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

The shift team formation problem in multi-shift manufacturing operations

This paper addresses the problem of assigning operators to teams that work in single-, two-, or three-day shift systems. The problem was motivated by, and illustrated with a case situation encountered in Dutch manufacturing industry. The problem addressed forms an extension of cell formation problems which are currently in the phase of addressing labor-related issues in cell design. A generalized goal problem formulation is presented to address multiple, conflicting objectives covering cross-training of workers, ensuring adequate levels of labor flexibility and minimizing labor-related costs. The proposed solution procedure consists of two phases. In the first phase, shift systems, in which applicable machines and the sizes of each shift team are identified. The next phase deals with assignment of operators to various teams and identification of specific cross-training needs for various workers. This phase involves the use of interactive goal programming. The methodology is illustrated by details from the case situation as well as a numerical example.

The 'Sophistication' Of Exports: A New Measure Of Product Characteristics

Trade data are often classified by product characteristics in the trade, technology and development literature to analyze trade patterns, competitive performance and structural change. However, existing classifications are constrained in that trade data are far more detailed than the industry data on which categories are based (e.g. current classifications only have 4-5 technology groups). We propose a new classification - 'sophistication' - as a means of analyzing product characteristics in great detail, based on the average income of exporting economies. Sophistication captures more than technical characteristics; it includes product differentiation, production fragmentation, resource availability and other factors. However, it has the advantage of providing unique continuous scores for each product at any level of detail. We calculate sophistication scores for 237 exports at the 3-digit SITC level and 766 exports at the 4-digit level for 1990 and 2000 (detailed values can be provided on request). We show sophistication scores for exports by selected countries, technology groups and industries. Our intention is to provide a database for further empirical analysis.

Human mismatches in machining

This main objectives of this study were to examine human aspects of machining and to obtain an understanding of the issues within the broad context of manufacturing. Emphasis was placed on operator mismatches and the relationships of these to basic human characteristics and the preferred levels of automation from the operators' perspective with regard to turning operations. [Continues.

Mises, Kantorovich and Economic Computation

An article that reviews the work of Kantorovich in the light of von Mises claim that rational calculations were impossible without markets. It gives a tutorial introduction to the use of Kantorovich's methods, compares his approach to that of Dantzig. An assesment is given of the extent to which new interior point methods of linear programming strengthen or weaken Kantorovich's claims.Kantorovich, Linear-programming, von-Mises

“Automation” of manufacturing in the late nineteenth century: the hand and machine labor study

Recent advances in artificial intelligence and robotics have generated a robust debate about the future of work. An analogous debate occurred in the late nineteenth century when mechanization first transformed manufacturing. We analyze an extraordinary dataset from the late nineteenth century, the Hand and Machine Labor study carried out by the US Department of Labor in the mid-1890s. We focus on transitions at the task level from hand to machine production, and on the impact of inanimate power, especially of steam power, on labor productivity. Our analysis sheds light on the ability of modern task-based models to account for the effects of historical mechanization.Published versio

The Rise and Fall of Unions in the U.S.

Union membership displayed an inverted-U-shaped pattern over the 20th century, while the distribution of income sketched a U. A model of unions is developed to analyze these phenomena. There is a distribution of firms in economy. Firms hire capital, plus skilled and unskilled labor. Unionization is a costly process. A union decides how many firms to organize and its members' wage rate. Simulation of the developed model establishes that skilled-biased technological change, which affects the productivity of skilled labor relative to unskilled labor, can potentially explain the above facts. Statistical analysis suggests that skill-biased technological change is an important factor in de-unionization.Computers; Distribution of Income; Flexible Manufacturing; Mass Production; Numerically Controlled Machines; Panel-Data Regression Analysis; Relative Price of New Equipment; Skill-Biased Technological Change; Simulation Analysis; Union Coverage; Union Membership; Deunionization

A Model-Driven Engineering Approach for ROS using Ontological Semantics

This paper presents a novel ontology-driven software engineering approach for the development of industrial robotics control software. It introduces the ReApp architecture that synthesizes model-driven engineering with semantic technologies to facilitate the development and reuse of ROS-based components and applications. In ReApp, we show how different ontological classification systems for hardware, software, and capabilities help developers in discovering suitable software components for their tasks and in applying them correctly. The proposed model-driven tooling enables developers to work at higher abstraction levels and fosters automatic code generation. It is underpinned by ontologies to minimize discontinuities in the development workflow, with an integrated development environment presenting a seamless interface to the user. First results show the viability and synergy of the selected approach when searching for or developing software with reuse in mind.Comment: Presented at DSLRob 2015 (arXiv:1601.00877), Stefan Zander, Georg Heppner, Georg Neugschwandtner, Ramez Awad, Marc Essinger and Nadia Ahmed: A Model-Driven Engineering Approach for ROS using Ontological Semantic

Global Mobility of Talent from a Perspective of New Industrial Policy: Open Migration Chains and Diaspora Networks

economic development, diaspora networks, search networks, serendipity