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Podcast "Irgendwas mit Logistik", Folge 82: Automatisierungstechnik richtig und fair abnehmen
(2021)
Professoren wird häufig unterstellt Theoretiker zu sein. Das haben wir zum Anlass genommen, um gemeinsam mit Stefan Galka, Professor an der OTH Regensburg, im Irgendwas mit Logistik Podcast über ein ziemlich praktisches Thema zu sprechen - Inbetriebnahmen von Automatisierungstechnik. Stefan, Jens und Thomas zäumen das Pferd von hinten auf und versuchen zu klären, wer welche Verantwortungen und (ggf. verdeckten) Interessen bei der Realisierung von technischen Gewerken hat, welche Richtlinien bei der Durchführung helfen können und definieren gemeinsam, wie die Inbetriebnahme zum Erfolg wird.
Bei der Planung von innerbetrieblichen Transportsystemen besteht die Aufgabe darin, Transportsysteme so zu gestalten und zu dimensionieren, dass Unternehmensziele unter Berücksichtigung gegebener Rahmenbedingungen möglichst ideal erfüllt werden. Ob ein geplantes System schlussendlich den an die Lösung gestellten Zielen gerecht wird und wie gut es "funktioniert", ergibt sich aus dem Zusammenspiel der in Kap. 2 beschriebenen Systemelemente. Da sich Ziele und Rahmenbedingungen je nach konkretem Einsatzfall unterscheiden und sich daraus unterschiedliche Anforderungen an Transportsysteme ergeben, müssen die Systembausteine in geeigneter Kombination ausgestaltet und dimensioniert werden. Ansätze dafür werden in Kap. 5 ausführlich vorgestellt.
Der Digitale Zwilling (DZ) ist ein wichtiger Bestandteil der Industrie 4.0 und ermöglicht Anwendungen wie Predictive Maintenance, virtuelles Prototyping oder die Steuerung von Produktions- und Logistikprozessen. Herausforderungen bei der Entwicklung des Digitalen Zwillings entstehen durch fehlende Struktur und Standards. Mit diesem Beitrag soll ein Vorgehensmodell für die Erstellung eines Digitalen Zwillings im Bereich der Produktion und Logistik aufgezeigt werden. Das Vorgehensmodell hilft bei der Einordnung, für welche Anwendungsfälle ein Digitaler Zwilling entwickelt werden kann, welche Schritte bei einer Umsetzung erfolgen müssen, und gibt einen Überblick über die Voraussetzungen und Komplexität bei der Entwicklung. Das zentrale Element bildet dabei die zielgerichtete Aufbereitung und Analyse der zugrunde liegenden Daten mittels des in der Industrie etablierten Vorgehensmodell CRISP-DM.
Digital twins are arousing great interest in both science and industry. There are a large number of papers that demonstrate and evaluate the potential of Digital Twins in different application areas. However, it must be noted that there is still no uniform definition of Digital Twins. This paper first examines the concept of Digital Twins and highlight how they differ in level, compared with other digital models. The focus of this paper lies in the conceptual development of a digital twin in order picking systems. The described approach in the paper aims at supporting the operational control in order picking systems. Both the architectural structure and the functions, e.g. the simulation, are described in detail. Overall, this thesis shows the benefits of Digital Twins. However, some functional extensions are still needed before the full potential can be achieved.
The minitrack contains contributions with a focus on modeling and decision making in manufacturing and logistics in the context of Industry 4.0.
Cyber-physical systems integrate the real world and the virtual world. For example, customer decisions and supply disruptions which directly influence manufacturing and logistics systems require immediate decisions in terms of planning and controlling the manufacturing and logistics systems. In order to immediately respond to these dynamics, models must have the capability to support decision making in manufacturing as well as in internal and external logistics in real-time. Two of the Mintrack papers describe concepts and models for digital twins. One paper provides an adaptive scheduling framework for solving multi-objective hybrid flow shop scheduling problems and one paper investigates matrix production systems and with their requirements and impacts on logistics planning.
Due to the complexity and the number of factors involved in factory layout planning, computers were identified as an efficient tool to support the process. However, so far no method for computer-aided layout planning has gained wide acceptance in practical application. One reason for this is that in present approaches either the user or the computer designs the layout, neglecting either the qualitative or the quantitative goals. To bridge this gap, this article introduces a concept for human-computer-integration based on evaluative feedback and inverse reinforcement learning. A key element of the concept is the interactive planning process in which user and computer alternately design and improve the layout until a satisfactory layout is found. The user evaluates the layouts according to qualitative criteria, adjusts them intuitively and specifies objectives and restrictions in an explorative way. The computer on the other hand – in form of a reinforcement algorithm – generates possible layouts and incorporates the user’s feedback into its policy. This synergy is expected to generate better results than an expert or an algorithm alone could. Furthermore, in the context of learning factories, it encourages critical thinking and allows students to develop a deeper understanding of the factors that contribute to efficient manufacturing processes. Both an architecture for the implementation is proposed and the requirements for the user interface are specified.
In the context of production and factory planning, the expansion of the factory must already be taken into account during initial planning. This results in an increase in planning complexity, as the involved planners have to know the expansion stages of the factory in the different time periods and have to evaluate concept modifications across all time periods. This paper presents an idea for a planning tool, which takes expansion stages into consideration. The data model contains all relevant information to generate a simulation model of the factory in an almost automated way. The aim is to enable factory planners to quickly investigate concept changes with the help of simulation, for example, to identify bottlenecks.
One major goal of green-field factory planning is to decide on space requirements in the plant. In this phase, detailed information about the supply chain network (e.g. which suppliers deliver which parts) is often unavailable. Nevertheless, typical decisions in supply chain management, like the definition of replenishment processes and quantities or make-or-buy decisions, impact space requirements in the factory and should therefore be considered in the factory planning. This research article proposes a simulation approach for a factory simulation in which raw material replenishment is integrated to evaluate the space requirements for raw materials.
Simulation Based Approach for Reconfiguration and Ramp up Scenario Analysis in Factory Planning
(2022)
Structural changes in production entail a potential economic risk for manufacturing companies. It is necessary to identify a suitable strategy for the reconfiguration process and to continue to meet the demand during the change in the factory structure and ramp-up phase. A simulation offers the possibility to analyze different ramp-up scenarios for the factory structure and to select a suitable concept for the reconfiguration process. A discrete event simulation approach is presented that can be used to evaluate variants of structural changes and serves as a basis for deciding on a reconfiguration strategy. This approach is demonstrated using a specific production step of a plant producing hydrogen electrolyzers, the results and generalized conclusions are discussed.