The evolution of data analytics through the lens of business cases

PurposeThe present study provides a comprehensive review of the evolution of data analytics using real-world cases. The purpose is to provide a distinct overview of where the phenomenon was derived from, where it currently stands and where it is heading.Design/methodology/approachThree case studies were selected to represent three different eras of data analytics: Yesterday (1950s–1990s), Today (2000s–2020s) and Tomorrow (2030s–2050s).FindingsRapid changes in information technologies more likely moving us towards a more cyber-physical society, where an increasing number of devices, people and corporations are connected. We can expect the development of a more connected cyber society, open for data exchange than ever before.Social implicationsThe analysis of technological trends through the lens of representative real-world cases helps to clarify where data analytics was derived from, where it currently stands and where it is heading towards. The presented case studies accentuate that data analytics is constantly evolving with no signs of stagnation.Originality/valueAs the field of data analytics is constantly evolving, the study of its evolution based on particular studies aims to better understand the paradigm shift in data analytics and the resulting technological advances in the IT business through the representative real-life cases.

“I'm Your Leader Now, but Do You Trust Me?”

Leadership succession is inevitable for most family businesses. To effectively face this challenging transition, next-generation leaders need to have the ability to gain their employees' trust which is typically very challenging due to previous generation' influence on the business. The chapter explores how trust in family leaders can impact succession when a business is passed from one generation to the next. This chapter presents two comparative examples of family business cases operating in the transportation sector in Italy. In the first business, the succession already took place and the next-generation leader is running the firm, while in the other firm, the incumbent generation is still in charge of the company and is not passing the baton. Results show that the incumbent and next-generation leader's perception of their leadership style correspond to non-family employees' perceptions. However, employees' trust in the incumbent is higher than the trust in the successor.

Virtual Prototyping: Evaluating the Digital Twin Based Virtual Factory for New Product Introduction

Shortening lifecycles and increasing complexity make product and production lifecycle processes more challenging than ever for manufacturing enterprises. Virtual Prototyping (VP) technologies promise a viable solution to handle such challenges in reducing time and physical builds as well as increasing quality. In previous studies, the Digital Twin (DT) based Virtual Factory (VF) concept showed significant potential to handle co-evolution by integrating 3D factory and product models with immersive and interactive 3D Virtual Reality (VR) simulation technology as well as real-time bidirectional data synchronisation between virtual and physical production systems. In this article, we present an extension to the paper “Demonstrating and Evaluating the Digital Twin Based Virtual Factory for Virtual Prototyping” presented at CARV2021. The study presents an evaluation by industry experts of the DT based VF concept for VP in the context of New Product Introduction (NPI) processes. The concept is demonstrated in two cases: wind turbine blade manufacturing and nacelle assembly operations at Vestas Wind Systems A/S. The study shows that the VF provides an immersive virtual environment, which allows the users to reduce the time needed for prototyping. The industry experts propose several business cases for the introduced solution and find that the phases that would have the most gain are the later ones (production) where the product design is more mature.

Capex-Free Production Facilities Optimization Opportunities by Exploiting Installed Automation Infrastructure and Unused Functionalities

Operational Excellence and Automation Excellence go hand in hand when it comes to enabling process optimization and cost-reduction opportunities in Upstream Oil & Gas Operations. As part of our Digital Journey in the Upstream Business, multiple streams and workflows have been created to successfully identify and explore the use of new technologies to foster collaboration, achieve higher levels of efficiency, lower operating costs, maximize production and asset integrity, improve decision making, and lower the carbon footprint of all of our exploration and production activities. While our Digital Journey has been highly successful in identifying, assessing and implementing new technologies and novel solutions, it has also made very clear, during the development of business-cases’, that a rather big number of existing and older facilities were not going to be good cost-effective candidates for the deployment of many of the identified solutions and technologies. As an established and rooted operator with a large number of aging facilities, these presented an additional challenge to dig deeper and look further for solutions to cover all of these remaining assets, no matter how old or small they could be.

Turning Crisis into Opportunities: How a Firm Can Enrich Its Business Operations Using Artificial Intelligence and Big Data during COVID-19

The COVID-19 pandemic has severe impacts on global health and social and economic safety. The present study discusses strategies for turning the COVID-19 crisis into opportunities to use artificial intelligence (AI) and big data in business operations. Based on the shared experience and theoretical ground, researchers identified five major business challenges during the COVID-19 pandemic: production and supply-chain disruption, appropriate business model selection, inventory management, budget planning, and workforce management. These five challenges were outlined with eight business cases as examples of companies that had already utilized AI and big data for their business operations during the COVID-19 pandemic. The outcomes of this study provide valuable insights into contemporary social science research and business management with AI and big data applications as a business response to any crisis in the future.

The perspective of IT decision makers on factors influencing adoption and implementation of AI-technologies in German Hospitals: Descriptive Analysis (Preprint)

BACKGROUND New AI tools are being developed at a high speed. Yet, strategies and practical experiences surrounding the adoption and implementation of AI in health care are lacking. This is likely due to AI’s high implementation complexity, legacy IT infrastructure and unclear business cases, thus complicating AI adoption. Research has recently started to identify factors influencing organizations' readiness for AI. OBJECTIVE Our study aimed to investigate the factors influencing AI readiness as well as possible barriers to AI adoption and implementation in German hospitals. We also tried to assess the status quo concerning the dissemination of AI tools in German hospitals. We focused on IT decision makers which is a seldom studied but highly relevant group. METHODS We created an online survey based on recent AI readiness and implementation literature. The survey was pretested. Possible participants were identified through a publicly accessible database and contacted via e-mail or via invitational leaflets sent by mail, in some cases accompanied by a telephonic pre-notification. Survey responses were analyzed through descriptive statistical methods. RESULTS Overall, we contacted 609 possible participants and our database recorded 40 fully completed surveys. Most participants agreed or rather agreed with the statement that AI will be relevant in the future, both in Germany (37/40, 92,5%) and in their own hospital (36/40, 89,5%). Participants were asked whether their hospital used or planned using AI technologies. 65% (26/40) answered this question with “yes”. Most AI technologies were used or planned in patient care, followed by biomedical research, administration, and logistics and central purchasing. The most important barriers to AI were lacking resources (staff, knowledge, financial). Relevant possible opportunities of using AI were increases in efficiency due to time saving effects, competitive advantages, and increases in quality of care. Most AI tools in use or in planning were developed with external partners. CONCLUSIONS Few tools have been implemented in routine care and many hospitals do not use or do not plan on using AI in the future. This can likely be explained by missing or unclear business cases or needed modern IT infrastructure to integrate AI tools in a usable manner, hence complicating decision making and resource attribution. Since most AI technologies already in use were developed in cooperation with external partners, these relationships should be fostered. IT decision makers in hospitals should assess their hospital’s readiness for AI individually with a focus on resources. Further research should continue to monitor the dissemination of AI tools and AI readiness factors to see if improvements can be made over time, especially in regards to governmentally supported investments in AI technologies that could alleviate financial burdens. Qualitative studies with hospital IT decision makers should be conducted to explore the reasons for slow AI adoption in more detail.

Business Models for Demand Response: Exploring the Economic Limits for Small- and Medium-Sized Prosumers

The European energy transition increasingly requires flexibility to ensure reliable operation of the electricity system, making use of demand response, a promising concept. With technological advances in the fields of big data analysis and the internet of things, small- and medium-sized prosumers could also provide flexibility services through aggregators. A lot of conceptual work has been conducted recently to formulate business models in this context, but their viability still remains unclear. In this paper, a quantitative validation is conducted of two business models that are frequently proposed in the scientific discussion. The aim of this work is to explore the economic limits of these business models and show under which conditions they can be profitable for small- and medium-sized prosumers. For this purpose, a multi-level contribution margin calculation for several scenarios, customer segments and target markets is conducted. The results show that the profitability for the participation of small loads is still very low under current market conditions. Especially for household consumers, transaction costs are too high to be covered by the revenues. Considering the quantitative results, in the future profitable business cases can only be expected for medium-sized tertiary consumers.

Aplicabilidade de estratégias militares na gestão estratégica de negócios / Applicability of military strategies in business management

Com a constante busca pela maior rentabilidade e eficiência nos negócios, os gestores têm buscado ideias de outras áreas de conhecimento para aplicarem na administração. Embora o conceito de estratégia militar esteja atrelado a aplicações bélicas, é natural que haja alguma correlação com a gestão estratégica empresarial. Neste sentido, este trabalho buscou avaliar se as táticas militares podem ser usadas pela alta administração de empresas para nortear suas operações. Foram analisados business cases de sucesso que demonstraram fortes correlações entre estes tipos de estratégia. Uma pesquisa de opinião com gestores e estudantes foi feita para avaliar a aplicabilidade de 10 estratégias militares específicas, e quase todas foram muito bem aceitas pelos entrevistados. Concluiu-se que as estratégias militares podem sim ser utilizadas por gestores na tomada de decisões, na definição do posicionamento da empresa no mercado, e em diversas outras aplicações que podem garantir a sobrevivência da empresa no competitivo campo de batalha dos negócios.