Data-driven based HVAC optimisation approaches: A Systematic Literature Review

2021 ◽  
pp. 103678
Author(s):  
Maher Ala’raj ◽  
Mohammed Radi ◽  
Maysam F. Abbod ◽  
Munir Majdalawieh ◽  
Marianela Parodi
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Data Driven

Big data-driven strategic orientation in international marketing

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shahriar Akter ◽  
Md Afnan Hossain ◽  
Qiang (Steven) Lu ◽  
S.M. Riad Shams
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Systematic Literature Review ◽  
International Marketing ◽  
Strategic Orientation ◽  
Data Driven ◽  
Future Research ◽  
Content Type ◽  
Strategic Orientations ◽  
Marketing Activities

PurposeBig data is one of the most demanding topics in contemporary marketing research. Despite its importance, the big data-based strategic orientation in international marketing is yet to be formed conceptually. Thus, the purpose of this study is to systematically review and propose a holistic framework on big data-based strategic orientation for firms in international markets to attain a sustained firm performance.Design/methodology/approachThe study employed a systematic literature review to synthesize research rigorously. Initially, 2,242 articles were identified from the selective databases, and 45 papers were finally reported as most relevant to propose an integrative conceptual framework.FindingsThe findings of the systematic literature review revealed data-evolving, and data-driven strategic orientations are essential for performing international marketing activities that contain three primary orientations such as (1) international digital platform orientation, (2) international market orientation and (3) international innovation and entrepreneurial orientation. Eleven distinct sub-dimensions reflect these three primary orientations. These strategic orientations of international firms may lead to advanced analytics orientation to attain sustained firm performance by generating and capturing value from the marketplace.Research limitations/implicationsThe study minimizes the literature gap by forming knowledge on big data-based strategic orientation and framing a multidimensional framework for guiding managers in the context of strategic orientation for international business and international marketing activities. The current study was conducted by following only a systematic literature review exclusively in firms' overall big data-based strategic orientation concept in international marketing. Future research may extend the domain by introducing firms' category wise systematic literature review.Originality/valueThe study has proposed a holistic conceptual framework for big data-driven strategic orientation in international marketing literature through a systematic review for the first time. It has also illuminated a future research agenda that raises questions for the scholars to develop or extend theory in this area or other related disciplines.

scholarly journals Data-Driven Approaches for Circular Economy in Manufacturing for Digital Technologies: A Review of Current Research and Proposed Framework

2018 ◽  
Author(s):  
Okechukwu Okorie ◽  
Konstantinos Salonitis ◽  
Fiona Charnley ◽  
Mariale Moreno ◽  
Christopher Turner ◽  
...  
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Circular Economy ◽  
Industry 4.0 ◽  
Review Paper ◽  
Digital Technologies ◽  
Data Driven ◽  
Empirical Literature ◽  
Future Research ◽  
Current Trends

The Circular Economy has been of growing significance within academic, policymaking and industry groups. Latest developments in the field of Circular Economy has led to an expansion of CE studies focused on interrogating CE as a paradigm, its relationship with sustainability and concepts and definitions of the Circular Economy. Research has also identified the significant potential of applying circular approaches to areas of the economy, including manufacturing and Industry 4.0, which, with data, is enabling latest the advances in digital technologies. This is the first review paper to integrate the fields of CE and digital technologies resulting in a framework which provides directions for policymakers and guidance for future research. To achieve this, we conduct a systematic literature review of the empirical literature related to digital technologies, industry 4.0 and circular approaches, from the point of the 9 Rs. The systematic literature review (SLR) is based on peer-reviewed articles published between 2000-2018. The findings reveal that while research on the circular economy has been on an annual rise, research on digital technologies enabled circular economy is still relatively an untouched area of research across all nine (9) circular approaches. As such this is an area rife for further research. This paper also presents illustrative charts and graphs to summarize the current trends in circular economy research in manufacturing. From this, a framework for future circular economy research for manufacturing for digital technologies is proposed.

scholarly journals UNDERSTANDING USAGE DATA-DRIVEN PRODUCT PLANNING: A SYSTEMATIC LITERATURE REVIEW

2021 ◽  
Vol 1 ◽  
pp. 3289-3298
Author(s):  
Maurice Meyer ◽  
Ingrid Wiederkehr ◽  
Christian Koldewey ◽  
Roman Dumitrescu
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Success Factors ◽  
Data Driven ◽  
Physical Systems ◽  
Usage Phase ◽  
Product Planning ◽  
Decision Making Processes ◽  
Research Questions ◽  
Usage Data

AbstractCyber-physical systems (CPS) are able the collect huge amounts of data about themselves, their users, and their environment during their usage phase. By feeding these usage data back into product planning, manufacturers can optimize their engineering and decision-making processes. Despite promising potentials, most manufacturers still do not analyze usage data within product planning. Also, research on usage data-driven product planning is scarce. Therefore, this paper aims to identify the main concepts, advantages, success factors and challenges of usage data-driven product planning. To answer the corresponding research questions, a comprehensive systematic literature review is conducted. From its results, a detailed description of usage data-driven product planning consisting of six main concepts is derived. Furthermore, taxonomies for the advantages, success factors and challenges of usage data-driven product planning are presented. The six main concepts and the three taxonomies allow for a deeper understanding of the topic while highlighting necessary future actions and research needs.

scholarly journals Mapping the Landscape of Behavioral Theories: Systematic Literature Review

2019 ◽  
Vol 35 (2) ◽  
pp. 161-179 ◽  
Author(s):  
Heeseo Rain Kwon ◽  
Elisabete A. Silva
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Agent Based Modeling ◽  
Data Driven ◽  
Agent Based ◽  
Search Results ◽  
Behavioral Theories

The term “behavioral” has become a hot topic in recent years in various disciplines; however, there is yet limited understanding of what theories can be considered behavioral theories and what fields of research they can be applied to. Through a cross-disciplinary literature review, this article identifies sixty-two behavioral theories from 963 search results, mapping them in a diagram of four groups (factors, strategies, learning and conditioning, and modeling), and points to five discussion points: understanding of terms, classification, guidance on the use of appropriate theories, inclusion in data-driven research and agent-based modeling, and dialogue between theory-driven and data-driven approaches.

scholarly journals Systematic Literature Review on Data-Driven Models for Predictive Maintenance of Railway Track: Implications in Geotechnical Engineering

Geosciences ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 425
Author(s):  
Jiawei Xie ◽  
Jinsong Huang ◽  
Cheng Zeng ◽  
Shui-Hua Jiang ◽  
Nathan Podlich
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Ensemble Methods ◽  
Predictive Maintenance ◽  
Data Driven ◽  
Railway Track ◽  
Data Sets ◽  
Critical Factors ◽  
Data Types ◽  
Railway Industry

Conventional planning of maintenance and renewal work for railway track is based on heuristics and simple scheduling. The railway industry is now collecting a large amount of data with the fast-paced development of sensor technologies. These data sets carry information about the conditions of various components in railway track. Since just before the beginning of the 21st century, data-driven models have been used in the predictive maintenance of railway track. This study presents a systematic literature review of data-driven models applied in the predictive maintenance of railway track. A taxonomy to classify the existing literature based on types of models and types of applications is provided. It is found that applying the deep learning methods, unsupervised methods, and ensemble methods are the new trends for predictive maintenance of railway track. Rail geometry irregularity, rail head defect, and missing rail components detection were the top three most commonly considered issues within the application of data-driven models. Prediction of rail breaks has received increasing attention in the last four years. Among these data-driven model applications, the collected data types are the most critical factors which affect selecting suitable models. Finally, this study discusses upcoming challenges in the predictive maintenance of railway track.

Industry 4.0 as a data-driven paradigm: a systematic literature review on technologies

2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Cristina Orsolin Klingenberg ◽  
Marco Antônio Viana Borges ◽  
José Antônio Valle Antunes Jr
Keyword(s):  
Content Analysis ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Industry 4.0 ◽  
New Technologies ◽  
Journal Citation Report ◽  
Data Driven ◽  
Process Data ◽  
Content Type ◽  
Enabling Technologies

Purpose The purpose of this paper is to identify current technologies related to Industry 4.0 and to develop a rationale to enhance the understanding of their functions within a data-driven paradigm. Design/methodology/approach A systematic literature review of 119 papers published in journals included in the Journal Citation Report (JCR) was conducted to identify Industry 4.0 technologies. A descriptive analysis characterizes the corpus, and a content analysis identifies the technologies. Findings The content analysis identified 111 technologies. These technologies perform four functions related to data: data generation and capture, data transmission, data conditioning, storage and processing and data application. The first three groups consist of enabling technologies and the fourth group of value-creating technologies. Results show that Industry 4.0 publications focus on enabling technologies that transmit and process data. Value-creating technologies, which apply data in order to develop new solutions, are still rare in the literature. Research limitations/implications The proposed framework serves as a structure for analysing the focus of publications over time, and enables the classification of new technologies as the paradigm evolves. Practical implications Because the technical side of the new production paradigm is complex and represents an evolving field, managers benefit from a simplified and data-driven approach. The proposed framework suggests that Industry 4.0 should be approached by looking at how data can create value and at what role each technology plays in this task. Originality/value The study makes a direct link between Industry 4.0 technologies and the key resource of this revolution, i.e. data. It provides a rationale that not only establishes relationships between technologies and data, but also highlights their roles as enablers or creators of value. Beyond showing the current focus of Industry 4.0 publications, this paper proposes a framework that is useful for tracking the evolution of the paradigm.

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