The evolution of manufacturing systems, influenced by changes along four axes - products, technology, business strategies and production paradigms - is presented. Adoption of human-centric decision ...making in meshed collaboration with intelligent systems is examined. Implications and preparedness for the shift towards more responsive, intelligent adaptive systems are reviewed. Research and industrial use cases are presented. A vision for the new future Adaptive Cognitive Manufacturing System (ACMS) paradigm and its characteristics, drivers and enablers are articulated highlighting the digital and cognitive transformations. Perspectives and insights are offered for future research, education, and work to realize the evolution of manufacturing systems.
Das Unmögliche wagen Schuh, Günther
Die Unternehmung (Bern),
01/2021, Volume:
75, Issue:
2
Journal Article
Peer reviewed
Mit dem unabhängigen Start-Up e.GO Mobile wollen wir die Mobilitätswende vorantreiben. Kann in Europa überhaupt ein Start-up in der überregulierten, kapitalintensiven Deep Tech Branche Automobilbau ...erfolgreich sein? Können die riesigen Markteintrittsbarrieren der Automobilbranche von einem Newcomer überwunden und hinreichende Einzigartigkeiten und Wettbewerbsvorteile erreicht werden? Inwieweit sind weit verbreitete und akzeptierte Prinzipien und Theorien der Betriebswirtschaftslehre hier gültig bzw. anwendbar? Oder führen sie sogar in die falsche Richtung?
Ich beschreibe unseren Feldversuch mit der e.GO Mobile SE, die ich 2015 auf dem RWTH Aachen Campus gegründet habe. Ein e-Auto Hersteller und Betreiber, der angetreten ist um die emissionsfreie, bezahlbare und wirklich nachhaltige Mobilität anzubieten. Der 2019 kurz zum Unicorn wurde und dem 2020 in der Pandemie vorübergehend das Geld ausging. Ich berichte, wie wir Markteintrittsbarrieren der Autoindustrie überwunden und uns über unsere Einzigartigkeiten finanzierbar gemacht haben. Haben wir daraus Wesentliches gelernt? Absolut ja! Das will ich mit diesem Beitrag teilen.
We want to push the mobility transition with our independent start up e.GO Mobile. Is it possible in Europe, that a startup could succeed in that overregulated capital intense deep tech car manufacture´s business? Could a Newcomer overcome these huge market entry barriers of the car industry and provide sufficient uniqueness and competitive advantages? Are widely acknowledged and accepted principles and theories of business administration still valid and useable here? Are they even misleading?
I describe our field test with e.GO Mobile SE, which I founded on the RWTH Aachen University Campus in 2015. A BEV (Battery Electric Vehicle) manufacturer and operator established to push emission free affordable and truly sustainable mobility forward. It became briefly a unicorn in 2019 and ran temporarily out of money in the pandemy 2020. I report how we overcame the entry barriers of the car industry and how our uniquenesses made us financeable. Significant learnings? Absolutely! That`s what I intend to share with this article.
Industrial companies are nowadays acting in global production networks (GPNs). A comprehensive scientific overview of those networks is still missing. To close this gap, a framework for designing and ...operating GPNs is introduced. It structures influencing factors, challenges, enablers and outlines the need for decision support systems. The state of the art in designing and operating GPNs is reviewed. Three trends are identified that help to transform historical grown networks into changeable GPNs with a focused network footprint. In conclusion, a need for future research in forming the production strategy, designing the network footprint and managing the network is given.
Continuous design of production networks is an essential element to overcome historically grown, inefficient production networks, as they are common for manufacturing companies. In order to enable ...continuous network design, fast and low-effort methods for investment and allocation decision making are required without losing decision quality. This paper introduces a decision making approach that reduces planning efforts by systematically focusing on main influencing factors and reducing their uncertainty. The approach was applied to a real allocation decision of a machine tool manufacturer.
To efficiently support ramp-up processes as well as the instruction of new employees in industrial production, increasing the integration of working and learning is one promising approach. In this ...context, cyber-physical systems in Industry 4.0 enable the creation of work environments with new opportunities to purposefully facilitate learning new tasks. Corresponding measures make use of the large available amount of real-time information and pre-processed production data. The informed selection of appropriate measures has already to be considered in the early planning stages of a production system. Thus, the specific suitability, effectiveness and operating efficiency of work- based learning solutions have to be quantified in advance. This requires an empirical investigation and evaluation of the particular influence in an authentic production setting. The Demonstration Factory of the RWTH Aachen Campus offers an ideal framework for this purpose. It features the real production of marketable products as well as an infrastructure tailored to experiment-based production research.
Manufacturing companies gather a vast amount of data in order to track production, assembly and logistical processes. Among others, these data are used for updating production schedules, deriving ...process interventions and calculating key performance indicators. However, the integrity of these production feedback data is regularly impaired by various inconsistencies and errors, which negatively affect the value of these data for any decision-making processes. In this paper, a new approach for increasing the integrity of production feedback data based on an adapted Data Mining algorithm is proposed to compensate for a typical data error in production feedback data.
A cost-optimized design of a global production network is a complex task. Several optimization tools exist that determine cost-minimized solutions for defined points of times in the future but often ...do not take into account the development of the network over time. The approach of this article is to analyze a series of different cost-optimized scenarios for several points of time in the future with distinct parameter settings and compare its network structures. The goal of such broad calculations of future scenarios according to the idea of “Industry 4.0” is to identify a path in the trade-off between the costs for migrating a network structure into another one and the total landed costs of the regarded series of future network scenarios. The approach will be validated using data from real industrial case studies.
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.
An increasing product individualization has led to workshop(-like) production in many companies. Mastering the resulting planning complexity has proven to be a challenging task. Today’s production ...planning and control in many cases does not succeed in providing reliable production plans. One reason is that transition times account for a major part of lead times, that are difficult to predict due to a high number of partly unknown and volatile influencing factors. In this paper, a data mining approach is presented that increases planning quality by a databased prediction of order-specific transition times, and thus supports mastering of planning complexity.
Production complexity has increased considerably in recent years due to increasing customer requirements for individual products. At the same time, continuous digitization has led to the recording of ...extensive, granular production data. Research claims that using production data in data mining methods can lead to managing production complexity effectively. However, manufacturing companies widely do not use such data mining methods. In order to support manufacturing companies in utilizing data mining, this paper presents both a literature review on definitions of data mining, artificial intelligence and machine learning as well as a categorization of existing approaches of applying data mining to manage production complexity.
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