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The use of large and diverse data in real time (called Big Data) affects many business processes and models. The tourism industry, characterized by manifold sub-sectors and players, provides a variety of starting points for Big Data usage. Examples are the optimization of transport offers using transaction data or a comprehensive analysis of destination trends based on social media posts. Big Data is a trending topic, however, the general discourse centres around potential ideas but fewer practical solutions. Based on a systematic literature analysis of initially 148 peer-reviewed journal articles, this article evaluates the current state of Big Data research within tourism. For this purpose, research articles centering around tourism-related Big Data were investigated according to the actual state of implementation of an IT solution, whether they truly grasp or represent Big Data in technological terms, and which added value they create for the tourism industry and research community. One key finding is that traditional data analysis is often wrongfully subsumed under the Big Data label. Further, the scientific literature predominantly discusses ideas or theoretical considerations, fewer tangible Big Data implementations, and fails to address and/or meet all requirements to be classified as Big Data. Only a minority of the presented solutions processes data in real time, whereas many rely on only one data source or structured data. Furthermore, most articles revolve around post-trip data analyses and are set to a destination context. In contrast, other tourism sectors as well as data interpretation and usage in pre-trip and on-trip phases are less represented. Lastly, this literature analysis provides an overview of true Big Data solutions already in operation and enables researchers to validly classify their own research activities in order to plan initiatives more specifically.

The explainability of artificial intelligence (AI) is one of the central challenges for the wider use of the new technology in many industries and applications. The more powerful and efficient the algorithms of AI work, the less it is usually comprehensible to users. While there is widespread agreement on the basic requirement of explainability for AI applications, the design of an adequate AI explanation is rarely defined. This contribution presents basic concepts of explainability as well as current approaches to explanations for AI. It describes which methods are fundamentally suitable for considering an explanation to be complete and how it must be designed in order to be assessed as interpretable for AI.

What is special about the German-speaking education system is that there are two types of higher education institutions. While classical universities do academic teaching, so-called universities of applied sciences (UAS) focus more on practical training for the labour market. This chapter presents the experiences of two professors with practice-integrated teaching at two UAS that ensure employability to a large extent. The common setting of the four courses presented is that students work on real business cases, practical challenges, and real fields of innovation. Methodologically, design think and SCRUM are used. It is described for these cases how the design of the course contributes to students developing innovative and digital approaches for real companies. The chapter concludes with a compilation of good practices that emerged from experiences. These can serve as input for other teachers to design their own practice-integrated courses.

PurposeIncreasingly, dynamic market environments lead to growing complexity in manufacturing and pose a severe threat for the competitiveness of manufacturing companies. Systematic guidance to manage this complexity, especially in the context of Industry 4.0 and the therewith rising trends such as digitalization and data-driven production optimization, is lacking. To address this deficit a case-based reasoning (CBR) system for providing knowledge about managing complexity in Industry 4.0 is presented.Design/methodology/approachFirst, the explicit knowledge representation for managing complexity in IT-based manufacturing is introduced. Second, the CBR process step to retrieve knowledge from an artificially composed case base with in total 70 cases of data-based complexity management in the context of Industry 4.0 is set out. Third, knowledge transfer alongside several maturity levels of information technology capabilities of manufacturing systems for reuse in new problem scenarios is introduced.FindingsThe paper comprises the conceptual approach for designing a CBR system to support data-based complexity management in manufacturing systems. Furthermore, the appropriateness of the CBR system to provide applicable knowledge for reducing and managing complexity in corporate practice is shown.Research limitations/implicationsThe presented research results are evaluated in the course of an embedded single case study and may therefore lack generalizability. Future research to test and enhance the appropriateness of the developed CBR system will strengthen the research contribution.Originality/valueThe paper provides a novel approach to systematically support knowledge transfer for data-based complexity management by transferring the well-known and established methodology of CBR to the rising application domain of manufacturing systems in the context of Industry 4.0.

The processes of an investment bank are considered to be particularly knowledge-intensive, because analysts need to extract or generate relevant knowledge from a variety of data. With increasing digitization, modern data science and business intelligence techniques are available to support or partially automate these activities. This study presents concrete use cases for front office processes of an investment bank as how knowledge management techniques can be used. For example, the article describes how expert systems can be used in the due diligence review or how fuzzy logic systems help in deciding whether to buy or sell securities. The article is based on 1079 texts (e.g. documented cases and articles) and serves researchers as well as practitioners as an application overview of data science techniques in the example area of knowledge-intensive banking processes.

The commissioning of manufacturing plants is considered a complex and at the same time critical phase in the life cycle of a production system, because errors and delays lead to long downtimes and cost-intensive re-work at the start of production. Optimizations and rationalisation of this phase can be implemented by employing the idea of virtual commissioning. This paper summarises conceptual approaches to virtual commissioning and concertises them with the help of subject-oriented process models. As reference models, these depict the plant design process and demonstrate the advantage of virtual commissioning in terms of time, cost, and quality. The discussion along recognised quality criteria show that the reference model is suitable for providing orientation for the people involved in the plant engineering process and that the model can be used to support the creation of company-specific models.

Data-driven business processes management (BPM) is regarded as a central future trend because automation often makes huge amounts of data (big data) available for the optimisation and control of workflows. Software manufacturers also use this trend and call their solutions big data applications, even if some features are reminiscent of traditional data management approaches. This chapter derives from the basic definitions of big data including 13 central requirements that a big data BPM solution must meet in order to be described as such. One hundred twenty-one process management solutions are evaluated on the basis of these to determine whether they are real big data applications. As a result, less than 5% of all solutions analysed meet all requirements.

Data science as the interdisciplinary collection of methods and techniques to support businesses is becoming more and more popular. This article begins with definitions and shows how systematically competitive advantages can be built up on the basis of digital data. Essential sources and types of data-driven knowledge are introduced. Then a classification of approaches of data science concepts is explained. A distinction is made between Business Analytics and Business Intelligence as different levels of analytical skills. The paper goes into depth with these concepts and presents concrete techniques, algorithms, and application scenarios. Thus, the contribution introduces State of the Art approaches to analysis, control, monitoring but also to advanced approaches such as prediction, simulation, and optimization.

Techniken der kunstlichen Intelligenz kommen in immer mehr Anwendungssystemen im E-Commerce zum Einsatz. Sie haben das Potenzial, Herausforderungen der Kostensenkung im Online-Handel (z. B. durch Automatisierung von Beratung und Retouren) mit der Erhohung von Kundenzufriedenheit (z. B. durch Individualisierung von Angeboten) zu verbinden. In der Studie, deren Ergebnisse in diesem Beitrag vorgestellt werden, wurden 50 State-of-the-Art-Applikationen kunstlicher Intelligenz in digitalen Transaktionsprozessen der Modebranche gesammelt. Diese werden in diesem Beitrag entlang der Wertschopfungskette des Endkunden systematisiert.

Approaches of business process management (BPM) are always changing, because underlying business strategies and technological implementation are constantly evolving. Likewise, methodical approaches, how business processes are planned, optimized, managed and controlled, come and go. This article collects and systematizes the trends discussed at major BPM conferences of the year 2019. The identified hot spots include e.g. process mining, predictive BPM, BPM trust, modeling, BPM platforms, team-driven BPM.

A key objective of strategy management is ensuring the implementation of strategies in the day-to-day operational business processes of enterprises. For this purpose, researchers and practitioners developed an enormous body of methods for strategy management. However, the tremendous variety of strategy management methods makes it difficult to choose the most appropriate methods for a given challenge. In an effort to provide guidance for practitioners and researchers, this paper provides an integrated dataset of the 50 most widespread strategy design and implementation methods and links these methods to a large body of 100 case studies. Based on this dataset, commonly utilized methods, common challenges, typical objectives, and specifics of industries are examined.

Process orientation is often seen as a central starting point for optimizing business performance. This is especially true for supply chain workflows because modeling, understood as specifying activities, data, resources, and control flows, is usually the first step in selecting and implementing digital approaches such as smart factories, virtual agents, and autonomous systems. Many different process notations are known (e.g., detailed vs. rough, flexible vs. standardized, etc.), and at the same time, fundamentally different SCM process types make various assumptions about suitable modeling (e.g., integration of value chain partners, standardization). This chapter presents an association model that suggests which notation fits best to which SCM type. The chapter discusses the benefits of specific notations for classical as well as modern challenges of SCM to help in the selection of notations. The resulting specifications serve as the basis for the holistic digitization of supply chains.

Many concepts of process management are being intensively discussed in current research. The background is often that within the megatrend of digitization, workflows are automated. Furthermore, processes are fundamentally reconsidered in the digital transformation of organizations. What contribution S-BPM can make in the future of digital process management is analyzed in this paper aiming at the anniversary conference of S-BPM. As basis for the analysis serves the idea that, the suitability of BPM concepts (such as S-BPM) for certain application domains depends on essential process properties. Based on an assessment model we analyze under what conditions subject orientation can be well used (e.g., focus on collaboration). Using these results, we are evaluating the opportunities of S-BPM for the digital future. The evaluation is based on the three exemplary trends (i) digital development/products, (ii) digital production and (iii) digital business transformation. In these trend, we asses which conditions change (e.g. fast changing environments, machine-to-machine-communication, user-centered thinking) and to what degree S-BPM can still contribute in the new digital world. In a nutshell, our findings reveal that subject orientation is well suited for collaborative processes involving many different human and non-human stakeholders, but has deficiencies when dealing with autonomous and unstructured systems, both upcoming trends in the digital transformation.