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Due to increasing globalization, market penetration, and technological progress, more products are becoming highly standardized and interchangeable. This has led to fierce price competition, forcing companies to compete for declining margins. To remain competitive in the long term, it is essential to consider both changeability and profitability during the early planning phases. The article proposes an approach for simulation studies using discrete-event simulation during the concept phase of factory planning. This approach aims to systematically dimension production resources while considering alternative scaling options, such as a higher degree of automation. By doing so, the approach facilitates the demand-oriented deployment of resources during the ramp-up phase, which helps to control and reduce manufacturing costs. The simulation experiments yield scaling paths that indicate the timing and quantity of resources required to meet changing demands. These paths form the basis for subsequent factory planning steps. For instance, the layout can incorporate the necessary space for a future automation solution that will be profitable, enabling faster adaptation to demand changes and securing competitive advantages. This is demonstrated in a use case where the approach was applied to the factory planning of a new production system for a sensor product family. [ABSTRACT FROM AUTHOR]

The use of Virtual Engineering is getting more and more common in production facility planning in the automotive sector. However, there are still discrepancies between the reality and the software generated models. That is why the process of quality assurance is crucial. This article aims at determining the current challenges in the planning process of production facilities, which are to be processed by means of Virtual Engineering, and limitations that occur in practice during the execution of Virtual Engineering in digital production facility engineering. We strive to maximize the utilization of underlying potentials by seeking out solutions. Our approach involves conducting a systematic literature review as well as seven expert interviews to investigate the current practical implementation of Virtual Engineering at Original Equipment Manufacturers (OEMs). Additionally, we aim to validate the influencing variables obtained from the literature in relation to the challenges, limitations, and potentials of Virtual Engineering. We show that there are current challenges in the planning process of production facilities, where the areas of functionality, equipment selection, safety, accessibility, and key performance indicators should already be secured in a virtual environment by means of Virtual Engineering. Our findings reveal significant limiting factors in the application of Virtual Engineering as quality assurance. This limitations arises as a result of the reliance on manual activities, which ultimately hinders progress in terms of time efficiency, quality assurance, and cost-effectiveness. In order to optimize this, we show that the potential of standardization of the work steps is required to define an automation that fully exploits the potentials of Virtual Engineering. [ABSTRACT FROM AUTHOR]

The production paradigm of "matrix production", newly discussed in the context of the digital transformation, is challenging traditional factory planning methods. In a turbulent market environment, more and more companies are striving to increase their competitiveness through more flexible and efficient production systems. Traditional factory planning approaches are still based on classical production systems. Conflicts and inefficiencies in the transformation project are inevitable. To avoid controversy, the question of how factory planning can be updated with the support of matrix production systems (MPSs) must be answered. Should an existing framework be adapted or a new one developed? A detailed literature review reveals the need for an update. In essence, it becomes clear that the topics of MPSs and factory planning have already been connected. These connections do not take into account the morphological requirements that are placed on the planning and operation of MPSs in a factory. The result is a conceptual modification of a common guideline for factory planning. Following the result, recommendations for the way to a successful validation are given. The article provides valuable insights into the core issues of MPSs and factory planning and stimulates scientific discourse. [ABSTRACT FROM AUTHOR]

There is a growing demand for more flexibility in manufacturing to counter the volatility and unpredictability of the markets and provide more individualization for customers. However, the design and implementation of flexibility within manufacturing systems are costly and only economically viable if applicable to actual demand fluctuations. To this end, companies are considering additive manufacturing (AM) to make production more flexible. This paper develops a conceptual model for the impact quantification of AM on volume and mix flexibility within production systems in the early stages of the factory-planning process. Together with the model, an application guideline is presented to help planners with the flexibility quantification and the factory design process. Following the development of the model and guideline, a case study is presented to indicate the potential impact additive technologies can have on manufacturing flexibility Within the case study, various scenarios with different production system configurations and production programs are analyzed, and the impact of the additive technologies on volume and mix flexibility is calculated. This work will allow factory planners to determine the potential impacts of AM on manufacturing flexibility in an early planning stage and design their production systems accordingly. [ABSTRACT FROM AUTHOR]

Manufacturing companies across multiple industries face an increasingly dynamic and unpredictable environment. This development can be seen on both the market and supply side. To respond to these challenges, manufacturing companies must implement smart manufacturing systems and become more flexible and agile. The flexibility in operational planning regarding the scheduling and sequencing of customer orders needs to be increased and new structures must be implemented in manufacturing systems' fundamental design as they constitute much of the operational flexibility available. To this end, smart and more flexible solutions for production planning and control (PPC) are developed. However, scheduling or sequencing is often only considered isolated in a predefined stable environment. Moreover, their orientation on the fundamental logic of the existing IT solutions and their applicability in a dynamic environment is limited. This paper presents a conceptual model for a task-based description logic that can be applied to factory planning, technology planning, and operational control. By using service-oriented architectures, the goal is to generate smart manufacturing systems. The logic is designed to allow for easy and automated maintenance. It is compatible with the existing resource and process allocation logic across operational and strategic factory and production planning. [ABSTRACT FROM AUTHOR]

The planning process of a factory is of high importance for the future performance of the respective manufacturing system. However, the planning process is characterized by its high complexity, given the large variety of possible layout variants. Recent research has demonstrated that reinforcement learning can be successfully used in the early stage of factory layout planning to generate and optimize a factory layout. However, dynamic aspects in the material flow that influence the manufacturing system's throughput time are usually neglected since only analytic optimization objectives are considered. This paper addresses this research gap by presenting a reinforcement learning-based planning approach that incorporates a discrete event material flow simulation as part of the environment. The primary focus of investigation is the formulation of an appropriate reward function since the reward function has a direct influence on the solution quality. The results of the investigation show that the reinforcement learning approach is able to successfully interact with the simulation and optimize the layout with respect to the total throughput time if the incentives given by the reward function are aligned with the optimization objective. The results are additionally compared to a manual planning approach and the computed optimal solution which demonstrates that the developed approach is a suitable support tool for factory layout planning. Concluding, the approach is applied to an application scenario with 25 functional units to investigate the scalability of the approach. [ABSTRACT FROM AUTHOR]

Due to competitive pressure and varying customer demands, product and technology life cycles are constantly shortening. Thus, manufacturing companies must handle the increasing discrepancy between long building life cycles and shortening product and technology life cycles. For economic and sustainability reasons, shortening the building life cycles is no viable option. Instead, factory buildings should be able to meet the changing production requirements and thus lay the foundation for a resilient manufacturing system. As it is not economically reasonable to build production buildings that simply maximize flexibility and changeability, an estimation of consistent future requirements is necessary. This estimation should focus on the requirements of the most rigid parts of the building. Traditional, statistic-based forecasting methods are not suitable because of the long planning horizons in building planning. However, methods used in corporate foresight promise good results. This paper gives a short overview of the research status and the relevant terminologies. After that, a method to cope with the described challenge is presented. The method identifies possible future developments of the production system. It then translates these developments into requirements on the building system and evaluates different degrees of requirement fulfillment. On this basis, the method helps to decide what specific developments the building should cover. Unlike existing approaches, this method focuses not only on the factory building itself as the system of highest relevance for long-term changeability but also reduces the execution effort and improves the integration of production planning and building planning. [ABSTRACT FROM AUTHOR]

The concept of the digital twin can be applied to a wide variety of areas in a company. A fundamental distinction can be made between digital twins of a product or service, digital twins of processes and digital twins of resources. The combination of process and resource twin forms the operational twin of a factory. In order to lay the foundations for the digital twin of the factory, corresponding requirements must already be taken into account in product development. In a structured literature review, this paper examines the interactions between product development and factory planning and operation at the data level. In addition, common use cases for the digital twin of the factory are examined to develop an understanding of its characteristics. On this basis, some possible activities for further research activities will be discussed. [ABSTRACT FROM AUTHOR]

The design of a product is shaped by different requirements. Some product requirements result from the available resources of the factory. Consequently, the planning of production resources must also be taken into account at an early stage during the product development phase to ensure suitable synchronization. This results in the demand for industrial planning to be carried out during product development and to ensure suitable synchronization. This paper therefore examines the interfaces between product development and factory planning. Furthermore, an overview of existing challenges in the cooperation between these two areas is given. Finally, fields of action for further research are discussed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Many industrial sectors are currently being challenged with regard to the flexibility and adaptability of their production and control processes, which can, inter alia, be attributed to macroeconomic trends such as digitisation. These trends not only shorten product lifecycles but also lead to a greater need for redesign and transformation of production systems within factories. Since this field of factory planning tends to play a subordinate role in SMEs anyway, the above-mentioned factors impede an efficient execution of corresponding projects even more. While large companies are more likely to have a factory planning department, SMEs often do not have sufficient resources to meet the aforementioned challenges through independent factory planning projects. Although network-based structures could be a realistic and practical way to meet the challenges of factory planning as they generally provide possibilities for resource and risk sharing, SMEs often manifest resentments towards network collaborations. This paper aims to validate these resentments. For this purpose, a profound expert study explores whether network cooperations could overcome companies' challenges. It derives implications towards a model that enables network cooperations in factory planning. [ABSTRACT FROM AUTHOR]

Manufacturing companies across multiple industries face an increasingly dynamic and unpredictable environment. This development can be seen on both the market and supply side. To respond to these challenges, manufacturing companies must implement smart manufacturing systems and become more flexible and agile. The flexibility in operational planning regarding the scheduling and sequencing of customer orders needs to be increased and new structures must be implemented in manufacturing systems’ fundamental design as they constitute much of the operational flexibility available. To this end, smart and more flexible solutions for production planning and control (PPC) are developed. However, scheduling or sequencing is often only considered isolated in a predefined stable environment. Moreover, their orientation on the fundamental logic of the existing IT solutions and their applicability in a dynamic environment is limited. This paper presents a conceptual model for a task-based description logic that can be applied to factory planning, technology planning, and operational control. By using service-oriented architectures, the goal is to generate smart manufacturing systems. The logic is designed to allow for easy and automated maintenance. It is compatible with the existing resource and process allocation logic across operational and strategic factory and production planning.

Layout planning is a central element of the factory planning process. Given its complexity, layout planning is often time consuming and involves creative processes. One possible way to deal with this complexity is the training of a machine learning algorithm, which enables to generate and optimize factory layouts. Consequently, this paper outlines a reinforcement learning based concept for automated layout planning. In particular, boundary conditions and objective functions are derived from the existing planning parameters of factory layouts. The presented approach will allow a multiobjective optimization of the layout and uses material flow and energy consumption as optimization criteria. [ABSTRACT FROM AUTHOR]

Planning a manufacturing factory involves a high number of planners who deliver their domain-specific planning information. In Building Information Modeling, a central entity needs to check the information from different planning domains for coherence to each other. Especially for non-geometrical information, this is a manual, time-consuming and error-prone approach. The paper at hand therefore aimed at developing an expert system that automates the decision making in the checking process of factory planning information. On the basis of a case study, we have applied knowledge engineering methods from the Semantic Web and calculated an F1 score for validating our approach. [ABSTRACT FROM AUTHOR]

Since the beginning of global networking, the environment of production systems has been considered a turbulent zone, in which the unpredictability of economic, technical and political trends characterizes a phase of sudden and high speed of change. These changes can lead to disturbances in factory operation resulting in economic losses. Therefore, each change requires time to adapt to reach and maintain a certain performance level. One element of a factory are the employees working on production processes, maintenance or further support functions. Employee specific learning behavior of production tasks is already investigated in detail and describes the constantly increasing performance based on a learning process. To reach a certain level of productivity, employees require a specific timeframe. The duration of the timeframe depends on further factors e.g. task complexity or individual learning ability. Learning behavior is similar to the adaption of factories to change events, which is why it offers a basis for describing the proven concept of changeability. Therefore, the goal of this paper is to relate the characteristics of learning curves to changeability of factories in order to develop a framework for assessing the impact of change. The framework contains starting points for influencing the learning process in terms of its speed or necessity, as well as for estimating the point in time at which a factory is ready for change again. For this purpose, possible influencing factors such as resilience or transformability, and the significance of production logistic key figures are examined in a more detailed analysis. [ABSTRACT FROM AUTHOR]

Factory layout planning is a recurring and important process since an inadequately planned layout can significantly impede the operation of a factory. A time-efficient and automated planning method is especially important as shortened product lifecycles, changing technologies, and market requirements lead to changes in the production program, which further require the re-planning of the factory layout. Reinforcement learning (RL) has proven to be a suitable approach to support decision-making in complex layout planning problems. However, recent studies only consider comparably small and exemplary problem statements with a maximum of 4 functional units. In consequence, these approaches are insufficient for industrial applications in which numerous functional units are considered. Regarding this background, this paper investigates the scalability of a RL-based planning approach, in which a Double Deep Q Learning agent is used to solve a layout planning task for a small batch production with 20 functional units. Considering that recent approaches lack scalability, this paper presents a new state representation in combination with an action masking method that optimizes the action selection process to ensure the scalability of the approach and reduce the training time. After the training, the algorithm can generate an optimized factory layout by reducing the resulting transportation time for industrial relevant problem sizes. [ABSTRACT FROM AUTHOR]

Factories are complex systems, which are characterized by interlinked and overlapping life cycles of the constituent factory elements. Within this context, the heterogeneity of these life cycles results in life cycle complexity and corresponding conflicts and trade-offs that need to be addressed in decision situations during the planning and operation of factory systems. Also with respect to the transformation need towards environmental sustainability, there is a need for methods and tools for life cycle oriented factory planning and operation. This paper systematically reviews existing life cycle concepts of factory systems as well as frameworks, models and methods for the evaluation and engineering of factory life cycles. In order to respond to the above challenges, a general understanding about the factory life cycle, e.g. life cycle stages, related activities and interdependencies, is developed and action areas of life cycle engineering are discussed that could supplement factory planning. Following that, the paper presents an integrated, model-based evaluation and engineering framework of factory life cycles.

Nowadays, production companies are facing an increasingly volatile environment. Due to increasing globalization, but also de-globalization, taking into consideration an internal production network is becoming more and more important for companies, all in order to be able to counteract in an agile way the uncertainties such as swings in the demand. Current production network planning procedures focus on (re-)locating decisions without delving into what happens inside the plants, neglecting the dynamics of production networks, following a rigid top-down approach during the configuration phase, and they do not integrate the effects of planning tasks at the factory level (tactical and operational planning). In order to be able to make strategic decisions with a well-founded database regarding the production network, the effects on the tactical and operational level must be considered in an iterative way during the strategic decision-making process. The aim of this research is to define the requirements for an approach to strategic production network planning, which considers the effects at the tactical and operational level in an iterative way, and to develop a process model, derived from the requirements, that in its five phases considers the deficiencies of the existing approaches.

The authors present the results of a systematic literature review on risk management in factory planning. Most importantly, standardized methods are not available yet; hence, approaches from supply chain management, manufacturing as well as project management are frequently applied to factory planning. Furthermore, FMEA, multi-criteria decision-making and fuzzy-logic-based risk assessment are promising techniques for current agile and informationally connected planning procedures. Concluding, the paper discusses how the findings could support the development of a holistic decision-making framework able to handle uncertain information, thus contributing to better risk management decisions and higher achievement levels of project target values in industrial practice. [ABSTRACT FROM AUTHOR]

Factories consist of numerous factory elements with individual life cycles. Besides technical aspects such as wear, their actual lifetime is influenced by change drivers from an increasingly dynamic market environment. As a result, factory elements may experience a premature end due to changed requirements before the end of their technical lifetime. The difficulty in factory planning is to understand the behavior of the life cycles in order to make management decisions. Therefore, the goal is to identify change drivers and evaluate their influence on the factory life cycle, while taking into account the technical functionality. [ABSTRACT FROM AUTHOR]