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Modern factory planning requires a holistic perspective taking economic as well as environmental sustainability over the entire factory life cycle into account. As a complex socio-technical system, the factory life cycle consists of multiple life cycles of the inherent factory elements. A holistic understanding of the individual life cycles and their interdependencies is missing for both planning and operation of a factory. Therefore, the goal is to develop a system understanding about life cycle-oriented factory planning and to analyze the contribution of relevant factory elements to the sustainability of a factory. As a result, a knowledge base for life cycle costing and assessment of the entire factory is established using an impact path model. The qualitative model supports factory planners in deriving planning measures for the sustainable design of a factory and in determining data requirements for the quantitative evaluation of the economic and environmental sustainability of a factory. It shows that the production and logistics concepts essentially define the sustainability potential during planning, while the resulting life cycle behavior of the process facilities and workers is responsible for the majority of costs and environmental impacts of a factory. Factory planners must therefore become aware of the implications of planning decisions on factory operation when developing concepts in the future.

The presented data article aims to provide the whole dataset obtained during an experiment of updating laser scan point clouds with photogrammetry meshes. In this context, the data quality and calculation time of photogrammetry models from different recording devices and different software solutions were compared. It was investigated whether photos from smartphones are also appropriate for updating point clouds by using photogrammetry in a factory environment. The photos of a technical installation were taken in 08:30 min with these three devices: Nikon D810 with Sigma art 24 mm, iPhone 6 and iPhone XS. With each of the mentioned devices, three datasets have been created to provide enough data for the comparisons. One dataset (photos in .TIFF) of the iPhone XS is provided. The results of the datasets are used for a photogrammetry mesh quality comparison and a calculation time comparison. For the mesh quality comparison, visual qualitative inspections were performed on the models and the results were compared. Furthermore, all settings in the RealityCapture BETA 1.0.3.9696 ppi and Meshroom 2019 2.0 software are provided. A comparison of the quality of the photogrammetric 3D meshes was performed by comparing the rendering results. The dataset of the iPhone XS can be used to compare further photogrammetry software or single algorithms. Besides the images, the initial point cloud of the laser scanner is provided. Also included is the combined file which consists of the laser scan point cloud and the photogrammetry mesh of the end of the experiment.

The 3D modelling of indoor environments and the generation of process simulations play an important role in factory and assembly planning. In brownfield planning cases, existing data are often outdated and incomplete especially for older plants, which were mostly planned in 2D. Thus, current environment models cannot be generated directly on the basis of existing data and a holistic approach on how to build such a factory model in a highly automated fashion is mostly non-existent. Major steps in generating an environment model of a production plant include data collection, data pre-processing and object identification as well as pose estimation. In this work, we elaborate on a methodical modelling approach, which starts with the digitalization of large-scale indoor environments and ends with the generation of a static environment or simulation model. The object identification step is realized using a Bayesian neural network capable of point cloud segmentation. We elaborate on the impact of the uncertainty information estimated by a Bayesian segmentation framework on the accuracy of the generated environment model. The steps of data collection and point cloud segmentation as well as the resulting model accuracy are evaluated on a real-world data set collected at the assembly line of a large-scale automotive production plant. The Bayesian segmentation network clearly surpasses the performance of the frequentist baseline and allows us to considerably increase the accuracy of the model placement in a simulation scene.

With increasing innovational dynamics and shorter product and technology lifecycles, companies are facing a huge number of production ramp-ups in shorter times at a higher complexity. To cope with these challenges, methods and tools are required to reduce instabilities during production ramp-ups and to enable a smooth and stable ramp-up curve. One solution approach is the application of factory standards. As standardized product architectures are already state of the art for many companies, standardized factory structures are the next step towards an overall standardized production architecture. The approach of factory standards and its benefits especially during ramp-up will be introduced in this paper.

Manufacturing companies face the challenge of understanding and improving complex factory systems in order to stay competitive in a turbulent environment. Interrelated and overlapping life cycles of products and physical factory elements (e.g. machine tools, technical building services, building shell) are challenges to be handled in factory planning and operation. This work discusses both qualitative and quantitative factory life cycle models, analyzing addressed sustainability goals. Due to the lack of quantitative life cycle description models on higher system levels, a concept for aggregating life cycle models from shop floor up to site level is developed. The concept is consequently applied in a case study where cost curves are calculated over the factory's life span and are aggregated to support factory planning and operation. © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.

Due to shorter product-life-cycles, innovations in production engineering have to keep pace with today's technologies. As a result, factory planning is more and more challenged by technologies being immature for series production. Usually, these immature technologies place special demands on production layout and quality management, for example. These demands have to be considered in the factory planning process. Moreover, technologies are part of the production process that is created by a series of technologies. Hence, a planning process has to ensure that the positive aspects of a new technology are not negated by arrangements to protect the technology chain against failure due to immature technologies. With Selective Laser Melting (SLM) used as example for an additive manufacturing technology, this paper presents a method of planning a production system by taking the technology maturity into account. Possible requirements of an immature technology interacting with the process chain will be addressed as well as adjustments to be made to the factory planning process.

Today, factories have to be adapted permanently in order to follow the developments towards fast changing customer demands and faster life cycles of products. Key aspects to cope with these developments are the reduction of the unit costs and the planning duration as well as the improvement of the planning quality. In order to overcome these challenges, digital tools can support all phases of factory and process planning. Most of them provide a wide range of functionalities, which require a large invest in software and high operation costs to use them efficiently. Often, small and medium enterprises (SME) cannot afford such invests and operation costs. Therefore, new digital factory planning tools tailored for SMEs supporting the production system planning are required. Main goal of the “WiES-Pro” project, funded by the Ministry of Finance & Economics Baden-Wurttemberg, was to develop an engineering environment for production system planning suitable for SMEs. The “WiES-Pro” approach connects small specific software tools for production system planning in an integrated platform, capable to share information and knowledge easily. The challenges in the development are to cope with both the heterogeneous data from different sources like machine master data, work plans or product data and the applicability to SMEs. This paper presents the results of the project, consisting of the approach and its implementation. Also the architecture of the browser-based platform and the developed digital factory planning tools for priority graph optimization, assembly process time planning, similarity-based product search and material flow simulation are presented shortly.

Energy efficiency has developed into an important objective for industrial enterprises. However, there is still a need for systematic approaches to reduce energy consumption in factories. Existing methods focus on the optimization of manufacturing processes and lack upon considering the entire factory system. Additionally, they are based on a detailed quantitative analysis of processes and thus, they need a high effort during the phase of data acquisition. Therefore, an approach for reducing energy consumption by providing energy efficiency measures to factory planning participants was developed in order to overcome these barriers. The general approach is described in this paper and supported with a use case that demonstrates the required information and possible outcomes in terms of energy efficiency information. Main advantages of this approach are reducing the effort to acquire energy data and the possibility to consider the factory system holistically.

Planning resource and energy efficient factories is a challenge for present approaches because of the additional knowledge required and because of the high variety of energy efficient measures theoretically available; modularity as promising solution allows case-adaptability and focusing most relevant energy drivers during planning. Planning requires interdisciplinary collaboration and smooth decision making in these modules because different factory elements interact and information needs to be processed. This paper describes the composition and interconnection of Green Factory Planning modules focusing the assignment to planning experts and the management procedure to have higher impact on green performance. A use case shows how planning modules can help identify relevant information and key team members to let them collaboratively find solutions.

In the modern world we are continuously surrounded by information. The human brain has to analyse and interpret this information to transform into useable knowledge that is then used in decision making activities. The advent and implementation of Industry 4.0 will make it a requirement for systems within factories to interact and share large quantities of information with each other. This large volume of information will make it even more difficult for the human resources within the factory to sift through the large amount of information required since there is a limit to the information that our brains can cope with. Just in time information retrieval (JITIR) within the digital factory environment aims to provide support to the human stakeholders in the system by proactively yet non-intrusively providing the required information at the right time based on the users context. This paper will therefore provide an insight into the cognitive difficulties experienced by humans in the digital factory and how JITIR can tackle these challenges. By validating the JITIR concept, several industry scenarios have been evaluated: an exemplary model, concerning the machine tool industry, is presented in the paper. The results of this research are a set of guidelines for the development of a digital factory support tool., peer-reviewed

Due to the reasons of sustainability, energy efficiency becomes a more and more important objective for industrial companies. Among others, the term “energy management” is often mentioned as a practical instrument to raise energy efficiency. Because of the long-term influence at an early stage of the factory life cycle, factory planning provides an important contribution to realize company goals regarding energy efficiency. Until now, there is a lack of methodical concepts considering the combination of energy management and factory planning. In this paper, the interaction between both of these topics is described and by this, the meaning of energy management as an integral part of energy-efficient factories is underlined. Generally, energy management is used in the phase of factory operation to improve the energy performance, including energy efficiency, energy supply security, energy use and energy consumption. Besides, the integration of energy-related tasks in the factory planning process should also be realized as far as possible, e.g. regarding the purchase, distribution, storage and use of energy. This leads to a more systematic and holistic consideration of energy efficiency in organizational and technical processes of a company.

The type of tools for sustainability assessment is manifold and is growing consistently to meet more and more of society's needs. This paper presents a review and evaluation study of existing assessment tools collected from a variety of sources. The aim is to clarify the difference between those sustainability assessments tools and increase factory planner's awareness towards the different tool properties. In this work representative assessment tools are evaluated and described with respect to their specific focus and benefits as well as drawbacks. Based on that a research gap is identified and further research directions can be derived.

Over the last couple of years several factors have changed the behavior of companies to interact and collaborate. This can be attributed mainly to a turbulent environment. In addition, the use of Virtual Reality (VR) as a tool for collaboration to exchange information and data has increased significantly over time in production-related areas. The purpose of this paper is to identify the recent state of application areas and stages for the expedient use of VR as a collaboration tool for factory planning. Therefore, a literature review is given on how to handle factors that influence companies in general, but also regarding the factory planning processes. Based on this, the scenario technique in the area of factory planning is presented. These scenarios can be used as a basis for long-term planning of strategic actions. This technique can be especially helpful in an unpredictable future to support long-term decisions. Furthermore, an overview is provided in regard to the phases of factory planning that are supported by VR applications. In contrast, phases are highlighted where a VR support is needed. The basis for a concept is given that strives towards a continuous VR supported factory planning process.

Information is one of the key enabler for transformability of factories. As factories have to be able to adapt their processes very fast according to the changing environment, the approach introduced here aims at supporting production process planning and optimization. The performed activities are guided by configuration concepts and methodologies. First the great potential of employing knowledge-based systems in production process planning is emphasized. Afterwards the questions will be answered, what knowledge is relevant, how this knowledge is modeled and how it is used for the configuration of production processes. The contribution closes with a roadmap of future activities.

Kurzfassung Dieser Beitrag stellt kurz die aktuellen Forschungsaktivitäten des Fraunhofer IPA in Stuttgart im Bereich „Digitale Fabrik“ vor. Theoretische Konzepte für eine durchgängige und kollaborative Fabrik- und Prozessplanung wurden im Rahmen des europäischen Projekt DiFac umgesetzt. Die Beschreibung der konzipierten und entwickelten Architektur der Hauptkomponente Factory Constructor der VR-basier-ten Produktionsumgebung DiFac bildet den Schwerpunkt dieses Artikels.

Part 1: Sustainable Production; International audience; Planning green factories implies increased complexity depending on specific planning requirements and contradictory planning targets; these aspects challenge present methods and tool for factory planning. To properly face these challenges with limited time and resources (typical of planning projects) , a factory planning approach that particularly addresses green has to be adaptable to specific cases and aligned with the green vision of the factory. This paper proposes a modular Green Factory Planning and describes its main components. The conceptual elements for a framework and a planning process are presented and differences in methods and tool applications are described in comparison to established approaches. The method and tools are combined to planning modules, linked with a clear information flow as well as responsibility definition. A use case for a typical module that specifically considers energy consumption shows the potential of green as integrated vision for factory planning.

Simulation systems are gaining an increasing influence. While different solutions are still being used on the way from the idea of a product to the production plant, comprehensive modelling will become feasible in the future.

International audience; A novel performance measurement and assessment frame called Performance Factory (PerFact) applied on a specific example focussing on mass customization in the context of the Factory of the Future is presented in this work. PerFact operates target-oriented towards the mission and vision of the company by connecting the overall mission and vision via related requirements with the Key Performance Indicators (KPIs) and their specific reference values. The performance is measured on manufactured customer-driven products, corresponding production processes and used resources. Furthermore, PerFact is able to monitor and assess the performance of both real production systems and simulated production scenarios. Moreover, the measurement system is balanced; it assesses the performance of the factory considering all perspectives relevant for each specific case.

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Cloud computing sets to make a change in business between enterprises over the Internet based on services providing dynamically reconfigurable and virtualized networks. It is difficult for an aircraft manufacturer to consider the performance and status of each factory (i.e., capacity, inventory, order, technology, etc.) because of the huge amount of suppliers. In legacy environments, the supplier consumed a long time to identify the machine and process required and the energy to be consumed in order to satisfy a new customer demand. If the process occurred with a new order should be optimized, additional time and cost were needed. Cloud computing can enable the relationship between the manufacturer and suppliers of aircraft, which is representative of mass customized products, to be flexible and efficient. In order to enhance the response time during contracting with suppliers, this paper focuses on an architecture, providing a service for a decision with the concerns about the time and cost as well as energy with regard to process planning. For realizing this service the manufacturer provides the process plan described using neutral language for numerical controllers called 'STEP-NC' which is independent from a specific machine tool. On the supplier side, the machinability check, tool path generation and energy use estimation should be identified with machine tool specifications described by STEP-NC. In the Cloud computing environment, the analysis is performed to check the possibility that a supplier can complete a new order from the manufacturer on the due date with the analysis of machine tools and on-going production schedule. This function is developed in a system which can be installed on a virtual platform and provided by a cloud service. (C) 2014 Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

In the last years the production paradigm of European Industry sector has changed. More and more, bigger companies are adopting Virtual Reality (VR) tools in their production chains, while the SMEs are still looking for more customised solution: more suitable for their dimension and less expensive. DiFac is an IST research project (FP6-2005-IST-5-035079) funded by the European Commission within the 6th Framework Programme priority "Collaborative Working Environment". The project aims to the development of an innovative Collaborative Manufacturing Environment (CME) for the next generation of Digital Factory in order to support SMEs competitiveness. The framework is based on three main pillars: presence, ergonomic and collaboration. They are composed by both tools and methodologies. This is the base on which the tools for supporting the manufacturing activities are developed on. This paper shortly presents the architecture of the Factory Constructor.