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Hypotheses for a Theory of Production in the Context of Industrie 4.0
Abstract
Significant increase in productivity of production systems has been an effect of all past industrial revolutions. In contrast to those industrial revolutions, which were driven by the production industry itself, Industrie 4.0 is pushed forward by an enormous change within the current society due to the invention and frequent usage of social networks in combination with smart devices. This new social behaviour and interaction now makes its presence felt in the industrial sector as companies use the interconnectivity in order to connect production systems and enhance collaboration. As employees bring their own smart devices to work the interconnectivity is brought into the companies as well and Industrie 4.0 is pushed into the companies rather than initiated by the companies themselves. On top of productivity improvement within production the fourth industrial revolution opens up new potentials in indirect departments such as engineering. This focus differentiates Industrie 4.0 from the first three industrial revolutions, which mainly focused on productivity increase by optimising the production process. Within the Cluster of Excellence “Integrative Production Technology for High-Wage Countries” of the RWTH Aachen University four mechanisms were developed which describe Industrie 4.0. The mechanisms “revolutionary product lifecycles”, “virtual engineering of complete value chains”, “better performing than engineered” and “revolutionary short value chains” can be achieved within an Industrie 4.0-environment. This environment is based on the four enablers “IT-Globalisation”, “single source of truth”, “automation” and “cooperation” and enhances collaboration productivity. Therefore the present paper examines and introduces hypotheses for a production theory in the context of Industrie 4.0. For each mechanism two hypotheses are presented which explain how the respective target state can be achieved. The transmission of these mechanisms into producing companies leads to an Industrie 4.0 capable environment strengthening competitiveness due to increased collaboration productivity within the direct and especially indirect departments. The specified hypotheses were developed within the framework of the Cluster of Excellence “Integrative Production Technology for High-Wage Countries” of the RWTH Aachen University.
... Understanding the impact of Industry 4.0 on sustainability within this context is essential for guiding these businesses as they transition towards more sustainable practices. Research Objectives and Hypotheses Development -The primary objective of this research is to develop hypotheses that explore the relationship between the adoption of Industry 4.0 technologies and sustainability performance in the manufacturing sector [1]. The study aims to identify the key factors that contribute to this relationship, taking into account the specific characteristics of different industries within India. ...
This paper explores the impact of Industry 4.0 on sustainability performance, a topic that has generated significant concern. The goal of this study is to develop hypotheses that examine how adopting Industry 4.0 technologies influences sustainability in the manufacturing sector. To do so, the research looks at various factors and formulates appropriate hypotheses, focusing on different types of industries in India. The study uses a survey-based approach to collect insights from small and medium-sized enterprises (SMEs) about the perceived benefits of adopting Industry 4.0 technologies. The data gathered will be analyzed using advanced statistical methods, such as regression analysis, to test the proposed hypotheses. The findings aim to bridge the gap between academic research and real-world applications, providing valuable guidance for SMEs as they navigate their digital transformation toward a more sustainable future.
... The most cuttingedge manufacturing technologies, including additive manufacturing, are needed to create a value chain that is so robust and deserving of admiration. (Dedrick, et al., 1999) According to , Schuh, et al., (2014) it's critical to simulate the value chain quickly to identify any issues and concerns in advance. One's ability to make decisions will be improved, and one will be able to adopt an active approach. ...
In this work, the product was selected sprocket style “B” 25B25SS by Tsubaki and CAD modeling was done in solid works 2021, and FEA was performed results showed that the sprocket can sustain chain tension of To= 50kg, and based on FEA the model was under the safe loading condition. A design optimization study was performed to see potential benefits from DFAM, where 83 iterations were successful, and the mass of the sprocket was reduced by nearly half of the original sprocket mass.
... A primeira revolução industrial ocorreu ao final do século XVIII, com substituição da energia produzida por pessoas e animais em força mecânica, sob a forma de caldeiras e máquinas a vapor, o que resultou, entre outros avanços materiais, no impulso dado às indústrias manufatureiras, com principal destaque para o setor têxtil, caracterizado pela elevada capacidade de produção (SCHUH et al., 2015). Na segunda metade do século XX, surgiu a terceira revolução industrial, denominando-se como a era da revolução tecnológica. ...
A Indústria 4.0 é um dos conceitos que mais cresce em todo o mundo, combinando o mundo físico e digital, que tem modificado não apenas a indústria de manufatura e inovação, mas a todos os setores produtivos e organizações, criando perspectivas e possibilidades.No Brasil, esse processo caminha de forma lenta, principalmente pelas dificuldades encontradas pelas indústrias nacionais em investir em inovação para melhoria de seus processos. No entanto, impulsionada pela competitividade interna e principalmente a global, tem motivado estas indústrias a acelerar este processo por busca de inovação. Com base neste cenário, surgiram algumas iniciativas públicas que também estão sendo implantadas, tais como o Decreto nº 10.534, de 2020 que Institui a Política Nacional de Inovação, a instituição da Câmara de Inovação e a Câmara Brasileira da Indústria 4.0, demonstrando assim, o início de uma implementação de uma política de inovação tecnológica para o Brasil.Neste artigo, analisou-se por meio de pesquisa bibliográfica o conceito da Indústria 4.0, suas tecnologias e sua aplicabilidade na indústria nacional sob este contexto. Buscando identificar os desafios e as oportunidades dentro deste novo cenário.
... Building on the individualization of products in the context of Industry 4.0 and the integration of customers and other relevant stakeholders (people) into the business processes, procurement creates value primarily through collaboration [19] and real-time information along the entire value chain [20]. McKinsey Studies [20] assume that the use of digitization within the procurement department among other changes enables a reduction up to 40% of maintenance costs. ...
In an increasingly competitive environment, suppliers are now seen as an important source of innovation. Long term partnerships enable companies to access the knowledge of suppliers to optimize their business. "Procurement 4.0" is one of the concepts that come to the fore when talking about digitalization of business processes. The major aim of this research is to discuss a conceptual model of "Procurement 4.0" and its potential to rethink the management of supplier relationships, which will be one of the main future tasks of procurement. The paper is based on a factual-analytical research approach that serves to continuously specify and supplement the elements of the frame of reference: Two challenging concepts, "Procurement 4.0" and Supplier Relationship Management, are merged to contribute to the fact that purchasing is indispensable as an "interface" within a global supply chain to reap the benefits of digitization. The factors that prove to be obstacles to digital supplier relationship management along the digital supplier journey - e.g. lack of guidelines, approaches or tools and a lack of understanding of the importance of long-term relationships - are reflected within the identified technologies of digital transformation. A comprehensive analysis of the given situation within digital supplier relationship management in Germany is provided. The most important digital supplier touchpoints are discussed in order to develop a traditional supplier relationship towards a digital relationship management. Thus, this paper succeeds in illustrating how the innovative concept of a supplier journey can be implemented in practice to counteract the future, entrepreneurial challenges.
... I 3.0 technology leaders realize that the now customized product value addition would captivate yet another customer prone to shell out a little to have a product with their personal characteristics inserted(KAGERMANN, et al. 2013). Silva (2015), citesSchuh et al. (2015), who point out four facilitators of Industry 4.0, responsible for increasing productivity: the globalization of information technology, the existence of a unified source of consistent data, automation and cooperation. ...
This paper proposes the retrofitting of equipment and processes available in a manufacturing didactic plant, composed of workstations embedded in its automated systems, formed by actuators and sensors connected to the AS-i network and at the PLC of each station, which are connected in PROFIBUS industrial network, concepts of Industry 3.0. The objective is to update it to work applying the concepts of Industry 4.0, emphasizing the integration of information through networks of Industrial Automation and Internet. To achieve this objective integration of Automation Technology (TA) with Information Technology (IT) is necessary. Allowing the integrated educational exercise of the technologies found in the Automated Industrial Manufacturing Systems, such as communication networks, process control and production management, control and supervision of complete automated assembly processes that takes place on the platform is possible through a remote connection via internet (remote laboratory). Thus, teaching and research groups in multiple locations can share information quickly and develop such simultaneously groups in multiple locations can share information quickly and develop such simultaneously.
... Such change in the production and consumption landscape that is commonly labelled as a new industrial revolution, also known ad industry 4.0, is extensively supported by the newly enabled possibilities for interaction and communication between humans and technology (also referred and cyber-physical-systems). In this approach, the production systems will be able to deliver personalized mass products, by relying on flexible processes, modularized product design and requiring efficient integration between supply chain players [6]. Such manufacturing systems have reconfiguration properties which allow for the definition of case-specific outputs enabling companies to adapt to changing production requirements in a cost-efficient way. ...
The remarkable developments that we continuously witness in information and communication technologies are dramatically reshaping the way we live and work. Future professions will require new competences and skills to fit increasingly digitized and responsive economies, whose fast pace will demand for continuous education and reskilling efforts. In Europe, the prospects of an aging population add to this scenario and will bring to the workforce new segments of the population, with very heterogeneous backgrounds and profiles that will demand for innovative learning approaches and tools. This paper offers a characterization of the concept and the applicability of piBook, a tool developed with the purpose of supporting the teaching and learning of computational thinking, suitable to diversified audiences. piBook supports the creation of interactive storytelling, using nonlinear narratives, and allows for the creation of textual games, interactive activities, tutorials, and alike, therefore offering opportunities new and engaging pedagogical methods.
... The huge amount of data enables data analytics to gain further and more specific information about the machine, e.g. the performance or even its future behavior [25]. Therefore, data information can be processed, which before were only stored [26]. They can be used to further optimize the production. ...
Cyber-physical Production Systems (CPPS) have become popular in the context of Industry 4.0. CPPS are related to interlink the entities of the production system (e.g. machines) as well as to decentralized production control. Decentralized production control means that the work pieces schedule themselves and determine their own production process in the production system. Thus, different production processes can even process two identical parts.
The concept of decentralization is discussed frequently in research. However, decentralized production control has implications on process planning. Process planning is conducted before scheduling to define the production process, the machine tool, as well as the tools. Hence, process planning determines the degree of freedom for the subsequent scheduling.
Until now, the discussion of Industry 4.0 focused mostly on scheduling. However, to make use of the full potential of Industry 4.0 and CPPS, this contribution investigates their implications on process planning, as this step is necessary for scheduling. Hereto, first the concept of Industry 4.0 is analyzed and the resulting changes in CPPS. After a general analysis, it is investigated which of the resulting changes impact process planning. This investigation is necessary as process planning is essential for a decentralized production control. Based on this, it is investigated how these changes can be used for a methodology for integrated process planning and scheduling.
... The fourth industrial revolution exploits different technologies that allow you to connect to the Internet any type of device. The purpose of this type of solution is essentially to monitor and control and transfer information and then carry out consequent actions (Schuh et al. 2015). This set of technologies is referred to by the term Internet of Things (IoT), used for the first time by Kevin Ashton, a researcher at MIT (Massachusetts Institute of Technology), to define the connection between material objects with a structure composed of virtual representations (Madakam et al. 2015). ...
Sustainable development and the circular economy are two important issues for the future and the competitiveness of businesses. The programs for the integration of sustainability into industrial activities include the reconfiguration of production processes with a view to reducing their impact on the natural system, the development of new eco-sustainable products and the redesign of the business model. This paradigm shift requires the participation and commitment of different stakeholder groups and industry can completely redesign supply chains, aiming at resource efficiency and circularity. Developments in key ICT technologies, such as the Internet of Things (IoT), help this systemic transition. This paper explores the phases of the transition from a linear to a circular economy and proposes a procedure for introducing the principles of sustainability (environmental, economic and social) in a manufacturing environment, through the design of a new Circular Business Model (CBM). The new procedure has been tested and validated in an Italian company producing ceramic tiles, using the digitalization of the production processes of the Industry 4.0 environment, to implement the impact assessment tools (LCA—Life Cycle Assessment, LCC—Life Cycle Costing and S-LCA—Social Life Cycle Assessment) and the business intelligence systems to provide appropriate sustainability performance indicators essential for the definition of the new CBM.
... Termo que remete ao fato da formação de fábricas que serão inteligentes, flexíveis, dinâmicas e ágeis (DA COSTA, 2017). Na indústria 4.0 ocorre a digitalização e interligação de todas unidades de uma fábrica (DA SILVA, 2015), possuindo como agentes facilitadores para o fenômeno da globalização a tecnologia de informação, a existência de uma fonte unificada de dados consistentes, a automatização e a cooperação (SCHUH et al., 2015). ...
A indústria 4.0 é um fenômeno ocorrido em escala global, revolucionando o conceito e métodos de trabalho através da criação de fábricas inteligentes que integram sistemas de automação, sistemas ciber-físicos e internet. Tal revolução passou a exigir novas competências relativas aos profissionais que irão se inserir no mercado de trabalho. Neste ensejo, surgem os efeitos da indústria 4.0 sobre o processo de aprendizagem que precisa ser alterado para se adequar as competências exigidas pelo mercado de trabalho e garantir uma formação multidisciplinar. Assim, o presente trabalho realiza uma análise das competências exigidas e efeitos sobre o processo de ensino-aprendizagem discutindo o papel do professor e estudante para formação de um profissional da engenharia com perfil desejado pelo mercado. A metodologia utilizada para a pesquisa foi baseada em uma revisão bibliográfica acerca do processo de aprendizagem, indústria 4.0 e competências para o mercado de trabalho. Como resultado é perceptível a necessidade de transformar o processo de aprendizagem no espaço das universidades, pois, devido as mudanças em um universo de caos epistemológico, o profissional que se insere no mercado deve chegar com as devidas competências requeridas pela indústria, bem como, os profissionais que já estão/estavam na indústria deve se reinventar procurando uma formação multidisciplinar. Logo, o processo de ensino- aprendizagem tem que ser reformulado e adequado ao novo mercado, destacando a aplicação de novas metodologias de ensino e revisão de componentes curriculares.
... Driven by the development of the society, new challenges are arising in the product realization process. Companies in almost all industries are confronted with shorter product lifecycles [1] and the trend of individualization [2]. Besides the resulting need for shorter innovation cycles, the decrease of lot sizes is one of the main issues arising from these changes. ...
A rising number of product variants together with decreasing lot sizes are a result of the trend of individualization. Besides the upcoming organizational issues, changes in the production technologies are required. Direct digital manufacturing contributes to solve this problem by enabling production of parts right from the CAD data.
Process capability analysis is applied in several industries to prove the reliable compliance of products with quality requirements. As it is based on statistical methods, new challenges arise in the context of single-part production.
The paper describes and compares different approaches for the adoption of process capability analysis for single-part production with special focus on additive manufacturing technologies. The statistical background and the applicability of different capability parameters are discussed. An overview of existing research work is given and supplemented by own approaches for the adoption of statistical methods for single-part production. The aim of the research work is to establish a first approach for the qualification of new technologies in single-part production.
... The potential for improvements enabled by digitalization are not limited to the manufacturing process. They also concern indirect departments like engineering or administration [2]. The consideration of indirect departments puts the focus on a value chain perspective instead of an isolated manufacturing perspective. ...
The increasing digitalization of value chains can help companies to handle rising complexity in their processes and thereby reduce the steadily increasing planning and control effort in order to raise performance limits. Due to technological advances, companies face the challenge of smart value chains for the purpose of improvements in productivity, handling the increasing time and cost pressure and the need of individualized production. Therefore, companies need to ensure quick and flexible decisions to create self-optimizing processes and, consequently, to make their production more efficient. Lean production, as the most commonly used paradigm for complexity reduction, reaches its limits when it comes to variant flexible production and constantly changing market and environmental conditions. To lift performance limits, which are inbuilt in current value chains, new methods and tools must be applied. Digitalization provides the potential to derive these new methods and tools. However, companies lack the experience to harmonize different digital technologies. There is no practicable framework, which instructs the transformation of current value chains into digital pervasive value chains. Current research shows that a connection between lean production and digitalization exists. This link is based on factors such as people, technology and organization. In this paper, the introduced method for the determination of digitally pervasive value chains takes the factors people, technology and organization into account and extends existing approaches by a new dimension. It is the first systematic approach for the digital transformation of lean production and consists of four steps: The first step of ‘target definition’ describes the target situation and defines the depth of the analysis with regards to the inspection area and the level of detail. The second step of ‘analysis of the value chain’ verifies the lean-ability of processes and lies in a special focus on the integration capacity of digital technologies in order to raise the limits of lean production. Furthermore, the ‘digital evaluation process’ ensures the usefulness of digital adaptions regarding their practicability and their integrability into the existing production system. Finally, the method defines actions to be performed based on the evaluation process and in accordance with the target situation. As a result, the validation and optimization of the proposed method in a German company from the electronics industry shows that the digital transformation of current value chains based on lean production achieves a raise of their inbuilt performance limits.
... Another important emerging paradigm is Industrie 4.0, which [1,22] specifically takes into account: sensor and actuator networks, intelligent network control systems and human in the loop principles; Intelligent Robots and Machines, including human-robot interaction, adaptive control, context awareness; Big Data; Network Quality of Service; Energy Efficiency And Decentralization; Virtual Industrialization in regard to the concept of "virtual plants and products"; Value Networks aiming at achieving digital integration along the supply chain and along different manufacturing processes and engineering models and methods [7,18]. ...
Systems that can tightly integrate physical with virtual components have represented a priority of research and development in the area of ICT. An intensive work has been concentrated in different domains, such as: Internet of Things, Internet of Services and lately in the domain of Cyber Physical Systems whose important driver is represented by the largescale integration of the physical and cyber worlds. Authors propose a Generic System Architecture, taking into account the paradigm of Cyber-Physical Systems with a main emphasis on the future agricultural enterprise (intelligent/smart farm) as a complex system, addressing sustainability and adaptability towards environmental and market changes.
... A primeira revolução teve como tecnologia disruptiva lançadora os teares mecânicos, de cuja força motriz eram os motores a vapor, que propiciaram a partir de 1780 na Inglaterra uma mudança significativa na capacidade de produção de tecidos. A produção caseira foi substituída progressivamente por ambientes de fábricas com centralização produtiva, elevando-se significativamente a produtividade (SCHUH, 2015). Ainda na primeira revolução industrial, outro importante avanço foi a mecanização da agricultura, trazendo melhora na produção de alimentos, conforme atesta Chukwueke (2016). ...
Espaço reservado para a comissão organizadora (não escreva nada nesta área) Resumo A Indústria 4.0 é um conceito produtivo concebido através de um esforço em conjunto de colaboração entre o governo, indústria, pesquisa e instituições acadêmicas na busca por impactar a indústria do futuro por produtividade e competitividade. Em essência, é uma tecnologia de máquinas inteligentes e computação de alto desempenho em análises preditivas e de elevada interface com as pessoas. Existem muitas oportunidades e desafios a serem superados para que as indústrias e nações se engajem profundamente nesta nova onda. Contudo, não são claros quais os componentes tecnológicos seriam pertinentes à Indústria 4.0, tampouco como a implantação dessas novas tecnologias impactará as receitas e a competitividade das indústrias e dos países. Nesse sentido, as contribuições deste artigo, estão na apresentação dos componentes da Indústria 4.0, bem como propor aplicações na projeção competitiva na fabricação do futuro no Brasil. Como resultados são identificados seis componentes relevantes da Indústria 4.0, suas aplicações e projeções na alavancagem competitiva comparada as três revoluções industriais precedentes. Levando em conta estes conceitos, a conclusão é de que a Indústria 4.0 está no início e que será muito importante incorporar as inovações tecnológicas no processo de manufatura para se alcançar ganhos expressivos, seja no Brasil ou no Mundo. Palavras-chave: Indústria 4.0; Competitividade; Fabricação Inteligente; Indústria do Futuro. Introdução Embora existam algumas divergências sobre o ano de origem e os ciclos das revoluções industriais, pode-se afirmar que as três primeiras revoluções industriais se deram dentro de um período de cerca de 200 anos. A primeira revolução teve como tecnologia disruptiva lançadora os teares mecânicos, de cuja força motriz eram os motores a vapor, que propiciaram a partir de 1780 na Inglaterra uma mudança significativa na capacidade de produção de tecidos. A produção caseira foi substituída progressivamente por ambientes de fábricas com centralização produtiva, elevando-se significativamente a produtividade (SCHUH, 2015). Ainda na primeira revolução industrial, outro importante avanço foi a mecanização da agricultura, trazendo melhora na produção de alimentos, conforme atesta Chukwueke (2016). Assim, o setor primário teve condições de liberar mão-de-obra para a indústria manufatureira em expansão, ao mesmo tempo em que a Grã-Bretanha expandia seu comércio importando matérias-primas. A revolução industrial seguinte teve como indústria pioneira a automobilística, baseada na energia do petróleo, desenvolvida mais de 100 anos depois a partir do marco fundamental
... Each element must contain information about its original, current and final condition, and include steps that allow autonomous control of production [8]. Another important characteristic of Industry 4.0 is the already mentioned ability to collect and use a large amount of data [11]. The act of data collection alone does not offer advantages. ...
Industry 4.0 is a current research topic in the field of production engineering. One common characteristic of Industry 4.0 is decentralization which can be implemented by a decentralized production control. Several researchers have already addressed decentralized production controls. This paper focuses on the characteristics of Industry 4.0 as well as decentralized control approaches and hierarchies. Different properties of approaches and architectures are compared to the objectives of Industry 4.0. Based on this comparison conclusions are drawn about how different architectures suit Industry 4.0, and need for action for the development of production controls of Industry 4.0 is derived.
... • Global Database: Industry 4.0 relies on information sharing among production and logistics centers of companies. To ensure that all the centers utilize the most current information, it is necessary to receive information from a single location, defined by Schuh et al [2] as the "single source of truth" and presented in this paper as the global company database. Since Industry 4.0 companies generate and utilize vast amounts of data, the global database is beyond what can be integrated into user devices. ...
Industry 4.0 is a combination of many elements, including distributed intelligence, network security, massive data, cloud computing, and analytics, among other things. Such elements are critical to the “Digital Factory”, a term that has been recently introduced by many companies indicating a comprehensive portfolio of seamlessly integrated hardware, software and technology-based services, with the aim to enhance manufacturing productivity and improving efficiency. Typically, industrial networks enable the gathering of extensive data from productionlines and plants, which are increasingly becoming distributed. The gathered data is transmitted to analysis centers where it is transformed into information and used to make better informed decisions. In addition, modern industrial networks allow plant data to be automatically filtered and transmitted to various production controllers. Ultimately, industrial networks enable Industry 4.0 to have the following benefits: improved safety, increase uptime, lower energy costs, and improved maintenance;all of which lead to manufacturing competitiveness in cyber-physical production systems supported by Smart Grid implementations. This paper presents the extent to which industrial networks are taught at the School ofEngineering Technology at McMaster University. Further, the paper covers teaching methods of industrial networks and their related applications within manufacturing plants and electrical grid.
... Systems will involve greater integration of humans and automation via various components such as informatics, robotics, mobile devices and sensors. Although it is claimed that "Industrie 4.0 is not initiated on a shop floor level" [4] there is no denying that the conventional shopfloor environments will be transformed. ...
As a result of significant advances in information and communications technology the manufacturing industry is facing revolutionary changes whereby production processes will become increasingly digitised and interconnected cyber-physical systems. A key component of these new complex systems will be intelligent automation and human-robot collaboration. Industrial robots have traditionally been segregated from people in manufacturing systems because of the dangers posed by their operational speeds and heavy payloads. However, advances in technology mean that we will soon see large-scale robots being deployed to work more closely and collaboratively with people in monitored manufacturing sytems and widespread introduction of small-scale robots and assistive robotic devices. This will not only transform the way people are expected to work and interact with automation but will also involve much more data provision and capture for performance monitoring. This paper discusses the background to these developments and the anticipated ethical issues that we now face as people and robots become able to work collaboratively in industry.
... The American philosopher and economist Jeremy Rifkin highlighted the role of power technologies connecting the future social and economic structure with development of alternative power (Rifkin 2011). In turn, scientists from the German Cluster of Excellence "Integrative Production Technology for High-Wage Countries" singled out the key features of the new industry 4.0, such as IT-globalization, single source of truth, automation, and cooperation (Schuh et al. 2015). ...
Social and economic evolution is described by numerous wave and cyclic concepts. Nevertheless, at certain historical periods, societies make great breakthroughs known as technological revolutions. Now we are on the threshold of the fourth industrial revolution characterized by a rapid development of such industries, as robotics, artificial intelligence, neuroscience, brain engineering, and 3D printing. Social and economic development always went in parallel with science. However, the role of science in economic processes has been changing throughout time. The focus of the present research is the university as a key actor of economic change. Historically, it is possible to allocate four types of universities by analogy with four industrial revolutions. Under the conditions of the fourth industrial revolution there is a radical shift in the university model. From R&D and technology transfer universities move to creation of intellectual capital. Universities do not simply conduct R&D for business, but also create essentially new industries. Universities become a centre round which new hi-tech enterprises grow. This phenomenon has been entitled an entrepreneurial university, which is considered to be the main actor of entrepreneurial (startup) economy. The research main objective is identification of key factors in the entrepreneurial university success. The authors analysed the Global University Venturing ranking leading universities. The research is not limited to the quantitative data; qualitative indicators are also of great importance. Various techniques to estimate the university entrepreneurial capacity (Reuters, EULP-Entrepreneurial Universities Leaders Program) have been considered, and their comparative analysis has been conducted. The final model is based both on quantitative or qualitative indicators; the model can be used not only for estimation of entrepreneurial capability, but for the development of university strategy as well.
... The application of these technologies to factories creates smart factories. Smart factories aim for adjusting their production processes based on produced information, e.g., by machines [28,10,29,30,12]. ...
To stay competitive, manufacturing companies need to adapt their processes in a regular basis to the most recent conditions in their corresponding domains. These adaptations are typically the result of turbulences, such as changes in human resources, new technological advancements, or economic crises. Therefore, to increase the efficiency of production processes, (i) automation, (ii) optimization, and (iii) dynamic adaptation became the most important requirements in this field. In this work, we propose a novel process modelling and execution approach for creating self-organizing processes: Production processes are extended by context-sensitive execution steps, for which sub-processes are selected, elected, optimized, and finally executed on runtime. During the election step, the most desired solution is chosen and optimized based on selection and optimization strategies of the respective processes. Moreover, we present a system architecture for modelling and executing these context-sensitive production processes.
Purpose: Increasingly, organizations are seeking technological innovations to improve their processes and production stages, configuring Industry 4.0, which, despite the evident gains, has generated tensions in the world of work. Thus, this article aims to analyze how workers in this context have experienced this transition. Methodology/Approach: In methodological terms, this study was conducted through a qualitative-descriptive approach, which investigated the perception of workers involved in organizations that went through technological innovations through semi-structured interviews analyzed in the light of content analysis. Findings: The results acknowledge the benefits of technological advancement to the substantial development of the production processes of these organizations but highlight the ambiguities of Industry 4.0, such as the replacement of human labor by machine, generating unemployment, conflicts, and tensions among workers. Research Limitation/implication: The limitations of this study are the size of its corpus due to the difficulty in joining the research, imposed by the social limits caused by the pandemic. Originality/Value of paper: The discussions observed in the research point to the ambiguities that accompany this phenomenon, which has different positions and visions and points to the urgency of inserting the human component in the debates involving various sectors of society.
Although Industry 4.0 and other initiatives predict widespread adoption of digitalised technology on the factory floor, few companies use new digitalised production technology holistically in their ecosystems; in practical implementation, companies often decide against digitalisation for financial reasons. This is due to a paradox (akin to the so called “productivity paradox”) caused by the complexity of value creation and value delivery within digitalised production. This article analyses and synthesises cross-disciplinary research using a grounded theory model, thus offering valuable insights for businesses considering investing in digitalised production. A qualitative model and an associated toolbox (complete with tools for practical application by business leaders and decision-makers) are presented to address organisational uncertainty and leadership disconnect that often contribute to the paradoxical gap between digital strategy and operational implementation.
Additive manufacturing is being increasingly focused on the production of end-use parts. Compared to the prototyping application, the production of end-use parts demands a higher level of repeatability and process quality. To achieve this, increased knowledge is required about the influence of various process parameters on the part characteristics and the parameter interrelations. Design of Experiment methods can be applied to gain knowledge on the process behavior, but the applicability of different DoE methods for AM processes has to be validated. This paper describes the application of a definitive screening design for the identification of influencing parameters in Laser Powder Bed Fusion of CoCrW alloy. The impact of various hatch parameters on the part porosity is analyzed. The experimental setup and results are described. The results are validated in an additional test series, comparing the part quality achieved by parameter-sets obtained by different optimization approaches. Furthermore, the correlation of the porosity towards mechanical properties is investigated. Finally, the opportunities and limitations of the method are discussed.
While companies struggle to implement Smart Factory initiatives, the emergence of decentralized Distributed Ledger Technology (DLT) promises to support Smart Factories. However, little is known about the extent to which DLT can support Smart Factory initiatives. Thus, this paper examines whether DLT is a useful addition to the Smart Factory concept in the context of Industry 4.0. The focus of the research lies on practical challenges that manufacturing companies are confronted with when creating Smart Factories and integrating them into their value chain. These challenges were worked out with the help of a literature review and interviews, which were conducted with employees of one of the most renowned industrial automation and digitization companies (undisclosed for confidentiality). Based on this, two DLT concepts were developed and discussed with the experts regarding their respective opportunities, risks, and feasibility. The DLT-based Audit Trail is intended to solve the challenge of creating a detailed, consistent and traceable overview of production processes, while the Crypto-based Agent Logic solves the challenge of setting priorities for orders in a fully automated production process. The results show that DLT integration in the context of the Smart Factory concept is to be regarded as useful and should be driven forward by further research.
A new configuration of vibration energy harvester is proposed, to equip a smart bearing with a power supply. The case of rolling mill is analyzed, to allow the identification of some design issues, related to the proposed hybrid energy scavenger. The energy conversion is performed by a contactless plucking of a permanent magnet upon the tip mass of a flexible structure, with surface-bonded piezoelectric layers. Since the system is embedded into a mechanical bearing, the dynamic excitation is applied on the vibrating structure, constrained to its fixed ring, by a permanent magnet lying on the rotating ring surface.
Despite of the acceleration of investments and the expansion of countries towards the Industry 4.0, companies have difficulties in planning the transition processes and implementation of the scenarios of Industry 4.0. To benefit from the Industry Approach 4.0, it is necessary to take technological and organizational transition processes into account, since the phenomenon involves interoperability between humans; between humans and machines; and between machines and production. This paper proposes to examine the transformation processes of the current industrial model to the Industry 4.0 model of FESTO AG, in addition to the framework proposition for the analysis of transformation processes for Industry 4.0. Through the face-to-face interviews and the institutional materials of FESTO, it was observed that the company inserted in its strategy of products and innovation the concept of Industry 4.0. To do so, FESTO planned and built a new production plant based on connectivity, sustainability, and collaborative environment, especially between man and machine. To support this orientation, FESTO has strengthened its technological base, culture, training of its productive, commercial, and management teams.
Keywords: Industry 4.0, competitiveness, industry, transformation processes
The School of Engineering Practice Technology (SEPT) at McMaster University is making a deliberate effort to train the next generation of engineers that are ready to work in Industry 4.0 environment. Under this effort we have developed equipment for teaching the technologies that support Industry 4.0, and this paper presents two sets of such equipment. The first set is used to teach machine-to-machine (M2M) communication, while the second is used to teach system control and automation data access. The accessed data is used in a multiplicity of students’ projects, including but not limited to SCADA systems, system simulation and control using fuzzy logic and artificial neural network, cloud based systems, and data analytics. In addition, this paper describes how the equipment is utilized to support graduate and undergraduate teaching through the experiential learning paradigm of laboratory based projects. The paper also presents example student projects that have been carried out using our equipment.
Consolidation of disruptive techonologies such as 3D printing, Big Data, Cloud Computing, the Internet of Things and Cyber-Physical Systems have led to the advent of the Industry 4.0 revolution. As a consequence, a new wave of automation and IT-enabled process transformations is coming. We find Business Process Reengineering/Redesign (BPR) remaining as a conceptual reference for fundamental rethinking and redesign of business processes. Stemming from the identification of the insufficiencies of reference frameworks to characterize the different types of business process changes, we propose a new conceptual framework: the IT-Enabled Business Process Transformation Matrix. The horizontal axis of the matrix represents the scope of the change, whereas the vertical axis represents the degree of transformation. Some examples illustrate its practical interest and flexibility to incorporate a great variety of process transformations based on technological innovations.
Business processes are designed to perform in an ideal environment where incidents that disturb regular working processes do not exist. However, this environment is fairly idealist, since business processes are affected by many different events, forcing changes in plans or solutions that allow for business continuity. In the context of hierarchical production planning, unexpected events, such as the lack of availability of materials, rush orders and faulty machines; have to be managed efficiently because they represent a risk for business continuity, depending on their impact and duration. In this sense, operational risk management, supported by decision support systems, allow enterprises to have contingency plans that show the decision maker different ways to manage the specific event through rules that check the event's impact and analyse provenance data stored in data warehouse. In the on-going research of inter-enterprise architecture, it has been labelled its main elements: framework, methodology and modelling languages. This paper proposes a decision-making and operational risk framework, looking for solutions that facilitate the decision-making process under the arrival of unexpected events that affect hierarchical production planning.
Manufacturing companies in high-wage countries—one of the pillars of the European national economies—are particularly exposed to changes in global markets and rising market volatility. It is therefore necessary that manufacturers in these countries not only focus on reducing costs, but instead address the entire set of commonly defined operational capabilities: cost, quality, flexibility, and delivery performance. Although the optimization of these factors has been viewed since long as being largely mutually exclusive, we argue that advances in modern production technology might enable the resolution of the involved dichotomous relationships. In this chapter, we hence aim at presenting a technology-oriented theory of production that operationalizes the link between technological advances and possibilities to strengthen the four competitive priorities of manufacturing companies. For this purpose, existing production theories are first reviewed to ground and classify our theory. We subsequently formalize the technology-oriented theory by adopting a profitability assessment perspective derived from the insights of all projects within the Cluster of Excellence Integrative Production for High-Wage Countries.
Das Instandhaltungs-Controlling bildet einen zentralen Baustein einer intelligenten und lernorientierten Instandhaltung (IH). Dank Industrie 4.0 wird die wirtschaftliche Erfassung und Speicherung von großen Datenmengen möglich. Diese Daten können vom IH-Controlling für die effektive Analyse, Steuerung und Kontrolle genutzt werden. Dazu ist es allerdings nötig einen Überblick über diese Daten zu behalten. Schlanke Kennzahlencockpits die auf den kritischen Erfolgsfaktoren der Instandhaltung beruhen können dabei helfen. In Kombination mit einem geschlossenen Regelkreis ermöglichen es diese, Maßnahmen abzuleiten und die Instandhaltung auf eine permanente Lernorientierung auszurichten. Dieser Artikel zeigt wie ein ganzheitliches IH-Controlling aufgebaut, IH-Cockpits entwickelt sowie die Beziehungen zwischen Key Performance Indikatoren identifiziert werden können.
In diesem Kapitel soll der Frage nachgegangen werden, was die in den vorangehenden Kapiteln vorgestellte Systematik einer integrierten Planungsrechnung mit den Visionen von Industrie 4.0 zu tun hat. Im Kontext dieser neuen industriellen Revolution stehen Schlagwörter wie Echtzeitsteuerung, wandlungsfähige Produktionssysteme und Individualisierung der Produktion bis zur viel beschworenen „Losgröße 1“. Dabei drängt sich jedoch die Frage auf, ob die heute im betrieblichen Rechnungswesen dominierenden Softwaresysteme für die Realisierung dieser Visionen geeignet sind. Wenn allerdings Planungsdauern von 4–6 Monaten die Regel und Simulationsmöglichkeiten alternativer Szenarien heute in der Praxis so gut wie unmöglich sind, dann kann daran ernsthaft gezweifelt werden.
Den meisten am Markt verfügbaren Softwaresystemen zur Planungsunterstützung fehlt die systematische Abbildung der Bestandsplanung (Lagerbilanzgleichungen), die Erfassung der zeitlichen Dynamik der Produktion (Vor- und Durchlaufzeiten) sowie das gesamte relevante Mengen- und Zeitgerüst der Leistungserstellung (Produktions- und Kostenfunktionen). Daraus folgt einerseits, dass nur ein sehr grobes und zudem verzerrtes Abbild des Unternehmens im Planungsprozess erstellt wird und andererseits Restriktionen nicht sichtbar werden und differenzierte Abweichungsrechnungen unmöglich sind. Damit sind diese Systeme nicht geeignet, eine umfassende Planungsrechnung für Industrieunternehmen abzubilden, und können daher auch nicht in Simulationsumgebungen im Industrie-4.0-Umfeld eingebunden werden.
This book focuses on emerging issues in ergonomics, with a special emphasis on modeling, usability engineering, human computer interaction and innovative design concepts. It presents advanced theories in human factors, cutting-edge applications aimed at understanding and improving human interaction with products and systems, and discusses important usability issues. The book covers a wealth of topics, including devices and user interfaces, virtual reality and digital environments, user and product evaluation, and limits and capabilities of special populations, particularly the elderly population. It presents both new research methods and user-centered evaluation approaches. Based on the AHFE 2016 International Conference on Ergonomics Modeling, Usability and Special Populations, held on July 27-31, 2016, in Walt Disney World®, Florida, USA, the book addresses professionals, researchers, and students dealing with visual and haptic interfaces, user-centered design, and design for special populations, particularly the elderly.
Aim of the present research is the introduction of a self-assessment instrument fostering the user-centered development and evaluation of human machine interfaces during ramp-ups of socio-cyber-physical production systems. This objective is addressed by first outlining the concept of socio-cyber-physical production systems and their specific design restrictions. Then existing user-centered design approaches are analyzed and guiding questions from a user-centered perspective are deduced. The questions are structured under consideration of the agile framework scrum. Applicability of the instrument is tested by conducting a self-assessment with “oculavis”, a software environment for smart glasses and other wearable technologies. Results show that the integration of agile and user-centered development remains a challenge in practice and seems to be approached more intuitively than methodical.
Due to the potentials of Industrie 4.0 such as cyber-physical systems, manufacturing companies will connect their intelligent products and machines. The resulting smart factory enables integrated engineering across the entire value chain. Furthermore, intelligent assistance systems will support employees so that they can focus on value adding activities. Within the Cluster of Excellence “Integrative Production Technology for High-Wage Countries” of the RWTH Aachen University, which is funded by the German Research Foundation DFG, four mechanisms were developed regarding these current developments to increase collaboration productivity and therefore facilitate a sustainable production system. The mechanisms “radically short product development processes”, “virtual engineering of complete value chains”, “better performing than engineered” and “revolutionary short value chains” do not only improve manufacturing processes but also product development processes and thus meet current challenges as resource and energy efficiency.
In today's process manufacturing environment, innovation is viewed as critical to sustainable growth and business profitability. While innovation is regarded as the answer, the companies can effectively measure the return on R&D investment, have acceptable product success rates, achieve acceptable promotional effectiveness, or have visibility into their compliance risks or operational readiness for new product launches. Companies must have repeatable, compliant and responsive business processes, global ICT information infrastructure that provides a single source of the truth, alignment across departments and solutions that evolve without coding. With holistic strategy and supporting infrastructure, companies can consistently minimize the time to scale, improve product success rates and promotional effectiveness, and enjoy sustainable and profitable growth. The companies must first focus on the needs of their customer, continually minimize time to scale, eliminate waste, drive out costs and improve. These are core concepts of a Lean strategy. This paper will describe how Lean concept with PLM business strategy can leverage Lean with integrated compliance, continual improvement and other PLM best practices to increase the return on R&D investments and provide sustainable and profitable growth for business processes mainly manufacturing processes. The purpose of this paper is to review PLM approach linked to Lean concepts in order to achieve sustainable and innovative business environment with profitable growth.
In retrospective industrial revolutions always lead to a significant increase in productivity. Thus, the question arises what mechanisms contribute to raise productivity in the current revolution “Industrie 4.0”. Whereas the initial point of all past industrial revolutions can be located in the industry, they resulted in a tremendous change in society. In the present industrial revolution it is the other way around: Reviewing the beginning of the current transformation process, it is not driven by the production industry itself. Instead one of its main drivers is the invention of social networks and smart devices in combination with the employees’ appealing to it. This development of interconnectivity pushes into the industrial sector today. For instance, there exists a desire of employees to bring their own device to work. According to a survey by Accenture 82 percent of the Chinese respondents would be “more resourceful” if they chose their own hardware and software for work. The first three revolutions had a strong focus on the shop floor. This is also true for the present industrial revolution: The public view is merely on its impact on production processes. Therefore, this paper expands this view and additionally analyses the effects of the relating transformation processes to the indirect departments. The paper first analyses the enablers which mainly contribute to Industrie 4.0. Subsequently a reference systems is deduced which consists of basic collaboration mechanisms to increase productivity in the direct and indirect departments. A wide transparency and understanding of those collaboration mechanisms empower producing companies to profit from Industrie 4.0 by deriving individual activities which lead to a growth in productivity and therefore competitiveness. The specified approaches were conducted within the framework of the Cluster of Excellence “Integrative Production Technology in High-Wage countries” of the RWTH Aachen University.
Many implementations of the OPC-UA are increasingly available. Until now, use of technologies like the OPC-UA was limited to embedded systems with a sufficient amount of memory resources. OPC-UA offers a scalable feature set that could be tailored according to application demands. We believe that a key to successfully enable Internet of Things for the industrial automation applications lies in bringing down the OPC-UA into low end resource-limited devices, such as sensors. Because the OPC-UA provides a powerful information representation that is exchangeable through interoperable services. This paper has investigated the OPC-UA as a middleware solution for such resource-limited devices. For a proof of concept we have implemented an OPC-UA server based on the “Nano Embedded Device Server profiles” of the OPC Foundation.
The adoption of Cyber-Physical Systems (CPS) in production networks enables new potential for improved efficiency, accountability, sustainability and scalability. In terms of production and transport processes, materialising this potential requires customised technological concepts, planning and control methods as well as business models. Even though CPS strongly rely on technological advancements, the creativity, flexibility and problem solving competence of human stakeholders is strongly needed for their operation. This paper introduces and reviews the social aspects of CPS and motivates future research towards Socio-Cyber-Physical Systems (SCPS) applied to production networks. In this frame, context-dependent behavioural aspects and implications related to the human stakeholders are delimitated. As a showcase for the relevance of these aspects the deficits arising from an insufficient communication among stakeholders in SCPS are analysed by means of a simulation experiment. The obtained results substantiate the dependence of SCPS on properly considering the aspects related to human stakeholders together with technology.
Technologically demanding products are manufactured by adoption of modern production technology. The flexibility required for the ambitious technological processes needs a new kind of controlling mechanisms, which can only be reached by sophisticated optimization approaches like Self-Optimization. For Self-Optimization different approaches for controlling technologies are available, especially tools using cognitive information processing techniques. These new technologies have to be evaluated concerning important performance indicators against the background of the production process characteristics. Aim of the benchmarking process in this context is to ensure model quality of models used in a cognitive software application that is presented in this paper. As an example, an optimization approach using a combination of Artificial Neural Networks and a Soar optimizer is presented, with a benchmarking example of Artificial Neural Networks modeling process parameters and product characteristics resulting.
While seemingly incompatible, combining large-scale global software development and agile practices is a challenge undertaken by many companies. Case study reports on the successful use of agile practices in small distributed projects already exist. How these practices could be applied to larger projects, however, remains unstudied. This paper reports a case study on agile practices in a 40- person development organization distributed between Norway and Malaysia. Based on seven interviews in the development organization, we describe how Scrum practices were successfully applied, e.g., using teleconference and Web cameras for daily scrum meetings, synchronized 4- week sprints and weekly scrum-of-scrums. Additional agility supporting practices for distributed projects were identified, e.g., frequent visits, unofficial distributed meetings and annual gatherings are described.
We estimated the world’s technological capacity to store, communicate, and compute information, tracking 60 analog and digital
technologies during the period from 1986 to 2007. In 2007, humankind was able to store 2.9 × 1020 optimally compressed bytes, communicate almost 2 × 1021 bytes, and carry out 6.4 × 1018 instructions per second on general-purpose computers. General-purpose computing capacity grew at an annual rate of 58%. The
world’s capacity for bidirectional telecommunication grew at 28% per year, closely followed by the increase in globally stored
information (23%). Humankind’s capacity for unidirectional information diffusion through broadcasting channels has experienced
comparatively modest annual growth (6%). Telecommunication has been dominated by digital technologies since 1990 (99.9% in
digital format in 2007), and the majority of our technological memory has been in digital format since the early 2000s (94%
digital in 2007).
To understand the economic value of computers, one must broaden the traditional definition of both the technology and its effects. Case studies and firm-level econometric evidence suggest that: 1) organizational "investments" have a large influence on the value of IT investments; and 2) the benefits of IT investment are often intangible and disproportionately difficult to measure. Our analysis suggests that the link between IT and increased productivity emerged well before the recent surge in the aggregate productivity statistics and that the current macroeconomic productivity revival may in part reflect the contributions of intangible capital accumulated in the past. Erik Brynjolfsson is Associate Professor of Management, Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts and Co-director of the Center for eBusiness at MIT. Lorin M. Hitt is Assistant Professor of Operations and Information Management, Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania. Their e-mail addresses are <erikb@mit.edu> and <lhitt@wharton.upenn.edu> and their websites are <http://ebusiness.mit.edu/erik> and <http://grace.wharton.upenn.edu/~lhitt>, respectively. 2 Computers and Economic Growth How do computers contribute to business performance and economic growth? Even today, most people who are asked to identify the strengths of computers tend to think of computational tasks like rapidly multiplying large numbers. Computers have excelled at computation since the Mark I (1939), the first modern computer, and the ENIAC (1943), the first electronic computer without moving parts. During World War II, the U.S. government generously funded research into tools for calculating the trajectories of artillery shells. The result was the develo...
Kurzfassung
Unter dem Begriff „Selbstoptimierende Produktionssysteme“ wird ein Konzept verstanden, das wertstromorientierte Maßnahmen bei gleichzeitig steigender Planungseffizienz umsetzt und dabei die Qualität von Prozessen und Produkten verbessert. Dabei wird bereits erworbenes Wissen wieder verwendet und auf ähnliche, neue Produktionsfälle übertragen. Selbstoptimierung bietet neue Sichten auf Fertigungs- und Montagesysteme, indem sowohl in technologischen als auch in organisatorischen Bereichen eine dynamische Anpassung des Systemverhaltens an veränderliche Ziele ermöglicht wird. Eine ganzheitlich gesteigerte Qualität des Produktionssystems ist damit ein Ansatz, der nachhaltig die Produktion in Hochlohnländern sichert und verbessert.
The continuous growth of production networks has led to a threefold complexity issue for multinational companies. Firstly, it lies in the tremendous number of design options taking into regard all product groups and their production processes which need to be allocated to the amount of existing or new production sites. The second complexity issue is characterized by a short amount of time available in companies for highly important decisions. The third complexity problem lies in the complexity evaluation within the production network taking into account product, manufacturing and organisational structures. These three challenges are addressed within the scope of an approach which avails itself of a digital tool using interactive computing methods. While the complexity of the solution space is handled through a mathematical optimization, visual components help to understand and analyze the given solution through interactive computing. The identification of complexity drivers within production networks constitutes the final challenge.
This paper deals with the concept for self-optimizing decision-making in production planning and control. The concept is based on a value stream that provides real-time production data. This data enables a qualified decision regarding production planning and control. Practice has shown that production systems with a high production process complexity—such as job shop production with low volume production—are difficult to control automatically. Therefore, employees have an important role to play but need to be supported regarding their decision-making. The goal is to highlight relevant decisions and put them into the correct context. An unconventional and interactive illustration that abandons classic numerical key performance indicators helps to derive the correct decisions. Varying levels of detail regarding the depicted data allow the user to “zoom” in or out of the state of his production system. By support of simulation and visualization tools, the aim of this paper is to present a concept for self-optimizing decision-making in production control in order to help user making the right decision.
Due to increasing globalization and the resulting segmentation of markets, complexity has been increasing steadily. Modular product and process platforms as defined by Lean Innovation are increasingly applied by companies in order to reduce scale effects. Due to the autonomy of global production and development locations, these platforms, however, often do not deploy their full potential, since local improvement activities “soften” defined standards and lead to decreased scale effects. In order to solve this conflict between Lean Innovation and an increasing globalization, current complexity management has to consider global aspects. Lean Innovation by global complexity management allows companies to restrict degrees of freedom in the product-production systems by structural knowledge and to conduct innovation activities more focused. This results in serving global niche markets while simultaneously realizing scale effects in global production and development networks.
Um zukunftsfähig zu bleiben, müssen Industrieunternehmen mehr denn je ihre Produktivität steigern, energie- und ressourceneffizienter arbeiten und ihre Flexibilität erhöhen. Nur so können sie gleichzeitig Kosten senken, Markteinführungszeiten reduzieren und die steigende Nachfrage nach höherer Produktvielfalt und Produktindividualisierung befriedigen. Das erfordert ständig effizientere Produktions- und Geschäftsprozesse – um eine hoch flexible Großserienfertigung („Mass Customization“) zu ermöglichen, um Kunden und Geschäftspartner optimal in immer komplexere Wertschöpfungsnetzwerke zu integrieren und um die Produktion noch stärker mit hochwertigen Dienstleistungen zu verbinden.
Nach Jahrzehnten der Optimierung bewährter Fertigungsprozesse steht die produzierende Industrie vor einem Paradigmenwechsel: Die zunehmende Verschmelzung von virtueller und realer Fertigungswelt durch modernste industrielle IT und Software wird die Art zu produzieren grundlegend verändern. Daran besteht genauso wenig Zweifel wie an der Tatsache, dass die Entwicklung und der intelligente Einsatz von leistungsstarker industrieller Software zum bestimmenden Faktor für die Fertigungs- und Prozessindustrie werden wird. In vielen Bereichen ist das bereits heute der Fall.
Die Zukunft einer Branche und eines einzelnen Unternehmens entscheidet sich also immer weniger allein in den Werkshallen. Vielmehr wird auch die Leistung der Softwareingenieure maßgeblich sein, deren Systeme es erst ermöglichen, sämtliche Produktionsschritte miteinander wie auch mit betriebswirtschaftlichen Ebenen und mit allen Wertschöpfungsstufen außerhalb des eigenen Unternehmens zu verknüpfen. Die zunehmende Verschmelzung der virtuellen und realen Welt durch industrielle Software birgt ein derart großes Produktivitätspotenzial, dass zukunftsorientierte Produktionsbetriebe diesem Thema oberste Priorität geben werden.
Effectuation has been acknowledged as an appropriate alternative to causal decision making in highly uncertain situations by shifting the focus from a logic of prediction to a logic of control (Sarasvathy, 2001). Problem-solving speed as part of the holistic problem solving competence is crucial for the establishment of competitive advantages within corporations, as it promotes the ability to develop and market innovative products in a timely manner (Atuahene-Gima and Wei, 2011). Problem- solving speed can be defined as the time needed to find, evaluate and implement an adequate solution to a particular problem (Atuahene-Gima and Li, 2004). We conducted a survey with employees from new product development (NPD) departments of industrial and service companies located in Germany and discovered, that embracing the unexpected as one of the four effectual dimensions has a positive impact on the problem solving speed in highly uncertain situations. In contrast the causal dimensions of goal- and competition-orientation slow down problem-solving speed. All effects depend on the moderating influence of uncertainty. Thus, building decisions on entrepreneurial decision-making logic in uncertain situations partially explains the differences in problem-solving speed and uncovers a novel source of competitive advantage.
While the discussion of product variety has well advanced into assessing the impacts of any complexity induced into the manufacturing system, little is known about how to measure variant driven complexity costs and how product variety differs between emerging and established markets. This paper aims to address this gap by analyzing the theory behind manufacturing complexity as a result of product variety and its main implications in the automotive industry supply chain. This analysis embraces a complexity cost model development proposal done by the University of Lueneburg and a comparison of the product variety among established and in emerging markets.
One of the central success factors for production in high-wage countries is the solution of the conflict that can be described with the term “planning efficiency”. Planning efficiency describes the relationship between the expenditure of planning and the profit generated by these expenditures. From the viewpoint of a successful business management, the challenge is to dynamically find the optimum between detailed planning and the immediate arrangement of the value stream. Planning-oriented approaches try to model the production system with as many of its characteristics and parameters as possible in order to avoid uncertainties and to allow rational decisions based on these models. The success of a planning-oriented approach depends on the transparency of business and production processes and on the quality of the applied models. Even though planning-oriented approaches are supported by a multitude of systems in industrial practice, an effective realisation is very intricate, so these models with their inherent structures tend to be matched to a current stationary condition of an enterprise. Every change within this enterprise, whether inherently structural or driven by altered input parameters, thus requires continuous updating and adjustment. This process is very cost-intensive and time-consuming; a direct transfer onto other enterprises or even other processes within the same enterprise is often impossible. This is also a result of the fact that planning usually occurs a priori and not in real-time. Therefore it is hard for completely planning-oriented systems to react to spontaneous deviations because the knowledge about those naturally only comes a posteriori.
The efficient dealing with the dynamic environment of production industries is one of the most challenging tasks of Supply
Chain Management in high-wage countries. Relevant and current information are still not used sufficiently, to handle the influence
of the dynamic environment on intra- and inter-company order processing adequately. Among other things, the problem is caused
by missing or delayed feedback of relevant data. As a consequence of that, planning results differ from the actual situation
of production. High Resolution Supply Chain Management describes an approach aiming on high information transparency in supply
chains in combination with decentralized, self-optimizing control loops for Production Planning and Control. The final objective
is to enable manufacturing companies to produce efficiently and to be able to react to order-variations at any time, requiring
process structures to be most flexible.
KeywordsProduction management–Production planning and control–Logistics–Realtime capability–Control theory–Viable system model
Sustainability is an important issue with a growing interest. Two ICT technologies provide useful support for the sustainability of industrial systems: service-oriented architecture (SOA) and cyber-physical systems (CPS). SOA has been adopted in a variety of industrial systems due to its integration flexibility and process composability. CPS is a new technology to bring computational intelligence to physical devices and to make them mission- and situation-aware. We study a real-time SOA architecture to enhance sustainability and predictability in CPS. The proposed real-time SOA middleware builds the support for service accountability and global resource management for real-time service processes. Given a service plan and known resource constraints, the middleware monitors the performance and reserves resources in advance for each service in the process to ensure its real-time feasibility. A prototype of RT-SOA ESB has been implemented and evaluated.
Increasing dynamics and a turbulent environment force industrial enterprises to ensure a highly efficient production. The field of production planning and control (PPC) and the sustainable optimization of its methods are hereby of utmost importance. This paper introduces a concept for a cognitive production planning and control system, in which so-called smart products store knowledge about the production process and its current state. The RFID (radio frequency identification) technology presents a promising approach to realize those smart products, to enhance the information management on the shop floor and to offer a precise image of individual product states in the production process. The knowledge on production sequences is represented in a graph-based model. The developed concept represents the executable production of every single resource in capability profiles that are used for the allocation of production steps to resources. Material transports are realized by an anticipatory transport control, which updates its model parameters autonomously. During runtime, the product-specific operation times are measured and stored on the smart product, which is subsequently used to update the overall planning data. Thus, the introduced production planning and control system is able to react to unforeseen events (e.g. missing material, insufficient product quality) and autonomously adapts the planning data to the actual elapsed values of the real production. First experiments showed promising results for the approach to provide and process information directly on the shop floor: the idleness of resources due to errors was reduced by 41% from 19.4% to 8.0% during a 3 h test run. The waiting time of resources caused by missing material can be reduced in specific cases by 17.7%.
Collaborative engineering is the practical application of collaboration sciences to the engineering domain. In today's highly connected technology-driven economy, the production industry must rely on the best practices of collaborative engineering to stay competitive when designing, manufacturing and operating complex machines, processes, and systems on a global scale. Despite its importance, collaborative engineering is currently more of a practiced art than a scientific discipline. A better understanding of how engineers should collaborate with all stakeholders to accomplish complex tasks that fulfill our increasing social responsibilities is a grand challenge. However, because we currently lack well-defined sciences of human collaboration, we must first establish a scientific foundation of collaborative engineering to develop this emerging field into a rigorous discipline. This paper reports on the CIRP community's collective efforts to establish such a scientific foundation according to the “Observation → Hypothesis → Theory” development pathway. Our objective is to spearhead the rigorous development of this new human-centered engineering discipline, so that useful knowledge can be generated to educate students and practical guidelines can be developed to enable engineers to become more productive collaboration leaders in the new global production industry.
Purpose
– Managing enterprise performance is an important, yet a difficult process due to its complexity. The process involves monitoring the strategic focus of an enterprise, whose performance is measured from the analysis of data generated from a wide range of interrelated business activities performed at different levels within the enterprise. This study aims to investigate management data systems technologies in terms of how they are used and the issues that are related to their effective management within the broader context of enterprise performance management (EPM).
Design/methodology/approach
– A range of recently published research literature on data warehousing, online analytic processing and EPM is reviewed to explore their current state, issues and challenges learned from their practice.
Findings
– The findings of the study are reported in two parts. The first part discusses the current business practices of these technologies, and the second part identifies and discusses the issues and challenges the business managers dealing with these technologies face for gaining competitive advantage for their businesses.
Originality/value
– The study findings are intended to assist the business managers to effectively understand the issues and technologies behind EPM implementation.
The limits of Taylorism are alive and well in today's manufacturing systems. Automation does have to constrain human ability creativity, judgement and skill, and undermine human dignity. The paper presents an interactive concept of manufacturing. “Human-Oriented Manufacturing Systems” (HOMS), which aims to achieve high flexibility and quality of production while creating an environment for happy working and joyful living.
We introduce a new hybrid approach to joint estimation of Value at Risk (VaR) and Expected Shortfall (ES) for high quantiles of return distributions. We investigate the relative performance of VaR and ES models using daily returns for sixteen stock market indices (eight from developed and eight from emerging markets) prior to and during the 2008 financial crisis. In addition to widely used VaR and ES models, we also study the behavior of conditional and unconditional extreme value (EV) models to generate 99 percent confidence level estimates as well as developing a new loss function that relates tail losses to ES forecasts. Backtesting results show that only our proposed new hybrid and Extreme Value (EV)-based VaR models provide adequate protection in both developed and emerging markets, but that the hybrid approach does this at a significantly lower cost in capital reserves. In ES estimation the hybrid model yields the smallest error statistics surpassing even the EV models, especially in the developed markets.
02.2014) High-Speed-Junkie für einen Tag
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