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Big Data (BD)-driven business intelligence (BI) is considered to be a new development stage within industrial engineering management to achieve higher business value creation. The article provides extensive research of BD-driven approaches to identify strategic knowledge and value-creating components within Internet of Things (IoT) manufacturing. Due to previous research studies focused mostly on partial solutions and lack a holistic view, this article topic is still far from being sufficiently explored and resolved. The article presents the results of an extensive research study of contemporary professional literature with a specific aim to provide a more holistic and systematical view on the current state and future trends in this issue. To solve this high complex research problem, both quantitative and qualitative research methodology approaches are used. The article is primarily based on a systematic literature review covering 187 essential and 36 additional current research papers within the Web of Science and Scopus database from 2012 to the present. Research findings identify the seven key strategic elements determining that Big Data analytics (BDA) can become a real and tremendous business value creation driver within IoT intelligent manufacturing. These research results provide important conclusions and implications for challenging future investigations into BDA and BI systems in the context of advancing the Industry 4.0 revolution. In conclusion, we propose a final suggestion in the form of a strategic future research chain from IoT through BDA to value creation based on BD intelligence within IoT manufacturing, whereas seven strategic elements might play a crucial. Author, Internal Grant Agency of Tomas Bata University in Zlin [IGA/FaME/2020/009-Process]

Continuous improvement of the fleet of technological equipment, in the conditions of existing production, is a difficult task, the solution of which must take into account many parameters, such as mechanization, automation and application of new technologies within the concept of Industry 4.0. One of the effective methods of improving the technical and economic indicators and reliability of production is the modernization of existing technological equipment, which is based on the need to use modern methods of development and implementation of information technology, such as Industrial Internet of Things, cyber-physical production systems. The subject of this study is the testing of methods for developing additive cyber design for automation of complex industrial equipment. The goal of this article is to develop a cyber-physical production control system for the modernization of the DA2238B hydraulic press. To achieve this goal it is necessary to solve the following tasks: to analyze the technical characteristics and control system of the hydraulic press DA2238B; to carry out research of the scheme of hydraulic basic and, taking it into account, to choose sensors and executive mechanisms, and also operating modes; to develop the scheme of inclusion of the hydro equipment; to develop an automated control system based on a modern single-board computer and to develop an intuitive additive cyber-design of the operator interface; to conduct experimental research. Conclusions: as a result of research, a block diagram was developed and an automated control system based on LattePanda was implemented. Selected pressure and temperature sensors for control of pressing processes, which allowed to ensure accurate compliance with the requirements of the technological process. An automated operator's workplace has been developed, in which touch control based on additive cyber-design has been implemented. The developed control system provides: collection and analysis of production data on the basis of which it is possible to improve production technology and to provide forecasting of achievement "Lean Manufacturing"., Постоянное совершенствование парка технологического оборудования, в условиях существующего производства, является сложной задачей, при решении которой необходимо учитывать много параметров, таких как механизация, автоматизация и применение новых технологий в рамках концепции Industry 4.0. Одним из эффективных методов повышения технико-экономических показателей и надежности производства является модернизация существующего технологического оснащения, которая основывается на необходимости использования современных методов разработки и внедрения информационных технологий, таких как Industrial Internet of Things, кибер-физических производственных систем. Предметом данного исследования является апробация методов разработки аддитивного кибер-дизайна для автоматизации сложного промышленного оборудования. Целью данной статьи является разработка кибер-физической производственной системы управления, для модернизации пресса гидравлического ДА2238Б. Для достижения поставленной цели необходимо решить следующие задачи: провести анализ технических характеристик и системы управления пресса гидравлического ДА2238Б; провести исследование схемы гидравлической принципиальной на базе которой выбрать датчики и исполнительные механизмы, а также режимы работы; разработать схему включения гидро- оборудования; разработать автоматизированную систему управления на базе современного одноплатного компьютера и разработать интуитивно понятный аддитивный кибер-дизайн интерфейса оператора; провести экспериментальные исследования. Выводы: в результате исследований была разработана структурная схема и реализована автоматизированная система управления на базе LattePanda. Выбраны датчики давления и температуры для контроля процессов прессования, что позволило обеспечить точное соответствие требованиям технологического процесса. Разработано автоматизированное рабочее место оператора, в котором реализовано сенсорное управление на базе аддитивного кибер-дизайна. Разработанная система управления обеспечивает: сбор и анализ производственных данных, на базе которых можно усовершенствовать технологию производства и обеспечить прогнозирование достижения "Lean Manufacturing"., Постійне вдосконалення парку технологічного обладнання, в умовах існуючого виробництва, є складним завданням, при вирішенні якого необхідно враховувати багато параметрів, таких як механізація, автоматизація і застосування нових технологій в рамках концепції Industry 4.0. Одним з ефективних методів підвищення техніко-економічних показників та надійності виробництва є модернізація існуючого технологічного оснащення, яка ґрунтується на необхідності використання сучасних методів розробки і впровадження інформаційних технологій, таких як Industrial Internet of Things, кібер-фізичних виробничих систем. Предметом даного дослідження є апробація методів розробки адитивного кібер-дизайну для автоматизації складного промислового обладнання. Метою даної статті є розробка кібер-фізичної виробничої системи керування, для модернізації преса гідравлічного ДА2238Б. Для досягнення поставленої мети необхідно вирішити такі завдання: провести аналіз технічних характеристик і системи керування преса гідравлічного ДА2238Б; провести дослідження схеми гідравлічної принципової на базі якої обрати датчики і виконавчі механізми, а також режими роботи; розробити схему включення гідро обладнання; розробити автоматизовану систему керування на базі сучасного одноплатного комп'ютера і розробити інтуїтивно зрозумілий адитивний кібер-дизайн інтерфейсу оператора; провести експериментальні дослідження. Висновки: в результаті досліджень було розроблено структурну схему та реалізовано автоматизовану систему керування на базі LattePanda. Обрані датчики тиску і температури для контролю процесів пресування, що дозволило забезпечити точне дотримання вимог технологічного процесу. Розроблено автоматизоване робоче місце оператора, в якому реалізовано сенсорне управління на базі адитивного кібер-дизайну. Розроблена система керування забезпечує: збір та аналіз виробничих даних, на базі яких можна вдосконалити технологію виробництва і забезпечити прогнозування досягнення "Lean Manufacturing".

With the concepts of Industry 4.0 and smart manufacturing gaining popularity, there is a growing notion that conventional manufacturing will witness a transition toward a new paradigm, targeting innovation, automation, better response to customer needs, and intelligent systems. Within this context, this review focuses on the concept of cyber–physical production system (CPPS) and presents a holistic perspective on the role of the CPPS in three key and essential drivers of this transformation: data-driven manufacturing, decentralized manufacturing, and integrated blockchains for data security. The paper aims to connect these three aspects of smart manufacturing and proposes that through the application of data-driven modeling, CPPS will aid in transforming manufacturing to become more intuitive and automated. In turn, automated manufacturing will pave the way for the decentralization of manufacturing. Layering blockchain technologies on top of CPPS will ensure the reliability and security of data sharing and integration across decentralized systems. Each of these claims is supported by relevant case studies recently published in the literature and from the industry; a brief on existing challenges and the way forward is also provided.

Integration of additive manufacturing (AM) into series production demands for comprehensive methods and tools. An agent-based process chain simulation framework is proposed to embed AM into a cyber-physical production system. A multi-dimensional evaluation supports the planning of AM process chains as well as their day-to-day operation. The detailed evaluation possibilities down to product and single simulation event level are illustrated by a case study, in which an automotive exterior part is produced using the multi-jet fusion technology. Multiple possible process chain configurations are assessed based on their modeled key performance indicators, summarized in a energy value stream map.

Designing meat processing systems can be a complex process since this type of cyber-physical production system has two distinguishing properties: divergent product routings due to the cutting up of meat (known as co-production), and variability in the outcome of production processes (known as random yield). In this paper, we present a model-based design framework for meat processing systems, which can be used for performance prediction and system validation of a designed system. We show how an agent-based production control layer can be integrated into a model-based design architecture for meat processing systems. Finally, this framework describes how to route products through a system with co-production and random yield.

With the inception of high-speed internet data services and ever-growing technical advancement in manufacturing technology, the integration of production systems and the internet of things to produce different types of jobs via cloud computing has become possible. The internet-enabled advanced automatic production systems can be referred to as the cyber-physical production systems (CPPS). The use of CPPS via cloud computing and the internet of things (IoT) can offer high productivity and high flexibility for the production of jobs in a dynamic production environment with varying specifications. The aim of this paper is to present a generalized synergic framework between different production facilities locating at different geographical locations to realize an energy-saving and efficient cyber-physical production system for the production of different types of jobs in the context of the industry 4.0 and beyond. In addition to the above, the study also identifies a need to address large scale multi-objective optimization issues to make the best decisions for different combinatorial production scenarios by using CPPS that are functioning in smart production facilities at different geographical locations.

Due to increased competitive pressure, modern organizations tend to rely on knowledge and its exploitation to sustain a long-term advantage. This calls for a precise understanding of knowledge management (KM) processes and, specifically, how knowledge is created, shared/transferred, acquired, stored/retrieved, and applied throughout an organizational system. However, since the beginning of the new millennium, such KM processes have been deeply affected and molded by the advent of the fourth industrial revolution, also called Industry 4.0, which involves the inter contentedness of machines and their ability to learn and share data autonomously. For this reason, the present article investigates the intellectual structure and trends of KM in Industry 4.0. Bibliometric analysis and a systematic literature review are conducted on a total of 90 relevant articles. The results reveal six clusters of keywords, subsequently explored via a systematic literature review to identify potential stream of this emergent field and future research avenues capable of producing meaningful advances in managerial knowledge of Industry 4.0 and its consequences.

Condition monitoring of industrial equipment, combined with machine learning algorithms, may significantly improve maintenance activities on modern cyber-physical production systems. However, data of proper quality and of adequate quantity, modeling both good operational conditions as well as abnormal situations throughout the operational lifecycle, are required. Nevertheless, this is difficult to acquire in a non-destructive approach. In this context, this study investigates an approach to enable a transition from preventive maintenance activities, that are scheduled at predetermined time intervals, into predictive ones. In order to enable such approaches in a cyber-physical production system, a deep learning algorithm is used, allowing for maintenance activities to be planned according to the actual operational status of the machine and not in advance. An autoencoder-based methodology is employed for classifying real-world machine and sensor data, into a set of condition-related labels. Real-world data collected from manufacturing operations are used for training and testing a prototype implementation of Long Short-Term Memory autoencoders for estimating the remaining useful life of the monitored equipment. Finally, the proposed approach is evaluated in a use case related to a steel industry production process.

Facility management is an evolving discipline that has received attention from both professionals and researchers in recent years. Modern facility management considers various interests related to material resources, and among others, social and environmental interests. An important opportunity for the improvement of this discipline derives from the introduction of Industry 4.0 technologies for the management of material resources. In this paper, we shall study the problem of industrial facility management, an area with important economic implications. Starting from a facility management model for the maintenance of industrial assets, we develop a general approach to maintenance based on the Internet of Things and Cyber-Physical Systems, which allows us to reason about the implementation of an effective Organisational Facility Management Unit. The objective is the continuous improvement of maintenance activities, from which also derives the improvement of the production process performance.

The subject of research in the article is production management processes based on cyber-physical systems. The purpose of the work is to automate the management of production processes using the cybernetic component and analyze the results. To achieve this goal, it is necessary to solve the following tasks: develop an analytical and logical model of the CPPS development management process taking into account the requirements specified in the terms of reference for the development CPPS; to present the analytical and logical structure of connections in the form of a model of interaction of the main windows and all the necessary graphical elements for the implementation of the full-featured interface of the developed CPPS; to carry out experimental researches of efficiency and practical approbation of the received theoretical results, by comparison of classical methods and the developed ones. Results: The targets, physical and cybernetic components of the CPPS development management process were combined in a single information space from the beginning of development to its implementation. The process of checking the achievement of the main goal of CPPS development has been automated, which makes it possible to make changes and manage the CPPS development process at any level and stage of the proposed technology. The process of managing the development of the cybernetic component is automated on the basis of synthesized algorithms of functioning using the GUI elements of object-oriented programming languages, which made it possible to increase the flexibility of the proposed architecture and technologies for automating the CPPS development process and to reduce the development time of the cybernetic component. Conclusions: the proposed models and methods will not only automate the process of managing the development of CPPS from scratch, but will also make it possible to automate the process of managing the development of the cybernetic component for the modernization and improvement of existing CPPS.

International audience; This paper presents our early-stage research on a Multi-Paradigm Modeling (MPM) approach as an initial step towards the definition of a Digital Twin (DT) for Cyber-Physical Production Systems (CPPSs). This work takes place in the context of the digitalization of the mail sorting process at La Poste, the French national postal service company. Indeed, La Poste is currently investing on robotics modules for automatically loading mail containers. The main objective is to reduce the painful work for human operators while optimizing the robots usage. We already worked on targeting such a balance in a past effort that resulted in the production of different kinds of models of the La Poste CPPS. However, these models were defined separately and are not directly related to the underlying business process in particular. Thus, we propose an MPM approach starting from this business process as now modeled explicitly in a BPMN model. Then, we refine the high-level business activities into finer-grained activities represented in a UML Activity model. From these latest, we derive the specification of a Multi-Agent System (MAS) developed with the JADE framework and emulating the behavior of the La Poste CPPS. Our longer term objective is to pave the way for supporting the definition of a DT for this CPPS, and potentially for other CPPSs in different contexts in the future.

The implementation of Industry 4.0 and smart factory concepts changes the ways of manufacturing and production and requires the combination and interaction of different technologies and systems. The need for rapid implementation is steadily increasing as customers demand individualized products which are only possible if the production unit is smart and flexible. However, an existing factory cannot be transformed easily into a smart factory, especially not during operational mode. Therefore, designers and engineers require solutions which help to simulate the aspired change beforehand, thus running realistic pre-tests without disturbing operations and production. New product lines may also be tested beforehand. Data and the deduced knowledge are key factors of the said transformation. One idea for simulation is applying artificial intelligence, in this case the method of multi-agent-systems (MAS), to simulate the inter-dependencies of different production units based on individually configured orders. Once the smart factory is running additional machine learning methods for feedback data of the different machine units may be applied for generating knowledge for improvement of processes and decision making. This paper describes the necessary interaction of manufacturing and knowledge-based solutions before showing an MAS use case implementation of a production line using Anylogic.

There is a considerable amount of interest in Industry 4.0, the so-called 4fh industrial revolution, however, the concept is not clear in the literature. This research by performing a literature review on Industry 4.0, aims to present an overview of the several industrial revolutions with emphasis on Industry 4.0 and its underlined dimensions. Industry 4.0 is characterized by the advanced digitalization and integration of industrial manufacturing and logistics processes, and the use of internet and “smart” objects (machines and products) and merging the physical and the virtual worlds by the adoption of information and communications technology (ICT). Industry 4.0 fosters novel human and production organization systems and new organizational business models, impacting the overall value chain, society and the environment. Contributions for such new business models that can support Industry 4.0 are proposed with envisioned potential benefits such as shorter operations cycle times, quick delivery times, faster time to market of new products and services, improved quality, and product/service customization, stronger consumer involvement and loyalty. Industry 4.0 can help organizations to address new and emerging markets by a differentiation strategy, or even create new disruptive business models. However, it is still in the early stages for most companies and the digital transformation will require a strong leadership, the right human competences and to overcome several barriers, for its successful implementation. And while this will lead to a significant improvement in job creation, there will be also considerable job losses for Employees with low skill levels. Considering that in 2015, only 14% of Small and Medium Enterprises were using internet channel and 40% of the European Union companies still had not adopted any of the new advanced digital technologies, there is a great need to further research Industry 4.0 drivers and success factors.

The main feature of cyber-physical production systems is its adaptability. They adapt quickly to new requirements such as new products or product variants. Nowadays, a bottleneck is the automation system, for which high manual engineering efforts are needed: Today, on-site technicians write and rewrite automation software, configure real-time communication protocols and create system configurations consisting of machine timing, physical dimensions of products, sensitivity, and motor control accelerations and velocities. Cyber-physical production systems often solve this dilemma by relying on reusable software components, which are composed in the overall automation software. However, this solution comes with a price, reusable software components need free parameters to adjust to the individual production configurations. This paper addresses this central research question and presents a novel parameter estimation approach to choose automatically optimal system configurations for cyber-physical production systems. Different scenarios from discrete manufacturing plants are used to evaluate the solution approach.

Industrial environments are heterogeneous systems that create challenges of interoperability limiting the development of systems capable of working collaboratively from the point of view of machines and software. Additionally, environmental issues related to manufacturing systems have emerged during the last decades, related to sustainability problems faced in the world. Thus, the proposed work aims to present an interoperable solution based on events to reduce the complexity of integration, while creating energetic profiles for the machines to allow the optimization of their energy consumption. A publish/subscribe-based architecture is proposed, where the instantiation is based on Apache Kafka. The proposed solution was implemented in two robotic cells in the automotive industry, constituted by different hardware, which allowed testing the integration of different components. The energy consumption data was then sent to a Postgres database where a graphical interface allowed the operator to monitor the performance of each cell regarding energy consumption. The results are promising due to the system’s ability to integrate tools from different vendors and different technologies. Furthermore, it allows the possibility to use these developments to deliver more sustainable systems using more advanced solutions, such as production scheduling, to reduce energy consumption.

Part 1: Lean Production; International audience; Lean Manufacturing and Industry 4.0 are at times portrait as conflicting paradigms. However, we take the stance that they are two sides of the same coin, and should be considered as mutually beneficial. Based on this understanding, this paper is part of a series where we discuss established Lean practices in the emerging Digital Lean Manufacturing World. In this paper, we specifically focus on the issue of “buffer waste”, and what that implies within a cyber-physical production system. We discuss the vicious cycle of Mura, Muri, and Muda, and provide observed examples in industry for “buffer waste” from four different, yet interdependent perspectives: (i) physical to physical, (ii) physical to digital, (iii) digital to physical, and (iv) digital to digital. The results of this study confirm that “buffer waste” is indeed an issue that deserves our attention as academics and practitioners in the emerging Digital Lean Manufacturing environment.

Part 1: Lean Production; International audience; In this paper, the authors present practical insights in order to propose a Quality Management Framework for Digital Lean Manufacturing. The rationale behind this proposed framework is that the individual ISO 9000:2015 Quality Management pillars are rather different when it comes to their objectives and their tasks at hand when using Human capabilities and/or Digital technologies capabilities in a strategic way for (i) Quality Planning, (ii) Quality Control, (iii) Quality Assurance, and (iv) Quality Improvement in the context of the emerging paradigm of Digital Lean Manufacturing Systems.

peer-reviewed One of the key enabling technologies for the smart factory is the Digital Twin, which can be described as a digital model of a physical entity. In smart manufacturing, a digital twin, also referred to as a cyber-physical model of a physical entity. In smart manufacturing, a digital twin, also referred to as a cyber-physical production system, comprises three integrated components: the product, the process and the machine. In this paper, the design, fabrication and development of a proof of principal manufacturing digital twin demonstrator is discussed. The manufacturing process is a V-bending operation of a thin metallic plate. All three components of a manufacturing digital twin are represented: the metallic plate product, the bending process and the bending machine. Low-cost, IoTenabled, open-source microcontrollers are used to communicate between the physical and the digital twins. The microcontroller is used to control machine operations, while also extracting sensor data from the system. It is demonstrated that the digital twin enables real-time stress prediction in the product during the bending process, while also tracking and recording machine performance. An information dashboard has also been developed which presents key performance indicators to the end user.

International audience; The aim of this article is to overview the transition of smart logistic companies towards the Industry 4.0. Industry 4.0 is a generic concept that subscribes to the idea of a general awareness of the importance of new technologies for the manufacturing industry. This reflection aims to conserve and develop a strong and innovative industrial activity to support the modernization of different fields. In the case of high volumes service companies, many reasons have led to this modernization; remarkable increase in the volume of items to deliver, complexity of managing the transportation system and the personnel scheduling, humanization of work conditions, etc. In this work, The French postal service company, La Poste, is given as a particular case of study. La Poste daily treats high volumes of mail and parcel items. In this article, the organization of the French postal service system from the collect to the delivery is explicitly described. A literature review of different concerned research axes is presented. Finally, we present what we think are the future needs for researches in these axes.

Biological collective systems have been an important source of inspiration for the design of production systems, due to their intrinsic characteristics. In this sense, several high level engineering design principles have been distilled and proposed on a wide number of reference system architectures for production systems. However, the application of bio-inspired concepts is often lost due to design and implementation choices or are simply used as heuristic approaches that solve specific hard optimization problems. This paper proposes a bio-inspired reference architecture for production systems, focused on highly dynamic environments, denominated BIO-inspired Self-Organising Architecture for Manufacturing (BIOSOARM). BIOSOARM aims to strictly adhere to bio-inspired principles. For this purpose, both shopfloor components and product parts are individualized and extended into the virtual environment as fully decoupled autonomous entities, where they interact and cooperate towards the emergence of a self-organising behaviour that leads to the emergence of the necessary production flows. BIOSOARM therefore introduces a fundamentally novel approach to production that decouples the system’s operation from eventual changes, uncertainty or even critical failures, while simultaneously ensures the performance levels and simplifies the deployment and reconfiguration procedures. BIOSOARM was tested into both flow-line and “job shop”-like scenarios to prove its applicability, robustness and performance, both under normal and highly dynamic conditions.

Rapid development in ICT and production engineering and the subsequent joining of both fields result currently in the creation of cyber-physical production systems. With these new technologies innovative didactic concepts are required to cope with new arising tasks on the job and in the development of such systems. Learning environments have proven to be effective instruments for developing competence in manufacturing training and education. To enable development of competence on cyber-physical production processes and systems a design approach for implementation in learning environments is presented. A case study illustrates the proposed implementation framework in a real learning factory.

This study aims to investigate how the use of cyber-physical production systems (CPPS) in small manufacturing firms can facilitate the implementation of the activity-based costing (ABC) method in calculating production costs, thereby increasing the reliability of costing information for decision-making purposes. The collaboration between the research team and managers allowed an in-depth knowledge of the manufacturing problems at the investigated firm, facilitating the analysis of the manufacturing process through both a costing and a managerial perspective. Findings show that the integration of CPPS investments with the ABC method, through real-time measurement of resources by sensors, apps and part programs, leads to useful information about operations, allowing firms to improve performance and reduce resource consumption. As a result, a model of integration between the factory control system, communication network and production system is proposed.

Ensuring uninterrupted interaction of modular industrial equipment units is one of the most important engineering tasks. The concept of Cyber-Physical Production Systems (CPPS) assumes that the distributed network should correspond to the current industrial process and be able to quickly reorganize it when changes occur. If composition of the equipment becomes more complicated, a standard topology with one central control node might get ineffective. This article describes the application of mesh-network technology to ensure the interaction of industrial devices and sensors included in the modular equipment. Virtual deployment of the network and a description network nodes interaction including new node registration in the dispatcher registry are given.

To fast react to changing market demands and customer requirements, Industry 4.0 envisions the digitalization of the manufacturing sector. By means of smart devices and intelligent technologies for a distributed automation control, one of the main goals of the Industry 4.0 is to enhance flexibility and reconfigurability of the production systems. In this regard, the EU H2020 FAR-EDGE project intends to support industries in their digital transformation by providing them with an open platform for factory automation based on edge computing and cyber-physical systems. Therefore, one of the key challenges is to identify adequate migration paths that take into account the digitalization process not only from a technical point of view but also considering the related operational and human impact aspects on existing production systems. The objective of this paper is to provide an overview of the holistic approach developed in FAR-EDGE to define migration strategies towards digital manufacturing automation through the implementation of FAR-EDGE technology solutions.

Lean production emerged as an alternative way of organizing and managing manufacturing operations in the 1990s, following the close examination and promotion of the Toyota Production System as a better way of working. More recently, the advent of Industry 4.0 and its associated Cyber-Physical Production Systems has materialised novel ways of optimizing production operations. Though it is generally agreed that manufacturers should not neglect one of the aforementioned approaches in favour of the other, there remains uncertainty as to how Industry 4.0 technologies should be integrated with existing lean production programmes. This paper presents an overview of both approaches, and provides an exploratory study that examines the integration of Lean and Industry 4.0 in practice. This is a post-peer-review, pre-copyedit version of an article published in IFIP Advances in Information and Communication Technology Locked until 25.8.2019 due to copyright restrictions. The final authenticated version is available online at: https://doi.org/10.1007/978-3-319-99707-0_44

Often deviations occur in the execution of a production schedule because prediction of productivity is unrealistic. Therefore, researchers have shown huge interest in understanding and modelling productivity factors to consider them in planning and design of manufacturing systems. In contrast, this paper examines how productivity can be considered in online-scheduling using past and real-time data and which effect this has on the overall system performance. The discrete event simulation exemplarily considering human productivity factors shows promising results but also the need for more complex forecasting methods Future work will also consider other factors such as tool wear and disturbances.

Industrial competitiveness today means shorter product lifecycles, increased product variety and shorter time-to-market. To face this challenge, the manufacturing industry is forced by different initiatives, namely Internet of Things, Industrie 4.0 and Cyber-Physical System to move from traditional control approaches towards intelligent manufacturing control systems that are dynamically adaptable to changing production environment and flexible to different processing tasks. For years several emergent intelligent approaches such as Multi-Agent Systems, Service-oriented Architecture, Plug-and-Produce systems and Cloud technologies have been developed in a variety of research fields. This paper reviews the state-of-the-art methodologies and technologies of flexible and reconfigurable manufacturing systems outlining the differences between them and arising benefits within the vision of the PERFoRM (Production harmonizEd Reconfiguration of Flexible Robots and Machinery) project. Current trends and challenges in industrial implementation of these methodologies are reported and research opportunities are described.

The increasing complexity and dynamics of cyber-physical production systems (CPPS) lead to a high vulnerability to disturbances during production processes. In the event of process disruptions, decisions must be made in short time in order to minimize the impact on production systems. In this paper, a simulation-based decision support for the disruption management process in a resilient cyber-physical production system is presented. Scenarios for disruption events and response strategies are modeled and simulated. The simulation results for each disruption event scenario are evaluated and the best possible strategy is recommended to the decision-maker including the expected impact on production processes.

Production technology in European steel industry has reached such a level, that significant improvements can only be reached by through process optimization strategies instead of separately improving each process step. Therefore, the connection of suitable technological models to describe process and product behavior, methods to find solutions for typical multi-criterial decisions, and a strong communication between involved plants is necessary. In this work, a virtual simulation platform for the design of cyber-physical production optimization systems for long production facilities focusing on thermal evolution and related material quality is presented. Models for describing physical processes, computers, software and networks as well as methods and algorithms for through process optimization were implemented and merged into a new and comprehensive model-based software architecture. Object-oriented languages are addressed and used because they provide modularity, a high-level of abstraction and constructs that allow direct implementation and usage of the cyber-physical production systems concepts. Simulation results show how the proper connection between models, communication, and optimization methods allows feasibility, safety and benefits of cyber-physical production systems to be established. Furthermore, the software architecture is flexible and general and thus, can be transferred to any steel production line as well as outside the steel industry.