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Today, manufacturing companies are facing the influences of a dynamic environment and the continuously increasing planning complexity. Using advanced data analytics methods, processes can be improved by analyzing historical data, detecting patterns and deriving measures to counteract the issues. The basis of such approaches builds a virtual representation of a product – called the digital twin or digital shadow.
Although, applied IT systems provide reliable feedback data of the processes on the shop-floor, they lack on a data structure which represents real-time data series of a product. This paper presents an approach for a data structure for the order processing which overcomes the described issue and provides a virtual representation of a product. Based on the data structure deviations between the production schedule and the real situation on the shop-floor can be identified in real time and measures to reschedule operations can be identified.
One of the major challenges facing today´s manufacturing industry is to differentiate from competition in a highly globalized world. As a consequence to the increasing competitive pressure, many companies transform their product centered business models towards service based business models to differentiate from competition. However, the transformation is often underestimated regarding its complexity and its management challenges to behavioral change.
As a consequence lots of transformation initiatives fail. Besides difficulties in structuring the magnitude of changes in processes and structures, many transformation managers do not perceive the risk of employee resistance against changes, which is one of the key factors causing the failure of transformation. The objective of this paper is to enhance the existing body of research on manufacturer´s organizational transformation towards Product-Service Systems. More detailed, the objective is to develop new knowledge to support the management during the decision-making process in the way how and by means of which instruments the change of behavior can be supported when transforming from a manufacturer to a solution.
We developed a reference framework which structures and defines the relevant dimensions of behavioral change. The identification and validation of the success factors build the second component of our research. We conducted an empirical investigation in the German manufacturing industry and got 79 data sets.
Structural equation modelling was applied for the analyses and the validation of the hypotheses. By this analysis we linked management practice with employee behavior and transformational success variables. On the basis of the gained insights decisions can be made concerning the successful transformation from manufacturer to a solution-oriented service provider.
Today’s manufacturers are facing numerous challenges such as highly entangled and interconnected supply chains, shortening product lifecycles and growing product complexity. They thus feel the need to adjust and adapt faster on all levels of value creation. Self-optimization as a basic principle appears a promising approach to handle complexity and unforeseen disturbances within supply chains, machines and processes. Therefore it will improve the resilience and competitiveness of manufacturing companies.
This paper gives an introduction to the concept of self-optimizing production systems. After a short historical review, the different levels of value creation from supply chain design and management to manufacturing and assembly are analyzed considering their specific demands and needs for self-optimization. Examples from each of these levels are used to illustrate the concept of self-optimization as well as to outline its potential for flexibility and productivity. This paper closes with an outlook on the current scientific work and promising new fields of action.
Real-time data analytics methods are key elements to overcome the currently rigid planning and improve manufacturing processes by analysing historical data, detecting patterns and deriving measures to counteract the issues.
The key element to improve, assist and optimize the process flow builds a virtual representation of a product on the shop-floor - called the digital twin or digital shadow. Using the collected data requires a high data quality, therefore measures to verify the correctness of the data are needed. Based on the described issues the paper presents a real-time reference architecture for the order processing.
This reference architecture consists of different layers and integrates real-time data from different sources as well as measures to improve the data quality. Based on this reference architecture, deviations between plan data and feedback data can be measured in real-time and countermeasures to reschedule operations can be applied.
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 need for a theoretical consideration of the influence of manipulable variables in various evaluation dimensions on the economic efficiency of a production system is obvious. Here it is necessary to link the relevant influencing variables and their mutual dependencies into a model, which represents the basis for the determination of the optimal operating points of the production system. In this model, formal sub-models are to be analysed and integrated, assur-ing that the state of research from various technical disciplines in production engineering, such as manufacturing technology, machine tools, logistics and production planning and control, are used to quantify the economic effect of the influencing variables.
Digital networking via the company and as well, the overall supply chain, can only succeed if digital planning reflects reality as accurately as possible and if production control can react to deviations in real time. In essence, this leads to a development of process control towards process regulation. While longterm production and resource planning is usually mapped by Enterprise Resource Planning (ERP) systems, detailed planning, including short-term deviations and real-time data at the production level, is increasingly supported by Manufacturing Execution Systems (MES) at the production control level. However, in order to bring the underlying system concepts into line with Industry 4.0 efforts in a standardized manner, mutual functional integration within the framework of interoperable production planning and control is of crucial importance. For this purpose, studies were carried out in particular into cause-effect relationships. Thus, the overarching research objective is a valid design model to increase the controllability of production planning and control systems (PPC) in the context of Industry 4.0.
Der Begriff „Digitaler Schatten“ steht für ein hinreichend genaues, digitales Abbild der Prozesse, Information und Daten eines Unternehmens. Dieses Abbild wird benötigt, um eine echtzeitfähige Auswertebasis aller relevanten Daten zu schaffen, um hieraus letztendlich Handlungsempfehlungen abzuleiten. Die Bildung des Digitalen Schattens ist damit ein zentrales Handlungsfeld von Industrie 4.0 und stellt die Grundlage für alle weitergehenden Aktivitäten dar.
Steigende Energiekosten sind ein zunehmendes Risiko für Unternehmen des deutschen Maschinen- und Anlagenbaus. Die Steigerung der Energieeffizienz kann somit zukünftig zu Wettbewerbsvorteilen führen. Aufgrund der Komplexität heutiger Produktionssysteme ist eine Analyse der Wechselwirkungen von Parametern der Produktionsplanung und -steuerung (PPS) auf die Energieeffizienz notwendig, um Maßnahmen zu identifizieren, die eine Steigerung der Energieeffizienz ermöglichen.
Der vorliegende Artikel stellt die Ergebnisse einer Simulationsstudie vor, in welcher der Einfluss der Losgrößenplanung auf die Energieeffizienz im Rahmen einer mehrstufigen Mehrproduktfertigung untersucht wird. Die Ergebnisse der Studie leisten einen Beitrag zum besseren Verständnis der komplexen Zusammenhänge und können als Ausgangspunkt für weitere Untersuchungen zu Wechselwirkungen von Produktionsparametern mit der Energieeffizienz dienen.
Manufacturing companies of the machinery and equipment industry find themselves more than ever exposed to a rapidly changing competitive environment. In particular, the resulting diversity of planning and control processes confronts organisations and information systems with a significant coordination effort. To this day, planning and execution of order processing – from offer processing to the final shipment of the product – is still a part of the production planning and control (PPC), which is almost entirely integrated into information systems. Though, in order to manage dynamic influences on processes within order processing, there can be found a deficiency in the processing of decision-relevant and real-time information. Partly, the reason for this is a missing or incorrect feedback of process relevant data, so that the planning results, gained by the use of information systems, differ to the current process situation.
The concept of Manufacturing Resource Planning (MRP II) still represents the central logic of production planning and control. However, the centralised and push-oriented MRP II planning logic is not able to plan and measure dynamic processes adequately, which, due to diverse disturbances, often occur in production environments. Furthermore, specific weaknesses of MRP II-based systems are the lack of support for order releases, the planning principle based on average values and the successive planning method as well as the use of limited partial models. As a result a successive planning method leads to a dissection of PPC-tasks into smaller work packages and so strides away from a holistic approach and the achievement of an optimal solution. Similarly, a planning, focusing on a general business objective system, using a partial planning approach due to isolated considerations is not possible. Insufficient consideration of the current load horizon and the current capacity utilization, non-existing or delayed feedback on order progress as well as faults and poor availability and transparency of information can be named as further weaknesses of MRP II-based systems.
Producing companies are confronted with a growing number of product ramp-ups, since product life cycles are decreasing and product diversity is increasing. Production Planning and Control (PPC) of ramp-up products is particularly challenging, as there is a significant lack of reliable experienced data.
The information deficit is exceptionally high for the first step of PPC process, namely Production Program Planning (PPP). The paper in hand proposes an innovative approach of cybernetic PPP that enables companies with numerous ramp-ups to design reliable and fast PPP processes that can react highly adaptable on unpredictable environmental disturbances. The Viable System Model (VSM) is used as frame of reference for the design of PPP processes in line with principles from management cybernetics.
Production systems are exposed to an increasing planning-related uncertainty and susceptibility. The inter-company coordination has not sufficiently been considered in contemporary concepts of supply chain management. Against this background, it is crucial to provide a suitable tool that increases the planning capability of the players and the robustness of the supply chain as a whole. Therefore, this article provides the relevant causes and effects of planning uncertainties within the production planning and presents based on that an inter-company supply chain planning concept.
Applying Game Theory in Procurement. An Approach for Coping with Dynamic Conditions in Supply Chains
(2014)
Producing companies are facing continually changing conditions accompanied by higher requirements with respect to the flexible configuration of their supply chain. The challenge resulting from this initial situation is to develop systems that have the availability of adjusting their planning procedures and aims depended on the situation and therefore accommodate the increasing demand for flexibility. To address this challenge game theory seems to be a new and promising approach. The aim and added-value of the research work described here is to develop a decision model for the area of procurement using solutions concepts of game theory. Especially in times of high volatility such a decision model can support material requirements planners better than today's common selective planning logics.
In this paper the model to be solved by game theoretic solution concepts is presented. A research study has been conducted which proved the need for combining existing methods of procurement quantity calculation by means of game theoretic solution concepts. Some of the results of this study are presented in this paper. In the last part of the paper a structure for classifying game theoretic models is presented. This structure should support in selecting the appropriate solution concept for real-life decision-situations and is able to support in any practical application-field finding out the most appropriate game theoretic solution concept.
Volatile electricity prices caused by an increase of renewable energy sources push producing companies towards taking in an active role in balancing the electricity grid. Possible actions at the customer side to actively adapt to volatile energy prices are called demand response actions. In production logistics such actions can be the modification of production schedules motivated by possible economic benefits. So far, the focus in scheduling problems has been the optimization in the dimensions of quality, time and costs. This paper presents the results of a simulation study on the economic benefits of demand response actions for a generic production system.
Auftragsmanagement
(2014)
Ausgelöst durch einen konkreten Kundenauftrag, plant, steuert und überwacht das Auftragsmanagement sämtliche Aktivitäten der Auftragsabwicklung von der Anfragenbearbeitung über die Konstruktion, den Einkauf, die Fertigung und Montage bis hin zum Versand des fertigen Produkts. Dabei wird im Auftragsmanagement das Ziel verfolgt, die Transparenz der Auftragsabwicklung zu erhöhen und damit die Reaktionsfähigkeit im Hinblick auf unternehmensinterne und -externe Störungen deutlich zu verbessern. Gleichzeitig unterstützt das Auftragsmanagement die Lösung von Interessenskonflikten zwischen verschiedenen Fachbereichen sowie die Ausregelung von Zielkonflikten im Sinne einer effizienten Erfüllung des Kundenauftrags.
Teilaufgaben des Auftragsmanagements sind die Angebotsbearbeitung, die Auftragsbearbeitung sowie die Auftragskoordination und das Auftragscontrolling. In diesem Kapitel werden zunächst die Kernaufgaben des Auftragsmanagements definiert und anschließend die wesentlichen Methoden und Verfahren zur Bearbeitung der verschiedenen Teilaufgaben innerhalb des Auftragsmanagements zusammengestellt. Abschließend werden die Aufgaben des Auftragsmanagements in ihrer zeitlogischen Abfolge in Form eines Referenzprozessmodells modelliert und dabei fertigungstypspezifisch detailliert.
Produktionssysteme sind einer steigenden planungsbezogenen Unsicherheit und Störanfälligkeit ausgesetzt. Der hieraus resultierende überbetriebliche Koordinationsbedarf wurde hinsichtlich seiner Bewältigung in kontemporären Konzepten des Supply-Chain-Managements bislang methodisch nicht ausreichend berücksichtigt. Vor diesem Hintergrund gilt es ein geeignetes Werkzeug bereitzustellen, das die Planungsfähigkeit der Akteure sowie die Robustheit der Wertschöpfungskette als Ganzes steigert. Zu diesem Zweck soll der vorliegende Artikel einen Beitrag leisten, indem zunächst die relevanten Ursachen und Wirkungen planerischer Unsicherheiten aufgezeigt werden, um im Anschluss das darauf aufbauende Kooperationsmodell zu skizzieren.
Der effiziente Umgang mit den dynamischen Rahmenbedingungen produzierender Unternehmen ist eine der wesentlichen Aufgaben des Supply Chain Managements in Hochlohnländern. Die echtzeitnahe Verfügbarkeit und Verarbeitung planungsrelevanter Informationen nimmt dabei eine Schlüsselrolle ein. Sie stellt die Grundlage für eine realistische Planung und Steuerung der Produktion dar. Die zentrale Herausforderung liegt dabei in der Komplexität der Informationsvielfalt und deren Bewältigung sowie der effektiven Integration menschlicher Intuition und Erfahrung in den Regelkreis des Supply Chain Management. High Resolution Supply Chain Management (HRSCM) beschreibt einen Ansatz, Organisationsstrukturen und -prozesse auf Basis einer hohen Informationstransparenz in die Lage zu versetzen, sich durch dezentralisierte Produktionskontrollmechanismen in Form eines kaskadierten Regelkreismodells selbstoptimierend an ständig verändernde Rahmenbedingungen anzupassen.
Entgegen der von Porter postulierten Inkompatibilität von Economies of Scale und Economies of Scope sind in Hochlohnländern produzierende Unternehmen in zunehmendem Maße herausgefordert, sowohl individuelle Kundenbedürfnisse zu befriedigen als auch gegen den Kostendruck globalisierter Märkte zu bestehen. Diese Herausforderung entspricht einer Auflösung der Scale-Scope-Dichotomie. Aufgrund der hochgradigen Interdependenz der strukturbildenden Elemente eines Produkt-Produktionssystems müssen diese zur Auflösung der Dichotomie in ihrem spezifischen Standardisierungsgrad aufeinander abgestimmt werden.
Diese Abstimmung entspricht der Aufgabenstellung der integrativen Bewertungs-und Konfigurationslogik, die im Folgenden präsentiert wird. Auf Basis eines integrierten Bewertungsmodells, das Produkt-Produktionssysteme in vier quantifizierbare Spannungsfelder gliedert, kann hierbei der aktuelle Betriebspunkt eines Produktionssystems analysiert werden. Über die gewonnenen Analyseergebnisse ermöglicht dieses Bewertungsmodell die Steuerung des Konfigurationsprozesses eines Produkt-Produktionssystems in Form einer Konfigurationslogik.
Industrial production in high-wage countries like Germany is still at risk. Yet, there are many counter-examples in which producing companies dominate their competitors by not only compensating for their specific disadvantages in terms of factor costs (e.g. wages, energy, duties and taxes) but rather by minimising waste using synchronising integrativity as well as by obtaining superior adaptivity on alternating conditions. In order to respond to the issue of economic sustainability of industrial production in high-wage countries, the leading production engineering and material research scientists of RWTH Aachen University together with renowned companies have established the Cluster of Excellence “Integrative Production Technology for High-Wage Countries”. This compendium comprises the cluster’s scientific results as well as a selection of business and technology cases, in which these results have been successfully implemented into industrial practice in close cooperation with more than 30 companies of the industrial production sector.