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As AI technology is increasingly used in production systems, different approaches have emerged from highly decentralized small-scale AI at the edge level to centralized, cloud-based services used for higher-order optimizations. Each direction has disadvantages ranging from the lack of computational power at the edge level to the reliance on stable network connections with the centralized approach. Thus, a hybrid approach with centralized and decentralized components that possess specific abilities and interact is preferred. However, the distribution of AI capabilities leads to problems in self-adapting learning systems, as knowledgebases can diverge when no central coordination is present. Edge components will specialize in distinctive patterns (overlearn), which hampers their adaptability for different cases. Therefore, this paper aims to present a concept for a distributed interchangeable knowledge base in CPPS. The approach is based on various AI components and concepts for each participating node. A service-oriented infrastructure allows a decentralized, loosely coupled architecture of the CPPS. By exchanging knowledge bases between nodes, the overall system should become more adaptive, as each node can “forget” their present specialization.
Technological advancements are giving rise to the fourth industrial revolution - Industry 4.0 -characterized by the mass employment of smart objects in highly reconfigurable and thoroughly connected industrialproduct-service systems. The purpose of this paper is to propose a theory-based knowledgedynamics model in the smart grid scenario that would provide a holistic view on the knowledge-based interactions among smart objects, humans, and other actors as an underlyingmechanism of value co-creation in Industry 4.0. A multi-loop and three-layer - physical, virtual, and interface - model of knowledge dynamics is developedby building on the concept of ba - an enabling space for interactions and theemergence of knowledge. The model depicts how big data analytics are just one component inunlocking the value of big data, whereas the tacit engagement of humans-in-the-loop - theirsense-making and decision-making - is needed for insights to be evoked fromanalytics reports and customer needs to be met.
Faced with the increasing needs of companies, optimal dimensioning of IT hardware is becoming challenging for decision makers. In terms of analytical infrastructures, a highly evolutionary environment causes volatile, time dependent workloads in its components, and intelligent, flexible task distribution between local systems and cloud services is attractive. With the aim of developing a flexible and efficient design for analytical infrastructures, this paper proposes a flexible architecture model, which allocates tasks following a machine-specific decision heuristic. A simulation benchmarks this system with existing strategies and identifies the new decision maxim as superior in a first scenario-based simulation.
In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.
In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.
Cyber-physical systems (CPS) have shaped the discussion about Industry 4.0 (I4.0) for some time. To ensure the competitiveness of manufacturing enterprises the vision for the future figures out cyber-physical production systems (CPPS) as a core component of a modern factory. Adaptability and coping with complexity are (among others) potentials of this new generation of production management. The successful transformation of this theoretical construct into practical implementation can only take place with regard to the conditions characterizing the context of a factory. The subject of this contribution is a concept that takes up the brownfield character and describes a solution for extending existing (legacy) systems with CPS capabilities.
The digital transformation sets new requirements to all classes of enterprise systems in companies. ERP systems in particular, which represent the dominant class of enterprise systems, are struggling to meet the new requirements at all levels of the architecture. Therefore, there is an urgent need to reconsider the overall architecture of the systems and address the root of the related issues. Given that many restrictions ERP pose on their adaptability are related to the standardization of data, the database layer of ERP systems is addressed. Since database serve as the foundation for data storage and retrieval, they limit the flexibility of enterprise systems and the chance to adapt to new requirements accordingly. So far, relational databases are widely used. Using a systematic literature approach, recent requirements for ERP systems were identified. Prominent database approaches were assessed against the 23 requirements identified. The results reveal the strengths and weaknesses of recent database approaches. To this end, the results highlight the demand to combine multiple database approaches to fulfill recent business requirements. From a conceptual point of view, this paper supports the idea of federated databases which are interoperable to fulfill future requirements and support business operation. This research forms the basis for renewal of the current generation of ERP systems and proposes to ERP vendors to use different database concepts in the future.
Manufacturing companies still have relatively few points of contact with the circular economy. Especially, extending life time of whole products or parts via remanufacturing is an promising approach to reduce waste. However, necessary cost-efficient assessment of the condition of the individual parts is challenging and assessment procedures are technically complex (e.g., scanning and testing procedures). Furthermore, these assessment procedures are usually only available after the disassembly process has been completed. This is where conceptualization, data acquisition and simulation of remanufacturing processes can help. One major constraining aspect of remanufacturing is reducing logistic efforts, since these also have negative external effects on the environment. Thus regionalization is an additional but in the end consequential challenge for remanufacturing. This article aims to fill a gap by providing an regional remanufacturing approach, in particular the design of local remanufacturing chains. Thereby, further focus lies on modeling and simulating alternative courses of action, including feasibility study and eco-nomic assessment.
Wandlungsfähigkeit von Informationssystemen ist zu einem wesentlichen Wettbewerbsfaktor geworden. Die derzeit unzureichende methodische Unterstützung zur Umsetzung von Wandlungsfähigkeit führt in Unternehmen häufig zu ungenutzten Potentialen einer leistungsfähigen Struktur durch die eingesetzte Informationstechnologie. Ziel des Forschungsprojektes CHANGE ist es, Methoden und Vorgehensmodelle zu entwickeln, die eine dauerhafte Wandlungsfähigkeit von Informationssystemen unterstützen. Dazu wird im Rahmen dieses Beitrages ein Verfahren vorgestellt, welches der Forderung zur Ermittlung der notwendigen Wandlungsfähigkeit unter Einbeziehung des Unternehmensumfeldes nachkommt. Als wesentliches Ergebnis wird ein Kennzahlensystem entwickelt, das zum einen die Umweltsituation als Indikator für den Wandlungsdruck eines Unternehmens beschreibt. Im nächsten Schritt werden Kriterien zur Ermittlung des Wandlungspotentials der eingesetzten IT herangezogen. Abschließend werden beide Dimensionen zusammengeführt und in ihrer Bedeutung für die IT Strategie eines Unternehmens interpretiert.
Accelerating knowledge
(2019)
As knowledge-intensive processes are often carried out in teams and demand for knowledge transfers among various knowledge carriers, any optimization in regard to the acceleration of knowledge transfers obtains a great economic potential. Exemplified with product development projects, knowledge transfers focus on knowledge acquired in former situations and product generations. An adjustment in the manifestation of knowledge transfers in its concrete situation, here called intervention, therefore can directly be connected to the adequate speed optimization of knowledge-intensive process steps. This contribution presents the specification of seven concrete interventions following an intervention template. Further, it describes the design and results of a workshop with experts as a descriptive study. The workshop was used to assess the practical relevance of interventions designed as well as the identification of practical success factors and barriers of their implementation.
A growing number of business processes can be characterized as knowledge-intensive. The ability to speed up the transfer of knowledge between any kind of knowledge carriers in business processes with AR techniques can lead to a huge competitive advantage, for instance in manufacturing. This includes the transfer of person-bound knowledge as well as externalized knowledge of physical and virtual objects. The contribution builds on a time-dependent knowledge transfer model and conceptualizes an adaptable, AR-based application. Having the intention to accelerate the speed of knowledge transfers between a manufacturer and an information system, empirical results of an experimentation show the validity of this approach. For the first time, it will be possible to discover how to improve the transfer among knowledge carriers of an organization with knowledge-driven information systems (KDIS). Within an experiment setting, the paper shows how to improve the quantitative effects regarding the quality and amount of time needed for an example manufacturing process realization by an adaptable KDIS.
Für die Wettbewerbsfähigkeit von Unternehmen hat der Kontinuierliche Verbesserungsprozess (KVP) eine hohe Bedeutung. Hinsichtlich der Qualität und Quantität der Beiträge für den KVP durch die Mitarbeitenden stoßen Unternehmen, insbesondere KMU, jedoch auf vielfältige Herausforderungen. Diesen Problemen können Unternehmen durch das KVP-Tool begegnen, welches im Projekt „Adaptive Spielifizierung im KVP“ entwickelt wird. Durch die Digitalisierung und Spielifizierung des Prozes- ses im KVP-Tool wird die kontinuierliche Beteiligung nachhaltig durch intrinsische Anreize gefördert. Die Neuartigkeit des Projektes ergibt sich aus der Adaptivität der Spielifizierung, also die Wechselwirkung zu den Nutzenden. Dabei werden zwei Aspekte fokussiert: unterschiedliche Spielertypen und Marktdynamik.
Process mining (PM) has established itself in recent years as a main method for visualizing and analyzing processes. However, the identification of knowledge has not been addressed adequately because PM aims solely at data-driven discovering, monitoring, and improving real-world processes from event logs available in various information systems. The following paper, therefore, outlines a novel systematic analysis view on tools for data-driven and machine learning (ML)-based identification of knowledge-intensive target processes. To support the effectiveness of the identification process, the main contributions of this study are (1) to design a procedure for a systematic review and analysis for the selection of relevant dimensions, (2) to identify different categories of dimensions as evaluation metrics to select source systems, algorithms, and tools for PM and ML as well as include them in a multi-dimensional grid box model, (3) to select and assess the most relevant dimensions of the model, (4) to identify and assess source systems, algorithms, and tools in order to find evidence for the selected dimensions, and (5) to assess the relevance and applicability of the conceptualization and design procedure for tool selection in data-driven and ML-based process mining research.
Faced with the triad of time-cost-quality, the realization of production tasks under economic conditions is not trivial. Since the number of Artificial-Intelligence-(AI)-based applications in business processes is increasing more and more nowadays, the efficient design of AI cases for production processes as well as their target-oriented improvement is essential, so that production outcomes satisfy high quality criteria and economic requirements. Both challenge production management and data scientists, aiming to assign ideal manifestations of artificial neural networks (ANNs) to a certain task. Faced with new attempts of ANN-based production process improvements [8], this paper continues research about the optimal creation, provision and utilization of ANNs. Moreover, it presents a mechanism for AI case-based reasoning for ANNs. Experiments clarify continuously improving ANN knowledge bases by this mechanism empirically. Its proof-of-concept is demonstrated by the example of four production simulation scenarios, which cover the most relevant use cases and will be the basis for examining AI cases on a quantitative level.
Since more and more production tasks are enabled by Industry 4.0 techniques, the number of knowledge-intensive production tasks increases as trivial tasks can be automated and only non-trivial tasks demand human-machine interactions. With this, challenges regarding the competence of production workers, the complexity of tasks and stickiness of required knowledge occur [1]. Furthermore, workers experience time pressure which can lead to a decrease in output quality. Cyber-Physical Systems (CPS) have the potential to assist workers in knowledge-intensive work grounded on quantitative insights about knowledge transfer activities [2]. By providing contextual and situational awareness as well as complex classification and selection algorithms, CPS are able to ease knowledge transfer in a way that production time and quality is improved significantly. CPS have only been used for direct production and process optimization, knowledge transfers have only been regarded in assistance systems with little contextual awareness. Embedding production and knowledge transfer optimization thus show potential for further improvements. This contribution outlines the requirements and a framework to design these systems. It accounts for the relevant factors.
The concept of adaptability has been widely recognised as research field in recent years. Business information systems play a key part in terms of business performance. Adaptability of information systems therefore is a primary goal of vendors and end-users. However, so far concepts that help to determine the adaptability of Information Systems are missing. Based on research results of the project CHANGE1 this contribution presents an integrated process model addressing the problem and a possible solution.
This paper presents an exploratory study investigating the influence of the factors (1) intermediary participation, (2) decision-making authority, (3) position in the enterprise, and (4) experience in open innovation on the perception and assessment of the benefits and risks expected from participating in open innovation projects. For this purpose, an online survey was conducted in Germany, Austria and Switzerland. The result of this paper is an empirical evidence showing whether and how these factors affect the perception of potential benefits and risks expected within the context of open innovation project participation. Furthermore, the identified effects are discussed against the theory. Existing theory regarding the benefits and risks of open innovation is expanded by (1) finding that they are perceived mostly independently of the factors, (2) confirming the practical relevance of benefits and risks, and (3) enabling a finer distinction between their degrees of relevance according to respective contextual specifics.
Application of knowledge management methods for the improvement of education and training needs
(2006)
This contribution presents an approach for requirement oriented team building in industrial processes like product development. This will be based on the knowledge modelling and description language (KMDL(R)) that enables the modelling and analysis of knowledge intensive business processes. First the basic elements of the modelling technique are described, presenting the concept and the description language. Furthermore it is shown how the KMDL(R) process models can be used as a basis for the team building component. Therefore, an algorithm was developed that is able to propose a team composition for a specific task by analyzing the knowledge and skills of the employees, which will be contrasted to the process requirements. This can be used as guidance for team building decisions.
Audit - and then what?
(2019)
Current trends such as digital transformation, Internet of Things, or Industry 4.0 are challenging the majority of learning factories. Regardless of whether a conventional learning factory, a model factory, or a digital learning factory, traditional approaches such as the monotonous execution of specific instructions don‘t suffice the learner’s needs, market requirements as well as especially current technological developments. Contemporary teaching environments need a clear strategy, a road to follow for being able to successfully cope with the changes and develop towards digitized learning factories. This demand driven necessity of transformation leads to another obstacle: Assessing the status quo and developing and implementing adequate action plans. Within this paper, details of a maturity-based audit of the hybrid learning factory in the Research and Application Centre Industry 4.0 and a thereof derived roadmap for the digitization of a learning factory are presented.
Im Beitrag wird dargestellt, wie der Einsatz von Wissensmanagementwerkzeugen einen wichtigen Beitrag zur Gestaltung eines erfolgreichen ERP-Betriebes leisten kann. Da diese Aufgabe einen hohen Anteil an wissensintensiven Prozessen aufweist, wird dazu eine Methode benötigt, welches die Prozesse strukturiert erfasst, durch die Modellierung analysiert und geeignete Maßnahmen des Wissensmanagements vorschlägt. Die Methode KMDL® ermöglicht durch die Einführung einer Wissensebene die Spezifikation, wann Wissen welchen Inhalts und in welcher Form im Prozess benötigt oder erzeugt wird. Dabei wird auch darauf eingegangen, wie durch die Anwendung der KMDL® der optimale Schulungsbedarf für ERP-Anwender ermittelt werden kann.
Die Auswahl von Standardsoftware stellt viele Unternehmen vor Herausforderungen. Gerade im deutschen Mittelstand kommen vermehrt eigenentwickelte Individuallösungen zum Einsatz. Ent- sprechende Unternehmen sind daher nicht mit komplexen Soft- wareauswahlprojekten vertraut. Das breite Angebot an ERP-Systemen erschwert die Vergleichbarkeit der Lösungen und die zielgerichtete Auswahl des idealen Systems zusätzlich.
Die teilweise sehr kurzfristig notwendige Reaktion auf Veränderungen erfordert von Unternehmen ein hohes Maß an Flexibilität und Reaktionsgeschwindigkeit. Anwendungssystemarchitekturen, die im Wesentlichen aus alten und selbst entwickelten Systemen bestehen, erfüllen häufig diese Anforderungen nicht. Investitionsmittel für neue Software sind jedoch begrenzt, daher müssen Prioritäten in der Ablösung von Altsystemen gesetzt werden. Eine effiziente Analysemethode zur Planung der Erneuerung der Anwendungssystemlandschaft stellt die Wandlungsfähigkeitsanalyse dar. Dieser Beitrag beschreibt Vorgehen und Ergebnisse am Beispiel eines international tätigen Automobilzulieferers.
Bon appétit!
(2007)
Collaborative Engineering is a promising concept to increase the competitiveness of companies. Target of this paper is to describe the industrial application of this approach, considering shipbuilding as an example. Besides the engineering partners needs to collaborate during the product development phase, there are many other stakeholders who are interested in the product ship along its whole life cycle. Therefore the Concept of Collaborative Engineering is extended by introducing the idea of Communities. Requirements on Communities in Engineering are deduced. Based on this an architectural framework for Collaborative Engineering Communities is described. Concluding research topics which have to be discussed for practical realization are outlined.
Competence development must change at all didactic levels to meet the new requirements triggered by digitization. Unlike classic learning theories and the resulting popular approaches (e.g., sender-receiver model), future-oriented vocational training must include new learning theory impulses in the discussion about competence acquisition. On the one hand, these impulses are often very well elaborated on the theoretical side, but the transfer into innovative learning environments - such as learning factories - is often still missing. On the other hand, actual learning factory (design) approaches often concentrate primarily on the technical side. Subject-oriented learning theory enables the design of competence development-oriented vocational training projectsin learning factories in which persons can obtain relevant competencies for digitization. At the same time, such learning theory approaches assume a potentially infinite number of learning interests and reasons. Following this, competence development is always located in an institutional or organizational context. The paper conceptionally answers how this theoryimmanent challenge is synthesizable with the reality of organizationally competence development requirements.
Coring on Digital Platforms
(2017)
Today’s mobile devices are part of powerful business ecosystems, which usually involve digital platforms. To better understand the complex phenomenon of coring and related dynamics, this paper presents a case study comparing iMessage as part of Apple’s iOS and WhatsApp. Specifically, it investigates activities regarding platform coring, as the integration of several functionalities provided by third-party applications in the platform core. The paper makes three contributions. First, a systematization of coring activities is developed. Coring modes are differentiated by the amount of coring and application maintenance. Second, the case study revealed that the phenomenon of platform coring is present on digital platforms for mobile devices. Third, the fundamentals of coring are discussed as a first step towards theoretical development. Even though coring constitutes a potential threat for third-party developers regarding their functional differentiation, an idea of what a beneficial partnership incorporating coring activities could look like is developed here.
Today’s mobile devices are part of powerful business ecosystems, which usually involve digital platforms. To better understand the complex phenomenon of coring and related dynamics, this paper presents a case study comparing iMessage as part of Apple’s iOS and WhatsApp. Specifically, it investigates activities regarding platform coring, as the integration of several functionalities provided by third-party applications in the platform core. The paper makes three contributions. First, a systematization of coring activities is developed. Coring modes are differentiated by the amount of coring and application maintenance. Second, the case study revealed that the phenomenon of platform coring is present on digital platforms for mobile devices. Third, the fundamentals of coring are discussed as a first step towards theoretical development. Even though coring constitutes a potential threat for third-party developers regarding their functional differentiation, an idea of what a beneficial partnership incorporating coring activities could look like is developed here.
The efficient use of human capital is one of the most important factors in todays' business competition. Competition is strongly influenced by qualified staff. In order to aid the human resources department to keep up with strategic decisions various competency management systems have been created that make the development of human resources easier and more precise. Competency management systems are only as good as the information that they are based on. The mostly used basic information is the skill catalogue. But there are nearly no applicable methods yet to create such a catalogue thoroughly. This paper introduces a reasonable approach to create such a catalogue with the description language for knowledge-intensive processes KMDL.
This meta-analysis synthesizes 332 effect sizes of various methods to enhance creativity. We clustered all studies into 12 methods to identify the most effective creativity enhancement methods. We found that, on average, creativity can be enhanced, Hedges’ g = 0.53, 95% CI [0.44, 0.61], with 70.09% of the participants in the enhancement conditions being more creative than the average person in the control conditions. Complex training courses, meditation, and cultural exposure were the most effective (gs = 0.66) while the use of cognitive manipulation drugs was the least and also noneffective, g = 0.10. The type of training material was also important. For instance, figural methods were more effective in enhancing creativity, and enhancing converging thinking was more effective than enhancing divergent thinking. Study effect sizes varied considerably across all studies and for many subgroup analyses, suggesting that researchers can plausibly expect to find reversed effects occasionally. We found no evidence of publication bias. We discuss theoretical implications and suggest future directions for best practices in enhancing creativity. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
In times of digitalization, the collection and modeling of business processes is still a challenge for companies. The demand for trustworthy process models that reflect the actual execution steps therefore increases. The respective kinds of processes significantly determine both, business process analysis and the conception of future target processes and they are the starting point for any kind of change initiatives. Existing approaches to model as-is processes, like process mining, are exclusively focused on reconstruction. Therefore, transactional protocols and limited data from a single application system are used. Heterogeneous application landscapes and business processes that are executed across multiple application systems, on the contrary, are one of the main challenges in process mining research. Using RFID technology is hence one approach to close the existing gap between different application systems. This paper focuses on methods for data collection from real world objects via RFID technology and possible combinations with application data (process mining) in order to realize a cross system mining approach.
Die Innovationstätigkeit im industriellen Umfeld verlagert sich durch die Digitalisierung hin zu Produkt-Service-Systemen. Kleine und mittlere Unternehmen haben sich in ihrer Entwicklungstätigkeit bisher stark auf die Produktentwicklung bezogen. Der Umstieg auf „smarte“ Produkte und die Kopplung an Dienstleistungen erfordert häufig personelle und finanzielle Ressourcen, welche KMU nicht aufbringen können. Crowdsourcing stellt eine Möglichkeit dar, den Innovationsprozess für externe Akteure zu öffnen und Kosten- sowie Geschwindigkeitsvorteile zu realisieren. Bei der Integration von Crowdsourcing-Elementen ist jedoch einigen Herausforderungen zu begegnen. Dieser Beitrag zeigt sowohl die Potenziale als auch die Barrieren einer Crowdsourcing-Nutzung im industriellen Umfeld auf.
Process analysis usually focuses only on single and selected processes. It is either existent processes that are recorded and analysed or reference processes that are implemented. So far no evident effort has been put into generalising specific process aspects into patterns and comparing those patterns with regard to their efficiency and effectiveness. This article focuses on the combination of dynamic and holistic analytical elements in enterprise architectures. Our goal is to outline an approach to analyse the development of business processes in a cyclical matter and demonstrate this approach based on an existent modelling language. We want to show that organisational learning can derive from the systematic analysis of past and existent processes from which patterns of successful problem solving can be deducted.
Increasingly fast development cycles and individualized products pose major challenges for today's smart production systems in times of industry 4.0. The systems must be flexible and continuously adapt to changing conditions while still guaranteeing high throughputs and robustness against external disruptions. Deep reinforcement learning (RL) algorithms, which already reached impressive success with Google DeepMind's AlphaGo, are increasingly transferred to production systems to meet related requirements. Unlike supervised and unsupervised machine learning techniques, deep RL algorithms learn based on recently collected sensorand process-data in direct interaction with the environment and are able to perform decisions in real-time. As such, deep RL algorithms seem promising given their potential to provide decision support in complex environments, as production systems, and simultaneously adapt to changing circumstances. While different use-cases for deep RL emerged, a structured overview and integration of findings on their application are missing. To address this gap, this contribution provides a systematic literature review of existing deep RL applications in the field of production planning and control as well as production logistics. From a performance perspective, it became evident that deep RL can beat heuristics significantly in their overall performance and provides superior solutions to various industrial use-cases. Nevertheless, safety and reliability concerns must be overcome before the widespread use of deep RL is possible which presumes more intensive testing of deep RL in real world applications besides the already ongoing intensive simulations.
Increasingly fast development cycles and individualized products pose major challenges for today's smart production systems in times of industry 4.0. The systems must be flexible and continuously adapt to changing conditions while still guaranteeing high throughputs and robustness against external disruptions. Deep rein- forcement learning (RL) algorithms, which already reached impressive success with Google DeepMind's AlphaGo, are increasingly transferred to production systems to meet related requirements. Unlike supervised and unsupervised machine learning techniques, deep RL algorithms learn based on recently collected sensor- and process-data in direct interaction with the environment and are able to perform decisions in real-time. As such, deep RL algorithms seem promising given their potential to provide decision support in complex environments, as production systems, and simultaneously adapt to changing circumstances. While different use-cases for deep RL emerged, a structured overview and integration of findings on their application are missing. To address this gap, this contribution provides a systematic literature review of existing deep RL applications in the field of production planning and control as well as production logistics. From a performance perspective, it became evident that deep RL can beat heuristics significantly in their overall performance and provides superior solutions to various industrial use-cases. Nevertheless, safety and reliability concerns must be overcome before the widespread use of deep RL is possible which presumes more intensive testing of deep RL in real world applications besides the already ongoing intensive simulations.
Business processes can be modelled and analysed extensively with well known and established methods. The simple signs of static knowledge do not fulfil the requirements of a comprehensive and integrated approach of process-oriented knowledge management. The Knowledge Modelling Description Language KMDL is able to represent the creation, use and necessity of knowledge along common business processes. Therefore KMDL can be used to formalise knowledge-intensive processes with a focus on certain knowledge-specific characteristics and to identify weak points in these processes. The tool K-Modeller is introduced for a computer-aided modelling and analysing.
Die Fabrikplanung sieht sich infolge veränderter Marktbedingung neuen Herausforderungen gegenüber gestellt. Die Anforderung an die Wandlungsfähigkeit eines Unternehmens ist zu einem Schlüsselfaktor geworden, der bereits in der Planungsphase einer Fabrik adressiert werden muss. Welche Einflüsse und Faktoren sind bei der Realisierung neuer Fabriken zu berücksichtigen? Zu dieser Thematik wurden Unternehmen im Bereich Fabrikplanung befragt.
As the complexity of learning task requirements, computer infrastruc- tures and knowledge acquisition for artificial neuronal networks (ANN) is in- creasing, it is challenging to talk about ANN without creating misunderstandings. An efficient, transparent and failure-free design of learning tasks by models is not supported by any tool at all. For this purpose, particular the consideration of data, information and knowledge on the base of an integration with knowledge- intensive business process models and a process-oriented knowledge manage- ment are attractive. With the aim of making the design of learning tasks express- ible by models, this paper proposes a graphical modeling language called Neu- ronal Training Modeling Language (NTML), which allows the repetitive use of learning designs. An example ANN project of AI-based dynamic GUI adaptation exemplifies its use as a first demonstration.
Modern production infrastructures of globally operating companies usually consist of multiple distributed production sites. While the organization of individual sites consisting of Industry 4.0 components itself is demanding, new questions regarding the organization and allocation of resources emerge considering the total production network. In an attempt to face the challenge of efficient distribution and processing both within and across sites, we aim to provide a hybrid simulation approach as a first step towards optimization. Using hybrid simulation allows us to include real and simulated concepts and thereby benchmark different approaches with reasonable effort. A simulation concept is conceptualized and demonstrated qualitatively using a global multi-site example.
Existing factories face multiple problems due to their hierarchical structure of decision making and control. Cyber-physical systems principally allow to increase the degree of autonomy to new heights. But which degree of autonomy is really useful and beneficiary? This paper differentiates diverse definitions of autonomy and approaches to determine them. Some experimental findings in a lab environment help to answer the question raised in this paper.
Traditional production systems are enhanced by cyber-physical systems (CPS) and Internet of Things. A kind of next generation systems, those cyber-physical production systems (CPPS) are able to raise the level of autonomy of its production components. To find the optimal degree of autonomy in a given context, a research approach is formulated using a simulation concept. Based on requirements and assumptions, a cyber-physical market is modeled and qualitative hypotheses are formulated, which will be verified with the help of the CPPS of a hybrid simulation environment.
Lately, first implementation approaches of Internet of Things (IoT) technologies penetrate industrial value-adding processes. Within this, the competence requirements for employees are changing. Employees’ organization, process, and interaction competences are of crucial importance in this new IoT environment, however, in students and vocational training not sufficiently considered yet. On the other hand, conventional learning factories evolve and transform to digital learning factories. Nevertheless, the integration of IoT technology and its usage for training in digital learning factories has been largely neglected thus far. Existing learning factories do not explicitly and properly consider IoT technology, which leads to deficiencies regarding an appropriate development of employees’ Industrial IoT competences. The goal of this contribution is to point out a didactic concept that enables development and training of these new demanded competences by using an IoT laboratory. For this purpose, a design science approach is applied. The result of this contribution is a didactic concept for the development of Industrial IoT competences in an IoT laboratory.
Wodurch zeichnen sich die besten ERP-Systeme aus? Diese Frage wird dem Autor dieses Beitrags immer wieder gestellt. Mit der Vorstellung der Balanced ERP Scorecard (BESC) besteht nun eine Möglichkeit, ein System aus verschiedenen Perspektiven zu bewerten und so individuell zu einer Einschätzung des für eine konkrete Unternehmenssituation besten Systems zu gelangen. Daher beschreibt dieser Beitrag zunächst die Perspektiven der BESC und dann mögliche Kriterien für eine Ausgestaltung dieser Scorecard.
Produkte werden zunehmend internetfähig. Gerade im Kontext der Industrie 4.0 statten beispielsweise Maschinen- und Anlagenbauer sowie Komponentenhersteller ihre Produkte mit Option zur Netzwerk- und Internetanbindung aus. Neben der technischen Realisierung beschäftigt Unternehmen die Frage, wie diese neue Geschäftsmodelle realisieren können. Gerade bei Produkten, die bei Kunden im Einsatz sind, kommt der Nutzung von IoT-Plattformen eine wichtige Bedeutung zu.
Für Unternehmen stellt sich immer wieder die Frage nach der Stabilität der Geschäftsprozesse. Wie flexibel bzw. wandlungsfähig müssen Unternehmensarchitekturen ausgelegt werden, damit Veränderungen in der Aufbau- oder Ablauforganisation ITseitig auch in Zukunft gefolgt werden kann? Dazu wird im folgenden Beitrag das Paradigma Wandlungsfähigkeit als Gestaltungsziel von Unternehmensarchitekturen charakterisiert und der Zusammenhang zwischen Veränderungen des Umfeldes eines Unternehmens und erforderlicher Anpassungsfähigkeit der Informationssysteme erläutert.
Digital Platforms (DPs) has established themself in recent years as a central concept of the Information Technology Science. Due to the great diversity of digital platform concepts, clear definitions are still required. Furthermore, DPs are subject to dynamic changes from internal and external factors, which pose challenges for digital platform operators, developers and customers. Which current digital platform research directions should be taken to address these challenges remains open so far. The following paper aims to contribute to this by outlining a systematic literature review (SLR) on digital platform concepts in the context of the Industrial Internet of Things (IIoT) for manufacturing companies and provides a basis for (1) a selection of definitions of current digital platform and ecosystem concepts and (2) a selection of current digital platform research directions. These directions are diverted into (a) occurrence of digital platforms, (b) emergence of digital platforms, (c) evaluation of digital platforms, (d) development of digital platforms, and (e) selection of digital platforms.
Digitale Plattformen
(2020)
Obwohl digitale Plattformen vornehmlich von Großunternehmen betrieben werden, bieten sie klein- und mittelständischen Unternehmen (KMU) Potenziale zur Verbreitung innovativer Technologien und für den Ausbau ihres Geschäftsmodells. Für die Umsetzung digitaler Plattformen stehen Unternehmen mehrere Strategien zur Verfügung. Der Beitrag vergleicht und bewertet grundlegende Strategien am Beispiel eines Maschinenbauunternehmens. Die Ergebnisse dienen als Grundlage für die Entscheidungsfindung von KMU.
Digitale Plattformen
(2020)
Obwohl digitale Plattformen vornehmlich von Großunternehmen betrieben werden, bieten sie klein- und mittelständischen Unternehmen (KMU) Potenziale zur Verbreitung innovativer Technologien und für den Ausbau ihres Geschäftsmodells. Für die Umsetzung digitaler Plattformen stehen Unternehmen mehrere Strategien zur Verfügung. Der Beitrag vergleicht und bewertet grundlegende Strategien am Beispiel eines Maschinenbauunternehmens. Die Ergebnisse dienen als Grundlage für die Entscheidungsfindung von KMU.
As Industry 4.0 infrastructures are seen as highly evolutionary environment with volatile, and time-dependent workloads for analytical tasks, particularly the optimal dimensioning of IT hardware is a challenge for decision makers because the digital processing of these tasks can be decoupled from their physical place of origin. Flexible architecture models to allocate tasks efficiently with regard to multi-facet aspects and a predefined set of local systems and external cloud services have been proven in small example scenarios. This paper provides a benchmark of existing task realization strategies, composed of (1) task distribution and (2) task prioritization in a real-world scenario simulation. It identifies heuristics as superior strategies.
Dieses Buch bietet eine Einführung in das Informationsmanagement in kommentierter Form. Es richtet sich in erster Linie an Studierende der Betriebswirtschaftslehre und der Wirtschaftsinformatik.
Der erste Teil des Buches gibt einen einführenden Überblick über die grundlegende Begriffe und Ansätze des Informationsmanagements. Es wird ein Modell des Informationsmanagements vorgestellt, auf dessen Basis die Aufgaben des Informationsmanagements in den folgenden Kapiteln vertieft werden. Der zweite Teil widmet sich der Informationswirtschaft und behandelt Informationsnachfrage und -angebot. Im dritten Teil des Buches werden die betrieblichen Informationssysteme mit ihren grundlegenden Bausteinen Daten und Prozesse thematisiert. Der vierte Teil gibt einen Überblick über Anwendungssysteme für die Produktion und die Aufgaben des Managements der Informations- und Kommunikationstechnik. Das abschließende Kapitel beinhaltet eine Diskussion relevanter Führungsaufgaben des
Dieses Buch bietet eine Einführung in das Produktionsmanagement in kommentierter Form. Der erste Teil des Buches gibt einen einführenden Überblick über grundlegende Begriffe der Produktionswirtschaft und systematisiert Produktionsfaktoren, Produktionssysteme und Produkte. Im zweiten Teil geht es um strategische Aspekte des Produktionsmanagements, wie Standortwahl und Produktstrategie, sowie um die Festlegung des Produktionsprogramms. Teil 3 behandelt mit der Gestaltung des Produktionssystems und menschlicher Arbeit die Potenzialfaktoren der Produktion. Gegenstand des vierten Teils ist die Versorgung der Produktion mit dem benötigten Material. Hier werden Fragen der Materialbedarfsermittlung, der Beschaffung und der Lagerhaltung beleuchtet. Im fünften Teil werden relevante Konzepte der operativen Produktionsplanung und -steuerung vorgestellt und die besonderen Planungsprobleme in der Einzel-, Auftrags- und Variantenfertigung betrachtet. Im Fokus des sechsten Teils steht die Serien- und Massenfertigung.
Dieses Buch bietet eine Einführung in die Wirtschaftsinformatik in kommentierter Form. Es richtet sich in erster Linie an Studierende der Betriebswirtschaftslehre und der Wirtschaftsinformatik.
Band 1 beinhaltet eine Betrachtung der nachfolgenden Themenpunkte: Der erste Abschnitt gibt eine Einführung in die Bedeutung der Wirtschaftsinformatik als Wissenschaftsfach und damit verbunden in ihre praktische Anwendungsorientierung. Der folgende Abschnitt liefert einen Überblick über die der Informatik zugrunde liegenden Konzepte und Techniken von Computer-Hardware und -Software sowie Begriffsbestimmungen und Beschreibungsmerkmale der Daten, Datenhaltung und -speicherung. Ein weiterer Abschnitt ist dem Komplex Netzwerke und Internet gewidmet. In kompakter Form werden Grundlagen der Netze und Netzwerkdienste erklärt. Das Thema Datenmodellierung bildet den Schwerpunkt dieses Bandes. Die Vorgehensweise zur Erstellung und Bearbeitung von Modellen wird in sehr ausführlicher Form dargelegt. Den Abschluss bildet die Verbindung zwischen Datenbanken und betrieblichen Anwedungssystemen.
Dieses Buch bietet eine Einführung in die Wirtschaftsinformatik in kommentierter Form. Es richtet sich in erster Linie an Studierende der Betriebswirtschaftslehre und der Wirtschaftsinformatik.
Band 1 beinhaltet eine Betrachtung der nachfolgenden Themenpunkte: Der erste Abschnitt gibt eine Einführung in die Bedeutung der Wirtschaftsinformatik als Wissenschaftsfach und damit verbunden in ihre praktische Anwendungsorientierung. Der folgende Abschnitt liefert einen Überblick über die der Informatik zugrunde liegenden Konzepte und Techniken von Computer-Hardware und -Software sowie Begriffsbestimmungen und Beschreibungsmerkmale der Daten, Datenhaltung und -speicherung. Ein weiterer Abschnitt ist dem Komplex Netzwerke und Internet gewidmet. In kompakter Form werden Grundlagen der Netze und Netzwerkdienste erklärt. Das Thema Datenmodellierung bildet den Schwerpunkt dieses Bandes. Die Vorgehensweise zur Erstellung und Bearbeitung von Modellen wird in sehr ausführlicher Form dargelegt. Den Abschluss bildet die Verbindung zwischen Datenbanken und betrieblichen Anwedungssystemen.
Dieses Buch bietet eine Einfuehrung in die Wirtschaftsinformatik in kommentierter Form. Es richtet sich in erster Linie an Studierende der Betriebswirtschaftslehre und der Wirtschaftsinformatik. Zu Beginn dieses Bandes wird eine Einfuehrung in die Funktionalitaet einer Oracle-Datenbank am Beispiel der Version Oracle 10g XE als frei verfuegbare Version des relationalen Datenbanksystems Oracle 10g gegeben. Der thematische Schwerpunkt im Band 2, die Datenbankabfragesprache SQL, baut auf dem Teil der Datenmodellierung des Bandes 1 auf. Zum besseren Verstaendnis der Zusammenhaenge ist ein durchgehend verwendbares Beispiel gewaehlt worden. An Hand dieses Beispiels werden sowohl Begriffe als auch Zusammenhaenge in der Umsetzung vom relationalen Datenmodell zur Datenbank erlaeutert. Im Deteil werden sowohl Syntax als auch Semantik der Datenbeschreibungssprache SQL erlaeutert.
Dieses Buch bietet eine Einführung in die Wirtschaftsinformatik in kommentierter Form. Es richtet sich in erster Linie an Studierende der Betriebswirtschaftslehre und der Wirtschaftsinformatik. Zu Beginn dieses Bandes wird eine Einführung in die Funktionalität einer Oracle-Datenbank am Beispiel der Version Oracle 10g XE als frei verfügbare Version des relationalen Datenbanksystems Oracle 10g gegeben. Der thematische Schwerpunkt im Band 2, die Datenbankabfragesprache SQL, baut auf dem Teil der Datenmodellierung des Bandes 1 auf. Zum besseren Verständnis der Zusammenhänge ist ein durchgehend verwendbares Beispiel gewählt worden. An Hand dieses Beispiels werden sowohl Begriffe als auch Zusammenhänge in der Umsetzung vom relationalen Datenmodell zur Datenbank erläutert. Im Detail werden sowohl Syntax als auch Semantik der Datenbeschreibungssprache SQL erläutert.
Enhancing economic efficiency in modular production systems through deep reinforcement learning
(2024)
In times of increasingly complex production processes and volatile customer demands, the production adaptability is crucial for a company's profitability and competitiveness. The ability to cope with rapidly changing customer requirements and unexpected internal and external events guarantees robust and efficient production processes, requiring a dedicated control concept at the shop floor level. Yet in today's practice, conventional control approaches remain in use, which may not keep up with the dynamic behaviour due to their scenario-specific and rigid properties. To address this challenge, deep learning methods were increasingly deployed due to their optimization and scalability properties. However, these approaches were often tested in specific operational applications and focused on technical performance indicators such as order tardiness or total throughput. In this paper, we propose a deep reinforcement learning based production control to optimize combined techno-financial performance measures. Based on pre-defined manufacturing modules that are supplied and operated by multiple agents, positive effects were observed in terms of increased revenue and reduced penalties due to lower throughput times and fewer delayed products. The combined modular and multi-staged approach as well as the distributed decision-making further leverage scalability and transferability to other scenarios.
Künstliche Intelligenz (KI) gewinnt in zahlreichen Branchen rasant an Bedeutung und wird zunehmend auch in Enterprise Resource Planning (ERP)-Systemen als Anwendungsbereich erschlossen. Die Idee, dass Maschinen die kognitiven Fähigkeiten des Menschen imitieren können, indem Wissen durch Lernen auf Basis von Beispielen in Daten, Informationen und Erfahrungen generiert wird, ist heute ein Schlüsselelement der digitalen Transformation. Jedoch charakterisiert der Einsatz von KI in ERP-System einen hohen Komplexitätsgrad, da die KI als Querschnittstechnologie zu verstehen ist, welche in unterschiedlichen Unternehmensbereichen zum Einsatz kommen kann. Auch die Anwendungsgrade können sich dabei erheblich voneinander unterscheiden. Um trotz dieser Komplexität den Einsatz der KI in ERP-Systemen erfassen und systembezogen vergleichen zu können, wurde im Rahmen dieser Studie ein Reifegradmodell entwickelt. Dieses bildet die Ausgangsbasis zur Ermittlung der KI-Reife in ERP-Systemen und grenzt dabei die folgenden vier KI- bzw. systembezogenen Ebenen voneinander ab: 1) Technische Möglichkeiten, 2) Datenreife, 3) Funktionsreife und 4) Erklärfähigkeit des Systems.
Mittelständische Industrieunternehmen setzen für ihre betrieblichen Abläufe Planungs- und Ausführungssysteme ein. Aufgrund der Turbulenzen auf Absatz- und Beschaffungsmärkten kann die Wirtschaftlichkeit und Wettbewerbsfähigkeit dieser Unternehmen nur durch permanente Anpassungen der Organisationsstrukturen und -abläufe erfolgen. In der Praxis zeigt sich eine unzureichende technologische Anpassungsfähigkeit der heute eingesetzten Standardsoftwaresysteme. Diese lassen zwar während der Einführungsphase vielfältige Konfigurationsmöglichkeiten zu, Veränderungen im laufenden Betrieb sind aber meist nur mit großem Aufwand möglich. Hier sind die Softwarehersteller in Zukunft zunehmend gefordert, wandlungsfähige Auftragsabwicklungssysteme zu entwickeln. Über die Entwicklungsphase (Build-Time) hinaus muss auch parallel zur Betriebsphase (Run-Time) der technische Fortschritt aufgrund von geänderten Anforderungen durch entsprechende Softwarereleases synchronisiert werden.
Obwohl Handelsplattformen zunehmend an Bedeutung gewinnen, besteht im deutschsprachigen Raum ein Mangel an umfassenden Marktübersichten. Dadurch fehlt es Verkäufern, potenziellen Plattformbetreibern und Kunden an einer soliden Grundlage für fundierte Entscheidungen. Das ändern wir mit folgendem Beitrag. Erfahren Sie hier das Wichtigste über den rasant wachsenden Markt der Handelsplattformen.
Erhöhung der Wandlungsfähigkeit von ECM-Lösungen unter Verwendung kartographischer Gestaltungsmittel
(2008)
Bei Entscheidungen über abzulösende oder neue Anwendungssysteme kann mit Hilfe funktionaler Anforderungen immer nur der gegenwärtige oder vorhersehbare Bedarf ermittelt werden. In einem turbulenten Umfeld sind die Anwendungssysteme jedoch häufig langere Zeit im Einsatz als die Anforderungen gültig sind, mit Hilfe derer sie ausgewählt wurden. An der Universität Potsdam wird im Rahmen des BMBF-Projektes CHANGE eine Vorgehensweise zur Ermittlung der Zukunftsfähigkeit unternehmensweiter Anwendungssysteme entwickelt, deren wesentliche Merkmale in diesem Beitrag beschrieben werden.
Bei Entscheidungen über abzulösende oder neue Anwendungssysteme kann mit Hilfe funktionaler Anforderungen immer nur der gegenwärtige oder vorhersehbare Bedarf ermittelt werden. In einem turbulenten Umfeld sind die Anwendungssysteme jedoch häufig langere Zeit im Einsatz als die Anforderungen gültig sind, mit Hilfe derer sie ausgewählt wurden. An der Universität Potsdam wird im Rahmen des BMBF-Projektes CHANGE eine Vorgehensweise zur Ermittlung der Zukunftsfähigkeit unternehmensweiter Anwendungssysteme entwickelt, deren wesentliche Merkmale in diesem Beitrag beschrieben werden.
ERP und MES : Teil 3
(2008)
Die Branche der Dienstleistungsunternehmen (Professional Services) hat einige Anforderungen, die sie von den „klassischen“ ERP-Branchen Industrie und Handel unterscheidet. Dieser Beitrag beschreibt einige der aktuellen Herausforderungen dieses immer wichtiger werdenden Wirtschaftszweigs und geht dann am Beispiel eines mittelständischen Ingenieurdienstleisters auf typische Anforderungen dieser Branche, infrage kommende Systeme und das Vorgehen zur Auswahl ein.
ERP-Systeme
(2021)
Neben kleineren, meist automatisierten Update-Aktivitäten zur Fehlerbehebung und zur Steigerung der Performance von ERP-Systemen, gibt es größere Updates mit umfangreicheren Aktualisierungen und Erweiterungen der Software – auch „Updateprojekte“ oder „Upgrades“ genannt. Ein ERP-Upgrade beschreibt einen größeren Änderungsprozess, der die Nutzung neuer Technologien ermöglicht und das System mit (neuen) Geschäftsstrategien in Einklang bringt. Upgrades tragen zur Verbesserung der Software bei und sind klar zu unterscheiden von geringfügigen Änderungen innerhalb einer Version eines ERP-Systems.
Process models are the basic ingredient for many attempts to improve business processes. The graphical depiction of otherwise not observable behavior in an enterprise is one of the most important techniques in the digital society. They help to enable decision making in the design of processes and workflows. Nevertheless it is not easy to correctly model business processes. Some approaches try to detect errors by an automated analysis of the process model. This contribution focuses on the creation of the first model from scratch. Which errors occur most frequently and how can these be avoided?
Process oriented knowledge management focuses on knowledge intensive business processes. For modelling and analysis of these processes the modelling technique KMDL (Knowledge Modeling and Description Language) has been developed. KMDL is a method to describe knowledge flows and conversions along and between business processes. Thereby KMDL identifies existing and utilized information as well as knowledge of individual participants and of the entire company. This research-in-progress contribution introduces a practical example in the field of software engineering, in which KMDL models are evaluated to identify process improvements, e.g. by adding knowledge management activities. Therefore three individual views focussing on selected aspects of interest are introduced.
Already successfully used products or designs, past projects or our own experiences can be the basis for the development of new products. As reference products or existing knowledge, it is reused in the development process and across generations of products. Since further, products are developed in cooperation, the development of new product generations is characterized by knowledge-intensive processes in which information and knowledge are exchanged between different kinds of knowledge carriers. The particular knowledge transfer here describes the identification of knowledge, its transmission from the knowledge carrier to the knowledge receiver, and its application by the knowledge receiver, which includes embodied knowledge of physical products. Initial empirical findings of the quantitative effects regarding the speed of knowledge transfers already have been examined. However, the factors influencing the quality of knowledge transfer to increase the efficiency and effectiveness of knowledge transfer in product development have not yet been examined empirically. Therefore, this paper prepares an experimental setting for the empirical investigation of the quality of knowledge transfers.