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Sustainability for the Holistic Ecosystem : Regulation Guide Designing for the Prevalent Technology Development of China Emerging Communications: Big Data, IoT, 5G etc.
... Their relatively small number confirms the observed gap of research on the nexus of EAM and Big Data or GDPR. The papers address either the EAM-Big Data relationship or the EAM-GDPR relationship, but only one paper (i.e., (Chao 2018) simultaneously addresses all three areas). The fact that most of those papers are published from 2018 onwards indicates that the research interests in applying EAM for big data protection and privacy compliance in environments with BDA applications, is quite recent. ...
... The research conducted by Chao (2018;Janssen 2017, 2020;Lněnička et al. 2017) supports the idea that EAM is an effective means to address these needs, as it enables organisations to easily adapt and manage, to a certain degree, the complexity of their corporate information systems, increase the business value of BDA besides developing the appropriate internal capabilities. In this regard (Kehrer et al. 2016a, b) pointed out that many organisations endeavour to integrate Big Data technologies into existing IT and business architectures. ...
Context
Big Data Analytics is a rapidly emerging IT practice whose applications offer benefits for a wide variety of business areas across an organisation. Given the wide scope of applications, the many types of processing involved, including those for purposes not yet foreseen, and the inherent privacy concerns resulting from collecting and storing personal data, the newly introduced General Data Protection Regulation (GDPR) poses specific challenges for safeguarding the security and protection of big data. These challenges are not limited to the IT function but extend across the entire organisation. This raises the question whether Enterprise Architecture Management (EAM), as an approach for ensuring the coherence, strategic alignment and focus on value creation of all organisational resources, offers guidance for addressing those challenges in a holistic manner, and thus provides a fruitful ground for developing an approach for complying to GDPR requirements in a Big Data context.
Objective
This study surveys the state-of-the-art in research on security, privacy, and protection of big data. The focus is on investigating which specific issues and challenges have been identified and whether these have been linked to GDPR requirements. Further, it examines whether previous research has investigated the potential of EAM in addressing those challenges and what the main findings of those studies are.
Method
We used Systematic Mapping Review (SMR), which is a methodology for literature review aimed at surveying the state-of-the-art in a research field as it is documented in the scientific literature. Further, we used Template Analysis, which is a thematic analysis technique, for coding the texts of the selected papers, classifying the research studies, and interpreting the different themes addressed in the literature.
Results
Our study indicates that only few researchers have explored the use of EAM practices in relation to data security and protection in a Big Data context. We further identified seven trends within the areas under consideration that could be subjects for further research.
Conclusions
Our study does not invalidate the potential of EAM to help addressing GDPR requirements in a Big Data context. However, how EAM practices may contribute to risk management and data governance in environments where big data are being processed, is still a huge research gap, which we intend to address in our future research.
... Other actors, such as Subjectivity, Corruption, Financial Resources, Ecosystems and Politics were examined less than 5 times. The ANT has guided BDAD research across various contexts entailing environment and sustainability (Ascui et al., 2018;Chao, 2018), social life (Ruppert, Law, and Savage, 2013), finance and banking (Campbell-Verduyn, Goguen, and Porter, 2017), journalism (Hammond, 2017), higher education (Keith and Van Belle, 2015), and music (Webster et al., 2016). For instance, Diniz, Luvizan, Hino, and Ferreira (2018) applied the ANT to examine BDAD in Brazilian banks by using a case study analysis of bank's managers from the moment that they became aware of the relevance of BDAD through to its integration into the firm's strategy. ...
Incorporating big data analytics into a particular context brings various challenges that rest on the model or framework through which individuals or organisations adopt big data to achieve their objectives. Although these models have recently triggered scholars’ attention in various domains, in-depth knowledge of using each of these models in big data research is still blurred. This study enriches our knowledge on emerging models and theories that shape big data analytics adoption (BDAD) research through a bibliometric analysis of 229 studies (143 journal articles and 86 conference papers) published in indexed sources between 2013 and 2019. As a result, twenty models on BDAD have emerged (e.g., “Dynamic Capabilities”, “Resource-Based View”, “Technology Acceptance Model”, “Diffusion of Innovation”, etc.). The analysis reveals that BDAD research to demonstrate attributes suggestive of a topic at an initial stage of development as it is broadly dispersed across different domains employs a wide range of models, some of which overlap. Most of the applied models are generic in nature focusing on variance-based relationships and snapshot prediction with little consensus. There is a conspicuous dearth of process models, firm-level analysis and cultural orientation in contemporary BDAD research. Insights of this bibliometric study could guide rigorous big data research and practice in various contexts. The study concludes with research implications and limitations that offer promising prospects for forthcoming research.
During the almost 27 years of its existence, the business ecosystem research has developed a substantial level of ambiguity and multifacetedness. Because to the technological advancements that promote interconnectedness and value co-creation, the field has consequently spun off into more domain-specific branches, such as the arena of digital business ecosystems that are driven by Internet of Things (IoT). Nonetheless, despite the efforts to mend the theoretical foundations and to close the gap between academia and empirical practice, the absolute majority of IoT-driven digital business ecosystem literature follows the trend of conceptual randomness while expanding the volume of publications exponentially. Therefore, in order to address this unfavourable increase in random adoption of distinct concepts that ultimately refer to the same subject matter, the author encourages other scholars involved in the research field of IoT-driven digital business ecosystems to make extended efforts and support the external validity of their research (as well as the relevance of the research stream as a whole) by bounding the IoT-driven digital business ecosystems on a rigorous basis through deploying the extant theory in a careful and appropriate manner. Via a thorough examination of the theoretical fundaments that underpin the concept of IoT-driven digital business ecosystem, and based on a concise thematic review of corresponding literature published until September 2020, this article articulates logic for viewing the conceptual hierarchy within the business ecosystem research and proposes six literature-based recommendations for developing further IoT-driven digital business ecosystem (DBE) research in a rigorous way.
The Internet of Things (IoT) is a promising technology which tends to revolutionize and connect the global world via heterogeneous smart devices through seamless connectivity. The current demand for Machine-Type Communications (MTC) has resulted in a variety of communication technologies with diverse service requirements to achieve the modern IoT vision. More recent cellular standards like Long-Term Evolution (LTE) have been introduced for mobile devices but are not well suited for low-power and low data rate devices such as the IoT devices. To address this, there is a number of emerging IoT standards. Fifth Generation (5G) mobile network, in particular, aims to address the limitations of previous cellular standards and be a potential key enabler for future IoT. In this paper, the state-of-the-art of the IoT application requirements along with their associated communication technologies are surveyed. Additionally, the 3rd Generation Partnership Project (3GPP) cellular-based Low-Power Wide Area (LPWA) solutions to support and enable the new service requirements for Massive to Critical IoT use cases are discussed in detail, including Extended Coverage Global System for Mobile Communications for the Internet of Things (EC-GSM-IoT), enhanced Machine-Type Communications (eMTC), and Narrowband-Internet of Things (NB-IoT). Furthermore, 5G New Radio (NR) enhancements for new service requirements and enabling technologies for the IoT are introduced. This paper presents a comprehensive review related to emerging and enabling technologies with main focus on 5G mobile networks that is envisaged to support the exponential traffic growth for enabling the IoT. The challenges and open research directions pertinent to the deployment of Massive to Critical IoT applications are also presented in coming up with an efficient context-aware congestion control (CACC) mechanism.
This chapter aims to provide methods and tools for sustainable values creation in the context of high value engineering. First, a sustainable value-driven life cycle design framework is proposed for product and process engineering innovation for sustainability and provides a conceptual linkage with the value-creating activities such as design, production, supply chains, partnerships, and distribution channels. Then, approaches for the integration of ecological assessment (i.e. LCA) with computer-aided product development are proposed as a useful tool to support sustainable value-oriented product and process engineering. The proposed approaches and tools are expected to help bring experts in fields of product and process engineering, industrial management, and ecological assessments to a common vision, and therefore accelerate development of more sustainable products, processes, and business strategies.
This chapter provides insights into how open innovation and global product development can add value in a high value engineering environment, and how the impacts can be monitored and managed effectively. The chapter begins by introducing engineering design in a global context, and how open innovation approaches can support value creation and capture for high value engineering. It then highlights the importance and impact of strategic decision-making in global product development, utilising insights from global engineering companies to provide recommendations for managerial decision support. Finally, building on well-established concepts from performance measurement, a framework to support the development of key performance indicators in global engineering design projects is developed with its application exemplified in industry.
Currently, the transfer of personal data to the USA raises several problems, since the Safe Harbor agreement between the European Commission and the US is no longer in effect. By now, companies can use the subsequent agreement called Privacy Shield. In the future, contractual arrangements are expected to become increasingly relevant. Whether this is a realistic long-term solution depends on the implementation of the ECJ’s guidelines.
Digitization has increased in importance for the agricultural sector and is described through concepts like Smart Farming and Precision Agriculture. Due to the growing world population, an efficient use of resources is necessary for their nutrition. Technology like GPS, and, in particular, sensors are being used in field cultivation and livestock farming to undertake automatized agricultural management activities. Stakeholders, such as farmers, seed producers, machinery manufacturers, and agricultural service providers are trying to influence this process. Smart Farming and Precision Agriculture are facilitating long-term improvements in order to achieve effective environmental protection. From a legal perspective, there are issues regarding data protection and IT security. A particularly contentious issue is the question of data ownership.
Scoring is an assessment procedure, especially for the purpose of credit assessment. Big data did not “create” that kind of procedure but influences the calculation of probability forecasts by opening up additional data sources and by providing enhanced possibilities of analyzing data. Scoring is negatively connoted. While being connected to risks, it opens up opportunities for companies as well as for the data subject. Since 2009, scoring is regulated by the German Federal Data Protection act, which entitles the data subject to get information free of charge once a year. Currently, a draft amendment concerning scoring is discussed in Parliament.
Old industry versus new industry: massive power struggles are expected within the automotive industry. Data sovereignty in the cockpit will bring substantial benefits in the future. With the assistance of cutting-edge IT, a variety of data will and already can be collected, processed and used in modern vehicles. Various parties pursue a keen interest in accessing a wide range of data sets in modern vehicles. For the legal classification of data the issue of personal reference, according to the Federal Data Protection Act (BDSG), plays a decisive role. This data may only be collected and processed within narrow limits. Fundamental questions must be discussed: To whom are the data legally assigned to? Who has the rights of disposal? And to whom do they “belong”? Autonomous driving is no longer just a dream of the future. Apart from the issues involving liability, ethical issues, in particular, need to be discussed. Concepts like “Privacy by Design” and “Privacy by Default” may be potential solutions. Data protection needs to be positioned on the same level as traffic safety and environmental protection. In this respect, the German automotive industry can assume a leading role.
Energy transformation is becoming digital: For several years now the EU has been pursuing the implementation of regulations for the digitalization of the energy networks. Electricity grids became more complex due to the focus on renewable energies; more efficient ways of energy management will be required: Smart Grid. The network load will be enhanced through gathering information regarding power consumption and production, as well as through automated decisions in the Smart Grid. Furthermore, the Smart Grid makes it possible to offer variable electricity rates; whereby, the price for the customer depends on the date of consumption. Legally, aspects of data protection and energy industry law play a major role. The rollout of smart meters in private households contains a substantial saving and optimization potential, and also enables the day-to-day lifecycles of households to be recorded in detail.
M2M-communication will play an increasing role in everyday life. The classic understanding of the term “declaration of intent” might need reform. In this regard, the legal construct of an electronic person might be useful. The use of autonomous systems involves several liability issues. The idea of “defects” that is laid down in the product liability law is of vital importance regarding these issues. To solve legal problems in the field of big data the main function of law as an element of controlling, organizing, and shaping needs to be kept in mind.
Big data is closely linked to the new, old question of data quality. Whoever pursues a new research perspective such as big data and wants to zero out irrelevant data is confronted with questions of data quality. Therefore, the European General Data Protection Regulation (GDPR) requires data processors to meet data quality standards; in case of non-compliance, severe penalties can be imposed. But what does data quality actually mean? And how does the quality requirement fit into the dogmatic systems of civil and data protection law?
5G encompasses the development of various key wireless communication technology standards. The development entails both technological advancement and social interaction. This paper analyzes 5G development and its significant shift from a strategy policy to a user-oriented development showing the coplay of technology and society in China. Based on this theory, relevant proposals are recommended for future technical development and industrial regulation. Also, to increase the throughput and spectrum efficiency, channel assignment and load balance are considered. A hybrid routing protocol TOHRP (tree-based and on-demand hybrid routing protocol) and a distributed channel assignment algorithm LBCA (Load Based Channel Assignment) in the multichannel environment are proposed and they solve the traditional problem of ( 1 ) waste of spectrum and ( 2 ) self-interference. The computer-simulated experiment shows that the algorithm improves the performance.
Big data analytics is the current, trending hot-topic in the research community. Several tools and techniques for handling and analysing structured and unstructured data are emerging very rapidly. However, most of the tools require high expert knowledge for understanding their concepts and utilizing them. This chapter presents an in-depth overview of the various corporate and open-source tools currently being used for analysing and learning from big data. An overview of the most common platforms such as IBM, HPE, SAPRANA, Microsoft Azure and Oracle is first given. Additionally, emphasis has been laid on two open-source tools: H2O and Spark MLlib. H2O was developed by H2O.ai, a company launched in 2011 and MLIB is an open source API that is part of the Apache Software Foundation. Different classification algorithms have been applied to Mobile-Health related data using both H2O and Spark MLlib. Random Forest, Naïve Bayes, and Deep Learning algorithms have been used on the data in H2O. Random Forest, Decision Trees, and Multinomial Logistic Regression Classification algorithms have been used with the data in Spark MLlib. The illustrations demonstrate the flows for developing, training, and testing mathematical models in order to obtain insights from M-Health data using open source tools developed for handling big data.
The main objectives of smart cities are to improve the well being of its citizens and promote economic development while maintaining sustainability. Smart cities can enhance several services including healthcare, education, transportation and agriculture among others. Smart cities are based on the ICT framework including the Internet of Things (IoT) technology. These technologies create voluminous amount of heterogeneous data, which is commonly referred to as big data. However these data are meaningless on their own. New processes need to be developed to interpret the huge amount of data gathered and one solution is the application of big data analytics techniques. Big data can be mined and modelled through the analytics techniques to get better insight and to enhance smart cities functionalities. In this chapter, four state-of-the-art big data analytics techniques are presented. Applications of big data analytics to five sectors of smart cities are discussed and finally an overview of the security challenges for big data and analytics for smart cities is elaborated.
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Driven by the advances in hardware and software technologies, the term Internet of things has emerged as a worldwide framework of ‘smart’ internet-based interconnected electronic devices through web having a significant impact in the betterment of our traditional living style. The use of these web connected embedded devices, as Information and communication technologies for re-shaping modern cities, lead to the concept of smart cities. This chapter surveys the most important domains of smart cities and illustrates the recent research and development in them. After identifying critical areas, the chapter also highlights and discusses the issues and research gaps in recent technologies. Finally, it presents the opportunities and research directions in future advancements of intelligent cities.
Despite the recent increase in the utilisation of the tools of “Big Data” and “Business Intelligence” (BI), the investigation that has been carried out regarding the inferences related to its implementation and performance is relatively scarce. Analytical tools have a significant impact on the development and sustainability of a company since the evaluation of the clients information are critical aspects and crucial in the progress towards a competitive market. All corporations at certain phase in their life cycle, require to implement different and improved data processing systems in order to optimize the decision making procedures. Enterprises utilise BI outcomes to pull together records that has been extracted from consolidated analyses from signals in the data grouping of BI information scheme. This, in turn, gives a marked advantage to companies in the development of activities based on predictions, and also to compete with competitors in the market. Business Intelligence applications are precise implements that resolve this matter for companies that require, data storing and administration. The chapter examines the most recommended Business Intelligence open source applications currently available: Pentaho and Jaspersoft, processing big data over and done with six databases of diverse sizes, with a special focus on their extract transform and load and Reporting procedures by calculating their routines through computer algebra systems. Moreover, the chapter correspondingly makes available a complete explanation of the structures, features, and comprehensive implementation scenario.
This paper examines the extent to which both the support services of technological business incubators (TBIs) and exogenous local factors facilitate the innovation activity of incubated new ventures. Using data on all 215 surviving Chinese incubators and their incubated firms from government surveys conducted over five consecutive years from 2009 until 2013, combined with information from nine case studies, we examine the effects of four incubator services on three levels of innovation in incubated firms, whilst also taking account of key exogenous factors. Technical service support from an incubator was found to have had a positive influence on all levels of innovation activity across all regions whilst incubator financial support had a positive effect on the making of more advanced innovations. The availability of venture capital had a significant impact on making lower order innovations whereas the availability of scientific knowledge resources influenced more advanced innovation activity. Whereas TBI support services in the more developed Eastern region are mainly concerned with leveraging external resources, those in the less developed Central and Western regions are more concerned with compensating for the lack of external resources to support innovation.
With the rapid development of smart factory of Industry 4.0, all kind of industrial devices are adopted in smart factory. As a result, the complexity of cooperative behaviors among industrial devices increases rapidly. It becomes more and more difficult for practicing engineers to ensure temporal correctness of cooperative behaviors. At the same time, smart factory has made its way into the domain of Safety-Critical Systems (SCS), where there is a higher requirement for temporal correctness of cooperative behaviors. Therefore, it faces great challenges for practicing engineers to ensure temporal correctness of cooperative behaviors. Nowadays, some methods have been proposed for analysis of cooperative behaviors. They can use graphic methods and formal methods to create the graphic model and formal model to analyze and ensure the temporal correctness. However, these methods lack the capability of multi-perspective analysis, that is exactly necessary for those systems that have a higher requirement for temporal correctness. To address this issue, in this paper, we propose a multi-perspective method, that can analyze cooperative behaviors from the macroscopic perspective and the microscopic perspective. Comparing to existing methods, it can provide a more thorough analysis for cooperative behaviors. The usage of our method is illustrated by a semiconductor manufacturing scenario.
Internet of Things (IoT), which will create a huge network of billions or trillions of “Things” communicating with one another, are facing many technical and application challenges. This paper introduces the status of IoT development in China, including policies, R&D plans, applications, and standardization. With China's perspective, this paper depicts such challenges on technologies, applications, and standardization, and also proposes an open and general IoT architecture consisting of three platforms to meet the architecture challenge. Finally, this paper discusses the opportunity and prospect of IoT
The telecommunications industry has been undergoing tremendous technological changes, and owning to continuous technological advancement, it has maintained sustained prosperity and development. In this paper, the interplay between technology, market and government in telecommunications is discussed briefly, and then we introduce technology and government into the traditional SCP (Structure-Conduct-Performance) paradigm to develop an industry analysis framework called TG-M(SCP) (Technology-Government-Market (Structure-Conduct-Performance)). Based on this framework, we present the spiral co-evolution model which elaborates on the interaction mechanism of technological innovation with government regulation and market dynamics from the perspective of industry evolution. Our study indicates that the development of the telecommunications industry is the result of the coevolution of technology, government regulation and market forces, and among the three actors, technology is the fundamental driving force. Relative to the 'invisible hand' (market) and 'visible hand' (government), we conceptualize technology as the 'third hand', which fundamentally drives the development of telecommunications industry in coordination with the other two hands. We also provide several policy implications regarding these findings.
This study provides an innovative application of virtual water trade in the traditional allocation of physical water resources in water scarce regions. A multi-objective optimization model was developed to optimize the allocation of physical water and virtual water resources to different water users in Beijing, China, considering the trade-offs between economic benefit and environmental impacts of water consumption. Surface water, groundwater, transferred water and reclaimed water constituted the physical resource of water supply side, while virtual water flow associated with the trade of five major crops (barley, corn, rice, soy and wheat) and three livestock products (beef, pork and poultry) in agricultural sector (calculated by the trade quantities of products and their virtual water contents). Urban (daily activities and public facilities), industry, environment and agriculture (products growing) were considered in water demand side. As for the traditional allocation of physical water resources, the results showed that agriculture and urban were the two predominant water users (accounting 54% and 28%, respectively), while groundwater and surface water satisfied around 70% water demands of different users (accounting 36% and 34%, respectively). When considered the virtual water trade of eight agricultural products in water allocation procedure, the proportion of agricultural consumption decreased to 45% in total water demand, while the groundwater consumption decreased to 24% in total water supply. Virtual water trade overturned the traditional components of water supplied from different sources for agricultural consumption, and became the largest water source in Beijing. Additionally, it was also found that environmental demand took a similar percentage of water consumption in each water source. Reclaimed water was the main water source for industrial and environmental users. The results suggest that physical water resources would mainly satisfy the consumption of urban and environment, and the unbalance between water supply and demand could be filled by virtual water import in water scarce regions.
Manufacturing-related engineering skill shortages are being experienced in the USA and the UK. In this chapter we compare two models of employer-engagement in the education of 14- to 19-year-olds. The first model, University Technical Colleges (UTC), are supported by the UK government and engage national employers in developing localised solutions to skill demand in science, technology, engineering and mathematics (STEM)-related occupations. The second approach, based in Chicago, USA, is the product of a local coalition of manufacturing firms and organisations, the Chicago Manufacturing Renaissance Council (CMRC). The CMRC has created a manufacturing-oriented training programme at a public high school called Austin Polytechnic Academy (APA). The APA provides specialist engineering-related education within a “normal” school environment. They pull in small- and medium-sized enterprises as partner organisations to offer work-based learning opportunities for APA students through summer internships, job shadowing, mentoring, and eventual job placement. These two examples offer alternative approaches to developing vocational education for the engineering sector. Employer-engagement in the education of young people is shown to add value in the form of exposure to the engineering sector, preparedness for work, potential opportunities for employment and ongoing career progression. This form of value is not identified in traditional academic performance measures and risks undermining the advantage of including businesses in the development of classroom and applied learning structures.
This chapter reviews the general context for policymaking; discusses the logic of economic development; and suggests various types of economic policy with a particular focus on advanced manufacturing and knowledge-intensive services. A range of policy instruments for high value engineering are introduced with an overall framework encompassing social context, physical infrastructure, factors of productions, factors of consumption, and social capital. The chapter also discusses the targeting of policies for high value engineering.
The aim of this chapter is to introduce the modelling and optimization methods in engineering value chain decision-making and show the effectiveness and advances of solving management problems by information technologies. There are many critical problems related to decision-making along the engineering value chain. Based on the characteristics of a specific problem, different decision methods are developed and applied. In this chapter, we summarize several typical value chain decision problems and related decision methods and introduce the popular decision-making methods based on mathematic models and optimization algorithms. A complex engineering value chain construction decision problem is investigated, a multi-objective model is then developed, and the genetic algorithm-based solution procedure is proposed. Finally, numerical experiment and discussion are conducted to demonstrate the benefit of the method. Further, future development trends in engineering value chain modelling and optimization are illustrated.
Scientists, engineers and other technologists have a crucial role to play in the development and deployment of technology for the economic benefit of society, and also to address challenges facing humanity, such as climate change and resource scarcity. In this context, the management of engineering and technology becomes increasingly important. Technological investment and effort needs to be aligned with organisational and wider social needs and aspirations throughout the life cycle from design, through to production and the creation of valuable services. In order to manage engineering and technology, processes are needed to align inputs and activities with desired outputs along the technology management process framework comprising five broad areas of activity. These process areas are elaborated in this chapter, posed as questions that managers, engineers and technologists must address for the effective management of engineering and technological knowledge and resources.
This chapter provides a systematic method to understand the innovation implications of complex engineering systems through value chain simulation. Particularly, we explore the usefulness of agent-based simulation for studying engineering value chains, a field of inquiry that has attracted increasing research interests. Essentially, given the network character of engineering value chains, we discuss why an agent-based simulation approach is particularly suitable for studying engineering value chains by reviewing related literature and discussing the state of the art in the field. A case of using agent-based simulation to study innovation diffusion (i.e. firm–customer relationship) in an engineering value chain is presented to demonstrate the value of the approach in studying high value engineering networks. We finally discuss opportunities and challenges of adopting agent-based simulation for future high value engineering network study.
The aim of this chapter is to unpack the concept of value, exploring what it means in a practical setting of engineering services (servitisation) using the service staircase. It consists of two sections. In the first section we will explore firms that are able to create value through services. The second section will create an insight into how the customer can create value through services created for them. Industrial firms are shifting their product-oriented strategies to services with the principal aim to expand their growth and market portfolios. For the customer it can be advantageous to take on advanced services from the service provider, and it will reduce his risk base and reduce costs for optimisation projects. The outcome would be for the customer to focus on its core business. The baseline of working together is to act as network partners instead of suppliers and build relationships instead of transactions. Overall, both the customer and the service provider have the ability to create and capture value through servitised offerings.
Manufacturing industry has been experiencing dramatic evolutions in last three decades. The boundaries of a manufacturing system are extended from factory towards various types of network relationships. The missions of manufacturing system are transformed and redefined. The output scopes are also expanded into servitisation from traditional product-focused concentration. This chapter, based upon recent observations and analysis in both globalised production and engineering networks, introduces an evolutionary picture of manufacturing system with its engineering function systems. The chapter demonstrates a theoretical framework that highlights both globalised manufacturing and engineering networks have not only very unique structures and configurations but also distinguished strategic capabilities. The chapter suggests that the classical manufacturing strategy and system design theories should be adapted into a contingency strategy to respond to the manufacturing evolutions and treat different systems interdependently. The chapter also seeks to clarify the two distinguished concepts and systems—production and engineering—based on the case analysis, and tries to achieve a synergy between those two different value creation systems on the global platform. The chapter suggests a new conceptual framework for further research on new manufacturing evolution and contingency strategy.
Sustainability is increasingly recognised as a key approach to long-term high value creation along the entire engineering value chain. Engineering for sustainable value creation requires business models that systematically integrate each stage of the engineering value chain to maximise total value creation. There is a need for methods and tools to help companies deliver such sustainable value. This chapter presents a conceptual model of engineering for sustainable value, and a practical tool (i.e. Sustainable Value Analysis Tool) to elicit value uncaptured across the whole life cycle and uncover new value opportunities through a structured and visual approach. The tool has helped a number of engineering companies to innovate business models for sustainable engineering operations in global value networks.
This chapter addresses coordination and integration issues of the engineering value chain. A comprehensive review of relevant studies is present, and challenges and some prospect solutions in the new business environment are analyzed in the first section. Then, the coordination issue is explored by considering practical problems involving information coordination and an available-to-promise (ATP)-based modeling, which are put forth in the Sects. 2 and 3. Conclusions are drawn in the Sect. 4
Recent trend of integrating ICT in engineering solutions has enabled superior engineering capabilities to create high-value-added products and services which lead to the disruption and transformation of traditional engineering industries. This chapter firstly highlights the ICT-driven industrial evolution to demonstrate the importance of ICT for building engineering capabilities in the international context. The typical ICT-enabled advanced manufacturing technologies and models are then introduced, which represent the most advanced engineering, manufacturing and management capabilities in the current business environment. By reviewing the ongoing ICT revolution, this chapter suggests high value engineering strategies for the major economies of the world and indicates the value creating dominance of ICT for engineering sectors in the future.
There was a strong movement for multinational corporations (MNCs) from developed countries to establish a transnational configuration of engineering capabilities in other developed countries in the 1980s and 1990s, and in emerging economies in the new millennium. In the last decade, MNCs from emerging countries such as China are trying to move up the value chain by investing in engineering capabilities overseas. This chapter tries to understand such new trends by incorporating the existing literature and practices of international research and development (R&D, as a core part of MNCs’ engineering capabilities), and to contrast them with Chinese MNCs’ current practices of expanding overseas engineering capabilities. We suggest that MNCs from China (and emerging countries) should learn from the experience of their Western counterparts, but more importantly, the unique challenges and opportunities should be considered in managing their international engineering strategies and decisions.
Purpose
In the past decade, various types of organizations all over the world have made numerous efforts to include sustainability in the management systems. These efforts have only partly been able to manage sustainability since these were focused to address specific needs of different type of organizations; mostly to comply with regulations. Therefore, a holistic consideration of sustainability in management systems was neglected. The current study is an attempt to fill this gap and highlight the imperative features of sustainability management from organizational point of view.
Methodology
This research is designed to conduct a comprehensive and systematic literature review which targets three different dimensions of sustainability management at organizational level; sustainability management and assessment frameworks published in academic journals, and standardized international guidelines for incorporating sustainability in management systems.
Findings
The findings of the literature review reveal that the operational parameters required to systematically undertake the essential elements of sustainability in management systems, and the inter-relationship of those parameters, have been largely ignored. Furthermore, integration of sustainability assessment in the management models has been overlooked.
Originality value
In order to fill this gap, a generic sustainability management framework is proposed with a three-dimensional insight on organizational sustainability management. The threefold focus ensures that the proposed framework is: (1) fundamentally correct, (2) consistent with the established international standardized guidelines, and (3) embedded with an assessment tool which keeps the focus of sustainability management system in the intended direction. The contribution of this paper is unique from existing relevant studies for two main reasons: (1) the authors have undertaken the management of sustainability as a distinct concept rather than the integration of existing MSs and MSSs, which many authors have done in past; and (2) the proposed framework establishes clear and distinct connections between the processes required to systematically manage sustainability, rather than trying to stitch what exists in the ad hoc systems.
Implications
The advantages of this framework include extensive involvement of stakeholders, enhanced transparency, adaptiveness, and organized development and assessment of the sustainability management system. Moreover, multi-dimensional focus of the proposed framework is one of its strengths; economic, quality, environmental, health and safety, and social challenges can all be managed through the application of this framework.
While there is a general recognition that breakthrough innovation is non-linear and requires an alignment between producers (supply) and users (demand), there is still a need for strategic intelligence about the emerging supply chains of new technological innovations. This technology delivery system (TDS) is an updated form of the TDS model and provides a promising chain-link approach to the supply side of innovation. Building on early research into supply-side TDS studies, we present a systematic approach to building a TDS model that includes four phases: (1) identifying the macroeconomic and policy environment, including market competition, financial investment, and industrial policy; (2) specifying the key public and private institutions; (3) addressing the core technical complements and their owners, then tracing their interactions through information linkages and technology transfers; and (4) depicting the market prospects and evaluating the potential profound influences on technological change and social developments. Our TDS methodology is illustrated using the field of Big Data & Analytics ("BDA").
Agricultural ecosystem management needs to ensure food production and minimize soil erosion and nitrogen (N) leaching under climate change and increasingly intensive human activity. Thus, the mechanisms through which climatic and management factors affect crop production, soil erosion, and N leaching must be understood in order to ensure food security and sustainable agricultural development. In this study, we adopted the GIS-based Environmental Policy Integrated Climate (EPIC) model to simulate crop production, soil erosion, and N leaching, and used a partial least squares regression model to evaluate the contributions of climate variables (solar radiation, precipitation, wind speed, relative humidity, and maximum and minimum temperature) and management factors (irrigation, fertilization, and crop cultivation area) on agricultural ecosystem services (AES) in the agro-pastoral transitional zone (APTZ) of northern China. The results indicated that crop production and N leaching markedly increased, whereas soil erosion declined from 1980 to 2010 in the APTZ. Management factors had larger effects on the AES than climate change. Among the climatic variables, daily minimum temperature was the most important contributor to the variations in ecosystem services of wheat, maize, and rice. Spatial changes in the cultivated area most affected crop production, soil erosion, and N leaching for majority of the cultivated areas of the three crops, except for the wheat-cultivated area, where the dominant factor for N leaching was fertilization. Although a tradeoff existed between crop production and negative environmental effects, compromises were possible. These findings provide new insights into the effects of climatic and management factors on AES, and have practical implications for improving crop production while minimizing negative environmental impacts.
Some developed countries have enacted extended producer responsibility regulations that facilitate the diffusion of environmental supply chain cooperation (ESCC) practices among manufacturers. Developing countries, such as China, have adopted similar but generally flexible and voluntary regulations and policies. Using exchange theory with a focus on regulatory aspects as the theoretical lens, this paper develops propositions to examine if awareness of voluntary environmental regulatory policies is different among manufacturers as well as the relationship to ESCC practices adoption. Results from cluster analysis and multivariate analysis of variance for 308 responses identify three categories of Chinese manufacturers with respect to their awareness of environmental regulatory policies. These three categories include savvy, attentive, and nescient manufacturers. It was found that manufacturers characterized with higher environmental regulatory awareness tend to implement ESCC practices more intensively. Hierarchical regression analysis was further used to examine the relationship between awareness of regulatory policies and ESCC practices. Awareness of domestic regulatory policies has positive effects on green purchasing only for savvy manufacturers. Regression results show a nonlinear relationship between awareness of domestic regulatory policies and customer cooperation with environmental concerns, from slightly positive for nescient manufacturers and slightly negative for attentive manufacturers to significantly positive for savvy manufacturers.
Smart city is a vision that aims to integrate multiple information and communication solutions to residents with essential services like smart parking inside the all streets. Today, the parking systems has been changed by new advances that are empowering urban communities to diminish levels of congestions altogether. Internet of Things (IoT) is also new advancement which helps in detection of vehicle occupancy and congestion by basic intelligence and computational capability to make a smart parking system. The main motivation of using IoT for parking is to collect the data easily for free parking slots. This work presents the prototype of IoT-based Real Time smart street Parking System (IoT-based RTSSPS) with accessibility of data to make it simpler for residents and drivers to locate a free parking slot at the streets. Firstly, this work presents the introduction of IoT for smart parking with technology backgrounds, challenges of accessing IoT and database. Secondly, this work presents the prototype design of IoT-based RTSSPS with architecture and algorithm. IoT-based RTSSPS architecture is divided into three parts IoT-based WSN centric smart street parking module, IoT-based data centric smart street parking module and IoT-based cloud centric smart street parking module with street parking algorithm, evaluation and future directions.
We are living in the information age where almost every aspect of our life, directly or indirectly, relies on information and communication technology (ICT). The uses of ICT through big data has increased which therefore ended to be everything can directly go through online and people are now able to upload, retrieve, store their information and collect information to big data. Through big data learning management system (LMS), student managed and stored their intangible assets such as knowledge and information, documents, report, and administration purpose. LMS is basically application software that is capable and designed to provide electronic learning, and has been acknowledged to yield an integrated platform providing content, the delivery as well as management of learning, while supplying accessibility to a wide range of users that include students, content creators, lecturers as well as administrators. Universities aim to successfully implement a LMS in order to ease the learning process. This successful implementation lead to Universities make Business Process Re-engineering for their learning activity. Throughout the years, successful implementations of LMS have proven to be a very beneficial tool, providing ease and convenience. LMS is used not only in academic institutions such as schools and universities, but is also popularly used in a number of private corporations to provide online learning, training and is also capable of automating the process of record-keeping as well as employee registration. The objectives of this study are to reveal big data as enabler of LMS as Business Process Re-engineering bring users specifically, various benefits of its multi-function ability.
The main agenda of IoT is to enable us monitoring and controlling physical environment by collecting, processing and analysing the data generated by smart objects, which is an advancement of automation technologies, making our life simpler and easier. Nowadays Internet of Medical Things also became popular in which medical devices are connected and provides integration for taking care of patients and other aspects related to healthcare. Before the technologies like microcontrollers and smart phones are introduced, establishing home automation was a real burden with interference of electricians, installer and monthly maintenance costing. IoT is providing us home automation system using smart devices to get over this hindrance, which allows us to easily control the home appliances. The presented chapter introduces the home automation system using BASCOM, which also includes the components, flow of communication, implementation and limitations.
Watering to plants or on whole field of crop is most important and pain taking task on daily basis. Quantity of water required by plant is one of the effective parameter for greenhouse effect on plants. To make this challenging work to informal, some analytical and historical data is required so that irrigation cycle of crop may become easy task for farmers. This project is made with the use of Node MCU board; consist of ESP8266 Microcontroller with in-built Wi-Fi module. Soil Moisture sensor is set in the field, which keeps track of moisture level in field soil. That collected data are sent over cloud to make people’s nurturing activity pleased and tranquil. Data from the cloud is collected and irrigation related graph report for future use for farmer is made to take decision about which crop is to be sown. “Smart Irrigation Analysis” is an IoT application which provides remote analysis of irrigation on the field to the end user which is better than traditional irrigation of crop on field. Smart irrigation application has an automated recurring watering schedule; sensing and analysis of water used for crop and also senses moisture level giving real time data.
The collaboration between wireless sensor networks and the distributed robotics has prompted the making of mobile sensor networks. However, there has been a growing enthusiasm in developing mobile sensor networks, which are the favoured family of wireless sensor networks in which autonomous movement assumes a key part in implementing its application. By introducing mobility to nodes in wireless sensor networks, the capability and flexibility of mobile sensor networks can be enhanced to support multiple mansions, and to address the previously stated issues. The reduction in costs of mobile sensor networks and their expanding capacities makes mobile sensor networks conceivable and useful. Today, many types of research are focused on the making of mobile wireless sensor networks due to their favourable advantage and applications. Allowing the sensors to be mobile will boost the utilization of mobile wireless sensor networks beyond that of static wireless sensor networks. Sensors can be mounted on, or implanted in animals to monitor their movements for examinations, but they can also be deployed in unmanned airborne vehicles for surveillance or environmental mapping. Mobile wireless sensor networks and robotics play a crucial role if it integrated with static nodes to become a Mobile Robot, which can enhance the capabilities, and enables their new applications. Mobile robots provide a means of exploring and interacting with the environment in more dynamic and decentralised ways. In addition, this new system of networked sensors and robots allowed the development of fresh solutions to classical problems such as localization and navigation beyond that. This article presents an overview of mobile sensor network issues, sensor networks in robotics and the application of robotic sensor networks.
New technologies and the revolution of Internet of Things (IoT) fuel innovation in every area of science and human life, providing anytime and anywhere access to information in novel ways and contexts and brings people, processes, data and things as well as places, organizations and facilities together in unprecedented ways. Despite the numerous benefits IoT offers, manufacturing, distribution, and utilization of IoT products and systems are the resource and energy intensive and accompanied by escalating volumes of solid and toxic waste. In order to minimize the potentially negative influence of technological development on human and environment, it is necessary to successfully deal with challenges such as increased energy usage, waste and greenhouse gas emissions, and the consumption of natural and non-renewable raw materials. This is the reason for moving towards a greener future, where technology, IoT and the economy will be substituted with green technology, green IoT and the green economy, respectively, what implies a whole world of potentially remarkable improvements of human well-being and hence contributes to the sustainable smart world. This chapter presents an analysis of the significance of greening technologies’ processes in sustainable development, exploring the principles and roles of G-IoT in the progress of the society through the examination of its potential to improve the quality of life, environment, economic growth and green global modernization. It has been shown that the G-IoT holds the potential to transform and bring numerous benefits (among environment protection, customer satisfaction, and increased profit are the most significant) in diverse sectors using the latest technology approaches and solutions alongside eliminated or minimized the negative impact on the human health and the environment.
In the smart cities, objects can smartly communicate with the people through Internet of Things (IoT). It will make smart cities a greener place by detecting pollution through IoT and environmental sensors. In order to maintain the sustainability of green place in smart cities, the emerging technology, i.e. Green IoT automatically and intelligently makes smart cities sustainable in a collaborative manner. Governments and a lot of organizations around the world are doing a lot of efforts to campaign the importance of the reduction of energy consumption and carbon production as well as emphasize on the Green IoT for smart cities. Various IoT related smart cities architectures are already presented in literature. But this work presents the concept of the “Green IoT” to create a green environment which will apprehend the idea of energy saving in smart cities. In this chapter, design of Green IoT architecture is proposed for smart cites with the focus to reduce energy consumption at each stage and ensure realization of IoT toward green. This proposed Green IoT architecture is based on the cloud based system which reduces the hardware consumption.
In the world, the revolution of any industry is to connect their product and appliances to the Internet and make it autonomous and remotely connected so any one can operate and watch it from anywhere at any-time. This idea is known as industrial Internet of things (IIoTs). For achieving this goal very first and compulsory needed thing is connectivity. For any IIoTs enabled world the most frequency model architecture is build once in a world and that connectivity is one time disruption which requires new technology to interrupt the old one and manage it with new features. For this challenging task we try to develop the small but open for all system as a part of IIoT for the world. In this we are using the devices which is already define in open standard unified point of sale devices (UPoS) in which they included all physical devices like sensors printer and scanner. Firstly use the method and device defined by the UPoS system for the particular device. Connect that device to the server and lastly connect with it to the cloud. This flow will help us for connecting different branches of the company ubiquitously. By this you can connect remote device anytime anywhere with the right access. To the beneficial this will help us in decision making to the administration of the industry for the industry growth. Furthermore it will connect the one common employee to the chairman of the industry.
The purpose of this chapter is to synthesize opportunities and challenges related to the IoT within the manufacturing environment based on a project at Villeroy & Boch (V&B). The chapter seeks to visualize the impact of IoT methodologies, big data [2, 3] and Predictive Analytics towards the ceramics production. Key findings and challenges are related to both to the technical and organizational dimension tended to overshadow optimism. Organizations, such as V&B have been working with big data sets but emerging business models are now looking to gain deep insights from the so-called “data exhaust” that’s now become a “data gold mine” for business optimization embracing the move toward revolutionizing their production through big data and Predictive Analytics.
In IoT, technology is on an gradually developed both in terms of software and hardware. The high speed with which humans interact with the internet, use social media and interconnect their devices with another device is growing quickly. Due to the interaction between machine to human and machine to machine communication the massive amount of data will be generated and to store this generated data in the database becomes more difficult to store, manage, process and analyses. The data management is a biggest problem in IoT due to the connectivity of billions of devices, objects which are generating big data. With the help of big data technology we can handle that data. However due to the nature of Bigdata it has become important challenge to achieve the real-time capability using the traditional technologies. Big data is some technologies to capture, manage, store, distributed and analyses petabyte or larger sized datasets with highest velocity and different structure. Hadoop is a best platform for structuring Bigdata. It is a best tool for data analysis as it works for distributed big data, Time stamped data, structured, unstructured and semi-structured data, streaming data, text data etc. This paper represents the layer architecture of big data system. In addition, how to use FLUME and HIVE tool for data analysis. For NoSQLdatabse we use Hive which is SQL like query language is used for some analysis and extraction. Flume is used to extract real time data into HDFS.
Internet of things (IoT) based wireless body area network in healthcare moved out from traditional ways including visiting hospitals and consistent supervision. IoT allow some facilities including sensing, processing and communicating with physical and biomedical parameters. It connects the doctors, patients and nurses through smart devices and each entity can roam without any restrictions. Now research is going on to transform the healthcare industry by lowering the costs and increasing the efficiency for better patient care. With powerful algorithms and intelligent systems, it will be available to obtain an unprecedented real-time level, life-critical data that is captured and is analyzed to drive people in advance research, management and critical care. This chapter included in brief overview related to the IoT functionality and its association with the sensing and wireless techniques to implement the required healthcare applications.
The articles in this special section focs on mobile and wireless communications technologies and services for deployment in smart cities.
This paper seeks to provide some explanation as to how demand-, supply- and institutions-related factors in China have affected the creation and diffusion of Internet of Things (IoT)-related products and services. Concerning demand side factors the paper demonstrates how potential market size and existing technology trajectory work in favor of IoT diffusion. As a related demand side factor the paper argues that, in terms of the technological trajectory, China has started farther from the frontier than most industrialized countries. The degree of incremental benefit from the IoT is thus higher in the country. As to the supply side factors, the article promotes an understanding of how Chinese technology companies have capitalized on a huge user base to develop IoT-based applications. It also suggests that technologies and expertise provided by foreign multinationals have also played crucial roles. Regarding formal institutions, the government's proactive policies have been a major factor in the IoT's evolution. It is also in the Chinese government's interest to develop IoT products to make censorship and surveillance more effective. Regarding informal institutions, Chinese consumers are less concerned than Westerners about being tracked and monitored, which provides a favorable condition for the adoption of IoT-enabled devices. Nonetheless, this condition is changing due to increasing abuse of consumer privacy. China and the U.S. are compared in terms of diffusion, key determinants, performance indicators and impacts of the IoT in order to understand the areas that China outperforms—and underperforms—the U.S. Some indicators are proposed to gauge the IoT-related performance and the impacts of the IoT.