•This article focuses on prescriptive analytics, which is the less mature area of business analytics in comparison with descriptive and predictive analytics.•Prescriptive analytics are positioned as ...the next step towards increasing data analytics maturity and leading to optimized decision making ahead of time.•The existing literature pertaining to prescriptive analytics is reviewed and prominent methods for its implementation are examined.•The article identifies research challenges and outlines directions for future research in the field of prescriptive analytics.
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.
Decision-making for manufacturing and maintenance operations is benefiting from the advanced sensor infrastructure of Industry 4.0, enabling the use of algorithms that analyze data, predict emerging ...situations, and recommend mitigating actions. The current paper reviews the literature on data-driven decision-making in maintenance and outlines directions for future research towards data-driven decision-making for Industry 4.0 maintenance applications. The main research directions include the coupling of decision-making with augmented reality for seamless interfacing that combines the real and virtual worlds of manufacturing operators; methods and techniques for addressing uncertainty of data, in lieu of emerging Internet of Things (IoT) devices; integration of maintenance decision-making with other operations such as scheduling and planning; utilization of the cloud continuum for optimal deployment of decision-making services; capability of decision-making methods to cope with big data; incorporation of advanced security mechanisms; and coupling decision-making with simulation software, autonomous robots, and other additive manufacturing initiatives.
In manufacturing enterprises, maintenance is a significant contributor to the total company’s cost. Condition based maintenance (CBM) relies on prognostic models and uses them to support maintenance ...decisions based on the predicted condition of equipment. Although prognostic-based decision support for CBM is not an extensively explored area, there exist methods which have been developed in order to deal with specific challenges such as the need to cope with real-time information, to predict the health state of equipment and to continuously update maintenance-related recommendations. The current work aims at providing a literature review for prognostic-based decision support methods for CBM. We analyse the literature in order to identify combinations of methods for prognostic-based decision support for CBM, propose a practical technique for selecting suitable combinations of methods and set the guidelines for future research.
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.
Purpose
– The purpose of this paper is to perform an extensive literature review in the area of decision making for condition-based maintenance (CBM) and identify possibilities for proactive online ...recommendations by considering real-time sensor data. Based on these, the paper aims at proposing a framework for proactive decision making in the context of CBM.
Design/methodology/approach
– Starting with the manufacturing challenges and the main principles of maintenance, the paper reviews the main frameworks and concepts regarding CBM that have been proposed in the literature. Moreover, the terms of e-maintenance, proactivity and decision making are analysed and their potential relevance to CBM is identified. Then, an extensive literature review of methods and techniques for the various steps of CBM is provided, especially for prognosis and decision support. Based on these, limitations and gaps are identified and a framework for proactive decision making in the context of CBM is proposed.
Findings
– In the proposed framework for proactive decision making, the CBM concept is enriched in the sense that it is structured into two components: the information space and the decision space. Moreover, it is extended in a way that decision space is further analyzed according to the types of recommendations that can be provided. Moreover, possible inputs and outputs of each step are identified.
Practical implications
– The paper provides a framework for CBM representing the steps that need to be followed for proactive recommendations as well as the types of recommendations that can be given. The framework can be used by maintenance management of a company in order to conduct CBM by utilizing real-time sensor data depending on the type of decision required.
Originality/value
– The results of the work presented in this paper form the basis for the development and implementation of proactive Decision Support System (DSS) in the context of maintenance.
In recent years, persuasive interventions for inducing sustainable mobility behaviours have become an active research field. This review paper systematically analyses existing approaches and ...prototype systems as well as field studies and describes and classifies the persuasive strategies used for changing behaviours in the domain of mobility and transport. We provide a review of 44 papers on persuasive technology for sustainable transportation aiming to (i) answer important questions regarding the effectiveness of persuasive technology for changing mobility behaviours, (ii) summarize and highlight trends in the technology design, research methods, strategies and theories, (iii) uncover limitations of existing approaches and applications, and (iv) suggest directions for future research.
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 ...analytics, and optimised decision making. The availability of massive sensor data streams over time opens new perspectives for extracting meaningful and timely insights from data-in-motion through streaming analytics. Logistics 4.0 is a relatively new field of research which demands the development of scalable and efficient software solutions and their deployment to successful real-life case studies. In this paper, we propose a software framework for streaming analytics in an edge-cloud computational environment aiming at covering the whole data analytics lifecycle in logistics processes and thus, advancing the evolution and realisation of the Logistics 4.0 concept. The proposed framework takes advantage of edge computing technologies, streaming analytics and proactive decision making in order to monitor, analyse and support decision making in the frame of Logistics 4.0. It is applied and evaluated in a maintenance service logistics use case from the aerospace industry.
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