Christina Reuter

Hypotheses for a Theory of Production in the Context of Industrie 4.0

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.

Cognition-Enhanced, Self-optimizing Production Networks

This research area focuses on the management systems and principles of a production system. It aims at controlling the complex interplay of heterogeneous processes in a highly dynamic environment, with special focus on individualized products in high-wage countries. The project addresses the comprehensive application of self-optimizing principles on all levels of the value chain. This implies the integration of self-optimizing control loops on cell level, with those addressing the production planning and control as well as supply chain and quality management aspects. A specific focus is on the consideration of human decisions during the production process. To establish socio-technical control loops, it is necessary to understand how human decisions are made in diffuse working processes as well as how cognitive and affective abilities form the human factor within production processes.

Industrie 4.0 – ein Beitrag zur Entwicklung von „Smart Networks“

Kurzfassung Eng verknüpft mit der Entwicklung zur Industrie 4.0 steht der Begriff Smart Factory. Die nächste industrielle Revolution bietet jedoch nicht nur Potenziale in Bezug auf den Einzelstandort, sondern auch für die effiziente Vernetzung von Standorten zu einem Smart Network. Entsprechend ist Industrie 4.0 in der Lage, einen wichtigen Beitrag zum Management globaler Produktionsnetzwerke zu leisten. Im vorliegenden Beitrag wird erläutert, welchen Einfluss Industrie 4.0 auf das strategische Management, die Gestaltung und die Steuerung von Produktionsnetzwerken haben kann. Dabei wird insbesondere aufgezeigt, welche Voraussetzungen auf dem Weg zu Smart Networks zu schaffen sind und inwieweit die Leistungsfähigkeit von Produktionsnetzwerken dadurch positiv beeinflusst wird.

Cyber Physical Production Control

Currently the control of constantly increasing market dynamics and the simultaneously increasing individualization of process chains represent the central challenges for manufacturing companies. These challenges are caused by a lack of transparency in production planning, non-real-time processing of data as well as poor communication between the planning and control level. The research project ProSense addresses this problem and intends to eliminate the current problems in production by developing a high-resolution, adaptive production control based on cybernetic support systems and intelligent sensors. Through the development of a cyber-physical production control as one part of the project, which forms the basis for an innovative self-optimizing advanced planning system, ProSense provides a contribution to accomplish the goals of industry 4.0.

Iterative Improvement of Process Planning Within Individual and Small Batch Production

Present challenges of small batch production are represented by the need to improve time-to-market and the reduction of costs. A promising approach to take up these challenges is the use of highly iterative development processes such as Scrum known from software development. A transfer of these principles to process planning enables the prediction of producibility of customer orders by iteratively learning from manufacturing data of similar jobs from the past. Based on the required data structures described in this paper, work plans for new orders can be generated automatically. The potential of the approach is validated by an industrial example.

Cost Modelling Approach for the Source Specific Evaluation of Alternative Manufacturing Networks

In order to seize the full potential of production on a global scale, companies need to constantly evolve their production networks and reconsider the number of sites. Decision making in this context forces corporations to process a substantial amount of information and complexity. Prominent examples of German companies (e.g. Stihl or Steiff) prove the present struggle of decision makers. Existing approaches are either too complex and effortful or they do not consider all decision relevant cost factors in a cause-fair way. This paper presents a pragmatic cost modelling approach which focuses on the identification and projection of decision relevant cost factors as well as their source specific allocation in terms of the impact on existing sites in the network.

Approach of Information Provision for High Resolution Production Control

Production information is essential for controlling a company’s production. The awareness and targeted use of data is decisive for constant and positive enhancements of a company. At the moment, many manufacturing companies are still at the very beginning of collecting and using data gathered during production processes. The cost-benefit ratio of the various steps on the way to a high resolution production control is not clear at all. To overcome the hesitation of many small and medium-sized companies in collecting and using more data from their production, migration paths with an explicit focus on the cost-benefit ratio of decisions have to be characterized. This approach will ease the way to gain a suitable information level of a production facility for different objectives. The aim of this paper is to present the targeted approach for the efficient provision of production information in order to enable a high resolution production control.

Aggregation of Production Data for the Strategic Planning of Global Production Networks

In the last two decades a large amount of companies decided to establish productions sites all over the world in order to profit from cost benefits and to gain access to new markets and know-how. This internationalisation trend leads to more and more complex production networks. According to this development managers are confronted with growing intransparencies within their companies due to increasing information asymmetries, growing amount of interfaces and a growing effort for coordination. Nowadays a lot of strategic decisions, e.g. location decisions are made according to someone’s instinct. The usage of a valid data base within decision making processes can improve the quality of taken decisions. There are two ways to establish a sound data base: Manual data gathering, which is always associated with great effort or the usage of “big data”. Technologies from the era “Industrie 4.0” enable companies to gather and store a large amount of data. This data amount can establish transparency to support an objective assessment. Therefore the aim of this paper is the development of a method to aggregate production data systematically to support the strategic planning of global production networks.

Towards a Technology-Oriented Theory of Production

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.

Steigerung der Kollaborationsproduktivität durch cyber-physische Systeme

Die Produktionssteuerung ist das zentrale Element, um einen stabilen Produktionsprozess zu realisieren. Um steigender Marktdynamik und wachsenden Kundenanforderungen gerecht zu werden, ist es unumganglich die zur Produktionssteuerung eingesetzten IT-Systeme auf die sich wandelnde Unternehmensumgebung anzupassen. Industrie 4.0 bietet die Grundlage fur eine Verbessrung der Produktionssteuerung. Zum einen konnen mit Hilfe intelligenter Sensorik zusatzliche Daten aus der Produktion generiert werden. Durch eine intelligente Verarbeitung innerhalb der Sensorik stehen den IT-Systemen aggregierte Informationen zur Verfugung. Zum anderen hat der Fertigungssteuerer durch eine schnellere Verarbeitung von Daten Echtzeitinformationen zur Entscheidungsunterstutzung zur Verfugung. Somit steigt die Kollaborationsproduktivitat des Produktionssteuerers mit dem planenden IT-System.