliegende Entscheidungsfindung nur schwer nachvollzogen werden und ungerechtfertigte Entscheidungen können dadurch unerkannt bleiben, weshalb die Implementierung einer solchen KI auch häufig als sogenannte Blackbox bezeichnet wird. Folglich steigt die Bedrohung, durch unfaire und diskriminierende Entscheidungen einer KI benachteiligt behandelt zu werden. Resultieren diese Verzerrungen aus menschlichen Handlungen und Denkmustern spricht man von einer kognitiven Verzerrung oder einem kognitiven Bias. Aufgrund der Neuigkeit dieser Thematik ist jedoch bisher nicht ersichtlich, welche verschiedenen kognitiven Bias innerhalb eines KI-Projektes auftreten können. Ziel dieses Beitrages ist es, anhand einer strukturierten Literaturanalyse, eine gesamtheitliche Darstellung zu ermöglichen. Die gewonnenen Erkenntnisse werden anhand des in der Praxis weit verbreiten Cross-Industry Standard Process for Data Mining (CRISP-DM) Modell aufgearbeitet und klassifiziert. Diese Betrachtung zeigt, dass der menschliche Einfluss auf eine KI in jeder Entwicklungsphase des Modells gegeben ist und es daher wichtig ist „mensch-ähnlichen“ Bias in einer KI explizit zu untersuchen.
Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks. Deep learning is a machine learning concept based on artificial neural networks. For many applications, deep learning models outperform shallow machine learning models and traditional data analysis approaches. In this article, we summarize the fundamentals of machine learning and deep learning to generate a broader understanding of the methodical underpinning of current intelligent systems. In particular, we provide a conceptual distinction between relevant terms and concepts, explain the process of automated analytical model building through machine learning and deep learning, and discuss the challenges that arise when implementing such intelligent systems in the field of electronic markets and networked business. These naturally go beyond technological aspects and highlight issues in human-machine interaction and artificial intelligence servitization.
Business process modeling is one of the most crucial activities of BPM and enables companies to realize various benefits in terms of communication, coordination, and distribution of organizational knowledge. While numerous techniques support process modeling, companies frequently face challenges when adopting BPM to their organization. Existing techniques are often modified or replaced by self-developed approaches so that companies cannot fully exploit the benefits of standardization. To explore the current state of the art in process modeling as well as emerging challenges and potential success factors, we conducted a large-scale quantitative study. We received feedback from 314 respondents who completed the survey between July 2 and September 6, 2017. Thus, our study provides in-depth insights into the status quo of process modeling and allows us to provide three major contributions. Our study suggests that the success of process modeling projects depends on four major factors, which we extracted using exploratory factor analysis. We found employee education, management involvement, usability of project results, and the companies’ degree of process orientation to be decisive for the success of a process modeling project. We conclude this report with a summary of results and present potential avenues for future research. We thereby emphasize the need of quantitative and qualitative insights to process modeling in practice is needed to strengthen the quality of process modeling in practice and to be able to react quickly to changing conditions, attitudes, and possible constraints that practitioners face.
In our globalized world, companies operate on an international market. To concentrate on their main competencies and be more competitive, they integrate into supply chain networks. However, these potentials also bear many risks. The emergence of an international market also creates pressure from competitors, forcing companies to collaborate with new and unknown companies in dynamic supply chain networks. In many cases, this can cause a lack of trust as the application of illegal practices and the breaking of agreements through complex and nontransparent supply chain networks pose a threat.
Blockchain technology provides a transparent, decentralized, and distributed means of chaining data storage and thus enables trust in its tamper-proof storage, even if there is no trust in the cooperation partners. The use of the blockchain also provides the opportunity to digitize, automate, and monitor processes within supply chain networks in real time.
The research project "Plattform für das integrierte Management von Kollaborationen in Wertschöpfungsnetzwerken" (PIMKoWe) addresses this issue. The aim of this report is to define requirements for such a collaboration platform. We define requirements based on a literature review and expert interviews, which allow for an objective consideration of scientific and practical aspects. An additional survey validates and further classifies these requirements as “essential”, “optional”, or “irrelevant”. In total, we have derived a collection of 45 requirements from different dimensions for the collaboration platform.
Employing these requirements, we illustrate a conceptual architecture of the platform as well as introduce a realistic application scenario. The presentation of the platform concept and the application scenario can provide the foundation for implementing and introducing a blockchain-based collaboration platform into existing supply chain networks in context of the research project PIMKoWe.