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>

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.

Industrial internet of things for smart manufacturing applications using hierarchical trustful resource assignment

Work ◽  
2021 ◽  
pp. 1-11
Author(s):  
Duan Pingli ◽  
Bala Anand Muthu ◽  
Seifedine Nimer Kadry
Keyword(s):  
Internet Of Things ◽  
Manufacturing Industry ◽  
Production Systems ◽  
Intelligent Manufacturing ◽  
Smart Manufacturing ◽  
The Internet ◽  
Resource Assignment ◽  
Industrial Internet Of Things ◽  
Assignment Method ◽  
Industrial Internet

BACKGROUND: The manufacturing industry undergoes a new age, with significant changes taking place on several fronts. Companies devoted to digital transformation take their future plants inspired by the Internet of Things (IoT). The IoT is a worldwide network of interrelated physical devices, which is an essential component of the internet, including sensors, actuators, smart apps, computers, mechanical machines, and people. The effective allocation of the computing resources and the carrier is critical in the industrial internet of Things (IIoT) for smart production systems. Indeed, the existing assignment method in the smart production system cannot guarantee that resources meet the inherently complex and volatile requirements of the user are timely. Many research results on resource allocations in auction formats which have been implemented to consider the demand and real-time supply for smart development resources, but safety privacy and trust estimation issues related to these outcomes are not actively discussed. OBJECTIVES: The paper proposes a Hierarchical Trustful Resource Assignment (HTRA) and Trust Computing Algorithm (TCA) based on Vickrey Clarke-Groves (VGCs) in the computer carriers necessary resources to communicate wirelessly among IIoT devices and gateways, and the allocation of CPU resources for processing information at the CPC. RESULTS: Finally, experimental findings demonstrate that when the IIoT equipment and gateways are valid, the utilities of each participant are improved. CONCLUSION: This is an easy and powerful method to guarantee that intelligent manufacturing components genuinely work for their purposes, which want to integrate each element into a system without interactions with each other.

Smart Manufacturing Through a Framework for a Knowledge-Based Diagnosis System

2017 ◽  
Author(s):  
Michael P. Brundage ◽  
Boonserm Kulvatunyou ◽  
Toyosi Ademujimi ◽  
Badarinath Rakshith
Keyword(s):  
Manufacturing Industry ◽  
Ad Hoc ◽  
Root Cause Analysis ◽  
Medical Community ◽  
Smart Manufacturing ◽  
Cause Analysis ◽  
Diagnosis System ◽  
Standard Representation ◽  
Root Cause ◽  
Knowledge Based

Various techniques are used to diagnose problems throughout all levels of the organization within the manufacturing industry. Often times, this root cause analysis is ad-hoc with no standard representation for artifacts or terminology (i.e., no standard representation for terms used in techniques such as fishbone diagrams, 5 why’s, etc.). Once a problem is diagnosed and alleviated, the results are discarded or stored locally as paper/digital text documents. When the same or similar problem reoccurs with different employees or in a different factory, the whole process has to be repeated without taking advantage of knowledge gained from previous problem(s) and corresponding solution(s). When discussing the diagnosis, personnel may miscommunicate over terms used in the root cause analysis leading to wasted time and errors. This paper presents a framework for a knowledge-based manufacturing diagnosis system that aims to alleviate these miscommunications. By learning from diagnosis methods used in manufacturing and in the medical community, this paper proposes a framework which integrates and formalizes root cause analysis by categorizing faults and failures that span multiple organizational levels. The proposed framework aims to enable manufacturing operations by leveraging machine learning and semantic technologies for the manufacturing system diagnosis. A use case for the manufacture of a bottle opener demonstrates the framework.

scholarly journals Modelling the continuous innovation capability enablers in Indonesia’s manufacturing industry

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Benny Lianto ◽  
Muhammad Dachyar ◽  
Tresna Priyana Soemardi
Keyword(s):  
Information Technology ◽  
Manufacturing Industry ◽  
Innovation Capability ◽  
Business Managers ◽  
Screening Process ◽  
Content Type ◽  
Continuous Innovation ◽  
Fuzzy Delphi ◽  
Manufacturing Sectors ◽  
Power And Dependence

Purpose The purpose of this paper is to identify and screen continuous innovation capability enablers (CICEs) in Indonesia’s manufacturing sectors, develop a relationship among these enablers and determine their driving power and dependence power in the sector. Design/methodology/approach The initial CICEs identification process is based on a literature review, while a fuzzy Delphi method (FDM) was used for the screening process of CICEs. Total interpretive structural modelling (TISM) was used to develop contextual relationships among various CICEs. The results of the TISM are used as an input for the matrix of cross-impact multiplications applied to classification (MICMAC) to classify the driving power and dependence powers of the CICEs. Findings This paper selected 16 CICEs classified in seven dimensions. TISM results and MICMAC analysis show that leadership, as well as climate and culture, are enablers with the highest driving power and lowest dependence powers; followed by information technology. The results of this study indicate that efforts to continuously develop innovation capabilities in the Indonesian manufacturing industries are strongly influenced by their leadership capability, climate and culture, also information technology-related capability. Practical implications The framework assessed in this study provides business managers and policymakers to obtain a bigger picture in developing policies with evidence-based strategy and priority in regard to continuous innovation capability. Originality/value The results will be useful for business managers and policymakers to understand the relationship between CICEs and identify key CICEs in Indonesia’s manufacturing sectors, which were previously non-existent.

E-Business in India

2011 ◽  
pp. 392-410
Author(s):  
Rajeev Dwivedi ◽  
Sushil ◽  
Sushil ◽  
K. Momaya
Keyword(s):  
Information Technology ◽  
Automotive Industry ◽  
Automobile Industry ◽  
Information Society ◽  
Manufacturing Industry ◽  
Recent Change ◽  
Flexible Systems ◽  
Business Transformation ◽  
Doing Business ◽  
Brick And Mortar

Business and industries have faced several changes from the agriculture society to information society. The recent change is due to Information Technology (IT) affecting many businesses and industries. It is changing the nature of business from the traditional way of doing business. The complete change in traditional business is due to IT. This is known as e-business transformation. The Indian manufacturing industry is undergoing this IT-enabled change and is still under process of click and brick system. Indian automobile companies are stressing the importance of e-business in the domestic automotive industry. The main aim of the chapter is to explain how the manufacturing and especially the automobile industry business has changed from traditional brick and mortar business to click and brick e-business. This chapter provides a study of e-business transformation in manufacturing industry in India using Flexible Systems (SAP-LAP) Methodology. The SAP-LAP stands for Situation-Actors-Process and Learning-Action-Performance. This methodology helps for understanding systematic nature of e-business transformation. The explanation of stakeholder flexibility due to e-business transformation is Industry will be explained.

scholarly journals Sustainable and Smart Manufacturing: An Integrated Approach

2020 ◽  
Vol 12 (6) ◽  
pp. 2280 ◽  
Author(s):  
Mohamed Abubakr ◽  
Adel T. Abbas ◽  
Italo Tomaz ◽  
Mahmoud S. Soliman ◽  
Monis Luqman ◽  
...  
Keyword(s):  
Manufacturing Industry ◽  
Sustainability Assessment ◽  
Negative Impact ◽  
Manufacturing Sector ◽  
Sustainable Manufacturing ◽  
Smart Manufacturing ◽  
Future Research ◽  
Bottom Line ◽  
Manufacturing Performance ◽  
Sustainability Concept

The necessity for decreasing the negative impact of the manufacturing industry has recently increased. This is getting recognized as a global challenge due to the rapid increase in life quality standards, demand, and the decrease in available resources. Thus, manufacturing, as a core of the product provision system and a fundamental pillar of civilized existence, is significantly influenced by sustainability issues. Furthermore, current manufacturing modeling and assessment criteria require intensive revisions and upgrades to keep up with these new challenges. Nearly all current manufacturing models are based on the old paradigm, which was proven to be inadequate. Therefore, manufacturing technology, along with culture and economy, are held responsible for providing new tools and opportunities for building novel resolutions towards a sustainable manufacturing concept. One of such tools is sustainability assessment measures. Revising and updating such tools is a core responsibility of the manufacturing sector to efficiently evaluate and enhance sustainable manufacturing performance. These measures should be adequate to respond to the growing sustainability concerns in pursuit of an integrated sustainability concept. The triple bottom line (TBL) that includes environment, economic, and social dimensions has usually been used to evaluate sustainability. However, there is a lack of standard sets of sustainable manufacturing performance measures. In addition to the sustainability concept, a new concept of smart manufacturing is emerging. The smart manufacturing concept takes advantage of the recent technological leap in Artificial Intelligent (AI), Cloud Computing (CC), and the Internet of Things (IoT). Although this concept offers an important step to boost the current production capabilities to meet the growing need, it is still not clear whether the two concepts of smart manufacturing and sustainability will constructively or destructively interact. Therefore, the current study aims to integrate the sustainable smart manufacturing performance by incorporating sustainable manufacturing measures and discussing current and future challenges that are faced by the manufacturing sector. In addition, the opportunities for future research incorporating sustainable smart manufacturing are also presented.

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 Blockchain Reference System Architecture Description for the ISA95 Compliant Traditional and Smart Manufacturing Systems

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6456 ◽  
Author(s):  
Erkan Yalcinkaya ◽  
Antonio Maffei ◽  
Mauro Onori
Keyword(s):  
Manufacturing Systems ◽  
Manufacturing Industry ◽  
Research Effort ◽  
Smart Manufacturing ◽  
Reference Architecture ◽  
Systematic Assessment ◽  
Blockchain Technology ◽  
Design Requirements ◽  
Manufacturing Technologies ◽  
Smart Manufacturing Systems

The next-generation technologies enabled by the industry 4.0 revolution put immense pressure on traditional ISA95 compliant manufacturing systems to evolve into smart manufacturing systems. Unfortunately, the transformation of old to new manufacturing technologies is a slow process. Therefore, the manufacturing industry is currently in a situation that the legacy and modern manufacturing systems share the same factory environment. This heterogeneous ecosystem leads to challenges in systems scalability, interoperability, information security, and data quality domains. Our former research effort concluded that blockchain technology has promising features to address these challenges. Moreover, our systematic assessment revealed that most of the ISA95 enterprise functions are suitable for applying blockchain technology. However, no blockchain reference architecture explicitly focuses on the ISA95 compliant traditional and smart manufacturing systems available in the literature. This research aims to fill the gap by first methodically specifying the design requirements and then meticulously elaborating on how the reference architecture components fulfill the design requirements.

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