Exploring the Potential of Digital Twins for Production Control & Monitoring

The achievement of a high level of logistical performance is a primary goal of manufacturing companies. In order to remain competitive, companies must constantly improve order processing to ensure short delivery times and high delivery reliability. Production planning and control is a core function of manufacturing companies and is responsible for routing production orders through the stations involved in order processing such as procurement, production and dispatch. Yet managing production efficiently and achieving a high logistical performance remains a genuine issue, even with increasingly digitalized and automated processes. The concept of the digital twin promises an improved database to enable companies to reach more informed decisions. As yet, the potential for utilizing this database has not been thoroughly explored in the context of a constant measurement of the backlog and output. In addition, there are various divergent definitions and approaches to the application of digital twins. This paper discusses the potentials of the different tasks of production control and monitoring in relation to the acquisition of dynamic data in real-time. A method is provided that continuously calculates the backlog and output. Furthermore, an example of application is presented that shows how this information can be used for production control. Our results indicate that by exploiting this information, logistic performance can be improved

Procedure Model for Dimensioning and Investment Cost Calculation in an Early Factory Planning Phase

Companies and their factories face constant change in today's world. Cost-intensive factory planning projects are being carried out in shorter intervals due to the increasing dynamism of the production environment. The related investments have a substantial impact on the liquidity of companies. Shorter production life cycles and changing consumer behaviour also require an adapted and more sustainable factory planning and cost estimation. However, especially in an early planning phase, available data and information are often uncertain and inaccurate. This effects in particular the outcome of the central dimensioning variables (operating resources, employees and area) for the planned factories. Incorrect dimensioning of these variables and thus of the associated costs can lead to substantial misinvestments. A holistic approach to obtain a reliable cost estimation of the factory project at an early stage is not yet available. This article therefore presents the development of a comprehensive procedure model for dimensioning and investment cost calculation in an early factory planning phase. For this purpose, relevant information and planning tasks with regard to dimensioning and cost estimation have to be identified first. Determined output values of the subsequent resource dimensioning represent the input values for the cost calculation. With the identification of surcharge factors, cost rates and calculation methods, the dimensioning variables, in particular the production area as the basis for the planned factory, can be estimated in terms of costs at an early stage

I4PE - Plattform zur Unterstützung der unternehmensweiten Digitalisierung von KMU

Produzierende Unternehmen sind aufgrund der zunehmenden Bedeutung von Industrie 4.0 mit großen technologischen Veränderungen konfrontiert. Diese Veränderungen beeinflussen den gesamten Wettbewerb. Volatile Märkte, die Fokussierung auf Kundenwünsche sowie die Reduzierung der Fertigungstiefe zwingen Unternehmen ihre Prozesse kontinuierlich anzupassen, um sich klar gegenüber den Wettbewerbern im Markt zu positionieren. Die Digitalisierung in allen Unternehmensbereichen ist ein entscheidendes Mittel für Unternehmen, um unter solch schwierigen Bedingungen langfristig erfolgreich am Markt agieren zu können. Besonders kleine und mittlere Unternehmen (KMU) haben durch ihre limitierten Möglichkeiten Schwierigkeiten, den digitalen Wandel ganzheitlich im Unternehmen voranzutreiben. Mittels einer internetbasierten Plattform können dem Nutzer niedrigschwellig Informationen und Erläuterungen zu Maßnahmen für eine individuelle und zukunftsorientierte Ausrichtung der Geschäftsprozesse bereitgestellt werden. Entscheidend ist hier insbesondere die didaktische Aufbereitung der Plattforminhalte, sodass dessen Nutzung mit keinem großen Aufwand verbunden ist und gleichzeitig vom Nutzer keine besondere Expertise verlangt wird. Dieser Artikel gibt eine kurze Zusammenfassung über die entwickelten Inhalte einer solchen Projektplattform mit dem Namen ‚Industrie 4.0-Projektplattform zur Einführung für KMU (I4PE)‘. Basierend auf dem wissenschaftlichen Ansatz wird zudem eine Methodik vorgestellt, mit der die Digitalisierungsmaßnahmen dem jeweiligen Entwicklungsstand und den strategischen Entwicklungszielen eines Unternehmens zugewiesen werden

Development Of A Digital Planning Tool For Dimensioning And Investment Cost Calculation In An Early Factory Planning Phase

As an interdisciplinary task, factory planning represents a key factor for logistics, supply chain and ultimately, the economic success of companies in the manufacturing sector. In factory planning projects, the focus is on the early planning phase, where costs and the associated misinvestments can still be significantly influenced. The challenge lies in the early valid dimensioning of the planned factory despite fuzzy data to provide decision-making support regarding the investment costs. In this context, this article presents the development of a digital service and planning tool based on a scientific procedure model. For this purpose, the research needs are first derived, reference is made to a scientific procedure model and the requirements analysis for the tool is presented. The tool developed on this basis aims to dimension and economically assess planned factories at an early planning stage. In this way, decision-makers in companies will be provided with data-based results to make future-oriented decisions between different project scenarios

Introduction of an Economic Assessment Approach for Factory Planning

Factories are characterised by a long life cycle and substantial investment costs. In particular, factory planning and reorganisation lead to capital-intensive investments and have a significant influence on the liquidity of companies. Reliable data are the basic prerequisite for a valid economic assessment of intended factory planning projects. However, especially in early planning phases, the available data are often uncertain and inaccurate. This problem significantly impedes the early and precise dimensioning of operating resources, production areas and employees as well as their cost calculation for planned factories. This also increases the risk of erroneous cost-benefit estimates and potential misinvestments. As a result, an early economic assessment and cost estimate for planned factories is essential. Until now, a holistic approach that can quickly and precisely determine investment costs of different planning variants in an early factory planning phase with only limited and uncertain information is not yet available. In order to close this research gap, a holistic approach for calculating investment costs in an early stage of factory planning is in development within the framework of "ELIAS", a collaborate research project of the Institute of Production Systems and Logistics at Leibniz University Hannover and the GREAN GmbH. The central objective of this research project is the software-based development of an investment cost calculator in order to carry out an economic efficiency assessment for potential factory planning projects at an early stage. This article provides both a brief summary of the need and innovative capability of the research idea and the structured procedure for creating a planning tool that closes the research gap described above. By this means, companies are supported in making future-proof decisions at an early stage despite uncertain and inaccurate data

Investment Feasibility Study for Factory Planning Projects

Companies and their factories are in a state of permanent change. Factory planning is interested in anticipating the changes that will occur and thinking ahead structural adjustments by dimensioning the operating resources, personnel and space. Often incremental adjustments over the space are no longer sufficient, so that further investments become necessary. Large factory planning projects, such as a greenfield or expansion planning, usually require extensive capital, which has a huge impact on a company's liquidity. The correct evaluation of the economic efficiency of a factory planning project, i.e. the comparison of costs and revenues, is therefore essential. Investment feasibility studies can help companies choosing a proper project. In such an early phase, investments have to be planned and initiated under increasing uncertainty. However, with common methods and tools, a precise evaluation of the investments costs in an early planning phase is only possible to a limited extent. Consequently, the risk of misinvestment increases. Factory planning experts are therefore dependent on tools and methods that minimise this risk through a precise calculation of investment costs under uncertainties. This article addresses the related challenges, shows the need for an improved decision support, and give a first framework to face these challenges