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Logistics 4.0 aims at enabling the sustainable satisfaction of customer demands with optimised costs of services with the use of emerging technologies, such as Internet of Things, streaming analyti...

Fog computing extends multi-cloud computing by enabling services or application functions to be hosted close to their data sources. To take advantage of the capabilities of fog computing, serverless and the function-as-a-service (FaaS) software engineering paradigms allow for the flexible deployment of applications on multi-cloud, fog, and edge resources. This article reviews prominent fog computing frameworks and discusses some of the challenges and requirements of FaaS-enabled applications. Moreover, it proposes a novel framework able to dynamically manage multi-cloud, fog, and edge resources and to deploy data-intensive applications developed using the FaaS paradigm. The proposed framework leverages the FaaS paradigm in a way that improves the average service response time of data-intensive applications by a factor of three regardless of the underlying multi-cloud, fog, and edge resource infrastructure.

Fog computing extends multi-cloud computing by enabling services or application functions to be hosted close to their data sources. To take advantage of the capabilities of fog computing, serverless and the function-as-a-service (FaaS) software engineering paradigms allow for the flexible deployment of applications on multi-cloud, fog, and edge resources. This article reviews prominent fog computing frameworks and discusses some of the challenges and requirements of FaaS-enabled applications. Moreover, it proposes a novel framework able to dynamically manage multi-cloud, fog, and edge resources and to deploy data-intensive applications developed using the FaaS paradigm. The proposed framework leverages the FaaS paradigm in a way that improves the average service response time of data-intensive applications by a factor of three regardless of the underlying multi-cloud, fog, and edge resource infrastructure.

The emergence of Artificial Intelligence (AI) reveals new opportunities in Industry 4.0 environments. However, the lack of appropriate data and the requirements for trustworthiness pose significant challenges in the applicability and the effectiveness of AI systems in manufacturing environments. On the other hand, Industry 4.0 enables new types of interactions between humans and AI, but also between digital and physical worlds in the context of Cyber Physical Systems (CPS). In this paper, a Human Cyber Physical System (HCPS) framework for Operator 4.0 – Artificial Intelligence Symbiosis is proposed and its main architectural building blocks are described.

The emergence of Industry 4.0 enhances the capabilities of predictive maintenance and paves the way for efficient and optimized maintenance operations. Until now, the technical implications of adopting predictive maintenance solutions in Industry 4.0 environments have been reported in various studies. However, the business perspective is usually not considered, although there are significant managerial barriers toward the 4th industrial revolution. In this article, we present the benefits, business opportunities, and managerial implications of predictive maintenance based upon our experience in the design, implementation, and deployment of related solutions.

Business analytics aims to enable organizations to make quicker, better, and more intelligent decisions with the aim to create business value. To date, the major focus in the academic and industrial realms is on descriptive and predictive analytics. Nevertheless, prescriptive analytics, which seeks to find the best course of action for the future, has been increasingly gathering the research interest. Prescriptive analytics is often considered as the next step towards increasing data analytics maturity and leading to optimized decision making ahead of time for business performance improvement. This paper investigates the existing literature pertaining to prescriptive analytics and prominent methods for its implementation, provides clarity on the research field of prescriptive analytics, synthesizes the literature review in order to identify the existing research challenges, and outlines directions for future research.

To procure products and materials in the context of modern, convoluted supply chains, companies need to consider a variety of available options regarding trading partners, products, materials and s...

Business process management (BPM) has emerged as a prominent information management approach focusing on the design, execution and governance of organizational business processes. The ability to deal with both foreseen and unforeseen changes in business processes is considered critical for contemporary business process management systems. This paper proposes an approach that couples an event-driven framework for detecting and reasoning in situations that pose the need for process adaptations with MCDM methods for selecting adaptations. The proposed approach has been implemented in an aspect-oriented extension of a BPMN2.0 engine in order to enact adaptations of business processes in real-time.