Refine
Year of publication
Document Type
- Conference Proceeding (41)
- Part of a Book (36)
- Contribution to a Periodical (19)
- Article (10)
- Lecture (5)
- Book (3)
- Working Paper (3)
- Report (2)
- Internet Paper (1)
Is part of the Bibliography
- no (120)
Keywords
- 02 (4)
- 03 (3)
- 7. EU-Forschungsrahmenprogramm (1)
- APMS (1)
- APS (1)
- Aachener PPS-Modell (1)
- Adaptability (1)
- Additive Fertigung (1)
- Advanced Planning System (1)
- Anlagenbau (1)
Institute
- Produktionsmanagement (120) (remove)
Die Dezentralität ist einer der bedeutsamsten Aspekte der Blockchain-Technologie. Dennoch gibt es große Unterschiede in der Dezentralität verschiedener Blockchain-Applikationen. Ziel der vorliegenden Arbeit ist es, eine strukturelle und funktionelle Durchdringung des Aspekts der Dezentralität zu erreichen und Eigenschaften zu finden, die es ermöglichen verschiedene Blockchain-Applikationen in ihrer Dezentralität zu differenzieren. Der vorliegende Beitrag legt dar, dass die Datenverteilung und die Zugangsberechtigungen (Lese- und Schreibzugang) entscheidende Eigenschaften für die Dezentralität der Blockchain Applikationen sind. Diese Eigenschaften werden mithilfe eine morphologische Analyse untersucht und es wird ein detaillierter Überblick über die verschiedenen Ausprägungen der genannten Eigenschaften und der Auswirkungen auf die Dezentralität gegeben.
Zur Planung und Steuerung setzen Unternehmen der produzierenden Industrie heute auf umfassenden Softwareeinsatz. Deshalb begründen sie eine geringe logistische Zielerfüllung bezüglich Lieferfähigkeit und Liefertreue häufig mit Defiziten der Software. Doch Praxiserfahrungen zeigen, dass die Industrieanwender die Bedeutung organisatorischer Defizite in der innerund" überbetrieblichen Auftragsabwicklung deutlich unterschätzen.
Deshalb untersuchten die drei Institute
• Fraunhofer Institut für Produktionstechnik und Automatisierung IPA, Stuttgart;
• Forschungsinstitut für Rationalisierung FIR, Aachen sowie
• Laboratorium für Werkzeugmaschinen und Betriebslehre WZL der RWTH
Aachen
den Einfluss dieser Defizite auf die Lieferterminermittlung und -erfüllung. Ausgangspunkt der Studie waren Thesen, die eine qualitative Befragung der Produktions- und Logistikverantwortlichen verifizieren sollte.
Supply Chain Management liefert eine Fülle von Ideen und Methoden zur Gestaltung der Lieferkette. Dabei kann jedes Konzept zu erheblichen Kosteneinsparungen und verbessertem Lieferservice führen. Allerdings ist das einzelne Konzept nicht für unterschiedliche Kunden und deren diversifizierte Anforderungen praktikabel. Daher kann eine einheitliche "one size fits all"-Supply Chain nicht zum Erfolg führen. Der Schlüssel liegt in der Segmentierung der Supply Chain.
Discrete Event Simulation (DES) is a well-known approach to simulate production environments. However it was rarely used for operative planning processes and to our knowledge never in terms of multiple disposition levels.In this paper we develop the necessary adjustments to use DES for this purpose and show some theoretical advantages.
Working capital management is one of the key disciplines that must be prudently monitored for a firm in pursuit of profits, liquidity and growth. The focus of this paper is on the engineer-to-order manufacturers, and the objective is to analyze the correlations between the reference processes of the engineer-to-order production approach with the key postulates of working-capital management and deliver a mathematical operating curves model, whose purpose and goal is basing on the rationale, that is underlying in the parent logistic operating curves theory. [https://link.springer.com/chapter/10.1007/978-3-319-66926-7_30]
Nowadays one of the most challenging tasks of producing companies is the growing complexity due to the globalization and digitalization. Especially in high wage countries, the ability to deliver fast and to a fixed date gets more and more important. To achieve this logistic target, it is necessary to optimize the Production Planning and Control (hereinafter PPC). This study investigates the effects of a change of the scheduling parameters on a target system. The focused research questions are: How can the effect of a scheduling parametersvariation on the target system of the PPC can be displayed efficiently? Is it possible to review the effect of the scheduling parameters-variation quantitatively and to derive action options?
In this paper, we firstly present a target system which is deduced to assess the economic profitability of reverse supply chains. Considering this, we analyse process reference models to define relevant components of an appropriate target system.
Subsequently, we define applicable business models which are the basis for the manufacturer to offer new services to its customers on the one hand and to manage a goal-oriented return, recovery and resell of used products and components on the other hand. This will be done based on the morphology methodology in order to understand the characteristics and attributes of reverse supply chains.
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.
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.