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This paper reviews the synergy of Industry 4.0 and additive manufacturing (AM) and discusses the integration of data-driven manufacturing systems and product service systems as a key component of the Industry 4.0 revolution. This paper aims to highlight the potential effects of Industry 4.0 on AM via tools such as digitalisation, data transfer, tagging technology, information in Industry 4.0 and intelligent features.

In successive phases of industrialisation, there has been a rise in the use of, and dependence on, data in manufacturing. In this review of Industry 4.0 and AM, the five pillars of success that could see the Internet of Things (IoT), artificial intelligence, robotics and materials science enabling new levels of interactivity and interdependence between suppliers, producers and users are discussed. The unique effects of AM capabilities, in particular mass customisation and light-weighting, combined with the integration of data and IoT in Industry 4.0, are studied for their potential to support higher efficiencies, greater utility and more ecologically friendly production. This research also illustrates how the digitalisation of manufacturing for Industry 4.0, through the use of IoT and AM, enables new business models and production practices.

The discussion illustrates the potential of combining IoT and AM to provide an escape from the constraints and limitations of conventional mass production whilst achieving economic and ecological savings. It should also be noted that this extends to the agile design and fabrication of increasingly complex parts enabled by simulations of complex production processes and operating systems. This paper also discusses the relationship between Industry 4.0 and AM with respect to improving the quality and robustness of product outcomes, based on real-time data/feedback.

This research shows how a combined approach to research into IoT and AM can create a step change in practice that alters the production and supply paradigm, potentially reducing the ecological impact of industrial systems and product life cycle. This paper demonstrates how the integration of Industry 4.0 and AM could reshape the future of manufacturing and discusses the challenges involved.

This paper aims to discuss the burgeoning area of radio‐frequency identification (RFID), which uses radio waves to automatically identify and track individual items.

The paper begins by outlining the benefits and utility of RFID technology, through an examination of its technological characteristics, current applications, state of the market and future trends. The paper focuses on consumer applications for RFID, such as sports, lifestyle and leisure, and personal safety. It then explores some of the important implications of the technology and concludes with considerations of a socio‐ethical nature, including concerns related to individual privacy and human development.

The paper finds that RFID technology has tremendous potential to ease life and to improve the human condition. Still, further innovation and industrial deployment of this technology should be done in parallel with a careful exploration of all related aspects. No one can deny that the expansion of such “anywhere, anytime” communication technologies, for “anyone and anything”, will bring about increased convenience, greater access and a whole host of innovative applications and services. However, the capacity of these technologies to impact human lives (private and public) will grow correspondingly. This brings to mind the notion of the “Faustian Bargain” in the context of technological change: while a given technological advancement may improve many aspects of daily life, it also runs the risk of reducing the advantages of earlier developments or earlier ways of life. It is only through an increasing awareness of this risk that humanity (and societal progress) can be preserved in what has become an ever‐expanding sea of technology and automation.

The study presents an overview of technological benefits while expressing a balanced approach as to the potential concerns and implications.

The impact of natural disasters on human life, the environment and the flora and fauna can be contained to large extent by intelligent human intervention. This study introduces the human capabilities which can be extended considerably with technology. Internet of things have always provided opportunities for predicting and managing manmade/natural disasters. The extreme reason for causing soil erosions, landslides, cloud bursts, floods, etc., are due to excessive rainfall. However, the flood is one of the most happening natural disasters, following Bihar to be the most affected region due to floods. Lots of lives and properties were lost and damaged.

This implemented researchers to introduce an advanced solution for such calamities. Expectations were developed that it would signalize authority as early as possible so that advanced measures are taken before the effect. The lack of sensing or alarming technology in India pushed researchers to develop a model using the Android app that basically detected the upcoming flood and other calamities.

Most importantly the entire model was programmed with IoT and its techniques so that the response is quicker and more accurate.

This research study is original.

The purpose of this paper is to identify cross-country differences in socio-economic sustainability, which are operationalized as relative efficiency of economic and social impacts of hyperconnectivity (usage intensity of information and communication technologies (ICT) devices). The authors have a particular interest in the emerging economies because they enjoy outstanding growth rates and prospects for market expansion, and have undertaken significant economic reforms and, thus, should be expected to lead other two groups in the efficiency of transforming hyperconnectivity into sustainable growth.

The authors use canonical correlation analysis (CCA) to confirm the existence of a strong and significant relationship between hyperconnectivity drivers and socio-economic outcomes on a country level. The authors test the difference in efficiency of transforming hyperconnectivity into socio-economic sustainability among three groups of countries: advanced, emerging and developing nations using data envelopment analysis (DEA).

The findings indicate that indeed emerging economies were the most effective ones to use infrastructure and digital content followed by developing and advanced countries, respectively. However, relatively better affordability of technologies in the emerging countries did not produce as much socio-economic impact as compared with developing nations. Favorable legislative conditions and high individual ICT usage in advanced economies did not contribute much to socio-economic sustainability either.

One of the limitations of this study stems from the classification of the countries. World Economy Forum and International Monetary Fund resources are utilized for the economy categories, but their basis for classification of counties is rather subjective. Lack of existing comparative efficiency studies on a country level prevents effective benchmarking of the results.

Since the key vehicles of transforming technology into socio-economic impact are organizations, they should design and implement an appropriate organizational architecture which would facilitate this transformation in the emerging markets more effectively.

In a climate of increasing public accountability, governments have been increasingly urged to introduce good administrative practices and performance standards to enable efficient utilization of their resources and enhance social implications within and across countries.

Although the impact of ICT on macro-economic development has been previously studied, the efficiency of this impact was not. Using CCA as a complementary tool for DEA approach in this study constitutes a methodological contribution to existing DEA research, mostly done in the area of operations management. Using DEA on a country level is a novel approach which contributes to the realm of application of this methodology.

Due to the rapid growth of blockchain technology in recent years, the fusion of blockchain and the Internet of Things (BIoT) has drawn considerable attention from researchers and industrial practitioners and is regarded as a future trend in technological development. Although several authors have conducted literature reviews on the topic, none have examined the development of the knowledge structure of BIoT, resulting in scattered research and development (R&D) efforts.

This study investigates the intellectual core of BIoT through a co-citation proximity analysis–based systematic review (CPASR) of the correlations between 44 highly influential articles out of 473 relevant research studies. Subsequently, we apply a series of statistical analyses, including exploratory factor analysis (EFA), hierarchical cluster analysis (HCA), k-means clustering (KMC) and multidimensional scaling (MDS) to establish the intellectual core.

Our findings indicate that there are nine categories in the intellectual core of BIoT: (1) data privacy and security for BIoT systems, (2) models and applications of BIoT, (3) system security theories for BIoT, (4) frameworks for BIoT deployment, (5) the fusion of BIoT with emerging methods and technologies, (6) applied security strategies for using blockchain with the IoT, (7) the design and development of industrial BIoT, (8) establishing trust through BIoT and (9) the BIoT ecosystem.

We use the CPASR method to examine the intellectual core of BIoT, which is an under-researched and topical area. The paper also provides a structural framework for investigating BIoT research that may be applicable to other knowledge domains.

The purpose is to assess Industry 4.0 (I4.0) readiness index using fuzzy logic and multi-grade fuzzy approaches in an automotive component manufacturing organization.

I4.0 implies fourth industrial revolution that necessitates vital challenges to be dealt with. In this viewpoint, this article presents the evaluation of I4.0 Readiness Index. The evaluation includes two levels with appropriate criteria and factors. Fuzzy logic approach is used for assessment. Furthermore, the results obtained from fuzzy logic have been benchmarked with multi-grade fuzzy approach.

The proposed assessment model has successfully utilized fuzzy logic approach for assessment of I4.0 readiness index of automotive component manufacturing organization. Based on fuzzy logic approach, readiness index of I4.0 has been found to be (4.74, 6.26, 7.80) which is further benchmarked using multi-grade fuzzy approach. Industry 4.0 readiness index obtained from multi-grade fuzzy approach is 6.258 and thus, validated. Furthermore, 20 weaker areas have been identified and improvement suggestions are provided.

The assessment module include two levels (Six Criteria and 50 Factors). The assessment model could be expanded based on advancements in industrial developments. Therefore, future researchers could utilize findings of the readiness model to further develop multi-level assessment module for Industry 4.0 readiness in organization. The developed readiness model helped researchers in understanding the methodology to assess I4.0 readiness of organization.

The model has been tested with reference to automotive component manufacturing organization and hence the inferences derived have practical relevance. Furthermore, the benchmarking strategy adopted in the present study is simple to understand that makes the model unique and could be applied to other organizations. The results obtained from the study reveal that fuzzy logic-based readiness model is efficient to assess I4.0 readiness of industry.

The development of model for I4.0 readiness assessment and further analysis is the original contribution of the authors. The developed fuzzy logic based I4.0 readiness model indicated the readiness level of an organization using I4RI. Also, the model provided weaker areas based on FPII values which is essential to improve the readiness of organization that already began with the adoption of I4.0 concepts. Further modification in the readiness model would help in enhancing I4.0 readiness of organization. Moreover, the benchmarking strategy adopted in the study i.e. MGF would help to validate the computed I4.0 readiness.

The tightening competition and performance pressure in companies often leave no time or space for the assessment of business impacts of different investments and projects. In addition, in many cases the assessment may be challenging and there is no experience available to undertake it. Despite that companies often commit to different projects and investments without careful planning and vision of the costs it may cause. The purpose of this paper is to create a decision support system in order to facilitate and increase the assessment of business impacts of different investments concerning to machine-to-machine (M2M) systems.

The created decision support system is composed of cost-benefit analysis including several investment decision methods. In order to deepen the understanding on it, the system was applied to two cases from the M2M business.

During the study it was found that different financial metrics might give contradictory results when deciding whether to undertake an investment. In addition, a significant finding was how much some variables may have significance to the eligibility of an investment than others. The study also gave understanding how long payback time can be and how risky the investments might be in different M2M applications.

The study describes the created decision support system and it is applied to two different M2M applications. The system provides a comprehensive combination of different financial metrics, which will help any manager make decisions whether an investment is eligible or not.

This article’s purpose is to develop The Model for Smart, Self-learning and Adaptive Resilience Building (SARB).

Products and patents of methods and systems analysis was carried out in the fields of BIM application, Smart, Self-learning and Adaptive Resilience Building. Based on other researchers’ findings, The SARB Model was proposed.

Analysis of the literature showed that traditional decisions on the informational modelling do not satisfy all the needs of smart building technologies owing to their static nature. The SARB Model was developed to take care of its efficiency from the brief stage to the end of its service life.

The SARB Model was developed to take care of its efficiency from the brief stage to the end of its service life. The SARB Model does have some limitations: (1) the processes followed require the collection of much unstructured and semi-structured data from many sources, along with their analyses to support stakeholders in decision-making; (2) stakeholders need to be aware of the broader context of decision-making and (3) the proposal is process-oriented, which can be a disadvantage during the model’s implementation.

Two directions can be identified for the practical implications of the SARB Model. The initial expectation is the widespread installation of SARB Model within real estate and construction organisations. Furthermore, development of the SARB Model will be used to implement the ERASMUS+ project, “Advancing Skill Creation to ENhance Transformation—ASCENT” Project No. 561712-EPP-1-2015-UK-EPPKA2-CBHE-JP.

The practical implications of this paper are valuable.