scholarly journals How Technological Innovation Affect China’s Pharmaceutical Smart Manufacturing Industrial Upgrading

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Su Wang ◽  
Yuwen Chen
Keyword(s):  
Technological Innovation ◽  
Manufacturing Industry ◽  
New Technologies ◽  
Digital Transformation ◽  
Pharmaceutical Manufacturing ◽  
Smart Manufacturing ◽  
Manufacturing Companies ◽  
Industrial Upgrading ◽  
Depth Analysis ◽  
Innovative Resources

In recent years, a new generation of information technology has provided sufficient technical support for the smart manufacturing industry. In order to promote the upgrading of China’s pharmaceutical smart manufacturing industry, the direction of industrial upgrading and transformation will be discussed from the perspective of technological innovation. According to the input and output data of technological innovation in China’s pharmaceutical manufacturing industry from 2007 to 2019, the DEA method is used to analyze the allocation of innovative resources in China’s pharmaceutical manufacturing industry in recent years. The study found that the efficiency of technological innovation in China’s pharmaceutical manufacturing industry fluctuated greatly from 2007 to 2019, with a low overall level and varying degrees of wasted resources. On this basis, an in-depth analysis of the system architecture of the pharmaceutical smart manufacturing industry under the Industry 4.0 environment was performed. Finally, four paths for the digital transformation of China’s pharmaceutical manufacturing industry are proposed. Chinese pharmaceutical manufacturing companies need to use new technologies to carry out comprehensive intelligent upgrading and digital transformation to improve innovation efficiency.

Labour 4.0: developing competences for smart production

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Astrid Heideman Lassen ◽  
Brian Vejrum Vejrum Waehrens
Keyword(s):  
Manufacturing Industry ◽  
New Technologies ◽  
New Technology ◽  
Competence Development ◽  
Digital Transformation ◽  
Manufacturing Companies ◽  
Industrial Transformation ◽  
Content Type ◽  
Individual Level ◽  
Development Framework

Purpose The purpose of this paper is to determine how companies develop and acquire competences to capture the benefits of Industry 4.0 (I4.0) technologies. The authors argue that this is a fundamental and often overlooked prerequisite for industrial transformation. Design/methodology/approach The authors conduct a process study of 33 small- and medium-sized companies engaged in the transformation of a manufacturing industry from the different perspectives of manufacturers or manufacturing solution providers. Findings Key findings indicate a strong link between the specific competence development approach, the specific intricacies of the application domain and the process outcomes. On this basis, a competence development framework is proposed. Research limitations/implications The conclusions are drawn from a Danish population of companies in the manufacturing industry and are based on particular contingencies, such as low volume/high mix, high skill, low tech and high cost. However, the findings are believed to be applicable across different sets of contingencies where the need to combine legacy and emerging technologies is present, and where the human factor is central to leveraging technology beyond predefined supplier specifications. Practical implications In a time of extraordinary investments in the manufacturing of technologies in support of digital transformation, the development of strategic and operational competences to support these investments is lagging behind. This paper develops a conceptual outset for closing this gap. Originality/value The research is based on the fundamental argument that to efficiently apply new technology, a strategic approach to the acquisition of new knowledge and skills is required. The empirical research demonstrates that new skills and knowledge are often assumed to follow automatically from the use of new technologies. However, we demonstrate that this perspective in fact limits the ability to capture the potential benefits ascribed to I4.0 technologies. The authors propose that the competence strategy needs to be expansive and cover not only the technological competences but also the organizational- and individual-level competences. These results add to our understanding of how the digital transformation of manufacturing companies unfolds.

Artificial Intelligence as a Technological Innovation to Accelerate Economic Development

2019 ◽  
Vol 12 (3) ◽  
pp. 125-133
Author(s):  
S. V. Shchurina ◽  
A. S. Danilov
Keyword(s):  
Artificial Intelligence ◽  
Economic Development ◽  
Technological Innovation ◽  
Manufacturing Industry ◽  
New Technologies ◽  
Retail Trade ◽  
High Tech ◽  
Industrial Complex ◽  
Technological Gap ◽  
The Government

The subject of the research is the introduction of artificial intelligence as a technological innovation into the Russian economic development. The relevance of the problem is due to the fact that the Russian market of artificial intelligence is still in the infancy and the necessity to bridge the current technological gap between Russia and the leading economies of the world is coming to the forefront. The financial sector, the manufacturing industry and the retail trade are the drivers of the artificial intelligence development. However, company managers in Russia are not prepared for the practical application of expensive artificial intelligence technologies. Under these circumstances, the challenge is to develop measures to support high-tech projects of small and medium-sized businesses, given that the technological innovation considered can accelerate the development of the Russian economy in the energy sector fully or partially controlled by the government as well as in the military-industrial complex and the judicial system.The purposes of the research were to examine the current state of technological innovations in the field of artificial intelligence in the leading countries and Russia and develop proposals for improving the AI application in the Russian practices.The paper concludes that the artificial intelligence is a breakthrough technology with a great application potential. Active promotion of the artificial intelligence in companies significantly increases their efficiency, competitiveness, develops industry markets, stimulates introduction of new technologies, improves product quality and scales up manufacturing. In general, the artificial intelligence gives a new impetus to the development of Russia and facilitates its entry into the five largest world’s economies.

Smart Production System Modeling: Bernoulli Serial Line Case

2020 ◽  
Author(s):  
Yuting Sun ◽  
Tianyu Zhu ◽  
Liang Zhang
Keyword(s):  
Manufacturing Industry ◽  
Ad Hoc ◽  
New Technologies ◽  
System Modeling ◽  
Critical Issue ◽  
Smart Manufacturing ◽  
Serial Production ◽  
Modeling Process ◽  
Serial Line

Abstract The manufacturing industry has entered the era of Industry 4.0/Smart Manufacturing. New technologies have dramatically changed the way manufacturing activities are carried out on the factory floor. In addition to an enhanced level of equipment automation, automation of decision-making has been one of the key objectives of these new initiatives. On the other hand, a critical issue that has been overlooked is the construction of mathematical models in manufacturing research and studies, which are typically done manually. This manual, ad-hoc nature of mathematical modeling is quite problematic when modeling the job flow in a manufacturing process. As a result, the quality of the models obtained may heavily depend on the experience and personal preference of the modeler. The goal of this paper is to develop a method to standardize and automate the modeling process using standard manufacturing key performance indices in the framework of Bernoulli serial production line model.

scholarly journals Systemic Functions Evaluation based Technological Innovation System for the Sustainability of IoT in the Manufacturing Industry

2019 ◽  
Vol 11 (8) ◽  
pp. 2342 ◽  
Author(s):  
Kao ◽  
Nawata ◽  
Huang
Keyword(s):  
Decision Making ◽  
Technological Innovation ◽  
Manufacturing Industry ◽  
Innovation System ◽  
Government Procurement ◽  
Smart Manufacturing ◽  
Technological Innovations ◽  
Industrial Competitiveness ◽  
Industrial Sustainability ◽  
Study Results

Technological innovations are regarded as the tools that can stimulate economic growth and the sustainable development of technology. In recent years, as technologies based on the internet of things (IoT) have rapidly developed, a number of applications based on IoT innovations have emerged and have been widely adopted by various public and private sectors. Applications of IoT in the manufacturing industry, such as manufacturing intelligence, not only play a significant role in the enhancement of industrial competitiveness and sustainability, but also influence the diffusion of innovative applications that are based on IoT innovations. It is crucial for policy makers to understand these potential reasons for stimulating IoT industrial sustainability, as they can facilitate industrial competitiveness and technological innovations using supportive means, such as government procurement and financial incentives. Therefore, there is a need to ascertain different factors that may affect IoT industrial sustainability and further explore the relationship between these factors. However, finding a set of factors that affects IoT industrial sustainability is not easy. Recently, the robustness of a theoretical framework, termed the technological innovation system (TIS), has been verified and has been used to explore and analyze technological and industrial development. Thus, it is suitable for this research to use this theoretical model. In order to find out appropriate factors and accurately analyze the causality among factors that influence IoT industrial sustainability, this research presents a Bayesian rough Multiple Criteria Decision Making (MCDM) model based on TIS functions by integrating random forest (RF), decision making trial and evaluation (DEMATEL), Bayesian theory, and rough interval numbers. The proposed analytical framework is validated by an empirical case of defining the causality between TIS functions to enable the industrial sustainability of IoT in the Taiwanese smart manufacturing industry. Based on the empirical study results, the cause group consists of entrepreneurial activities, knowledge development, market formation, and resource mobilization. The effect group is composed of knowledge diffusion through networks’ guidance of the search, and creation of legitimacy. Moreover, the analytical results also provide several policy suggestions promoting IoT industrial sustainability that can serve as the basis for defining innovation policy tools for Taiwan and late coming economies.

scholarly journals “Industrie 4.0” and Smart Manufacturing – A Review of Research Issues and Application Examples

2017 ◽  
Vol 11 (1) ◽  
pp. 4-16 ◽  
Author(s):  
Klaus-Dieter Thoben ◽  
◽  
Stefan Wiesner ◽  
Thorsten Wuest ◽  
◽  
...  
Keyword(s):  
Manufacturing Industry ◽  
Business Models ◽  
Industrial Revolution ◽  
The United States ◽  
Industrie 4.0 ◽  
Business Strategies ◽  
Smart Manufacturing ◽  
Future Research ◽  
Manufacturing Companies ◽  
Research Issues

A fourth industrial revolution is occurring in global manufacturing. It is based on the introduction ofInternet of thingsandservitizationconcepts into manufacturing companies, leading to vertically and horizontally integrated production systems. The resultingsmart factoriesare able to fulfill dynamic customer demands with high variability in small lot sizes while integrating human ingenuity and automation. To support the manufacturing industry in this conversion process and enhance global competitiveness, policy makers in several countries have established research and technology transfer schemes. Most prominently, Germany has enacted itsIndustrie 4.0program, which is increasingly affecting European policy, while the United States focuses onsmart manufacturing. Other industrial nations have established their own programs on smart manufacturing, notably Japan and Korea. This shows that manufacturing intelligence has become a crucial topic for researchers and industries worldwide. The main object of these activities are the so-called cyber-physical systems (CPS): physical entities (e.g., machines, vehicles, and work pieces), which are equipped with technologies such as RFIDs, sensors, microprocessors, telematics or complete embedded systems. They are characterized by being able to collect data of themselves and their environment, process and evaluate these data, connect and communicate with other systems, and initiate actions. In addition, CPS enabled new services that can replace traditional business models based solely on product sales. The objective of this paper is to provide an overview of the Industrie 4.0 and smart manufacturing programs, analyze the application potential of CPS starting from product design through production and logistics up to maintenance and exploitation (e.g., recycling), and identify current and future research issues. Besides the technological perspective, the paper also takes into account the economic side considering the new business strategies and models available.

Measuring the Digital Transformation of Education and Teaching

2019 ◽  
pp. 379-401
Author(s):  
Christian Glahn
Keyword(s):  
Technological Innovation ◽  
Performance Indicators ◽  
New Technologies ◽  
Organizational Innovation ◽  
Digital Transformation ◽  
Faculty Members ◽  
Educational Institutions ◽  
Diffusion Of Technology ◽  
Innovation Potential ◽  
Combined Use

The digital transformation has reached higher education and many faculty members find teaching in the digital environment hard. A key question for educational institutions is whether the uptake of blended learning within their digitization strategies matches the pace of technological innovation. This chapter discusses a model for monitoring the progress of educational digitization that has been in use throughout four years at HTW Chur, Switzerland. The model connects technologies to practices rather than abstracting technologies from them. This helps identifying performance indicators in campus-wide information systems for understanding the diffusion of technology uses among the faculty, and it helps categorizing new technologies towards their organizational innovation potential. The combined use of these performance indicators with the model supports tailoring faculty development activities for digitization strategies that are based on the actual development needs within the institution.

Measuring the Digital Transformation of Education and Teaching

2021 ◽  
pp. 908-930
Author(s):  
Christian Glahn
Keyword(s):  
Technological Innovation ◽  
Performance Indicators ◽  
New Technologies ◽  
Organizational Innovation ◽  
Digital Transformation ◽  
Faculty Members ◽  
Educational Institutions ◽  
Diffusion Of Technology ◽  
Innovation Potential ◽  
Combined Use

The digital transformation has reached higher education and many faculty members find teaching in the digital environment hard. A key question for educational institutions is whether the uptake of blended learning within their digitization strategies matches the pace of technological innovation. This chapter discusses a model for monitoring the progress of educational digitization that has been in use throughout four years at HTW Chur, Switzerland. The model connects technologies to practices rather than abstracting technologies from them. This helps identifying performance indicators in campus-wide information systems for understanding the diffusion of technology uses among the faculty, and it helps categorizing new technologies towards their organizational innovation potential. The combined use of these performance indicators with the model supports tailoring faculty development activities for digitization strategies that are based on the actual development needs within the institution.

scholarly journals Technological innovation in neurosurgery: a quantitative study

2015 ◽  
Vol 123 (1) ◽  
pp. 174-181 ◽  
Author(s):  
Hani J. Marcus ◽  
Archie Hughes-Hallett ◽  
Richard M. Kwasnicki ◽  
Ara Darzi ◽  
Guang-Zhong Yang ◽  
...  
Keyword(s):  
Technological Innovation ◽  
Image Guidance ◽  
New Technologies ◽  
Technology Development ◽  
New Technology ◽  
Growth Curves ◽  
Search Term ◽  
Depth Analysis ◽  
Boolean Search ◽  
Highly Correlated

OBJECT Technological innovation within health care may be defined as the introduction of a new technology that initiates a change in clinical practice. Neurosurgery is a particularly technology-intensive surgical discipline, and new technologies have preceded many of the major advances in operative neurosurgical techniques. The aim of the present study was to quantitatively evaluate technological innovation in neurosurgery using patents and peer-reviewed publications as metrics of technology development and clinical translation, respectively. METHODS The authors searched a patent database for articles published between 1960 and 2010 using the Boolean search term “neurosurgeon OR neurosurgical OR neurosurgery.” The top 50 performing patent codes were then grouped into technology clusters. Patent and publication growth curves were then generated for these technology clusters. A top-performing technology cluster was then selected as an exemplar for a more detailed analysis of individual patents. RESULTS In all, 11,672 patents and 208,203 publications related to neurosurgery were identified. The top-performing technology clusters during these 50 years were image-guidance devices, clinical neurophysiology devices, neuromodulation devices, operating microscopes, and endoscopes. In relation to image-guidance and neuromodulation devices, the authors found a highly correlated rapid rise in the numbers of patents and publications, which suggests that these are areas of technology expansion. An in-depth analysis of neuromodulation-device patents revealed that the majority of well-performing patents were related to deep brain stimulation. CONCLUSIONS Patent and publication data may be used to quantitatively evaluate technological innovation in neurosurgery.

scholarly journals Labor indicators and manufacturing companies ownership patterns in Russia and its regions: results of quantitative analysis

2018 ◽  
Vol 9 (2) ◽  
pp. 261-285 ◽  
Author(s):  
Vladislav Spitsin ◽  
Alexander Mikhalchuk ◽  
Irina Pavlova ◽  
Alexander Mikhalchuk ◽  
Irina Pavlova ◽  
...  
Keyword(s):  
Manufacturing Industry ◽  
New Technologies ◽  
National Level ◽  
Value Added ◽  
Manufacturing Companies ◽  
Sharp Drop ◽  
Stable Conditions ◽  
Its Regions ◽  
Joint Ownership ◽  
Ownership Patterns

Research background: There has been an extensive process of foreign and joint ownership enterprises establishment in the Russian economy since 2006. Domestic manufacturing industry has been experiencing certain pressure on behalf of foreign direct investment bringing new technologies and higher labor requirements. Purpose of the article: The aim of this paper is to investigate differences in employment strategies and labor indicators in the case of enterprises in foreign and joint ownership (FJO) and domestic enterprises in Russian ownership (RO). We analyze the manufacturing industry in Russia and its regions under conditions of stable and crisis periods. Methods: The study enhances the analysis of Rosstat’s statistical data for 2005–2016 and applies ANOVA method to compare the employment results for companies with different ownership patterns. The research is carried out both at the national level of the Russian Federation and at the regional level according to the regions. Findings & Value added: The study identifies significant decline in employment and in-crease in productivity for the period of 2005–2016. In contrast to the crisis of 2008–2009, in 2014–2016 there has been no sharp drop in employment. However, there is a substantial decline in real salaries which is comparable to the crisis of 2008-2009. According to ANOVA, statistically significant differences in labor indicators between FJO and RO companies are manifested. RO companies dominate in employment and payroll funds, while FJO enter-prises have better productivity results with a higher average salary. FJO companies demonstrated faster growth in employment and payroll fund in relatively stable conditions (2012–2013). However, they reacted with a significant reduction in employment for a new crisis (2014–2016), although the creation of new FJO enterprises continued in separate regions of Russia. The results can be used in social policy to regulate the employment and earnings of industrial workers in the current economic conditions.

scholarly journals Technological Embedding, Industrial Integration, Industrial Upgrading----Discuss about the Role of Information Technology in Industrial Upgrading and Transformation

2016 ◽  
Vol 8 (2) ◽  
pp. 39
Author(s):  
Lin Xue-jun ◽  
Lv Han ◽  
Hao Luo ◽  
He Nie
Keyword(s):  
Information Technology ◽  
Manufacturing Industry ◽  
Smart Manufacturing ◽  
German Industry ◽  
Intelligent Network ◽  
Industrial Upgrading ◽  
Chinese Industry ◽  
Industry Integration ◽  
Total Integration ◽  
Economic Vitality

<p>At present, transformation and upgrading traditional industries are key to our country’s economic development. Discussion on how to transform traditional industries utilizing the information technology is hot in academic world. Transformation of traditional industries using information technology can fall into three categories: total Integration, embedded Integration, and general Integration. The result is to form a new industry, transform traditional industries, or increase the original industry productivity respectively. German industry version 4.0 is a case in point of industry Integration through intelligent factories, smart production, intelligent network, intelligent service etc. to build a smart manufacturing industry. China should vigorously utilize “Internet +” to upgrade China’s manufacturing industry, through which intelligent factories improve enterprise’s productivity, intelligent production increases the productivity of the industry, intelligent network improves the productivity of the whole society, intelligent service improves the economic vitality of the whole society, hence build the Chinese industry version 4.0.</p>

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