The United Nations’ Sustainable Development Goals (SDGs) set out to improve the quality of life of people in developed, emerging, and developing countries by covering social and economic aspects, with a focus on environmental sustainability. At the same time, data-driven technologies influence our lives in all areas and have caused fundamental economical and societal changes. This study presents a comprehensive literature review on how data-driven approaches have enabled or inhibited the successful achievement of the 17 SDGs to date. Our findings show that data-driven analytics and tools contribute to achieving the 17 SDGs, e.g., by making information more reliable, supporting better-informed decision-making, implementing data-based policies, prioritizing actions, and optimizing the allocation of resources. Based on a qualitative content analysis, results were aggregated into a conceptual framework, including the following categories: (1) uses of data-driven methods (e.g., monitoring, measurement, mapping or modeling, forecasting, risk assessment, and planning purposes), (2) resulting positive effects, (3) arising challenges, and (4) recommendations for action to overcome these challenges. Despite positive effects and versatile applications, problems such as data gaps, data biases, high energy consumption of computational resources, ethical concerns, privacy, ownership, and security issues stand in the way of achieving the 17 SDGs.

The Cross-Industry Standard Process for Data Mining (CRISP-DM) is a widely accepted framework in production and manufacturing. This data-driven knowledge discovery framework provides an orderly partition of the often complex data mining processes to ensure a practical implementation of data analytics and machine learning models. However, the practical application of robust industry-specific data-driven knowledge discovery models faces multiple data- and model development-related issues. These issues need to be carefully addressed by allowing a flexible, customized and industry-specific knowledge discovery framework. For this reason, extensions of CRISP-DM are needed. In this paper, we provide a detailed review of CRISP-DM and summarize extensions of this model into a novel framework we call Generalized Cross-Industry Standard Process for Data Science (GCRISP-DS). This framework is designed to allow dynamic interactions between different phases to adequately address data- and model-related issues for achieving robustness. Furthermore, it emphasizes also the need for a detailed business understanding and the interdependencies with the developed models and data quality for fulfilling higher business objectives. Overall, such a customizable GCRISP-DS framework provides an enhancement for model improvements and reusability by minimizing robustness-issues.

The United Nations’ 17 Sustainable Development Goals stress the importance of global and local efforts to address inequalities and implement sustainability. Addressing complex, interconnected sustainability challenges requires a systematic, interdisciplinary approach, where technology, AI, and data-driven methods offer potential solutions for optimizing resources, integrating different aspects of sustainability, and informed decision-making. Sustainability research surrounds various local, regional, and global challenges, emphasizing the need to identify emerging areas and gaps where AI and data-driven models play a crucial role. The study performs a comprehensive literature survey and scientometric and semantic analyses, categorizes data-driven methods for sustainability problems, and discusses the sustainable use of AI and big data. The outcomes of the analyses highlight the importance of collaborative and inclusive research that bridges regional differences, the interconnection of AI, technology, and sustainability topics, and the major research themes related to sustainability. It further emphasizes the significance of developing hybrid approaches combining AI, data-driven techniques, and expert knowledge for multi-level, multi-dimensional decision-making. Furthermore, the study recognizes the necessity of addressing ethical concerns and ensuring the sustainable use of AI and big data in sustainability research.

Background: By examining the recent advancements in technology, particularly the transformation of material into digital flows and its impact on customer demands, the aim of this paper is to investigate supply chain management (SCM) by analyzing seven key constructs: uncertainty, perspective, topicality, coordination, flow, job to be done, and connection. These constructs were chosen since they reflect evolving SCM practices that motivate this study. Methods: We conducted a broad, structured narrative review to comprehensively address the extensive SCM literature. This approach allowed us to evaluate the current state of SCM research and offer recommendations for overcoming prevailing challenges. Results: Our findings reveal the significant impact of technological advancements on SCM operations, requiring companies to adapt and remain competitive. We envision future supply chains as dynamic networks of networks, necessitating the adoption of a value architecture concept that extends a firm’s business model to an ecosystem business model. Conclusions: Considering these changes, our study recommends exploiting uncertainty, adopting demand-driven systems, offering on-demand customized services and products, utilizing prescriptive analytics, prioritizing information flows and services, and embracing open systems with high interoperability. Summarizing these opportunities and challenges that arise with changes in SCM provides interesting venues for future research and valuable insights for practitioners.

Currently, studies involving a digital twin are gaining widespread interest. While the first fields adopting such a concept were in manufacturing and engineering, lately, interest extends also beyond these fields across all academic disciplines. Given the inviting idea behind a digital twin which allows the efficient exploitation and utilization of simulations such a trend is understandable. The purpose of this paper is to use a scientometrics approach to study the early publication history of the digital twin across academia. Our analysis is based on large-scale bibliographic and citation data from Scopus that provides authoritative information about high-quality publications in essentially all fields of science, engineering and humanities. This paper has four major objectives. First, we obtain a global overview of all publications related to a digital twin across all major subject areas. This analysis provides insights into the structure of the entire publication corpus. Second, we investigate the co-occurrence of subject areas appearing together on publications. This reveals interdisciplinary relations of the publications and identifies the most collaborative fields. Third, we conduct a trend and keyword analysis to gain insights into the evolution of the concept and the importance of keywords. Fourth, based on results from topic modeling using a Latent Dirichlet Allocation (LDA) model we introduce the definition of a scientometric dimension (SD) of digital twin research that allows to summarize an important aspect of the bound diversity of the academic literature.

This study investigates data-driven business model innovation (DDBMI) for incumbent manufacturers, underscoring its importance in various strategic and managerial contexts. Employing topic modeling, the study identifies nine key topics of DDBMI. Through qualitative thematic synthesis, these topics are further refined, interpreted, and categorized into three levels: Enablers, value creators, and outcomes. This categorization aims to assess incumbent manufacturers’ preparedness for DDBMI. Additionally, a knowledge framework is developed based on the identified nine key topics of DDBMI to aid incumbent manufacturers in enhancing their understanding of DDBMI, thereby facilitating the practical application and interpretation of data-driven approaches to business model innovation.