Dr.-Ing.

Kuhn, Thomas

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User experience key performance indicators for industrial IoT systems: A multivocal literature review

2023 , Trendowicz, Adam , Groen, Eduard Christiaan , Henningsen, Jens , Siebert, Julien , Bartels, Nedo Alexander , Storck, Sven , Kuhn, Thomas

In software systems, user experience (UX) is a quality that provides value to its intended users. Therefore, we consider it critical to manage UX and its link to business value quantitatively. Key performance indicators (KPIs) have been widely adopted for objectively measuring factors that influence business success, including measuring UX characteristics and their business contribution in the context of software systems in general. Yet, measuring UX in the context of complex Internet of Things (IoT) systems, especially Industrial IoT (IIoT) systems, remains a challenge. This paper presents a multivocal literature review (MLR) of the UX-KPIs proposed in the context of (I)IoT systems, along with factors influencing UX and challenges of quantifying UX. We investigate the status quo and derive recommendations for measuring the UX of (I)IoT systems. Our analysis of 64 publications shows that UX has so far received relatively little attention in the context of (I)IoT. Although we identified 605 KPIs, 78 influencing factors, and 119 challenges, most of the UX features discussed were defined in a broader context of software systems, i.e., they were less specific to IoT, but rather focused on universal system quality characteristics such as functional suitability, performance efficiency, usability, and reliability. Finally, no generally accepted approach for measuring UX in (I)IoT systems and how these contribute to the overall business value has been proposed yet. We also found little evidence that improving UX or achieving a good UX currently plays an explicit role in the design and development of IoT systems.

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A Quality 4.0 Model for architecting industry 4.0 systems

2022 , Oliveira Antonino, Pablo , Capilla, Rafael , Pelliccione, Patrizio , Schnicke, Frank , Espen, Daniel , Kuhn, Thomas , Schmid, Klaus

The increasing importance of automation and smart capabilities for factories and other industrial systems has led to the concept of Industry 4.0 (I4.0). This concept aims at creating systems that improve the vertical and horizontal integration of production through (i) comprehensive and intelligent automation of industrial processes, (ii) informed and decentralized real-time decision making, and (iii) stringent quality requirements that can be monitored at any time. The I4.0 infrastructure, supported in many cases by robots, sensors, and algorithms, demands highly skilled workers able to continuously monitor the quality of both the items to be produced and the underlying production processes. While the first attempts to develop smart factories and enhance the digital transformation of companies are under way, we need adequate methods to support the identification and specification of quality attributes that are relevant to I4.0 systems. Our main contribution is to provide a refined version of the ISO 25010 quality model specifically tailored to those qualities demanded by I4.0 needs. This model aims to provide actionable support for I4.0 software engineers that are concerned with quality issues. We developed our model based on an exhaustive analysis of similar proposals using the design science method as well as expertise from seasoned engineers in the domain. We further evaluate our model by applying it to two important I4.0 reference architectures further clarifying its application.

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Continuous Systems and Software Engineering for Industry 4.0: A disruptive view

2021 , Nakagawa, Elisa Yumi , Antonino, Pablo Oliveira , Schnicke, Frank , Kuhn, Thomas , Liggesmeyer, Peter

Context: Industry 4.0 has substantially changed the manufacturing processes, leading to smart factories with full digitalization, intelligence, and dynamic production. The need for rigorous and continuous development of highly networked software-intensive Industry 4.0 systems entails great challenges. Hence, Industry 4.0 requires new ways to develop, operate, and evolve these systems accordingly. Objective: We introduce the view of Continuous Systems and Software Engineering for Industry 4.0 (CSSE I4.0). Method: Based on our research and industrial projects, we propose this novel view and its core elements, including continuous twinning, which is also introduced first in this paper. We also discuss the existing industrial engagement and research that could leverage this view for practical application. Results: There are still several open issues, so we highlight the most urgent perspectives for future work. Conclusions: A disruptive view on how to engineer Industry 4.0 systems must be established to pave the way for the realization of the fourth industrial revolution.

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Digitaler Zwilling

2017 , Kuhn, Thomas

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Verwaltungsschalenbasierte Datenräume. Möglichkeiten, Herausforderungen und Lösungsansätze auf dem Weg zu datengetrieben resilienten Wertschöpfungsnetzwerken

2023 , Adler, Rasmus , Kuhn, Thomas , Schnicke, Frank

Der Austausch von Unternehmensdaten ist eine wichtige Grundlage, um fragile Lieferketten in resiliente und nachhaltige Wertschöpfungsnetzwerke zu transformieren. Im Rahmen dieser Transformation werden die Verfügbarkeit von Daten und die Fähigkeit, diese zu nutzen, die lukrativsten Geschäftsmodelle der Zukunft prägen. Intelligente Systeme können die Daten nutzen, um autonome Entscheidungen bezüglich der Wandlung von Produktionssystemen zu treffen oder Empfehlungen zu geben. Damit diese Transformation gelingt, werden für die notwendigen Entwicklungen verwaltungsschalenbasierte Datenräume benötigt.

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Continuous engineering for Industry 4.0 architectures and systems

2022 , Oliveira Antonino, Pablo , Capilla, Rafael , Kazman, R. , Kuhn, Thomas , Schnicke, Frank , Treichel, Tagline , Bachorek, Adam , Müller-Zhang, Zai , Salamanca, V.

Traditionally, the quality of a software or system architecture has been evaluated in the early stages of the development process using architecture quality evaluation methods. Emergent approaches like Industry 4.0 require continuous monitoring of both run-time and development-time quality properties, in contrast to traditional systems where quality is evaluated at specific milestones using techniques such as project reviews. Considering the dynamics and minimum down-time imposed by the industrial production domain, it must also be ensured that Industry 4.0 system evaluations are continuously performed with high confidence and with as much automation as possible, using simulations, for instance. In this regard, there is a need to develop new methods for continuously monitoring and evaluating the quality properties of software-based systems for Industry 4.0, which must be supported by automated quality evaluation techniques. In this research we analyze traditional architecture evaluation methods and Industry 4.0 scenarios, and propose an approach based on Digital Twins and simulations to continuously evaluate runtime quality aspects of the architecture and systems of industrial production plants. The evaluation is based on the instantiation of our approach for a concrete demand of an automation plant in the automotive domain.

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Industrie-4.0-Middleware geht in die Anwendung

2021 , Kuhn, Thomas

In dem Forschungsprojekt ""Basissystem für die unternehmensübergreifende Produktionsunterstützung"" (kurz: BaSys überProd) arbeiten 21 Partner aus Wissenschaft und Wirtschaft an Lösungen für den Wandel hin zur digitalisierten, flexiblen Industrie-4.0-Produktion. Dabei kommt vor allem die Open-Source-Middleware Eclipse BaSyx zum Einsatz. Das Ziel: Repräsentative Anwendungsfälle in Wirtschaftsunternehmen umsetzen und das Wiederverwendungspotenzial der Lösungen für andere Kontexte herausarbeiten.

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Towards live decision-making for service-based production: Integrated process planning and scheduling with Digital Twins and Deep-Q-Learning

2023 , Müller-Zhang, Zai , Kuhn, Thomas , Oliveira Antonino de Assis, Pablo

Production flow is becoming increasingly complex since manufacturers must react quickly to changing markets demands and diverse customer requirements. In order to ensure production efficiency, it is essential to have an adequate scheduling system capable of managing diverse process flows and handling unforseen changes. In this paper, we present an approach leveraging Digital Twins (DTs) and Deep-Q-Learning to perform integrated process planning and scheduling for service-based production. DTs of production assets provide live information about their physical entities for our approach to perform live decision-making based on the current operation conditions. We use Deep-Q-Learning which is a deep Reinforcement Learning (RL) algorithm to perform integrated process planning and scheduling. We present two RL-designs that deal with different situations of live decision-making. We have evaluated the learning efficiency and scalability of the RL-designs on a virtual aluminum cold rolling mill developed by the SMS Group,1 in the context of the BaSys 4.2 project.2 The results show that the first RL-design is suitable for deriving schedules for individualized production with small lots where process plans must be re-calculated frequently, while the second RL-design is optimal for production with large job quantities where jobs arrive continuously.

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Industry 4.0 reference architectures: State of the art and future trends

2021 , Nakagawa, Elisa Yumi , Antonino, Pablo Oliveira , Schnicke, Frank , Capilla, Rafael , Kuhn, Thomas , Liggesmeyer, Peter

Industry 4.0 has led to a dramatic shift in manufacturing processes, which must be accomplished by interacting end-to-end industrial systems. While Industry 4.0 is still a big challenge for many manufacturing companies, reference architectures have been increasingly adopted in different domains to guide engineers on how their systems should interoperate and be structured. Companies have made different experiences with reference architectures for Industry 4.0. However, depending on the use cases addressed, a reference architecture may be more or less suited to support the transformation of a particular company. Besides, a complete understanding of existing representative architectures does not exist. The main goal of this work is to review existing reference architectures for Industry 4.0 and analyze them concerning their suitability for supporting Industry 4.0 processes and solutions. For this, we systematically researched these architectures and thoroughly analyzed and characterized them. We also address their use and technologies/tools that could support their implementation. As a result, we found that existing architectures still have a long way to go; hence, we present the most urgent steps for the near future. We conclude that the Industry 4.0 community is right in investing in reference architectures considering the future of Industry 4.0.

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A Service-Based Production Ecosystem Architecture for Industrie 4.0

2019 , Kuhn, Thomas , Sadikow, Siwara , Antonino, Pablo

Changeability is one major goal of Industrie 4.0. Existing production architectures limit changeability, because programmable logic controllers (PLC) that are responsible for the execution of real-time production steps also define the order of production steps that are executed for every product. PLC programming therefore implicitly defines the production process. Consequently, any change of a production process requires changes in PLC code, causes potential side effects due to unknown controller dependencies, and requires extensive testing. We propose a service-based architecture approach that encapsulates production steps into re-useable services. Production cells invoke services, and comparable to multi-agent systems autonomously decide about optimal service invocations based on shared information. In this article, we outline our service-based architecture concept and describe a use-case that illustrates the decentral organization of production systems and the cooperative optimization of production steps.

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