Business Intelligence in Industry 4.0: State of the art and research opportunities

Abstract

Data collection and analysis have been at the core of business intelligence (BI) for many years, but traditional BI must be adapted for the large volume of data coming from Industry 4.0 (I4.0) technologies. They generate large amounts of data that need to be processed and used in decision-making to generate value for the companies. Value generation of I4.0 through data analysis and integration into strategic and operational activities is still a new research topic. This study uses a systematic literature review with two objectives in mind: understanding value creation through BI in the context of I4.0 and identifying the main research contributions and gaps. Results show most studies focus on real-time applications and integration of voluminous and unstructured data. For business research, more is needed on business model transformation, methodologies to manage the technological implementation, and frameworks to guide human resources training.

Introduction to the Minitrack on The Digital Supply Chain of the Future: Technologies, Applications and Business Models

“…The results of the study show that the largest part of the literature addresses real-time applications as well as the integration of structured and unstructured data. Publications on business model transformation in the digital world, implementation methods for data-driven services as well as guidelines and frameworks for human resource training are still missing [3].…”

Shipping digitalization management: conceptualization, typology and antecedents

“…Innovative digital solutions, in turn, shape and are being shaped by the emergence of innovative digital business concepts and practices that are intertwined with digital solutions. This transformation process embedded in digital solutions and enacted with digital business models and practices is defined as digital transformation and captures the organizational changes and business model innovations induced by digital technologies (Atzori et al 2010;Beck et al 2017;Bharadwaj et al 2013;Bordeleau et al 2018;Demirkan et al 2015;Kane et al 2015). Our baseline conceptual model is illustrated in Fig.…”

Semantic-Driven Architecture for Autonomic Management of Cyber-Physical Systems (CPS) for Industry 4.0

“…In the FCW paradigm, the amount of collectable data grows exponentially and the need for its processing, very often in real time, is essential to produce meaningful information and in particular to create new knowledge. Existing solutions in the area of Business Intelligence (BI), Analytics or Artificial Intelligence (AI) have been successfully applied for years on the basis of data gathered from traditional information systems in order to support strategic decision making [15]. However, the quantity, heterogeneity and frequency of the potentially collectable data under the FCW paradigm of the Industry 4.0, reveal new challenges that will lead important innovations in the area of Big Data [16], [17].…”