Predictive maintenance as an internet of things enabled business model : A taxonomy

Predictive maintenance (PdM) is an important application of the Internet of Things (IoT) discussed in many companies, especially in the manufacturing industry. PdM uses data, usually sensor data, to optimize maintenance activities. We develop a taxonomy to classify PdM business models that enables a comparison and analysis of such models. We use our taxonomy to classify the business models of 113 companies. Based on this classification, we identify six archetypes using cluster analysis and discuss the results. The “hardware development”, “analytics provider”, and “all-in-one” archetypes are the most frequently represented in the study sample. For cluster analysis, we use a visualization technique that involves an autoencoder. The results of our analysis will help practitioners assess their own business models and those of other companies. Business models can be better differentiated by considering the different levels of IoT architecture, which is also an important implication for further research. © 2020, The Author(s)

Management of applied analytics: The role of executives for the shift to analytics-based decision-making in a corporate context

This publication-based dissertation examines human-related success factors for the implementation and application of data analytics tools and methods within the decision-making process of organizations. Generated insights on human-related factors are outlined and described in six chapters. First, a general introduction to the subject is provided and the research is positioned within a broader overall context. Additionally, the first section comprises a summary of the research papers included, along with publication information. Chapter 2 presents a systematic literature review summarizing the capabilities of Big Data analytics (BDA) with regard to firm performance. Five key capability clusters have been identified to categorize all relevant human-related capabilities across existing research to date. Chapter 3 presents an empirical research paper examining the relevant managerial aspects that must be considered when shifting from intuitive to analytics-based decision-making. Introducing a six-factor framework, the chapter outlines the findings of an indepth single case study of a German manufacturing organization that has already implemented analytical methods and tools within its decision processes. Chapter 4 contains the second empirical paper, which outlines the crucial role that executives play within the process of a firm’s digital transformation toward the application of analytics. Based on conducted interviews, four managerial archetypes are identified, with detailed descriptions of their characteristics, capabilities, and contribution to transformation. Chapter 5 introduces a teaching case study that sheds light on best practices relevant to the application of analytics. This case study describes the most critical factors for success in the use of an AI tool using an example from Wilo, a leading German manufacturer of pumps and pump systems. Finally, Chapter 6 summarizes the findings of this publication-based dissertation, outlines its contributions to academia and practice, and presents its limitations and potential avenues for future research

Digital transformation in the manufacturing industry : business models and smart service systems

The digital transformation enables innovative business models and smart services, i.e. individual services that are based on data analyses in real-time as well as information and communications technology. Smart services are not only a theoretical construct but are also highly relevant in practice. Nine research questions are answered, all related to aspects of smart services and corresponding business models. The dissertation proceeds from a general overview, over the topic of installed base management as precondition for many smart services in the manufacturing industry, towards exemplary applications in form of predictive maintenance activities. A comprehensive overview is provided about smart service research and research gaps are presented that are not yet closed. It is shown how a business model can be developed in practice. A closer look is taken on installed base management. Installed base data combined with condition monitoring data leads to digital twins, i.e. dynamic models of machines including all components, their current conditions, applications and interaction with the environment. Design principles for an information architecture for installed base management and its application within a use case in the manufacturing industry indicate how digital twins can be structured. In this context, predictive maintenance services are taken for the purpose of concretization. It is looked at state oriented maintenance planning and optimized spare parts inventory as exemplary approaches for smart services that contribute to high machine availability. Taxonomy of predictive maintenance business models shows their diversity. It is viewed on the named topics both from theoretical and practical viewpoints, focusing on the manufacturing industry. Established research methods are used to ensure academic rigor. Practical problems are considered to guarantee practical relevance. A research project as background and the resulting collaboration with different experts from several companies also contribute to that. The dissertation provides a comprehensive overview of smart service topics and innovative business models for the manufacturing industry, enabled by the digital transformation. It contributes to a better understanding of smart services in theory and practice and emphasizes the importance of innovative business models in the manufacturing industry

BIG DATA AND ANALYTICS AS A NEW FRONTIER OF ENTERPRISE DATA MANAGEMENT

Big Data and Analytics (BDA) promises significant value generation opportunities across industries. Even though companies increase their investments, their BDA initiatives fall short of expectations and they struggle to guarantee a return on investments. In order to create business value from BDA, companies must build and extend their data-related capabilities. While BDA literature has emphasized the capabilities needed to analyze the increasing volumes of data from heterogeneous sources, EDM researchers have suggested organizational capabilities to improve data quality. However, to date, little is known how companies actually orchestrate the allocated resources, especially regarding the quality and use of data to create value from BDA. Considering these gaps, this thesis – through five interrelated essays – investigates how companies adapt their EDM capabilities to create additional business value from BDA. The first essay lays the foundation of the thesis by investigating how companies extend their Business Intelligence and Analytics (BI&A) capabilities to build more comprehensive enterprise analytics platforms. The second and third essays contribute to fundamental reflections on how organizations are changing and designing data governance in the context of BDA. The fourth and fifth essays look at how companies provide high quality data to an increasing number of users with innovative EDM tools, that are, machine learning (ML) and enterprise data catalogs (EDC). The thesis outcomes show that BDA has profound implications on EDM practices. In the past, operational data processing and analytical data processing were two “worlds” that were managed separately from each other. With BDA, these "worlds" are becoming increasingly interdependent and organizations must manage the lifecycles of data and analytics products in close coordination. Also, with BDA, data have become the long-expected, strategically relevant resource. As such data must now be viewed as a distinct value driver separate from IT as it requires specific mechanisms to foster value creation from BDA. BDA thus extends data governance goals: in addition to data quality and regulatory compliance, governance should facilitate data use by broadening data availability and enabling data monetization. Accordingly, companies establish comprehensive data governance designs including structural, procedural, and relational mechanisms to enable a broad network of employees to work with data. Existing EDM practices therefore need to be rethought to meet the emerging BDA requirements. While ML is a promising solution to improve data quality in a scalable and adaptable way, EDCs help companies democratize data to a broader range of employees

Mapping domain characteristics influencing Analytics initiatives: The example of Supply Chain Analytics

Purpose: Analytics research is increasingly divided by the domains Analytics is applied to. Literature offers little understanding whether aspects such as success factors, barriers and management of Analytics must be investigated domain-specific, while the execution of Analytics initiatives is similar across domains and similar issues occur. This article investigates characteristics of the execution of Analytics initiatives that are distinct in domains and can guide future research collaboration and focus. The research was conducted on the example of Logistics and Supply Chain Management and the respective domain-specific Analytics subfield of Supply Chain Analytics. The field of Logistics and Supply Chain Management has been recognized as early adopter of Analytics but has retracted to a midfield position comparing different domains. Design/methodology/approach: This research uses Grounded Theory based on 12 semi-structured Interviews creating a map of domain characteristics based of the paradigm scheme of Strauss and Corbin. Findings: A total of 34 characteristics of Analytics initiatives that distinguish domains in the execution of initiatives were identified, which are mapped and explained. As a blueprint for further research, the domain-specifics of Logistics and Supply Chain Management are presented and discussed. Originality/value: The results of this research stimulates cross domain research on Analytics issues and prompt research on the identified characteristics with broader understanding of the impact on Analytics initiatives. The also describe the status-quo of Analytics. Further, results help managers control the environment of initiatives and design more successful initiatives.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

The use of differentiating communication tools to attract and retain different generational cohorts: case of a commercial bank in South Africa.

Published ThesisIt is inconceivable for any organisation to think that communicating with all of its clients using the same communications tools would make these clients more loyal. The problem of using the right tools of communication becomes more complex when the organisation deals with different generations. Previous scholars have emphasised the importance of Customer Relationship Management (CRM), both as a business philosophy and as part of an organisation’s IT systems to attract and retain clients. The IT systems are put in place so that clients can easily communicate with the organisation and vice versa. The CRM business philosophy is meant to change the method of dealing with clients as a top-down approach. This means top management will create the type of environment in the organisation that positions the needs of customers first. The primary objective of this study was to investigate the use of different communication tools by a commercial bank to attract and retain clients from different generations. The researcher identified four different branches from the same commercial bank in Bloemfontein to conduct the study. The location of these branches in and around Malls was important because it allowed the researcher to get a wide variety of different clients of the bank. A total of 50 clients of the bank per branch were asked to complete a questionnaire. The statistical calculations that were used were frequency tables, cross tables, McNemar test and the Chi-Square test. The research findings revealed that respondents from both generations made use of a variety of traditional and modern communication tools that were given in the questionnaire. It also indicates that this commercial bank at times utilises the wrong communication tools to communicate with these two cohorts, whether it is traditional or modern communication tools. The usage of each specific traditional and modern communication tool is also important. The results indicate that the usage of the specific communication tools for both traditional and modern communication tools vary during the course of the day. This is true for both generational cohort respondents. Based on the findings of this empirical study, the bank should focus more on utilising the specific communication tools that these two generations prefer, whether it is traditional or modern communication tools. The bank should also pay specific attention to the times of the day that these aforementioned communication tools are being used most by the respondents to ensure maximum marketing exposure. This study illustrates that there is no universal rule that dictates that a specific generation will only use a specific communication tool - in this case the Baby Boomer and Generation Y generation. The bank should investigate which modern or traditional communication tools are preferred by their clients the most and then continue with productive two way communication using those tools. This can facilitate the process of making clients more loyal and the process of attracting new clients simpler