Industry 4.0: The Future of Indo-German Industrial Collaboration

Industry 4.0 can be described as the fourth industrial revolution, a mega- trend that affects every company around the world. It envisions interconnections and collaboration between people, products and machines within and across enterprises. Why does Industry 4.0 make for an excellent platform for industrial collaboration between India and Germany? The answers lie in economic as well as social factors. Both countries have strengths and weakness and strategic collaboration using the principles of Industry 4.0 can help both increase their industrial output, GDP and make optimal use of human resources. As a global heavy weight in manufacturing and machine export, Germany has a leading position in the development and deployment of Industry 4.0 concepts and technology. However, its IT sector, formed by a labor force of 800,000 employees, is not enough. It needs more professionals to reach its full potential. India, on the other hand, is a global leader in IT and business process outsourcing. But its manufacturing industry needs to grow significantly and compete globally. These realities clearly show the need for Industry 4.0-based collaboration between Germany and India. So how does Industry 4.0 work? In a first step, we look at the technical pers- pective – the vertical and horizontal integration of Industry 4.0 principles in enterprises. Vertical integration refers to operations within Smart Factories and horizontal integration to Smart Supply Chains across businesses. In the second step, we look at manufacturing, chemical industry and the IT sector as potential targets for collaboration between the two countries. We use case studies to illustrate the benefits of the deployment of Industry 4.0. Potential collaboration patterns are discussed along different forms of value chains and along companies’ ability to achieve Industry 4.0 status. We analyse the social impact of Industry 4.0 on India and Germany and find that it works very well in the coming years. Germany with its dwindling labor force might be compensated through the automation. This will ensure continued high productivity levels and rise in GDP. India, on the other hand has a burgeoning labor market, with 10 million workers annually entering the job market. Given that the manufacturing sector will be at par with Europe in efficiency and costs by 2023, pressure on India’s labor force will increase even more. Even its robust IT sector will suffer fewer hires because of increased automation. Rapid development of technologies – for the Internet of Things (IoT) or for connectivity like Low-Power WAN – makes skilling and reskilling of the labor force critical for augmenting smart manufacturing. India and Germany have been collaborating at three levels relevant to Industry 4.0 – industry, government and academics. How can these be taken forward? The two countries have a long history of trade. The Indo-German Chamber of Commerce (IGCC) is the largest such chamber in India and the largest German chamber worldwide. VDMA (Verband Deutscher Maschinen- und Anlagenbau, Mechanical Engineering Industry Association), the largest industry association in Europe, maintains offices in India. Indian key players in IT, in turn, have subsidia- ries in Germany and cooperate with German companies in the area of Industry 4.0. Collaboration is also supported on governmental level. As government initiatives go, India has launched the “Make in India” initiative and the “Make in India Mittelstand! (MIIM)” programme as a part of it. The Indian Government is also supporting “smart manufacturing” initiatives in a major way. Centers of Excellence driven by the industry and academic bodies are being set up. Germany and India have a long tradition of research collaboration as well. Germany is the second scientific collaborator of India and Indian students form the third largest group of foreign students in Germany. German institutions like the German Academic Exchange Service (DAAD) or the German House for Research and Innovation (DWIH) are working to strengthen ties between the scientific communities of the two countries, and between their academia and industry. What prevents Industry 4.0 from becoming a more widely used technology? Recent surveys in Germany and India show that awareness about Industry 4.0 is still low, especially among small and medium manufacturing enterprises. IT companies, on the other hand, are better prepared. There is a broad demand for support, regarding customtailored solutions, information on case studies and the willingness to participate in Industry 4.0 pilot projects and to engage in its platform and networking activities. We also found similar responses at workshops conducted with Industry 4.0 stakehold- ers in June 2017 in Bangalore and Pune and in an online survey. What can be done to change this? Both countries should strengthen their efforts to create awareness for Industry 4.0, especially among small and medium enterprises. Germany should also put more emphasis on making their Industry 4.0 technology known to the Indian market. India’s IT giants, on the other hand, should make their Industry 4.0 offers more visible to the German market. The governments should support the establishing of joint Industry 4.0 collaboration platforms, centers of excellence and incubators to ease the dissemination of knowledge and technology. On academic level, joint research programs and exchange programs should be set up to foster the skilling of labor force in the deployment of Industry 4.0 methods and technologies

Study on interoperation and its' implementation of MES to support virtual factory

© 2020 Published under licence by IOP Publishing Ltd. The data interoperation between VF (virtual factory) platform and MES (Manufacturing Execution System) plays an important role in intelligent factory construction. The study focuses on the integration strategy between the VF and the MES by incorporating VF manufacturing assets in two ways, i.e., vertical integration (used for production line performance evaluation) and the horizontal integration (cloud manufacturing based on manufacturing assets services discovery and their composition). The VF platform which integrates the manufacturing assets in two manners is designed as the bottom layer in the entire integration framework. It has been applied to build a four tiers integration model in an intelligent production system construction of a domestic ship manufacturer and verified its feasibility and availability

Cloud manufacturing system for sheet metal processing

Cloud computing is changing the way industries and enterprises run their businesses. Cloud manufacturing is emerging as an approach to transform the traditional manufacturing business model, while helping the manufacturer to align production efficiency with its business strategy, and creating intelligent factory networks that enable collaboration across the whole enterprise. Many production planning and control (PPC) problems are essentially optimisation problems, where the objective is to develop a plan that meets the demand at minimum cost or maximum profit. Because the underlying optimisation problem will vary in the different business and operation phases, it is important to think about optimisation in a dynamic mechanism and in a number of interlinked sub-problems at the same time. Cloud manufacturing has the potential to offer decision support as a service and medium of communication in PPC. To solve these problems and produce collaboration across the supply chain, this paper provides an overview of the state of the art in cloud manufacturing and presents a model of cloud-based production planning and production system for sheet metal processing.fi=vertaisarvioitu|en=peerReviewed

Digital Manufacturing as a basis for the development of the Industry 4.0 model

The digital manufacturing (DM) is base for Industry 4.0, that have following dimensions: (i) digital manufacturing based on advanced digital-oriented technologies, (ii) smart products (advanced production mode and new characteristics), and (iii) smart supply - chain (procurement of raw materials and delivery of finished products). Bidirectional exchange of information in collaborative production, using it exchange also for digital platforms of design of the innovative products. This paper presents developed model of Serbian digital factory with selected examples, specifically for the Manufacturing Execution System (MES) area

Digital Manufacturing as a basis for the development of the Industry 4.0 model

The digital manufacturing (DM) is base for Industry 4.0, that have following dimensions: (i) digital manufacturing based on advanced digital-oriented technologies, (ii) smart products (advanced production mode and new characteristics), and (iii) smart supply - chain (procurement of raw materials and delivery of finished products). Bidirectional exchange of information in collaborative production, using it exchange also for digital platforms of design of the innovative products. This paper presents developed model of Serbian digital factory with selected examples, specifically for the Manufacturing Execution System (MES) area

Smart Manufacturing Technology Adoption for Improving Productivity: A Systematic Literature Review

This paper is to present a review of the Smart manufacturing application as Industry 4.0, specifically on the conceptual approaches to define it. Two conceptual approaches to the application of the concept of smart manufacturing are distinguished. The method using the previous articles of scientific journals from 2017-2022 (thirty-two international paper) were selected based on previous works, with the scope to investigate and analyze in-depth the extent to which literature used to discuss results in the selected studies of applying the Smart Manufacturing Technology in manufacture enterprises. The results of the previous studies are discussed and investigated in a table format

Cloud manufacturing – scheduling as a service for sheet metal manufacturing

Cloud manufacturing refers to a new concept of using centralized cloud computing for manufacturing information systems to support distributed and dynamic collaborative manufacturing environment. The core of cloud manufacturing is to provide service to geographically distributed manufacturers centralized services. This paper introduces a cloud based production scheduling system for sheet metal manufacturing and discusses the requirements of scheduling as a service. A genetic algorithm based scheduling application has been developed to serve distributed manufacturing lines in form of cloud manufacturing. The characteristics of the prototype system are described and performance estimates are tested.fi=vertaisarvioitu|en=peerReviewed

Comparing Research Trends and Industrial Adoption of Manufacturing Operations Management Solutions

For decades, the operations on the shop floor of manufacturing organizations have been supported by Manufacturing Execution Systems. In this paper, we investigate the trends of Manufacturing Operations Management in the research community and analyze of the adoption in the industry. Our literature review identifies the following trends for Manufacturing Operations Management: distributed system architectures, cloud technology, and use of standards. We conducted a survey targeting Manufacturing Operations Management solution providers and adopters to explore the adoption of these trends. The survey results show that the use of standards is already addressed to some extent by the industry. Practitioners anticipate distributed system architectures for Manufacturing Operations Management solutions in the future. However, practitioners are still reluctant towards cloud-only technology and will continue to be so in the foreseeable future

Integration Framework of MES Toward Data Security Interoperation

© 2020, Springer Nature Switzerland AG. The core problem of the application of MES (Manufacturing Execution System) in intelligent manufacturing systems is integration, which solves the problem of the data interoperation between the distributed manufacturing systems. The previous researches on MES integration rarely considered the problem of system data security access. A three-level data security access mechanism based on the independence of the system administrators, security administrators, and security auditors is proposed which integrated into the MES integration framework to guarantee the business and engineering data security access for the related distributed clients. The principle is using the domain to make the logical isolation for different clients and data sources and applying the pre-defined data sharing rules for safe access. In the proposed MES integration framework model, the data interoperation between MES and the engineering software systems is discussed which includes ERP (Enterprise Resource Management), CAPP (Computer Aided Process Planning), DNC (Distribution Numerical Control), WMS (Warehouse Management System), and SCADA (Supervisory Control and Data Acquisition), etc., the implementation method of personalized data display GUI is discussed as well. The study is based on the KMMES developed by Wuhan KM-Software of China, and it has been deployed in over forty companies from the sections of aerospace, automotive, shipbuilding and other industries

Industry 4.0 for SME

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsIndustry 4.0 has been growing within companies and impacting the economy and society, but this has been a more complex challenge for some types of companies. Due to the costs and complexity associated with Industry 4.0 technologies, small and medium enterprises face difficulties in adopting them. This thesis proposes to create a model that gives guidance and simplifies how to implement Industry 4.0 in SMEs with a low-cost perspective. It is intended that this model can be used as a blueprint to design and implement an Industry 4.0 project within a manufactory SME. To create the model, a literature review of the different fields regarding Industry 4.0 were conducted to understand the most suited technologies to leverage within the manufacturing industry and the different use cases where these would be applicable. After the model was built, expert interviews were conducted, and based on the received feedback, the model was tweaked, improved, and validated