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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?
The authors propose that while tacit knowledge is a valuable resource for developing new business models, its externalization presents several challenges. One major challenge is that individuals often don’t recognize their tacit knowledge resources, while another is the reluctance to share one’s knowledge with others. Addressing these challenges, the authors present an application-oriented serious game-based haptic modeling approach for externalize tacit knowledge, which can be used to develop the first versions of business models based on tacit knowledge. Both conceptual and practical design fundamentals are presented based on elaborated theoretical approaches, which were developed with the help of a design science approach. The development of the research process is presented step by step, whereby we focused on the high accessibility of the presented research. Practitioners are presented with guidelines for implementing their serious game projects. Scientists benefit from starting points for their research topics of externalization, internalization, and socialization of tacit knowledge, development of business models, and serious games or gamification. The paper concludes with open research desiderata and questions from the presented research process.
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.
As part of the digitization, the role of artificial systems as new actors in knowledge-intensive processes requires to recognize them as a new form of knowledge bearers side by side with traditional knowledge bearers, such as individuals, groups, organizations. By now, artificial intelligence (AI) methods were used in knowledge management (KM) for knowledge discovery, for the reinterpreting of information, and recent works focus on the studying of different AI technologies implementation for knowledge management, like big data, ontology-based methods and intelligent agents [1]. However, a lack of holistic management approach is present, that considers artificial systems as knowledge bearers. The paper therefore designs a new kind of KM approach, that integrates the technical level of knowledge and manifests as Neuronal KM (NKM). Superimposing traditional KM approaches with the NKM, the Symbiotic Knowledge Management (SKM) is conceptualized furthermore, so that human as well as artificial kinds of knowledge bearers can be managed as symbiosis. First use cases demonstrate the new KM, NKM and SKM approaches in a proof-of-concept and exemplify their differences.
Faced with the triad of time-cost-quality, the realization of knowledge-intensive tasks at economic conditions is not trivial. Since the number of knowledge-intensive processes is increasing more and more nowadays, the efficient design of knowledge transfers at business processes as well as the target-oriented improvement of them is essential, so that process outcomes satisfy high quality criteria and economic requirements. This particularly challenges knowledge management, aiming for the assignment of ideal manifestations of influence factors on knowledge transfers to a certain task. Faced with first attempts of knowledge transfer-based process improvements [1], this paper continues research about the quantitative examination of knowledge transfers and presents a ready-to-go experiment design that is able to examine quality of knowledge transfers empirically and is suitable to examine knowledge transfers on a quantitative level. Its use is proven by the example of four influence factors, which namely are stickiness, complexity, competence and time pressure.
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.
Since more and more business tasks are enabled by Artificial Intelligence (AI)-based techniques, the number of knowledge-intensive tasks increase as trivial tasks can be automated and non-trivial tasks demand human-machine interactions. With this, challenges regarding the management of knowledge workers and machines rise [9]. Furthermore, knowledge workers experience time pressure, which can lead to a decrease in output quality. Artificial Intelligence-based systems (AIS) have the potential to assist human workers in knowledge-intensive work. By providing a domain-specific language, contextual and situational awareness as well as their process embedding can be specified, which enables the management of human and AIS to ease knowledge transfer in a way that process time, cost and quality are improved significantly. This contribution outlines a framework to designing these systems and accounts for their implementation.