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A conceptual model of knowledge dynamics in the industry 4.0 smart grid scenario
Abstract
Technological advancements are giving rise to the fourth industrial revolution – Industry 4.0 – characterized by the mass employment of smart objects in highly reconfigurable and thoroughly connected industrial product-service systems. The purpose of this paper is to propose a theory based knowledge dynamics model in the smart grid scenario that would provide a holistic view on the knowledge-based interactions among smart objects, humans, and other actors as an underlying mechanism of value co-creation in Industry 4.0. A multi-loop and three-layer – physical, virtual, and interface – model of knowledge dynamics is developed by building on the concept of ba – an enabling space for interactions and the emergence of knowledge. The model depicts how big data analytics are just one component in unlocking the value of big data, whereas the tacit engagement of humans-in-the-loop – their sense-making and decision making – is needed for insights to be evoked from analytics reports and customer needs to be met.
... In recent times, manufacturers have been challenged to create value for customers using the growing interconnectivity between technology, objects and people, derived from Industry 4.0 (I4.0) (Lepore et al., 2021). In fact, I4.0 technology advances improved machines' capability to identify non-obvious, hidden information and patterns (Dragicevic et al., 2019). Even more, I4.0 have changed how knowledge is developed in companies and they are constantly demanding for new managerial skills to facilitate learning (Lepore et al., 2021). ...
... In the past decade, significant publications about KM from different perspectives have been published (Caputo et al., 2019;Dragicevic et al., 2019). Among them, Abubakar et al. (2019) proposed a theoretical framework composed of six KM processes, namely 'creation', 'capture', 'organisation', 'storage', 'dissemination' and 'application'. ...
... By different researchers (Li et al., 2019;Schniederjans et al., 2020;Cimini et al., 2020) I4.0 has been found to be a key element to create new knowledge coming from the use of new technologies in manufacturing processes and also from data gathered from suppliers and customers that become active partners in the design process. The use of I4.0 technologies has transformed industries in a broad way, however, companies still need to manage multiple levels of knowledge related to the different technologies and business processes (Dragicevic et al., 2019;Cimini et al., 2020 (2019) and Abubakar et al. (2019) have studied the role of external interactions of KM with suppliers and customers for sustaining competitive advantage. However, the full potential of technologies has not been exploited due to the higher integration of different information systems and many I4.0 technologies (Capestro and Kinkel, 2020). ...
This paper aims at investigating the potential of Knowledge Management (KM) as a mediator on the association between Industry 4.0 technologies (I4.0) and operational performance. For that, we gathered data through a survey with 112 managerial-level employees of manufacturers located in Brazil. Information was then analysed by means of multivariate data techniques. This research found that there is indeed a direct and positive relationship between I4.0 technologies, specific KM processes and operational performance. However, our findings reveal that a general approach to KM cannot be defined and specific reflections must be developed with reference to each company, context or situation.
... Those principles tend to guide individuals' behaviours and decisions, contributing to a cultural change in the organisation towards the Fourth Industrial Revolution (Hermann et al., 2016;Tortorella et al., 2021a). Hence, it is expected that the combination of effective KM practices and I4.0 could lead to more innovative organisations (Cheah & Tan, 2021;Dragicevic et al., 2019). However, unless the proper behaviours are in place, digitalisation efforts are less likely to support the improvement of innovation performance. ...
... However, the extensive digitalisation of organisations may lead to complex and conflicting implications that can eventually impair the effects of KM practices on innovation performance (Dragicevic et al., 2019). For instance, one of the digitalisation's major consequences is that tacit knowledge tends to become scarcer and, hence, aggravates the difficulty of handling this type of knowledge. ...
... Our regression models 3A and 3B showed R 2 values of 0.689 and 0.646, respectively, being relatively close to the threshold of 0.6 suggested above. Innovation performance in organisations, represented here by PDI and PRI, has been frequently associated with the level of technology integration and knowledge management practices (Cheah & Tan, 2021;Dragicevic et al., 2019). Thus, we argue that the R 2 values found in our models are reasonable in light of the common belief. ...
This paper aims at examining the role played by Industry 4.0 (I4.0) on the relationship between knowledge management (KM) practices (i.e., knowledge acquisition, knowledge dissemination, and responsiveness to knowledge) and innovation performance (represented by process and product innovation). For that, 153 practitioners from manufacturing firms in India and Brazil were surveyed. The data were analyzed through multivariate data techniques. This study was grounded on the concepts from the socio-technical systems theory. Our findings indicated that I4.0 design principles positively moderate the relationship between KM practices and innovation performance. In particular, this moderation seems to be more prominent for product innovation performance, although it was also found for process innovation performance. I4.0 design principles determine the expected mindset and behaviors in companies undergoing digital transformation. Our research showed that the effect of KM practices on innovation performance may be boosted when I4.0 design principles are extensively integrated into organizations. Although the separate implementation of I4.0 design principles and KM practices may yield improvements in the innovation performance, we evidenced that their joint implementation is likely to offer a balanced combination between socio and technical elements.
... Keywords: Knowledge management, Digital transformation, Digital technologies, Deep learning, Digital wallets, International Journal of Education and Knowledge Management (IJEKM) https://rpajournals.com/ijekm need to analyse the thick data (qualitative). There remains an associate degree of an inherent risk of how decision-makers use, interpret, and apply strategic data effectively in their enterprises to realise and sustain competitive advantage, which is still not clear (Edwards and Taborda, 2019;Dragicevic, 2019). Existing literature suggests that decision-makers usually have a limited understanding of the role of strategic data management and its influence on the competitive advantages of the enterprise (Grant, 1996;Zack, 1999;von Krogh, Nonaka, and Aben, 2001;Casselman and Samson, 2007;Choi et al., 2008;Venkitachalam and Willmott, 2015;Venkitachalam and Willmott, 2016). ...
... Oversight of strategic data management within the context of the growing stress of digital transformation across numerous industries worldwide generates substantial challenges, such as non-adaptive and dysfunctional data processes, including but not limited to the creation, transfer, use, and application of data (Heinze et al., 2018). Consequently, such inefficiencies could result in reinvention and loss of data assets at substantial costs to the enterprise (Venkitachalam and Willmott, 2016;Dragicevic et al., 2019;Bartik et al., 2020). Hence, it is enthralling that decision-makers develop a better understanding of the intersection between strategic knowledge management and the need to adopt a digital transformation at the enterprise. ...
... Inadvertence towards strategic knowledge management in the growing emphasis on digital transformation across diverse industries can present enormous concerns like non-adaptive and dysfunctional knowledge processes such as creation, transfer, use, and application. Consequently, such inadvertence can result in reinvention and loss of knowledge assets and massive organisations costs (Venkitachalam and Willmott, 2016;Dragicevic et al., 2019). Business decision-makers must beware that not all innovative ideas and digitalisation strategies are suitable or ideal. ...
Digital transformations challenge enterprises' adaptability, development, technology integration, and resilience to evolve their business models. Accelerated by the COVID-19 pandemic, businesses are increasingly characterised by a pervasive role of business digitalisation redefining organisations' management of customer experience efficiencies. It redefines value creation strategies. This paper argues that enterprises should rethink the creation and delivery of value to their customers, not based on technologies but on knowledge management strategies instead. As technologies transform business processes, KM is a catalyst in the evolving nature, knowledge assets, and transformation preparedness of enterprises' value drivers. Integrating Osterwalder and Pigneur's Business Model Canvas, Parmar et al.'s five patterns for value creation, and Goncalves' cloud enterprise transcoding proxy, a few cases are briefly discussed. Digital transformation drivers and the role of KM for strategic relevance are underlined through digitalised knowledge processes and customer-centric global marketing strategies to access and manage resources, core competencies, and dynamic capabilities. This research raises awareness of adopting KM as a catalyst for digital business transformation's role as a strategy to respond to the acceleration of digitalization during and after the COVID-19 pandemic, along with knowledge management principles.
... Indeed, digital technologies can provide timely access, tremendous possibilities, as well as challenges for organisations. Considering the rising influence and dependence on digital technologies and applications in many different sectors/industries, the relevance and importance of managing strategic knowledge in organisations has a more significant impact than ever before in the growth and sustenance of organisational competitiveness and value (Dragicevic et al., 2019;Venkitachalam and Willmott, 2015). Besides the significance of strategic knowledge, the extant literature on digitally connected conceptualisations [e.g. ...
... And somebody then has to do something new or different as a result of the new insights, or it won't have been done to any purpose". However, the inherent risk of how managers apply and use SKM effectively in their organisations to achieve and sustain competitive advantage is not entirely clear (Edwards and Taborda, 2016;Dragicevic et al., 2019). ...
... Rising evidence of political, economic and social changes, rapid technological innovations and developing dynamics in the contemporary business environment have contributed to the impact and relevance of SKM in organisations (Dragicevic et al., 2019;Venkitachalam and Willmott, 2015). The existing literature contends that managers responsible for decisionmaking often have a curtailed understanding of the role of SKM and their influence on the competitiveness of organisations (Casselman and Samson, 2007;Choi et al., 2008;Grant, 1996;Venkitachalam and Willmott, 2015;Venkitachalam and Willmott, 2016;von Krogh et al., 2001;Zack, 1999). ...
... Indeed, digital technologies now offer ubiquitous and timely access to a vast array of business and market resources, opening significant prospects across the globe and, with it, several business challenges as well. Considering the rising influence and dependence on digital technologies and applications across global markets and business sectors/industries, the ability and level of connectedness of managed business data and strategies are encompassing a more significant impact on the growth and sustainability of competitive advantages (Venkitachalam and Willmott, 2015;Dragicevic et al., 2019). Besides the importance of strategic data, the extant literature on digitally connected conceptualisations, such as big and thick data, cloud computing, deep learning, Internet of Things (IoT), augmented reality, virtual worlds, digital wallets, applied business analytics, highlights several challenges associated with attaining and maintaining business value for customers and stakeholders (Pauleen and Wang, 2017;Uden and He, 2017). ...
... For that, we need to analyse the thick data (qualitative). There remains an associate degree of an inherent risk of how decision-makers use, interpret, and apply strategic data effectively in their enterprises to realise and sustain competitive advantage, which is still not clear (Edwards and Taborda, 2019;Dragicevic, 2019). ...
... Oversight towards strategic data management within the context of the growing stress of digital transformation across numerous industries worldwide generates substantial challenges, such as non-adaptive and dysfunctional data processes, including but not limited to the creation, transfer, use, and application of data (Heinze et al., 2018). Consequently, such inefficiencies could result in reinvention and loss of data assets at substantial costs to the enterprise (Venkitachalam and Willmott, 2016;Dragicevic et al., 2019). Hence, it is enthralling that decision-makers develop a better understanding of the intersection between strategic knowledge management and the need to adopt a digital transformation at the enterprise. ...
Accelerated by the COVID-19 pandemic, businesses are increasingly characterised by a pervasive role of business digitalisation redefining organisations' management of customer experience efficiencies. Digital transformation challenges enterprises' adaptability, development, technology integration, and resilience in evolving their business models. It redefines value creation strategies. This paper argues that enterprises should rethink the creation and delivery of value to their customers, not based on technologies but knowledge management strategies instead. As technologies transform business processes, KM is a catalyst in the evolving nature, knowledge assets, and transformation preparedness of enterprises' value drivers. Integrating Osterwalder and Pigneur's Business Model Canvas, Parmar et al.'s five patterns for value creation, and Goncalves' cloud enterprise transcoding proxy, few cases are briefly discussed. Digital transformation drivers and the role of KM for strategic relevance are underlined through digitalised knowledge processes and customer-centric global marketing strategies to access and manage resources, core competencies, and dynamic capabilities.
... A Indústria 4.0, também conhecida como Smart Manufacturing, Fourth Industrial Revolution, Smart Industry e Integrated industry (SILVA et al., 2019;HERMANN et al., 2016), teve sua origem nos avanços tecnológicos (DRAGICEVIC et al., 2019). Alguns autores apontam que um projeto de manufatura avançada, apoiada pelo governo alemão no ano de 2011, também influenciou o surgimento desse conceito que, desde então, tornou-se muito utilizado (KAGERMANN et al., 2011; e que, de acordo com Lu (2017), refere-se à quarta revolução industrial projetada para a produção descentralizada, visando obter personalização e eficiência de recursos (BRETTEL et al. 2014). ...
... Entretanto, o BDA é apenas uma análise que descobre o valor do BD, o envolvimento do conhecimento humano na tomada de decisão ainda é muito necessário para que as anseios dos clientes possam ser conhecidos. Essa colaboração é conhecida como Humans-in-the-loop (DRAGICEVIC et al., 2019). A Indústria 4.0 busca garantir que as pessoas envolvidas sejam capacitadas e com foco no ser humano ao invés de dividir e desumanizar (SCHWAB, 2017). ...
... A revisão bibliométrica possibilitou a visualização de alguns pontos a serem considerados, como o campo de estudo do ciclo de vida do produto, que gera uma grande quantidade de dados que precisam ser analisados (ZHANG et al., 2017), a necessidade do desenvolvimento de modelos de compartilhamento e visualização de dados (RAMZI et al., 2019) e o incentivo a participação humana dentro da Indústria 4.0 (DRAGICEVIC et al., 2019;SCHWAB, 2017), entre outros aspectos. ...
Big Data (BD) is one of the pillars of Industry 4.0, currently considered the fourth industrial
revolution. Given a large amount of data involving the connections between the two areas of study, coupled with the constantly changing technological landscape, there are several opportunities, challenges, and technologies to be explored continuously. The objective of this research is to perform a literature review in a bibliometric format in order to identify the interactions between Industry 4.0 and BD, what the analyzes and tools used are, and to point out the opportunities and challenges found by several authors. The study provided insight into the technologies used, programming languages, and data analysis, as well as opportunities and challenges for future studies, such as new data sharing models, increased Humans-in-the-loop engagement, and other factors presented throughout the course of the article.
... • IT/cloud-based BMs: the result of technological enablers in I4.0, which can directly connect customers to a company (Müller et al., 2018). Knowledge creation and management are essential issues here (Dragicevic et al., 2020), as well as the use of big data (Lee, 2018) and cloud computing (Wu et al., 2020). • Service-based BMs: BMs based on product-service systems, that is, the servitization of BMs that originally were more focused on selling products. ...
... Every company can use these connections to explore new business opportunities, even if they are not directly linked to its core business. Knowledge creation and management are essential issues here (Dragicevic et al., 2020), as well as the use of 'Big Data' (Lee, 2018) and cloud computing (Wu et al., 2020). Customer experience represents the efforts to provide more than one product to the customer in terms of design, associated service, and communication throughout the product lifecycle. ...
This paper proposes a new Readiness Model, 3D-CUBE, to assess the current state of manufacturing companies in the digital transformation context. Using a systematic literature review with 8-Steps-Search-Flow and a hypothetical-deductive framework (considering maturity as an 'input' enabler and not as an 'output'), the best information of 63 existing models was selected from 486 studies found in 10 databases. The 3D-CUBE was elaborated, with 3 dimensions (X = Organizational , Y = Technological , and Z = Process Maturity), 6 sub-dimensions, and 21 elements, including a scale 0-5 to assess the company readiness level. For the company’s Data Collection, a 3D-CUBE Questionnaire was developed, which provides a radar graph and calculates the company’s score with a readiness vector R=(X,Y,Z). Based on the existing model’s shortcoming, 3D-CUBE is a new contribution to this research stream, to help companies in getting ready for Industry 4.0. .
... In particular, these studies have pointed out that the data itself does not generate value, but the firm's capability of harnessing it and the manner of data management are the crucial criteria Müller & Jensen, 2017;Zeng & Glaister, 2018). Moreover, regarding big data as the main driver of the emergence of the "value co-creation" paradigm, some studies have tried to strengthen the concept of value co-creation from the point of view of an organization, customers, and other third parties (Dragicevic et al., 2020;Jayashankar et al., 2019;Kunz et al., 2017;Line et al., 2020;Xie et al., 2016). ...
... The first research stream, primarily through empirical research, examines the relationship between the dynamics capabilities enabled by big data analytics technologies on competitive advantage and firm performance (Bozic & Dimovski, 2019;Conboy et al., 2020;Côrte-Real et al., 2017;Mikalef et al., 2018Mikalef et al., , 2019Mikalef et al., , 2020Wang & Hajli, 2017). The second research stream tried to broaden the strategic theories and research frameworks related to the interactions and changes between knowledge management and big data analytics ( Corte-Real et al., 2020;Dragicevic et al., 2020;Khan & Vorley 2017;Pauleen & Wang 2017;Zeng & Glaister 2018). Finally, the firms' adoption of open innovation strategy and ecosystem is another field of study for creating value from big data analytics (Trabucchi et al., 2018;Urbinati et al., 2019;Zeng & Glaister 2018). ...
The emergence of big data is a radical shift in the business context, leading to a change in value creation and capture. This phenomenon is a newborn concept in the business and management literature confirmed by the growing number of publications over recent years. This paper presents an updating comprehensive bibliometric analysis to describe and assess the scientific landscape of value creation and capture based on leveraging big data in the literature. Bibliometrix and VOSviewer were selected as software tools for descriptive and network bibliometric analysis based on the Web of Science Core Collection database from 2011 till 2020. By implementing bibliometric analysis such as analysis of citations and co-occurrence of keywords, we have recognized the most prominent and influential authors, papers, journals, countries, and four potential clusters of current trends in studies. These four trends of value creation and capture from big data studies are: 1) strengthening the basic knowledge of value creation in the big data era, 2) data-driven business model and value capturing, 3) dynamic capabilities and centrality of knowledge, and 4) digital transformation of the service industry. Finally, by identifying the existing research gaps, future research directions in each cluster are demonstrated.
... Industry 4.0, first proposed in 2011 [57], refers to the intelligent production process in the manufacturing industry, which mainly covers the Internet of things (IoT), cloud computing, big data, artificial intelligence, etc. Industry 4.0 is characterized by the extensive use of intelligent objects in highly reconfigurable and fully connected industrial product service systems [58], which has brought unprecedented damage to all traditional production/service systems and business models (value chains) and accelerated the demand for activity redesign and digitization [59][60][61]. With the expansion of these emerging or disruptive technologies, the disruptive and transformative wave of industry 4.0 has incredibly transformed many industries such as education, energy, agriculture, and healthcare. ...
... To sum up, [57], refers to the intelligent production process in the manufacturing industry, which mainly covers the Internet of things (IoT), cloud computing, big data, artificial intelligence, etc. Industry 4.0 is characterized by the extensive use of intelligent objects in highly reconfigurable and fully connected industrial product service systems [58], which has brought unprecedented damage to all traditional production/service systems and business models (value chains) and accelerated the demand for activity redesign and digitization [59][60][61]. With the expansion of these emerging or disruptive technologies, the disruptive and transformative wave of industry 4.0 has incredibly transformed many industries such as education, energy, agriculture, and healthcare. ...
This paper aims to summarize the publishing trends, current status, research topics, and frontier evolution trends of health technology between 1990 and 2020 through various bibliometric analysis methods. In total, 6663 articles retrieved from the Web of Science core database were analyzed by Vosviewer and CiteSpace software. This paper found that: (1) The number of publications in the field of health technology increased exponentially; (2) there is no stable core group of authors in this research field, and the influence of the publishing institutions and journals in China is insufficient compared with those in Europe and the United States; (3) there are 21 core research topics in the field of health technology research, and these research topics can be divided into four classes: hot spots, potential hot spots, margin topics, and mature topics. C21 (COVID-19 prevention) and C10 (digital health technology) are currently two emerging research topics. (4) The number of research frontiers has increased in the past five years (2016–2020), and the research directions have become more diverse; rehabilitation, pregnancy, e-health, m-health, machine learning, and patient engagement are the six latest research frontiers.
... Finally, the fourth industrial revolution is also called industry 4.0 because of the introduction of the German term "Industrie 4.0" in 2011 by Hannover Fair, and equivalent to what General Electric called industrial internet in the United States. Established conceptually exante, therefore previously, consists of the adoption of cyber-physical systems in the industry, in a phenomenon still in its initial phase in the beginning of the 21st century (Dragicevic, Ullrich, Tsui, & Gronau, 2019;Ghobakhloo, 2018;Kagermann et al., 2013). ...
... During the literature review stage, special attention was given to productions that has proposed models that at least partially has some intersection with that defined as objective for this present research. A total of 10 main models have been found attending that criteria, as described on Table 1 A special highlight is due to the work "A conceptual model of knowledge dynamics in the industry 4.0 smart grid scenario" (Dragicevic et al., 2019), which lies pretty close to the objective of the present research, not reaching it by the fact that it considered only a very specific sector (energy) and, in addition, mainly focused on the client perspective of the supply chain without broadly addressing more wide aspects, challenges or strategies for KM along the Supply Chain as a hole. ...
Organizational Knowledge Management (OKM) has a great potential to contribute to companies and nations. The highest complexity of its application may be found in the scope of the Supply Chain (SC), due to being composed of numerous interrelated organizations, often with quite different characteristics. In addition, the current context of the fourth Industrial Revolution, also called Industry 4.0 (I4.0) that, when occurring within SC, gave birth to what has been called Supply Chain 4.0 (SC4.0), the context in which even more complex challenges to KM could be expected. Since scientific investigations at the intersection of these themes (KM and SC4.0) are still scarce, this research conducts a systematic literature review and a content analysis, both aiming to consolidate the related state-of-the-art of scientific development, capable of sustaining further advances in the field to tackle its new and challenging aspects. It complements and expands previous literature reviews in terms of its focus on SC4.0. In addition, it proposes, grounded in the results achieved, a conceptual model for KM in the context of SC4.0. It was identified that I4.0 brought important changes to the management of supply chains of the Industry 4.0 era and KM within it, although human factor-mainly behavior and relationships-remain a central aspect despite the increased adoption of a broad range of new technologies. Factors that enhance or limit KM in SC4.0 were also discussed and summarized, as well as the main relationships between them. In addition, gaps, limitations, and opportunities for future research are presented.
... It is characterized by a digital, physical and biological world supported by emerging technologies such as 3D printing, artificial intelligence, the internet of things, and robotics (Philbeck and Davis, 2018;de Sousa Jabbour et al., 2018b;Despeisse et al., 2017). All these technologies are capable of harnessing big data capabilities to help reuse, recycle and reduce the use of resources, thus supporting the objectives of the circular economy (Dragicevic et al., 2020;Nascimento et al., 2019;Müller et al., 2018;de Sousa Jabbour et al., 2018a). In today's world, an incredible amount of data is generated by various connected objects. ...
... To address this, cities are utilizing smart grids, where with the help of big data analytics, they can manage distribution efficiency and promote effective energy solutions with minimal waste (Iqbal et al., 2020;Rathore et al., 2018). Smart grids can obtain the real time requirements of consumers at different locations and balance the supply-demand equation to further optimize operations and upstream energy distribution (Dragicevic et al., 2020). The same concept can be applied to the efficient management of resources such as batteries for electric vehicles in Shenzhen city, where it can be decided whether the batteries need to be owned, rented or charged at traditional gas stations. ...
This study is focused on presenting a unique landscape for big data-enabled circular economy that involves stakeholders as important decision makers. This research is designed based on five case studies from emerging markets with a focus on circular models to propose a framework for large scale decision making. In these cases, different linear economy problems are addressed that further utilizes the integration of big data and large-scale group decision making by stakeholders to achieve circularity. The findings of our study indicate a four-step design (enabling technologies, business significance, deriving value, and circular goals) to implement the 10R's of the circular economy through emerging technologies such as big data and related mobile applications along with cloud-based platforms. The study highlights how cases from emerging markets can be useful for other firms and ecosystems, ranging from e-commerce to manufacturing, that employ large number of decision makers with the aim of creating a circular economy. At the end, the study presents theoretical and practical implications along with the scope for future research.
... Finally, the fourth industrial revolution is also called industry 4.0 from the introduction of the German term "Industrie 4.0" in 2011 by Hannover Fair, and equivalent to what General Electric called industrial internet in the United States. Conceptually established ex-ante, therefore previously, it basically consists in the adoption of cyber-physical systems in industry, a phenomenon still in its initial phase in this beginning of the 21st century (Ghobakhloo, 2018;Kagermann, Wahlster and Helbig, 2013;Dragicevic, 2019). ...
Academic research in Knowledge and knowledge management tends to focus on issues related to producing, storing, organizing, sharing, and retrieving data, information, and knowledge for and from humans, and on how to make use of machines for those and other related purposes. Therefore, hardware and software are usually seen more as support and means but, as technology exponentially evolves, there are already many machine learning algorithms, artificial intelligence, and other resources where it is hard for a human mind to fully comprehend the rationale behind its outcomes, results, predictions, processing, or decisions taken, even though they might be shown to be precise and of high quality. There are theoretical e technical efforts to address it, such as the concept of Explainable AI, but it is conceivable that knowledge from machines may not be, in the present or in the future, both efficient and adequately translatable to traditional human-comprehensive knowledge. That knowledge might one day be only usable by other machines in a yet unknown approach of knowledge sharing between them, in a specific way designed for them and perhaps, in the future, also by them: a machine perspective of knowledge and knowledge management. In addition, machine knowledge may not be available only in the explicit form but also in a manner somehow analog to human tacit knowledge, as for instance, a given AI may acquire a rationale that is beyond what its stored bytes can express. That might be also evidence of a context in which perhaps it may be only able to be socialized between machines, in a tacit to tacit “transfer”, not with nor for humans. Furthermore, keeping machine knowledge secure might be far more complex than mere data storage security and policy, as a simple copy of those data may be insufficient for representing and recovering a previously developed machine knowledge, implying that traditional information management is no longer enough. Much is still needed to advance on the topic of machine knowledge, as an approach to data, information, and knowledge from and for machines is needed, in what could be called machine knowledge management (MKM). But that is not the final step needed, as from these machine knowledge and knowledge management concepts emerge the need for a unified theory with human counterparts, that addresses the complex aspects of coexistence and interactions of both clusters of knowledge, with implications for Human-Autonomy Teaming (HAT), and how both can work together in the present and future challenges. Therefore, the aim of this research is to advance toward the proposal of a theoretical model for machine knowledge and knowledge management, on how that can be integrated with the analog human versions in a unified human-machine model, and what might play the mediator role. Subsidiarily, it also discusses the need for a standardized and expanded concept of information and knowledge consistent with that model. Finally, topics are proposed for future research agenda. To achieve these research goals, the main methodologies adopted were the literature review and the grounded theory.
... Technological advances trigger digital transformation processes that alter value-creating paths in organisations (Vial, 2019) and lead to the emergence of the fourth industrial revolution (Beier et al., 2020;Dragicevic et al., 2020). The role of managers in making digital efforts successful is critical; however, digital transformation initiatives often ex-perience difficulties because managers experience role ambiguity and do not know how to act (Ellström et al., 2021;Fitzgerald et al., 2014). ...
... A large part of the research is currently devoted to the issues of the Fourth Industrial Revolution. They are predominantly devoted to the possibilities of increasing management efficiency, including the use of time and resources [1][2][3][4]. Contemporary socio-economic changes and the related challenges of the 21st century, also in the social sphere (e.g., aging, unemployment, income and property inequalities, poverty and social exclusion, discrimination, migration), entail searching for new, economically and socially rational solutions to social problems [5,6]. Nowadays, strengthening and using social capital and solidarity are essential to increase the competitiveness of the economy [7]. ...
Major transformations in the sphere of the economy that Industry 4.0 brings are also reflected in young people’s expectations regarding the development of their professional career. Existing social relations are being modified nowadays and new concepts of building them are being developed. The aim of the present article is to present the expectations, fears and hopes of young people related to the course of Industrial Revolution 4.0 in the context of their future life. For a simpler perception of the research objectives of students, the research was narrowed down to the topic of building relationships with robots, which are one of the pillars of Industry 4.0. The research methods are based on the literature studies and an experiment conducted among the students graduating from economic faculties and entering a strongly changing labour market. The experiment was qualitative. The students wrote a short essay on the topic of whether a friendship between a human and a robot is possible. One group of students was shown a short emotional clip about the relationship between the boy and the robot. Regardless of the attempt to influence the message with a film, both groups of students hardly noticed the negative effects of digitisation on building relationships and social trust. The relationship between human being and advanced technology will develop in the future, which will result in the emergence of new relationships between humans and artificial intelligence.
... The new concept relies on cyber-physical systems, implementing communication and coordination of cybernetic and physical elements by utilization of online technologies. Due to the ability to use the "digital trace", cyber-physical systems integrate with production processes, resulting in a qualitative transformation of production chains and more efficient business and customer management [18][19][20][21][22]. ...
The Fourth Industrial Revolution is an objective result of technological progress, which will radically change the traditional ways of doing business. This will also contribute to radical changes in the human’s role in the labour market. Employees with high qualifications and developed social-behavioural skills will experience high demand in the labour market, and a consequence of this will be necessary transformations of the talent management concept. This study aims to demonstrate the need to change the talent management concept in the era of the Fourth Industrial Revolution. The conducted research is based on the analysis of statistical data regarding labour productivity in the economic system EC-27. Economic and mathematical modelling techniques have been used for the analysis of selected statistical indicators (places of convergence and divergence, places of growth, declining and side trend have been found). As a result of the studies performed, a method of calculating the original Index the lack of talents in the economic system. The results of the completed research demonstrate: on the one hand, on the market, there is a shortage of talent and a transformation of the labour market is required for organizational and technological reasons; on the other hand, the Fourth Industrial Revolution and the accompanying transformation of economic processes create new requirements for human resources. In such conditions, the role of human capital development process management increases significantly, and the necessary transformation of the talent management concept is increasingly becoming a key element that directly determines the sustainable development of social and economic systems. The transition to this concept should proceed through modern technologies and digital solutions, pursuing the main goal, being to provide a continuous process of not only searching for talents but also creating talents by delivering knowledge and forming skills as required by technological progress.
... Industry 4.0 is characterized by the mass employment of smart objects in highly reconfigurable and thoroughly connected industrial product-service systems (Dragicevic et al., 2019;Javaid et al., 2021). The key characteristic of Industry 4.0 is a continuous communication between humans, machines, and manufacturing during the production process. ...
Triggered by the development of information and communications technologies, Industry 4.0 opens up a new era for the manufacturing industry.Currently, Industry 4.0 has attracted much attention from industry and academia. Research on Industry 4.0 is still evolving towards the development of frameworks linking Industry 4.0’s enabling technologies to specific goals and to their impact on the manufacturers’ businesses.Accordingly, this study presents a systematic review of the scope of Industry 4.0, its goals and implementations, as well as the barriers to the implementation of Industry 4.0. Solutions for overcoming the barriers and challenges are discussed.
... One of the prominent classifications of knowledge is the tacit-explicit (Becerra et al., 2008) where explicit knowledge is knowledge stored in physical storage such as books, computers, etc. and tacit knowledge is situational and stored in practices, routines, and feelings (Chuang et al., 2016). The rapid contemporary developments of technology allowed not only storing but communicating, interpreting and assimilating knowledge through big data analytics, virtual reality, augmented reality and robotics (Dragicevic et al., 2019). However, we still have to respond to ongoing calls for a better understanding of socio-materiality of knowledge in this digital medium. ...
This chapter intends to explore the use of the Twitter social media platform as a microblog to share Covid-19 prescribed knowledge through observing the Twitter accounts of the five most student populated UK Universities. The paper provides valuable practical insight to UK Universities practitioners, students and concerned stakeholders on the use of Twitter microblogs to share or retrieve knowledge required to cope with the current Covid-19 transition. The paper sheds light on the unique characteristics of knowledge shared by UK Universities through Twitter in relation to the current Covid-19 pandemic. The paper also highlights the unconventional use of Twitter by UK Universities to share Covid-19 prescribed knowledge with its stakeholders.
... In the SG scenario, one unit called the grid collects the realtime data (also referred to as meter reading in a conventional meter) from smart meters deployed at different locations such as homes and industries. Most of the data collected are in lowlevel operations and this allows data analysts to discover sig-nificant data outcomes and help coordinate subsequent usage, followed by even more complex analytics and planning [260]. This technology has become a significant part of any country's success, encouraging its power consumers to use smart meters to manage and efficiently control power consumption. ...
Blockchain technology has taken on a leading role in today's industrial applications by providing salient features and showing significant performance since its beginning. Blockchain began its journey from the concept of cryptocurrency and is now part of a range of core applications to achieve resilience and automation between various tasks. With the integration of Blockchain technology into different industrial applications, many application designs, security and privacy challenges present themselves, posing serious threats to users and their data. Although several approaches have been proposed to address the specific security and privacy needs of targeted applications with functional parameters, there is still a need for a research study on the application, security and privacy challenges, and requirements of Blockchain-based industrial applications, along with possible security threats and countermeasures. This study presents a state-of-the-art survey of Blockchain-based Industry 4.0 applications, focusing on crucial application and security and privacy requirements, as well as corresponding attacks on Blockchain systems with potential countermeasures. We also analyse and provide the classification of different security and privacy techniques used in these applications to enhance the advancement of security features. Furthermore, we highlight some open issues in industrial applications that help to design secure Blockchain-based applications as future directions.
... Inadvertence towards strategic knowledge management in the context of growing emphasis on digital transformation across diverse industries can present enormous concerns like non-adaptive and dysfunctional knowledge processes such as creation, transfer, use, and application. Consequently, such inadvertence can result in reinvention and loss of knowledge assets and massive organisations' costs (Venkitachalam and Willmott, 2016;Dragicevic et al., 2019). Considering the rising influence and dependence on digital technologies and applications in many different sectors/industries, the relevance of managing strategic knowledge in learning organisations has a more significant impact than ever before. ...
The annual death rate of small and medium enterprises (SMEs) is a strategic issue facing global businesses. In Nigeria, SMEs have been unable to achieve sustainable growth. The purpose of this paper is to examine the problems in the sustainable growth of Nigerian SMEs and to provide relevant solutions based on a comprehensive review of the literature. This review contributes to the literature on SMEs by identifying and explaining the issues that inhibit the sustainable growth of Nigerian SMEs, which may guide future research. It also offers valuable insights to SME owners, the government, and policy makers on ways to improve SME growth.
Keywords: Problems, solution, small and medium enterprises, sustainable growth, Nigeria
... The ACM merges both kinds of intellectual interactions. Such merging of informal and formal methods of knowledge management in one instrumental procedure seems to be promising as to unifying knowledge formats used by humans and computer agents in modern technologies of digital society (Dragicevic et al., 2020). ...
In this paper, we argue that qualitative data analysis software lacks a tool that can be used to fulfill an algorithmic evaluation of conceptualization carried out in qualitative studies. We propose the context-oriented models of coding that conjugate single codes, that is, brief denotations made in natural language, by unusual local relationships called context-fixed elucidation (CFE). CFE is a local relationship between miscellaneous aspects of a case under study. The set of separate CFEs, originated by the analyst during conceptualization and called thesaurus, represents the case as a whole. On the basis of CFE structure and using the thesaurus’ single codes as data, there is proposed an algorithm which calculates, without the involvement of the expert, whether there is or not global coherence of single codes used by analyst within the thesaurus. The tool thus obtained emulates for the codes originated in qualitative study the relationships known in the object-oriented programming, such as polymorphism, visibility, encapsulation, inheritance. A probe application of the new tool is demonstrated by the conceptualization of textual evidence. The application was performed with the help of a pilot computer package which architecture is based on the context-oriented models. Thanks to the models, QDAS can obtain special tools that would make researchers' analytical work more intelligible and coherent. The models proposed can find applications outside of research discourse including computer technologies used in various social spheres where people communicate in natural language.
... A digital (information) component is an information system responsible for processing data on relevant physical processes. Smart sensors and activators implementing the IoT concept serve as a connecting component between these components [2] In other words, the principle of cyber-physical systems looks like this: the "brain" of the system advances information technology, which receives data from sensors that monitor current physical processes. [1] There are several basic technical prerequisites that have made CPS possible: The first is an increase in the number of devices with embedded processors and storage: touch networks operating in all long technical infrastructures; medical equipment; smart homes, etc. The second is integration, which allows you to achieve the greatest effect by combining individual components into large systems: the Internet of Things (IoT), World Wide Sensor Net, Smart Building Environment, defense systems of the future. ...
In the information society, no human activity stands still, including the electric power industry. As a result, there has been a shift from a traditional grid architecture to a new information and communication technology architecture. The object of the study of this article is a smart power supply system. The purpose of the study is to consider problems for implementing the concepts of "Smart Grid" and "Internet of energy." To do this, a brief overview was made of the traditional electricity supply system, as well as promising renewable energy sources and its promising directions. In order for several RSE to exist in the same power grid without any problems, it is necessary to use energy routers that are able to connect several power grids operating on different sources. The received system monitor by the power grid management systems (SCADA, distributed control system). There are also discussed the SCADA tasks and features. The above all leads to the implementation of two innovative concepts in the field of energy: Smart Grid and Internet energy.
... In a CSTS, the analysis of autonomous or semi-autonomous machines demands for specific knowledge creation/conversion. Under Industry 4.0 paradigm, research has focused on knowledge-based interactions among smart objects, humans and other actors participating in value co-creation (Dragicevic et al., 2019). For example, these latter scholars propose a framework for knowledge dynamics where smart objects are capable of learning, adjusting and acting in the environment by mimic human decisionmaking. ...
Modern work domains are constituted by an intertwined set of social and technical actors with different, often conflicting, functional purposes. These agents act jointly to ensure system’s functioning under both expected and unexpected working conditions. Considering the increasing digitalization and automation of work processes, socio-technical systems are progressively including interconnected cyber technical artefacts, thus becoming cyber-socio-technical systems (CSTSs). Adopting a natural science perspective, this paper aims to explore knowledge creation and knowledge conversion within CSTSs, as rooted in an in-depth analysis of work practices and work contexts. The paper proposes a conceptual framework which unveils the relationships between different work representations, i.e. relying on Work-As-Imagined, Work-As-Done, Work-As-Disclosed, Work-As-Observed, intended as knowledge entities generated by different agents, i.e. sharp-end operators, blunt-end operators, and analysts. The recursive and fractal nature of the proposed WAx (Work-As-x) framework ensures its adaptability for different granularity levels of analysis, fostering the understanding, modeling, and analysis of work practices, while abandoning reductionist and over-simplistic approaches.
... One of the prominent classifications of knowledge is the tacit-explicit where explicit knowledge is knowledge stored in physical storage such as books, computers, etc. and tacit knowledge is situational and stored in practices, routines, and feelings (Chuang et al., 2016). The rapid contemporary developments of technology allowed universities not only to store but communicate, interpret, and assimilate organisational knowledge through social media, big data analytics, virtual reality, augmented reality, and robotics (Dragicevic et al., 2019). However, we still have to respond to ongoing calls for a better understanding of knowledge created, exchanged, absorbed, stored, and disseminated on social media platforms which entails the social aspect of knowledge to be revisited (Treem et al., 2020). ...
This paper seeks to conceptually explore the use of social media platforms such as Twitter as a microblog to share Covid-19 prescribed knowledge through developing a conceptual framework of university ecosystem knowledge regime. The framework outlines three ecosystem artefacts; teaching, assessment, and student experience and what knowledge-sharing strategies that may help representing these artefacts to the wider community of the ecosystem. The paper provides valuable practical insight to UK Universities’ practitioners, students, and concerned stakeholders on the use of Twitter microblogs to share or retrieve knowledge required to cope with the current Covid-19 transition. The paper sheds light on the unique characteristics of knowledge sharing by UK Universities through Twitter in relation to the current Covid-19 pandemic. The paper also highlights the unconventional use of Twitter by UK Universities to share Covid-19 prescribed knowledge with its stakeholders.
... The concept of "Industry 4.0" is interpreted as a transition to fully automated digital production controlled by intelligent systems in real time in constant interaction with the external environment, going beyond the boundaries of one enterprise, with the prospect of uniting into a global industrial network of things and services [4]. The fourth industrial revolution changes the [5,6,7]. ...
The problem of effective search, explication, and analysis of contextual knowledge is associated with an ever-increasing volume of available information, its non-formalization and poor structuredness, a high rate of updating of scientific knowledge, the polysemy of the terminological base used, the transfer of terms from one subject area to another without the necessary interpretation and adaptation. The existing tools for searching, explicating and analysing contextual knowledge are of little demand due to the lack of meaningful information both about the tools themselves and about the methods and algorithms for their application in science and practice, as well as the limited set of services provided by analytical software. The educational-methodical complex “Technologies of data extraction and mining in scientific research” is aimed at the formation of research and analytical competencies of undergraduates. The results of mastering the educational product will allow undergraduates to consciously apply in research practice an integrated approach (synthetic method) to the search and analysis of contextual knowledge, to use analytical software and environments with built-in services for explication, clustering, and statistical processing of scientific texts on the basis of this approach.
... Industry 4.0 has been considered a new industrial stage in which several emerging or disruptive technologies including Internet of ings (IoT), artificial intelligence (AI), 3D printing, and big data are converging to provide digital solutions [26,27]. Industry 4.0 is characterized by the mass employment of smart objects in highly reconfigurable and thoroughly connected industrial product-service systems [28]. In this respect, industry 4.0 phenomenon is bringing unprecedented disruptions for all traditional production/ service systems and business models (value chains) and hotfooting the need for redesign and digitization of activities [29][30][31][32]. ...
Very well into the dawn of the fourth industrial revolution (industry 4.0), humankind can hardly distinguish between what is artificial and what is natural (e.g., man-made virus and natural virus). Thus, the level of discombobulation among people, companies, or countries is indeed unprecedented. The fact that industry 4.0 is explosively disrupting or retrofitting each and every industrial sector makes industry 4.0 the famous buzzword amongst researchers today. However, the insight of industry 4.0 disruption into the industrial sectors remains ill-defined in both academic and nonacademic literature. The present study aimed at identifying industry 4.0 neologisms, understanding the industry 4.0 disruption and illustrating the disruptive technology convergence in the major industrial sectors. A total of 99 neologisms of industry 4.0 were identified. Industry 4.0 disruption in the education industry (education 4.0), energy industry (energy 4.0), agriculture industry (agriculture 4.0), healthcare industry (healthcare 4.0), and logistics industry (logistics 4.0) was described. The convergence of 12 disruptive technologies including 3D printing, artificial intelligence, augmented reality, big data, blockchain, cloud computing, drones, Internet of Things, nanotechnology, robotics, simulation, and synthetic biology in agriculture, healthcare, and logistics industries was illustrated. The study divulged the need for extensive research to expand the application areas of the disruptive technologies in the industrial sectors.
... This will affect and change the work behavior of traditional workers, which will become more complex, equipped with mobile devices, virtual reality and other technologies. It is difficult to say if the 4.0 sector will be a pleasant environment for qualified workers, but the need for greater specialization, flexibility and adaptability [22][23][24] and a potentially smaller range of knowledge and skills will be very different [1]; however, in general, the requirements from employees will increase in the form of responsibility in decision making [25]. ...
The European Union (European Parliament) understands industry 4.0 as a term for an environment of fast transformations of production systems and products. The basic characteristic of the change in the methods of creating added value in the conditions of the fourth industrial revolution is digitalization. Digitalization changes people management in two stages. The first stage is the adaptation of systems to the integration of physical inputs into digital systems, and the second stage is the redefinition of values for the internal and external customer. The purpose of this paper is to examine the content of the first digitalization stage and its impact on the transformation of values of corporate people management in the second stage of digitalization. The study published in this paper points out the level of digitalization applied towards the internal and external customer. The research results verify relations in the portfolio of corporate value and prove their present implementation of digitalization and its and importance for the future sustainability of the business. The study confirmed the independence of the levels of corporate digitalization and companies’ value portfolios. Furthermore, the study proved the universal nature of corporate value orientation, irrespective of the size, business focus or performance of the people management system. Meaningfulness, communication and cooperation dominate in terms of importance for business sustainability. The results of the study in Slovakia support the opinions of published foreign research, which emphasize the importance of introducing technological innovations aimed at employees to a much greater extent.
... In this sense, Industry 4.0 opens another issue for the manufacturing companies that choose to implement and use the new technologies successfully, namely the acquisition of new skills and competences (Schwab, 2017). In this case, new knowledge to manage (acquisition, conversion, application) data and processes needs to be acquired by employees and managers (Dragicevic, Ullrich, Tsui, & Gronau, 2019). People have to gain knowledge that will enable the development of digital thinking so that they may manage the process in a new way (Schniederjans, Curado, & Khalajhedayati, 2019). ...
The recent Industry 4.0 paradigm is revolutionizing the manufacturing processes, the way companies create value and interact with suppliers and customers. The new technologies allow manufacturing companies to gather huge amounts of data that they can use to tailor production, develop customized products and services, as well as improve operation activities in terms of efficiency, productivity, and flexibility. In this new technological scenario, new digital skills and competences (i.e., data management) become strategically important as they could assure new knowledge manufacturing companies to achieve superior competitive advantage. Such new knowledge depends not only on the use of Industry 4.0 technologies but also on the interactions with suppliers and customers as well as on the upgrading of employees’ competences. With the aim of deepening the understanding of these dynamics, the chapter reviews the empirical studies related to the adoption of Industry 4.0, by highlighting the role of knowledge management.
... What is missing in the analyzed body of literature are complex scenarios and in-depth analyses of what these developments might imply for employees or the environment. Especially when considering that humans still remain at the center of the dynamics between data and knowledge in Industry 4.0 (Kagermann et al., 2013;Dragičević et al., 2019) and constitute the decisive factor for operationalizing sustainable development with the aid of information and communication technologies (Seele and Lock, 2017). ...
Industry 4.0 has had a strong influence on the debate on the digitalization of industrial processes, despite being criticized for lacking a proper definition. However, Industry 4.0 might offer a huge chance to align the goals of a sustainable development with the ongoing digital transformation in industrial development. The main contribution of this paper is therefore twofold. We provide a de-facto definition of the concept “Industry 4.0” from a sociotechnical perspective based on its most often cited key features, as well as a thorough review of how far the concept of sustainability is incorporated in it.
... The concept of Industry 4.0 has presented reference to the Fourth Industrial Revolution (Park; Lee, 2017) and is configured as a smart factory. It is evidenced by several research and strategic initiatives, proposed mainly in developed countries, which seek to develop smarter and more sustainable industrial systems for the production of goods and services (Dragicevic et al., 2019). This concept affects not only the production systems, but the ways in which people organize themselves and act at work (Benešová;Tupa, 2017, Kazancoglu;Ozkan-Ozen, 2018). ...
With the emergence of new technologies, oriented to Cyber Physical Systems (CPSs) and Internet of Things (IoT) mainly, a next revolution is projected. The concept of Industry 4.0 has presented reference to the smart factory and Fourth Industrial Revolution. The implementation of this concept in companies encompasses a number of requirements and changes to be developed gradually, including the human factor management. The objective of this study is to identify the benefits and challenges of smart industry concept to the human factor, based on the concept of Industry 4.0. A systematic literature review was elaborated, based on structured protocols for the selection of a bibliographic portfolio of articles. A bibliometric analysis of the data and content analysis was performed. The discussions lead us to ponder on human factor in smart industries in the categories physical and mental health at work, and human performance and professional career in general. The conclusions points to the need to ensure adequate working conditions in cognitive and psychic aspects, among others.
Despite a general awareness of the potential of big data in terms of public interest, several obstacles prevent their effective sharing. This study, linking the discourse on data to the concepts of data value and accountability, aims at emancipating the scientific debate from the emphasis on administrative transparency and the protection of privacy, tracing new perspectives for future research. The present research examines the main peer-reviewed articles published by journals that have dealt with data value and accountability across the public and private dimensions. The bibliometric analysis carried out indicates a propensity by current literature to consider the issue of data value creation either only in the private (data as input to improve business performance or customer relations) or in the public dimension (open data government models). This means that research on behavioral data for public governance has so far been underestimated. Evidence shows that big data value creation is closely associated with a collective process where multiple levels of interaction and data sharing develop among private and public actors in a multilayered accountability environment.
Knowledge is an intangible asset that can increase employees' and organisation's effectiveness. Limited research has been done to upgrade existing knowledge management systems in the Industry 4.0 environment. Hence, this study identifies thirteen critical factors for developing a knowledge management system in Industry 4.0 environment. Interpretive Structural Modelling (ISM) is utilised to develop the structural relationships among these factors. Finally, fuzzy MICAMC is used to categorise the factors based on driving-dependence value. The results reveal the direct and indirect effects of these factors in developing a KM system in the digital age. Findings suggest that top management support, development of knowledge management strategy, knowledge friendly culture, creation and maintenance of digital infrastructure, and employees training are the major drivers for developing a KM system in Industry 4.0 environment. Organizations can use the proposed framework to prioritise their actions to develop a KM system in the Industry 4.0 environment. ARTICLE HISTORY
This chapter intends to explore the use of the Twitter social media platform as a microblog to share COVID-19 prescribed knowledge through observing the Twitter accounts of the five most student-populated UK universities. The chapter provides valuable practical insight to UK universities practitioners, students, and concerned stakeholders on the use of Twitter microblogs to share or retrieve knowledge required to cope with the current COVID-19 transition. The chapter sheds light on the unique characteristics of knowledge shared by UK universities through Twitter in relation to the current COVID-19 pandemic. The chapter also highlights the unconventional use of Twitter by UK universities to share COVID-19 prescribed knowledge with their stakeholders.
The smart grid plays a vital role in energy management systems. It helps to mitigate the demand side management of electricity by managing the microgrid. In the modern era, the concept of hybrid microgrids emerged which helps the smart grid management of electricity. Additionally, the Internet of Things (IoT) technology is used to integrate the hybrid microgrid. Thus, various policies and topologies are employed to perform the task meticulously. Pakistan being an energy deficient country has recently introduced some new policies such as Energy Wheeling Policy (EWP), Energy Import Policy (EIP), and Net Metering/Distributed Generation Policy (NMP) to manage the electricity demand effectively. In addition, the Energy Efficiency and Conservation Act (EECA) has also been introduced. In this paper, we present the overview and impact of these policies in the context of the local energy market and modern information and communication mechanisms proposed for smart grids. These new policies primarily focus on energy demand-supply for various types of consumers such as the demand for bulk energy for industrial ventures and the distributed production by consumers. The EWP deals with obtaining power from remote areas within the country to ease the energy situation in populated load centers and the EIP highlights energy import guidelines from foreign countries. The NMP deals with the integration of renewable energy resources and EECA is more focused on the measures and standardization for energy efficiency and conservation. The benefits and challenges related to EWP, NMP, and EIP have also been discussed concerning the present energy crisis in Pakistan. The generalized lessons learned and comparison of a few aspects of these policies with some other countries are also presented.
The COVID-19 pandemic has altered many organizations' operations management and accelerated the failure of those without resilient supply chains. It has greatly accelerated organizations' adoption of digital technologies and digital transformation. Digital technologies such as robotic process automation (RPA) play important roles in companies' operations-management activities and digital supply-chain transformation in the COVID-19 era. However, empirical research on RPA implementation in supply chains remains scarce. To fill this research gap, this case study was conducted to examine a global retail company's RPA implementation initiative to enhance its digital supply-chain capabilities. The authors examined three key project phases: pre-implementation, implementation, and post-implementation. They identified patterns of managerial practices and challenges related to digital technology implementation. The findings could help other organizations understand the most important issues to be addressed when seeking to implement RPA for operations activities and supply-chain processes.
This paper aims at analysing the collaborative and cooperative didactic strategies and methods for teaching of Italian L2 / LS for specific purposes (ISP). Due to the global processes which involves the Italian economy and the higher education system, there is a growing need for ISP teaching, because of the increasing number of international students enrolled in academic mobility programmes, as well as undergraduate and graduate students, on one hand, and because of high number of foreign employees hired by Italian and international companies operating in Italy, on the other hand. In the light of the new paradigm of the foreign language education, the analysis of Russian and international scientific literature has allowed us to justify the appropriateness and effectiveness of collaborative and cooperative learning strategies, to help students to reach quite encouraging results and to improve their foreign language skills as well as communication and intercultural skills in a relatively short period of time.
Keywords: сollaborative learning, cooperative learning, Italian for Specific Purposes and business communication, intensive language teaching methodologies.
Blockchain technology has taken on a leading position in today’s industrial applications by providing salient features and showing significant performance since its beginning. Blockchain began its journey from the concept of cryptocurrency and is now part of a range of core applications to achieve resilience and automation between various tasks. However, with the integration of Blockchain technology into different industrial applications, many application designs, security, and privacy challenges present themselves, posing serious threats to users and their data. Although several approaches have been proposed to address the specific application, security and privacy challenges of targeted applications with limited security enhancement solutions, there is still a need for a comprehensive research study on the application design, security and privacy challenges, and requirements of Blockchain-based industrial applications, along with possible security threats and countermeasures. This study presents a comprehensive and state-of-the-art survey of Blockchain-based Industry 4.0 applications, focusing on potential application design, security and privacy requirements, as well as corresponding attacks on Blockchain systems with potential countermeasures. We also analyse and provide the classification of security and privacy techniques used in these applications to enhance the advancement of security features. Furthermore, we highlight some open issues of integrating Blockchain technology into industrial applications that help design secure Blockchain-based applications as future directions.
Purpose
The present study is an attempt of identifying the human capital skills and HR-related challenges faced by top management in the perspective of industry 4.0 in emerging economies. In addition, the importance or key resources related to human assets that help in attaining competitive advantages while adopting newer digital technologies are also identified.
Design/methodology/approach
For identifying the dimensions of human capital skills in the perspective of industry 4.0, an extensive review of literature was performed. Along with that, feedback from the expert was used to conceptualize the importance and relationship of the skills in the context of industry 4.0. After that, a qualitative survey was launched and triangulate method was applied for identifying the skills. AHP and DEMATEL was used to analyze the relationship among the skills and subskills and to rank them based on their importance.
Findings
The qualitative survey resulted in skills such as “Cognitive, Emotional and Behavioural skills” and subskills of them. AHP results indicated that “Cognitive skills” was found as the most important skill followed by “Emotional skills” and “Behavioural skills”. In addition to this, DEMATEL was applied for seeking the inter-relationship and identifying the “Cause” and “Effect” relationship of skills and sub-skills.
Originality/value
This study prioritizes factors in a coordinated manner and also finds the relative importance in the context of industry 4.0. It will help further in identifying and deploying human capital with the right skills and will play a significant role at the time of formulating organizational and HR level strategies.
The study explores the knowledge system and organisational learning activities of enterprises serving the elderly using the case study of two Chinese enterprises, and the following findings were obtained. The analysis shows that the complexity of the knowledge system of comprehensive enterprises for the elderly is not weaker than that of the professional technical service industry. The knowledge system of enterprises serving the elderly presents sequential characteristics. We ague that the types of knowledge elements of enterprises serving the elderly should be integrated and contained to form a knowledge system with holographic characteristics. Furthermore, the knowledge accumulation of enterprises serving the elderly depend on the selection of appropriate organisational learning strategies, showing an obvious spiral process of stimulus-response model. This paper provides valuable insights for the enterprises serving the elderly to determine the development direction and path.
In recent decades, one of the main trends in the development of economy and society has been the penetration of information technologies into various spheres of human activity. The digital transformation of the economy poses challenges to economic science and management, as the socio-economic institutions of society change dramatically, the same holds for the conditions and methods of doing business under the influence of technological changes in the economy. The problem is that traditional economic laws (economies of scale, value chain) are no longer functioning, and new economic actors (digital companies) are emerging that do not fit into traditional models of performance and business indicators. In addition, in the context of digitalization of the economy, the management of economic entities is the relevant issue. In order to play a dominant role in the global computer economy, a country must pay special attention to the production of innovations and to the domestic employment opportunities. For each country, the production and support of technical skills is an important component of economic development, employment, economic growth and development. The article analyses the development trends and the size of the digital economy in Ukraine and in other countries of the world. Key trends that will determine the direction of this type of economy are identified. It is proved that digitalization should be carried out in accordance with the principles of equal access, creating benefits, economic growth, promoting the development of the information society and the orientation towards cooperation. The advantages of digitalization of the Ukrainian economy are presented, as well as the threats and risks that will arise as a result of this process are specified. Thus, the developmental role of many countries, including Ukraine, is associated with unlimited access and transformation of new forms of economic development, taking into account the use of intellectual skills.
The paper analyzes the problem of expressing tacit knowledge by participants in social processes interacting through ICT. This unsolved problem prevents organizing the human–machine interaction, which with due completeness and efficiency would take into account the vast social experience of people. The research question is how we can extract individual tacit knowledge and so formalize it? Answered this question, we could process information with ICT more meaningfully especially when humans need to team working. To solve the problem of expressing tacit knowledge by participants in social processes, we propose that any human within ICT would express his tacit knowledge by means of natural language. In this way, we preserve its essential role in social communication. However, the tacit knowledge should assume not the traditional form of a text stream, but structural view obtained by using special visual linking mechanism (VLM) associating natural language utterances. The paper examines the foundations of the introduction and structure of the VLM. How a person can apply the mechanism is explained through an example of the expression of tacit knowledge, functioning as part of human common ideas.
Both technology and business are changing in the world. The new paradigm of the world is emerging in the form of systems, affecting all aspects of the activities of society and market players. The scale and complexity of transformation will be different from what humanity has experienced before. It is not yet possible to predict with great precision how it will unfold, but one thing is clear: the answer must be integrated and comprehensive, from the public and private sectors in scientific community, business and society. In the new economic environment, economic agents have to go through the processes of digital transformation that are necessary to improve. The purpose of the article is to define the main directions of the development of digitalization and to analyse Ukraine’s place in the world by the level of development of digitalization. Methodical tools of the study were methods of analysis and synthesis, deduction and induction, search for causal relationships. The article presents the results of empirical analysis of the main trends in the Ukrainian market during the pandemic and their relationship with the processes of digitalization. The article analyzes the development trends and the size of the digital economy in Ukraine and in other countries of the world. Key numerical trends have been identified that will determine the direction of this type of economy. It has been proved that digitalization must be carried out in accordance with the principles of equal access, benefit creation, economic growth, the promotion of the information society and the orientation towards cooperation. The advantages of the digitalization of Ukrainian economy are presented, as well as the threats and risks that will arise from this process are indicated.
The purpose of this article is to explore the new concept of TQM 4.0 as a way of adapting quality management (QM) in Industry 4.0 (I4.0), guiding industries to this new phase, which has generated adaptations in numerous areas, one of which is QM and human resources.
A systematic review of the literature was carried out. Methodi Ordinatio was applied to build the portfolio of articles with scientific relevance, which is the source of data collections and content analysis. To help out in the analysis, NVivo 12 and VOSviewer software programs were used.
The results demonstrate that when adapting the QM to the technologies of I4.0, the result is an ecosystem that supports the integration between technology, quality and people in the industrial scenario.
This article presents a systematic review of the literature, but without delving into specific issues such as the different industrial sectors and the culture of countries in which industries may be inserted, for example, which characterizes a limitation of this research.
This study provides an ecosystem model that can guide future research, regarding the concept of TQM 4.0, in addition to pointing out some ways of combining technologies, quality and people in the industrial context.
This is one of the first articles to employ a systematic review of the literature using Methodi Ordinatio to build a bibliographic panorama on the intertwining of the themes total QM (TQM) and I4.0, focusing on the emerging concept of TQM 4.0.
Industry 4.0 is being implemented with the help of advanced technologies. Big data, Artificial Intelligence (AI), Robotics, Internet of Things (IoT), Cloud computing, and 3D printing are the major technologies used to adopt Industry 4.0 successfully. Here, the study's need is to discuss the major potential of big data for Industry 4.0. These technologies' primary purpose is to collect the right data to solve the relevant issue during manufacturing and other required services. This technology plays a significant role in creating advancements in this fourth industrial revolution. Conclusively, big data applications are useful for in-process management and productivity improvement in the automation sector. Complex systems of drivers and intelligent sensors can be easily optimised based on information collected using this technology. Big data is the key to gain a competitive leap by reconnoitring the fundamental issues like deviations during the process, quality discriminations, and energy efficiency squander in a manufacturing process. The study discusses the significant applications of Big Data in Industry 4.0. For a proper surveillance system, industries need to have an immensely technical or personalised way, making big data a valuable source for predicting analysis and operation management based on market insight statistics or information. In upcoming days, big data will provide further advancement in Industry 4.0 and is supposed to play an efficient role in its successful adoption.
The paper presents the concept of Sociocultural Information Urban Space (SIUS) considered as a logical development of the intercultural-communicational, technical-resource approaches and social machine concept, proposed for studying, supplementation and identification the features, possibilities and base information flows of the smart city sociocultural face. The SIUS idea helps to recognize or improve the data driving city concept, because of smart and gamification technology use, so that greater range of digital services related to the sociocultural city facets can be provided to different stakeholders. The study includes exploring the interaction of pervasive processes of the Sociocultural Information Urban Space, specification of its architecture and concept. Based on the principles and practices of a pervasive approach, the main mechanisms of citizens’ involvement into the active creative development of the SIUS are identified. Finally, the study discusses two application scenarios which demonstrate how pervasive processes of sustainable development and pervasive self-regulation mechanisms of the Sociocultural Information Urban Space can be used to meet the actual needs of citizens and decision-making.
The purpose of the chapter is to determine the main possible scenarios of development of Industry 4.0 in the conditions of knowledge economy’s formation and to determine their consequences for modern economic systems. The methodology of the research, which is conducted in this chapter, is based on using the method of macro-economic scenario analysis. This analysis is of the qualitative character, as it is performed not by the example of specific socio-economic systems but abstract (generalized) modern economic systems. This method is supplemented by analysis of causal connections and the method of formalization of scientific data. The performed analysis allowed determining the main scenarios of development of Industry 4.0 in the conditions of knowledge economy’s formation and showed that their consequences could be different. Lack of statistical data and experience of formation and development of Industry 4.0 is a basis for setting the same probability for all distinguished scenarios. The most pessimistic scenario is the one envisaging implementation of hidden threats to the concept of Industry 4.0 in the process of its practical implementation, as its consequences are imbalance and crisis of economic systems and of the global economy on the whole. There’s also scenario that envisages impossibility of practical implementation of the concept of Industry 4.0. It belongs to pessimistic scenarios, as it is related to implementation of innovational macro-economic project—formation of Industry 4.0. Optimistic scenarios include turning Industry 4.0 into the tool of formation of knowledge economy, which could be accompanied by appearance of large problems and their absence, which determine the time period of implementation of these scenarios. Positive consequences of these scenarios for modern economic systems are related to stimulation of quick formation of knowledge economy. The most optimistic scenario is the one that envisages implementation of the Fourth Industrial Revolution, related to transition to Industry 4.0, on the basis of knowledge economy. Consequences of this scenario include the change of technological mode, innovational development of economic systems, and studying anti-crisis effect.
The purpose of the article is to develop the priorities of development of Industry 4.0 in modern economic systems, characterized by different progress in the sphere of formation of knowledge economy. The methodology of the research includes the method of prioritizing and dialectical and logical methods, which are used for determining the logic of the process of Industry 4.0 development and priorities of managing this process depending on the progress in the sphere of formation of knowledge economy. For graphic interpretation of the conclusions and compiled recommendations, the authors use the method of formalization of data. The author classifies the goals of development of Industry 4.0 according to the criterion of advantages for knowledge economy and offer a logical scheme of development of Industry 4.0 in modern economic systems depending on the progress in the sphere of formation of knowledge economy. As a result, it is concluded that management of development of Industry 4.0 should be conducted in view of the achieved progress in the sphere of formation of knowledge economy. The offered priorities and the developed logical scheme of managing the development of Industry 4.0 in modern economic systems depending on the progress in the sphere of formation of knowledge economy takes into account this peculiarity and allows using it in the best way for the economic system. They allow for successful adaptation of this process to any economic systems due to flexibility of management.
Section 1305 of the Energy Independence and Security Act (EISA) of 2007 (Pub. L. 110–140) directs NIST ‘‘to coordinate the development of a framework that includes protocols and model standards for information management to achieve interoperability of smart grid devices and systems.’’ To meet these statutory goals, in January 2010, NIST published the NIST Framework and Roadmap for Smart Grid Interoperability Standards, Release 1.0 (Release 1.0), and in February 2012, NIST published the NIST Framework and Roadmap for Smart Grid Interoperability Standards, Release 2.0 (Release 2.0), which updated the material discussed in Release 1.0. This document is Release 3.0 and builds upon the work in previous releases with an update on the progress since Release 2.0; a description of the Smart Grid Interoperability Panel (SGIP); updated architecture, cybersecurity, and testing and certification chapters; and a new chapter on cross-cutting issues and future directions. Since the release of the last edition of the NIST Framework and Roadmap for Smart Grid Interoperability Standards (Release 2.0), advances in smart grid infrastructure have been implemented. Examples include the widespread deployment of wireless-communication power meters, the availability of customer energy usage data through the Green Button initiative, remote sensing for determining real-time transmission and distribution status, and protocols for electric vehicle charging, supported by standards development across the entire smart grid arena. This release updates NIST’s ongoing efforts to facilitate and coordinate smart grid interoperability standards development and smart grid-related measurement science and technology, including the evolving and continuing NIST relationship with the SGIP.
Purpose
The rise of new information and communication technologies forms the cornerstone for the future development of work. The term Industry 4.0 refers to the vision of a fourth industrial revolution that is based on a network of autonomous, self-controlling, self-configuring, knowledge-based, sensor-based and spatially distributed production resources. All in all, different forms of the application of the Industry 4.0 concept can be observed, ranging from autonomous logistic transport systems drawn upon the idea of swarm intelligence to smart knowledge management systems. This paper aims to develop a theoretical framework to analyze different applications of Industry 4.0 on an organizing continuum. The general research questions are: What forms of organizing digitalized work lead to the reproduction of routines, and what forms foster innovation within Industry 4.0? The authors thus analyze the consequences of different forms of organizing work on workers’ perceptions and the results of the working process.
Design/methodology/approach – This paper provides case studies for different stages of the organizing continuum in the context of Industry 4.0. The cases and a further analysis of all 295 funded projects are based on the Platform Industry 4.0 Map, which is part of the Industry 4.0 initiative of the German Federal Ministry of Economic Affairs and Energy and the German Federal Ministry of Education and Research. The consequences for people acting in such organizational and digitally supported structures are discussed.
Findings
A variety of applications of Industry 4.0 can be found. These applications mainly vary in the dimensions of the degree of formalization, the location of control authority, the location of knowledge and the degree of professionalization. At the right side of the organizing continuum, the digitalization organizes a work environment that supports highly qualified humans. They have broad leeway and a high degree of autonomy to design and create innovative forms of digitalization for tomorrow. At the left side of the organizing continuum, Industry 4.0 structures a work environment with narrow leeway, a low degree of autonomy and a top-down structure of control authority predetermined by digital applications. In this case, employees fill the gaps the machines cannot handle.
Research limitations/implications
As the paper focuses on Industry 4.0 developments in Germany, the comparability with regard to other countries is limited. Moreover, the methodological approach is explorative, and broader quantitative verification is required. Specifically, future research could include quantitative methods to investigate the employees’ perspective on Industry 4.0. A comparison of Industry 4.0 applications in different countries would be another interesting option for further research.
Practical implications – This paper shows that applications of Industry 4.0 are currently at a very early stage of development and momentarily organize more routines than innovations. From a practical point of view, professional vocational and academic training will be a key factor for the successful implementation of digitalization in future. A joint venture of industry and educational institutions could be a suitable way to meet the growing demand for qualified employees from the middle to the right-hand of the organizing continuum in the context of Industry 4.0.
Social implications – Industry 4.0 is designed by men, and therefore, humans are responsible for whether the future work situation will be perceived as supportive or as an alienated routine. Therefore, designers of Industry 4.0, as well as politicians and scientists, absolutely must take the underlying outcomes of digitalized work into account and must jointly find socially acceptable solutions (e.g. unconditional basic income to absorb negative societal effects of unemployment caused by digitalization).
Originality/value
This paper provides a promising avenue for future research on Industry 4.0 by analyzing the underlying organizational structures of digital systems and their consequences for employees. Moreover, the paper shows how Industry 4.0 should be organized to simply reproduce routines or to support innovation.
Smart meters provide large amounts of data and the value of this data is getting increased attention because a better understanding of the characteristics of consumers helps utilities and retailers implement more effective demand response programs and more personalized services. This paper investigates how such characteristics can be inferred from fine-grained smart meter data. A deep convolutional neural network (CNN) first automatically extracts features from massive load profiles. A support vector machine (SVM) then identifies the characteristics of the consumers. Comprehensive comparisons with state-of-the-art and advanced machine learning techniques are conducted. Case studies on an Irish dataset demonstrate the effectiveness of the proposed deep CNN-based method, which achieves higher accuracy in identifying the socio-demographic information about the consumers.
Smart Grids (SGs) are expected to be equipped with a number of smart devices able to generate vast amounts of data about the network status, becoming the key components for an efficient State Estimation (SE) of complex grids. To exploit their potentials, the ICT infrastructure needs to be scalable to follow the increasing amount of data flows and flexible to give the possibility to assign and re-assign grid functions and data flow control policies at runtime, possibly in a context-aware manner. In this scenario, this paper proposes and validates a Cloud-IoT-based architectural solution for SE in SG that combines cloudcapabilities and edge-computing advantages and uses virtualization technologies to decouple the handling of measurement data from the underlying physical devices. Case studies in the field of distribution networks monitoring are also analyzed, demonstrating that the proposed architecture is capable to accomplish the assigned operational tasks, while satisfying the needed quality level from both the communication and the grid perspectives with a significant degree of flexibility and adaptability with respect to state of the art solutions.
Purpose
Knowledge strategy and its planning are affected by uncertainty and environmental turbulence. This paper discusses these issues and presents knowledge strategy planning as an integrated approach for facing these conditions.
Design/methodology/approach
Based on an extensive survey and an original re-elaboration of the literature, the paper addresses these research questions: a) what is the meaning of knowledge strategy, and how can this term be related to the more general issues of strategic thinking, business strategy, and knowledge management in organizations? b) What are the limitations of pure rational approach to knowledge strategy in turbulent environments and under uncertainty? c) What approaches can be consequently proposed to formulate knowledge strategies?
Findings
The study provides a critical reading of the current literature. In addition, it proposes an integrated approach that sees planning as a continuous effort of learning and adaptation to needs and opportunities that dynamically emerge from daily practices.
Research and implications The proposed framework can inspire a new research agenda to detect how knowledge strategies are planned in companies and how they are continuously adapted on the basis of a dialog between rational contributions and perceptions of reality, influenced by practical views, intuitions, and emotions. This can also inspire a new agenda for company strategists and KM professionals.
Originality/value. In the literature, little attention has been devoted to knowledge strategy planning. The paper contributes to fill this gap and proposes a new way to see knowledge strategy as an integration of rational thinking and dynamic learning.
Purpose
The purpose of this paper is to explore the relationship between big data and knowledge management (KM).
Design/methodology/approach
The study adopts a qualitative research methodology and a case study approach was followed by conducting nine semi-structured interviews with open-ended and probing questions.
Findings
Useful predictive knowledge can be generated through big data to help companies improve their KM capability and make effective decisions. Moreover, combination of tacit knowledge of relevant staff with explicit knowledge obtained from big data improvises the decision-making ability.
Research limitations/implications
The focus of the study was on oil and gas sector, and, thus, the research results may lack generalizability.
Originality/value
This paper fulfills an identified need of exploring the relationship between big data and KM which has not been discussed much in the literature.
Purpose – What is really ‘new’ in Big Data? Big Data seems to be a hyped-up concept that has emerged during recent years. But it requires thorough discussion beyond the common 4V (velocity, volume, veracity and variety) approach.
Design/methodology/approach - We established an expert think tank to discuss the notion of Big Data, identify new characteristics, and re-think what really is new in the idea of Big Data, by analysing over 60 literature resources. We identified typical baseline scenarios (traffic, business processes, retail, health, and social media) as a starting point, from which we explored the notion of Big Data from different perspectives.
Findings - We concluded that the idea of Big Data is simply not new, and recognised the need to re-think a new approach towards Big Data. We also introduced a 5-Trait Framework for ‘Cognitive Big Data’, socio-technical system, data space, data richness, knowledge management/decision making, and visualization / sensory presentation.
Research limitations/implications – Our viewpoint is centred on cognitive processes as KM process.
Practical implications - Data needs to be made available in an understandable form, for the right application context, and in the right portion size that it can be turned into knowledge and eventually wisdom. We need to know about data that can be ignored, data that we are not aware of (dark data), and data that can be fully utilised for analysis (light data). In the foreground is human and machine understandability.’ – In form of Cognitive Big Data.
Social implications – Cognitive Big Data implies a socio-technological knowledge system.
Originality/value – Introduction of Cognitive Big Data as concept and framework.
Traditional production systems are enhanced by cyber-physical systems (CPSs) and Internet of Things. As kind of next generation systems, those cyber-physical production systems (CPPSs) are able to raise the level of autonomy of its production components. To find the optimal degree of autonomy in a given context, a research approach is formulated using a simulation concept. Based on requirements and assumptions, a cyber-physical market is modeled and qualitative hypotheses are formulated, which will be verified with the help of the CPPS of a hybrid simulation environment. Big Data Analytics can be used to extract influence factors which explain the optimal degree of autonomy.
Industry 4.0 factories become more and more complex with increased maintenance costs. Reducing costs by cyber-physical (CP) controllers should ensure the commercialization of the CPS for smart factory project results. We implement multi-adaptive CP controllers in the following domains: industrial robot arms, car manufacturing, steel industry, and assembly lines in general. The main objective is to implement such controllers for these application domains and let the industry partners provide feedback about the cost reduction potential. In this paper, we describe the technical infrastructure including deep learning and knowledge acquisition submodules, followed by anomaly detection modules and intelligent user interfaces in the IoT (Internet of Things) paradigm. In addition, we report on three concrete use case implementations of industrial robots and anomaly modeling, knowledge management and anomaly treatment in the steel domain, and anomaly detection in the energy domain.
The purpose of this paper is to present a new perspective on knowledge dynamics. This perspective is based on the energy metaphor and the theory of multifield knowledge spectrum. That makes a significant step forward with respect to all the other knowledge dynamics models, which are based on the stocks and flows metaphor since the new perspective uses the entropy principle from thermodynamics and not the Newtonian logic. While the metaphor of knowledge flows leads to the physical motion in space, the metaphor of knowledge energy allows us to interpret dynamics as an irreversible transformation between two well-defined fields. Also, the paper presents the entropic transformation of the potential organizational knowledge into the operational organizational knowledge performed by organizational integrators.
Classical productions systems are migrating step-by-step into cyber-physical production systems. The addition of much more computing power and object-bound data storage will lead to new possibilities for the advancement of autonomy in production systems. Autonomous message exchange and coordination can help to prevent quality problems (for instance wrong pairing of tool and work piece) and improve the disturbance management (for instance by faster information about current and probable disturbances). Due to the fact that nearly all improvements of existing production systems with cyber-physical systems take place in real and active manufacturing sites, on-site experiments for determining an appropriate degree of autonomy for production objects are not feasible. Therefore, a lab approach is necessary. In this contribution a hybrid lab approach to simulate various degrees of autonomy is presented [1]. The paper starts with a definition of autonomy and suggests diverse measurement methods [2]. After a short introduction into the lab concept, the results of some test runs are presented where autonomous objects perform the same production program as “dumb” production objects. Finally, an outlook for further research is given.
Cyber manufacturing is transformative concept that involves the translation of data from interconnected systems into predictive and prescriptive operations to achieve resilient performance. It intertwines industrial big data and smart analytics to discover and comprehend invisible issues for decision making. With the advent of Internet-of-Things (IoT) and smart, predictive analytics technologies, companies have been building a networked data-rich environment, which calls for a systematic methodology to transform raw data into meaningful and actionable operations. This paper introduces a fundamental framework and architecture for cyber manufacturing systems.
Future industrial systems can be realized using the cyber–physical systems (CPSs) that advocate the coexistence of cyber and physical counterparts in a network structure to perform the system’s functions in a collaborative manner. Multiagent systems share common ground with CPSs and can empower them with a multitude of capabilities in their efforts to achieve complexity management, decentralization, intelligence, modularity, flexibility, robustness, adaptation, and responsiveness. This work surveys and analyzes the current state of the industrial application of agent technology in CPSs, and provides a vision on the way agents can effectively enable emerging CPS challenges.
The Naturalistic Decision Making (NDM) community defines intuition as based on large numbers of patterns gained through experience, resulting in different forms of tacit knowledge. This view contrasts with Fast and Frugal Heuristics (FFH) researchers, who view intuition in terms of general purpose heuristics. The NDM view also differs from the Heuristics and Biases (HB) community, which sees intuitions as a source of bias and error. Seven suggestions are offered to assist the FFH and H&B communities in improving intuitive decision making and in conducting research that has greater potential for application.
The era of Big Data has begun. Computer scientists, physicists, economists, mathematicians, political scientists, bio-informaticists, sociologists, and other scholars are clamouring for access to the massive quantities of information produced by and about people, things, and their interactions. Significant questions emerge. Will large-scale search data help us create better tools, services, and public goods? Or will it usher in a new wave of privacy incursions and invasive marketing? Will data analytics help us understand online communities and political movements? Or will it be used to track protesters and suppress speech? Will it transform how we study human communication and culture, or narrow the palette of research options and alter what 'research' means? Given the rise of Big Data as a socio-technical phenomenon, we argue that it is necessary to critically interrogate its assumptions and biases. In this article, we offer six provocations to spark conversations about the issues of Big Data: a cultural, technological, and scholarly phenomenon that rests on the interplay of technology, analysis, and mythology that provokes extensive utopian and dystopian rhetoric.
Smart meters have been deployed in many countries across the world since early 2000s. The smart meter as a key element for the smart grid is expected to provide economic, social, and environmental benefits for multiple stakeholders. There has been much debate over the real values of smart meters. One of the key factors that will determine the success of smart meters is smart meter data analytics, which deals with data acquisition, transmission, processing, and interpretation that bring benefits to all stakeholders. This paper presents a comprehensive survey of smart electricity meters and their utilization focusing on key aspects of the metering process, different stakeholder interests, and the technologies used to satisfy stakeholder interests. Furthermore, the paper highlights challenges as well as opportunities arising due to the advent of big data and the increasing popularity of cloud environments.
Size is the first, and at times, the only dimension that leaps out at the mention of big data. This paper attempts to offer a broader definition of big data that captures its other unique and defining characteristics. The rapid evolution and adoption of big data by industry has leapfrogged the discourse to popular outlets, forcing the academic press to catch up. Academic journals in numerous disciplines, which will benefit from a relevant discussion of big data, have yet to cover the topic. This paper presents a consolidated description of big data by integrating definitions from practitioners and academics. The paper's primary focus is on the analytic methods used for big data. A particular distinguishing feature of this paper is its focus on analytics related to unstructured data, which constitute 95% of big data. This paper highlights the need to develop appropriate and efficient analytical methods to leverage massive volumes of heterogeneous data in unstructured text, audio, and video formats. This paper also reinforces the need to devise new tools for predictive analytics for structured big data. The statistical methods in practice were devised to infer from sample data. The heterogeneity, noise, and the massive size of structured big data calls for developing computationally efficient algorithms that may avoid big data pitfalls, such as spurious correlation.
In 2008, Chris Anderson (2008), at that time the Editor-in-Chief of Wired, proposed that in the age of the petabyte, there was no longer any need for the scientific method, nor for models or theories. Although it might be contended that this was more provocation and journalistic hubris than formal or substantiated claim, the issue was taken up and has gathered momentum ever since. Indeed within a year or so of Anderson's article, and a series of rejoinders published on the Edge Web site, 'The Age of Big Data' was being heralded, and the measure had increased from petabytes to exabytes, zettabytes, and yottabytes. Diebold (2012) usefully distinguishes between Big Data 'the phenomenon', 'the term', and 'the discipline'; arguing that the phenomenon 'continues unabated', the term is 'firmly entrenched', and the discipline is 'emerging'. In what follows we focus initially on the term and the phenomenon, but our main objective is to argue that it is critical that there is general understanding of the emerging discipline. In particular we aim to justify the assertion that in the age of Big Data the ability to be able to develop abstractions and concepts is at least as important as it was previously; perhaps even more so. Moreover that these skills and techniques need to be understood and available to all of us in an era where we are all analysts and researchers at least to the extent of our use of the internet and its potential for affording search and investigation of online resources. We seek to offer some critical insights into these activities - modeling, conceptualizing, and theorizing - by comparing and contrasting Knowledge Discovery from Data (KDD) with the Grounded Theory Method (GTM). The former a technical orientation, that although predating Big Data, lies at the heart of the emerging tools and techniques. The latter a widely used approach to qualitative research aimed at developing conceptual models 'grounded in the data'.
Recent advances in manufacturing industry has paved way for a systematical deployment of Cyber-Physical Systems (CPS), within which information from all related perspectives is closely monitored and synchronized between the physical factory floor and the cyber computational space. Moreover, by utilizing advanced information analytics, networked machines will be able to perform more efficiently, collaboratively and resiliently. Such trend is transforming manufacturing industry to the next generation, namely Industry 4.0. At this early development phase, there is an urgent need for a clear definition of CPS. In this paper, a unified 5-level architecture is proposed as a guideline for implementation of CPS.
One of the most significant directions in the development of computer science and information and communication technologies is represented by Cyber-Physical Systems (CPSs) which are systems of collaborating computational entities which are in intensive connection with the surrounding physical world and its on-going processes, providing and using, at the same time, data-accessing and data-processing services available on the internet. Cyber-Physical Production Systems (CPPSs), relying on the newest and foreseeable further developments of computer science, information and communication technologies on the one hand, and of manufacturing science and technology, on the other, may lead to the 4th Industrial Revolution, frequently noted as Industry 4.0. The key-note will underline that there are significant roots generally – and particularly in the CIRP community – which point towards CPPSs. Expectations and the related new R&D challenges will be outlined.
Today, in an Industry 4.0 factory, machines are connected as a collaborative community. Such evolution requires the utilization of advance- prediction tools, so that data can be systematically processed into information to explain uncertainties, and thereby make more “informed” decisions. Cyber-Physical System-based manufacturing and service innovations are two inevitable trends and challenges for manufacturing industries. This paper addresses the trends of manufacturing service transformation in big data environment, as well as the readiness of smart predictive informatics tools to manage big data, thereby achieving transparency and productivity.
The promise of data-driven decision-making is now being recognized broadly, and there is growing enthusiasm for the notion of "Big Data," including the recent announcement from the White House about new funding initiatives across different agencies, that target research for Big Data. While the promise of Big Data is real -- for example, it is estimated that Google alone contributed 54 billion dollars to the US economy in 2009 -- there is no clear consensus on what is Big Data. In fact, there have been many controversial statements about Big Data, such as "Size is the only thing that matters." In this panel we will try to explore the controversies and debunk the myths surrounding Big Data.
The emergence of the Smart Grid era fuels a new generation of innovative applications and services that are built upon fine-grained monitoring and control capabilities pertaining the underlying infrastructure, such as that of future Smart Cities. Collection, processing and analytics on the Internet of Things of massively generated data, as well as potential management functions will emerge; therefore making a reality informed real-time decision making as well as its enforcement in a timely manner over complex infrastructures. The prevalence of the Cloud and its services, can very well complement the Internet of Things when it comes to massive data management, giving rise to the Cloud of Things (CoT). For the next generation applications, the CoT can enable access to generic multi-modal energy services, on-top of which development of more sophisticated solutions can be realized. We depict here such Smart Grid services for the Smart City of the future, as well as experiences from their realization.
The era of Big Data has begun. Computer scientists, physicists, economists, mathematicians, political scientists, bio-informaticists, sociologists, and other scholars are clamoring for access to the massive quantities of information produced by and about people, things, and their interactions. Diverse groups argue about the potential benefits and costs of analyzing genetic sequences, social media interactions, health records, phone logs, government records, and other digital traces left by people. Significant questions emerge. Will large-scale search data help us create better tools, services, and public goods? Or will it usher in a new wave of privacy incursions and invasive marketing? Will data analytics help us understand online communities and political movements? Or will it be used to track protesters and suppress speech? Will it transform how we study human communication and culture, or narrow the palette of research options and alter what ‘research’ means? Given the rise of Big Data as a socio-technical phenomenon, we argue that it is necessary to critically interrogate its assumptions and biases. In this article, we offer six provocations to spark conversations about the issues of Big Data: a cultural, technological, and scholarly phenomenon that rests on the interplay of technology, analysis, and mythology that provokes extensive utopian and dystopian rhetoric.
This paper explores the emergent nature of organizational knowledge, which has not been addressed sufficiently in the current Knowledge Management (KM) research. For the task, we reconsider the concept of knowledge by looking at four distinct discourses on knowledge; namely, knowledge as object, knowledge as interpretation, knowledge as process, and knowledge as relationship. Then the fundamental nature of the emergence of knowledge will be discussed where we will argue that human interaction is the source of knowledge emergence. Based on the theoretical discussions, we examine a case study of a fire crisis threatening the supply chain between Aisin Seiki and Toyota in Japan. It demonstrates significant self-organizing, emergent KM practices that effectively coordinated various human and physical resources and contextual information in this chaotic situation. Finally we discuss the implications for future KM practices, by considering in particularly the institutional aspects of information and communication technologies in KM practices.
How should we understand why firms exist? A prevailing view has been that they serve to keep in check the transaction costs arising from the self-interested motivations of individuals. We develop in this article the argument that what firms do better than markets is the sharing and transfer of the knowledge of individuals and groups within an organization. This knowledge consists of information (e.g., who knows what) and of know-how (e.g., how to organize a research team). What is central to our argument is that knowledge is held by individuals, but is also expressed in regularities by which members cooperate in a social community (i.e., group, organization, or network). If knowledge is only held at the individual level, then firms could change simply by employee turnover. Because we know that hiring new workers is not equivalent to changing the skills of a firm, an analysis of what firms can do must understand knowledge as embedded in the organizing principles by which people cooperate within organizations.
Based on this discussion, a paradox is identified: efforts by a firm to grow by the replication of its technology enhances the potential for imitation. By considering how firms can deter imitation by innovation, we develop a more dynamic view of how firms create new knowledge. We build up this dynamic perspective by suggesting that firms learn new skills by recombining their current capabilities. Because new ways of cooperating cannot be easily acquired, growth occurs by building on the social relationships that currently exist in a firm. What a firm has done before tends to predict what it can do in the future. In this sense, the cumulative knowledge of the firm provides options to expand in new but uncertain markets in the future.
We discuss at length the example of the make/buy decision and propose several testable hypotheses regarding the boundaries of the firm, without appealing to the notion of “opportunism.”
Knowledge has widely been acknowledged as one of the most important factors for corporate competitiveness, and we have witnessed an explosion of IS/IT solutions claiming to provide support for knowledge management (KM). A relevant question to ask, though, is how systems and technology intended for information such as the intranet can be able to assist in the managing of knowledge. To understand this, we must examine the relationship between information and knowledge. Building on Polanyi's theories, the author argues that all knowledge is tacit, and what can be articulated and made tangible outside the human mind is merely information. However, information and knowledge affect one another. By adopting a multi perspective of the intranet where information, awareness, and communication are all considered, this interaction can best be supported and the intranet can become a useful and people-inclusive KM environment.
Despite the widely recognised importance of knowledge as a vital source of competitive advantage, there is little understanding of how organisations actually create and manage knowledge dynamically. Nonaka, Toyama and Konno start from the view of an organisation as an entity that creates knowledge continuously, and their goal in this article is to understand the dynamic process in which an organisation creates, maintains and exploits knowledge. They propose a model of knowledge creation consisting of three elements: (i) the SECI process, knowledge creation through the conversion of tacit and explicit knowledge; (ii) ‘ba’, the shared context for knowledge creation; and (iii) knowledge assets, the inputs, outputs and moderators of the knowledge-creating process. The knowledge creation process is a spiral that grows out of these three elements; the key to leading it is dialectical thinking. The role of top management in articulating the organisation's knowledge vision is emphasised, as is the important role of middle management (‘knowledge producers’) in energising ba. In summary, using existing knowledge assets, an organisation creates new knowledge through the SECI process that takes place in ba, where new knowledge, once created, becomes in turn the basis for a new spiral of knowledge creation.
The Fourth Industrial Revolution is changing everything - from the way we relate to each other, to the work we do, the way our economies work, and what it means to be human. We cannot let the brave new world that technology is currently creating simply emerge. All of us need to help shape the future we want to live in. But what do we need to know and do to achieve this?
In Shaping the Fourth Industrial Revolution, Klaus Schwab and Nicholas Davis explore how people from all backgrounds and sectors can influence the way that technology transforms our world. Drawing on contributions by more than 200 of the world's leading technology, economic and sociological experts to present a practical guide for citizens, business leaders, social influencers and policy-makers this book outlines the most important dynamics of the technology revolution, highlights important stakeholders that are often overlooked in our discussion of the latest scientific breakthroughs, and explores 12 different technology areas central to the future of humanity.
Emerging technologies are not predetermined forces out of our control, nor are they simple tools with known impacts and consequences. The exciting capabilities provided by artificial intelligence, distributed ledger systems and cryptocurrencies, advanced materials and biotechnologies are already transforming society. The actions we take today - and those we don't - will quickly become embedded in ever-more powerful technologies that surround us and will, very soon, become an integral part of us.
By connecting the dots across a range of often-misunderstood technologies, and by exploring the practical steps that individuals, businesses and governments can take, Shaping the Fourth Industrial Revolution helps equip readers to shape a truly desirable future at a time of great uncertainty and change.
The massive accessing of unbalance and nonlinear loads to a power distribution network may cause serious problems on power quality and result in additional power loss and safety risks. Distributed generation (DG) with a converter interface has a flexible adjusting ability and can provide certain unbalance and harmonic compensations to power distribution networks. In this study, an optimal operation method of an active distribution network (ADN) involving the unbalance and harmonic compensation of a converter was proposed. First, the power regulating characteristics of DG were analyzed. Second, an optimization model was constructed to minimize ADN power loss, sum of harmonic voltage squares, and total unbalance of the negative sequence voltage, and multiple constraints, such as converters and network, were considered in the model. Third, the optimization model was transformed into a semidefinite programming model based on the characteristics of the nonconvex and nonlinear original model, thus assuring global convergence and decreasing difficulties in problem solving. Finally, the validity of the proposed algorithm was verified by using IEEE 33-node and 123-node test systems.
The advances in smart grids are enabling huge amount of data to be aggregated and analyzed for various smart grid applications. However, the traditional smart grid data management systems cannot scale and provide sufficient storage and processing capabilities. To address these challenges, this paper presents a smart grid big data eco-system based on the state-of-the-art Lambda architecture that is capable of performing parallel batch and real-time operations on distributed data. Further, the presented eco-system utilizes a Hadoop Big Data Lake to store various types of smart grid data including smart meter, images and video data. An implementation of the smart grid big data eco-system on a cloud computing platform is presented. To test the capability of the presented eco-system, real-time visualization and data mining applications were performed on real smart grid data. The results of those applications on top of the eco-system suggest that it is capable of performing numerous smart grid big data analytics.
Smart meters are being deployed replacing conventional meters worldwide and to enable automated collection of energy consumption data. However, the massive amounts of data evolving from smart grid meters used for monitoring and control purposes need to be sufficiently managed to increase the efficiency, reliability and sustainability of the smart grid. Interestingly, the nature of smart grids can be considered as a big data challenge that requires advanced informatics techniques and cyber-infrastructure to deal with huge amounts of data and their analytics. For that, this unprecedented smart grid data require an effective platform that takes the smart grid a step forward in the big data era. This paper presents a framework that can be a start for innovative research and take smart grids a step forward. An implementation of the framework on a secure cloud-based platform is presented. Furthermore, the framework has been applied on two scenarios to visualize the energy, for a single-house and a smart grid that contains over 6000 smart meters. The application of the two scenarios to visualize the grid status and enable dynamic demand response, suggests that the framework is feasible in performing further smart grid data analytics.
Purpose
This viewpoint article makes the case that the field of Knowledge Management (KM) must respond to the significant changes that big data/analytics is bringing to operationalizing the production of organizational data and information.
Design/methodology/approach
This article expresses the opinions of the co-editors of the special Journal of Knowledge Management issue, Does Big Data mean Big Knowledge?: KM Perspectives on Big Data and Analytics.
Findings
A Big Data/Analytics-Knowledge Management (BDA-KM) model is proposed that illustrates the centrality of knowledge as the guiding principle in the use of big data/analytics in organizations.
Research limitations/implications
This is an opinion piece and the proposed model still needs to be empirically verified.
Practical implications
It is suggested that academic and practitioners in KM must be capable of controlling the application of big data/analytics and calls for further research investigating how KM can conceptually and operationally use and integrate big data/analytics to foster organizational knowledge for better decision-making and organizational value creation.
Originality/value
The Big Data/Analytics-Knowledge Management (BDA-KM) model is one of the early models placing knowledge as the primary consideration in the successful organizational use of Big Data/analytics.
The potential transformation cyber-physical systems can bring to a broad variety of domains is widely discussed in academia and industry. Despite the expected benefits in the industrial domain of further automatization of production processes and the possibility to produce “batch size one” at large-scale production costs, the majority of organizations hesitate in the implementation of cyber-physical systems. This can be attributed to uncertainty decision makers feel, about how to choose right applications of cyber-physical systems and if chosen how to implement these applications to the unique and specific needs of their organization. To address this problem this chapter introduces an application map which includes the spheres smart factory, industrial smart data, industrial smart services, smart products, product-related smart data and product-related smart services. Based on this model, the decision makers are provided a scheme of application fields for utilizing cyber-physical architectures adjusted to their unique business situation.
