Fertigungsplanung und -steuerung für die vernetzte Produktion von morgen*/Production planning and control for the networked production of tomorrow

2018 ◽  
Vol 108 (04) ◽  
pp. 235-239
Author(s):  
B. Denkena ◽  
M. Dittrich ◽  
S. Jacob ◽  
F. Uhlich
Keyword(s):  
Production Planning ◽  
Learning Process ◽  
Production System ◽  
Process Models ◽  
Production Planning And Control ◽  
Optimal Operating ◽  
Planning And Control ◽  
Short Notice ◽  
And Control ◽  
Effective Analysis

Der anhaltende Trend zur Produktindividualisierung stellt für Unternehmen eine große Herausforderung dar. Insbesondere der Reaktionsfähigkeit der Fertigung kommt eine zunehmende Bedeutung zu. Zugleich stellt sich die Frage nach dem optimalen Betriebspunkts einer vernetzten Produktion. Eine effektive Analyse und Verarbeitung von Fertigungsdaten kann das hierfür benötigte Wissen bereitstellen. In diesem Artikel wird gezeigt, wie mit diesem Wissen eine vernetzte Produktion unter Verwendung lernender Prozessmodelle geplant und gesteuert werden kann.   Companies are particularly challenged by the trend towards individualized products. As a result, the ability of a production system to react on short notice becomes increasingly more important. Identifying the optimal operating point of a cross-linked production is a major challenge. Effective analysis and processing of manufacturing data can provide the required knowledge. This article shows how the knowledge can be used to plan and control a cross-linked production using learning process models.

Simulationsbasierte PPS-Optimierung für Instandhaltungsunternehmen*/Simulation-based optimization of production planning and control in maintenance companies

2015 ◽  
Vol 105 (04) ◽  
pp. 215-219
Author(s):  
B. Denkena ◽  
A. Georgiadis
Keyword(s):  
Production Planning ◽  
Spare Parts ◽  
Production Planning And Control ◽  
Wird Eine ◽  
New Approach ◽  
Complex Products ◽  
Planning And Control ◽  
Short Notice ◽  
Simulation Based ◽  
And Control

Die Produktionsplanung und -steuerung (PPS) steht bei der Instandhaltung komplexer Produkte vor der Herausforderung, mittelfristig Entscheidungen ohne detaillierte Kenntnis des Bauteilzustandes treffen zu müssen. Je nach Bauteildiagnose sind dann kurzfristig die Fertigungskapazitäten und Lagerbestände anzupassen. Die getroffenen Entscheidungen beeinflussen die Wirtschaftlichkeit des gesamten Instandhaltungsprozesses maßgeblich. Im Rahmen des Fachbeitrags wird eine simulationsbasierte Methode vorgestellt, um die PPS in der Instandhaltung mittels Ersatzteildisposition und Reihenfolgebildung zu optimieren. Dabei werden die Entwicklung und Ergebnisse des Ansatzes zur Ersatzteildisposition beschrieben.   Production planning and control (PPC) systems concerned with maintaining complex products are faced with the challenge of making decisions without knowing the product’s condition. After the diagnosis, the production and warehouse capacities are adapted at short notice. This article presents a simulation-based method for optimizing PPC in maintenance companies. It focuses on the development and the results of a new approach for the management of spare parts.

scholarly journals Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study

2021 ◽  
Author(s):  
Olumide Emmanuel Oluyisola ◽  
Swapnil Bhalla ◽  
Fabio Sgarbossa ◽  
Jan Ola Strandhagen
Keyword(s):  
Machine Learning ◽  
Production Planning ◽  
Production System ◽  
Service Oriented Architecture ◽  
Big Data Analytics ◽  
Smart Manufacturing ◽  
Production Planning And Control ◽  
Planning And Control ◽  
And Control

AbstractIn furtherance of emerging research within smart production planning and control (PPC), this paper prescribes a methodology for the design and development of a smart PPC system. A smart PPC system uses emerging technologies such as the internet of things, big-data analytics tools and machine learning running on the cloud or on edge devices to enhance performance of PPC processes. It achieves this by using a wider range of data sources from the production system, capturing and utilizing the experience of production planners, using analytics and machine learning to harness insights from the data and allowing dynamic and near real-time action to the continuously changing production system. The proposed methodology is illustrated with a case study in a sweets and snacks manufacturing company, to highlight the key considerations and challenges production managers might face during its application. The case further demonstrates considerations for scalability and flexibility via a loosely coupled, service-oriented architecture and the selection of fitting algorithms respectively to address a business requirement for a short-term, multi-criteria and event-driven production planning and control solution. Finally, the paper further discusses the challenges of PPC in smart manufacturing and the importance of fitting smart technologies to planning environment characteristics.

scholarly journals A heijunka study for the production of standard parts included in a customized finished product

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260515
Author(s):  
Paulina Rewers ◽  
Jacek Diakun
Keyword(s):  
Control System ◽  
Production Planning ◽  
Production System ◽  
Crucial Role ◽  
Simulation Method ◽  
Production Planning And Control ◽  
Production Flow ◽  
Planning And Control ◽  
And Control ◽  
Order Execution

Efficient order execution plays a crucial role in the activity of every company. In production planning it is important to find a balance between the fluctuations of orders and stability of production flow regarding the company. One of the methods of achieving this goal is heijunka (production leveling). This paper presents a study of choosing the best variant of the production planning and control system for the production of standard parts. Three variants are investigated regarding delays in order delivery. The analysis of variants was conducted using a simulation method. The method of choosing the best variant for the production system being investigated is also proposed. The results show that the best variant is a mix of production leveling and production "for stock".

scholarly journals Development and evaluation of risk treatment paths within energy-oriented production planning and control

2021 ◽  
Author(s):  
Stefan Roth ◽  
Vincent Kalchschmid ◽  
Gunther Reinhart
Keyword(s):  
Production Planning ◽  
Electricity Market ◽  
Production System ◽  
Efficient Solutions ◽  
Production Planning And Control ◽  
Price Forecast ◽  
Planning And Control ◽  
Risk Treatment ◽  
Flexibility Measures ◽  
And Control

AbstractProduction planning and control pursues high delivery reliability and short delivery time of the production system at the lowest possible costs. Especially in energy-intensive industries, energy cost account for a significant amount of manufacturing costs. The consideration of variable electricity market prices using energy-flexibility measures facilitates reduced costs by adapting the load profile of production to an electricity price forecast. However, it also increases the production planning and control system’s complexity by additional input variables and possible risks due to the influence of flexibility measures on the production system. In the case of unexpected events, such as failure of machines or faulty materials, it is difficult to adapt the complex production system to the new situation quickly. There is a risk of high additional costs by various causes, such as delays in deadlines or load peaks. Therefore, this paper presents an approach for developing and evaluating risk treatment paths, which include possible combinations of risks and measures for the mitigation of risk effects. The advantage of these paths compared to a situational reaction is that all effects and possible further interactions can be considered and thus overall cost-efficient solutions can be found. The approach is based on the determination of interactions through interpretive structural modelling and the calculation of conditional probabilities using Bayesian Networks. The approach was implemented in MATLAB® and applied using real order and energy data from a foundry. The results show that the presented approach enables structured and data-based comparison of risk treatment paths.

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