Dario Antonelli
email: dario.antonelli@polito.it

Giulia Bruno
email: giulia.bruno@polito.it

Ontology-based Framework to Design a Collaborative Human-Robotic Workcell

Keywords: Collaborative robots, cyber physical systems, ontology

Exploiting the collaboration between human and robots is a fundamental target for industrial Cyber-Physical Systems. Several studies have already addressed the evaluation of collaborative robotic cells, especially in automotive industry. Feasible tasks assignment to workers and robots were proposed in a few use-cases. However, previous studies start from an existing configuration of the collaborative assembly cell. Due to the moderate diffusion of collaborative robotic applications in the industry, it would be better to define a method orienting the design of a new instances of collaborative cells, by taking into account the different classifications of collaboration deriving by the new ISO 15066 standard. The classification depends on the kind of information that must be made available within the cell, and the possible methods of acquisition and communication of such information. This knowledge base will be represented in the form of ontology, as an extension of the CORA (Core Ontologies for Robotics and Automation) ontology, by IEEE Robotics and Automation Society. By adopting this ontology, it will be possible to support the design of new collaborative cell. An industrial case-study will prove the efficacy of the proposed method.

Introduction

The design of collaborative environments where robots can work side by side with human operators for the execution of complex industrial tasks is having an increasing interest from both academics and robot manufacturers. In a survey on innovative flexible approaches to automotive assembly technologies, considerable importance is given to cooperation among humans and robots [START_REF] Michalos | Automotive assembly technologies review: challenges and outlook for a flexible and adaptive approach[END_REF]. For example, some complex assembly procedures require both precise handling and other assembly operations, such as inserting fasteners and connecting wire harnesses. Some of these tasks require the precision and speed of automation, while others benefit from the dexterity and intelligence of human operators.

Human-Robot Collaboration (HRC) brings benefits to industrial applications in terms of speed, efficiency, better quality of the production and better quality of the workplace (ergonomic) [START_REF] Helms | rob@ work: Robot assistant in industrial environments[END_REF]. However, as far as now, robotic automation was rarely applied in small batch productions, due to the variety of products and of variable production schedules [START_REF] Antonelli | Applicability of human-robot collaboration to small batch production[END_REF]. The workplace organization leans towards flexibility where manual systems are advantaged. Now, several robot manufacturers are designing special robot architectures, named collaborative robots, allowing the human workers to execute their tasks in the same workplace as the robots, e.g., KUKA LBR [START_REF] Bischoff | The KUKA-DLR Lightweight Robot arm-a new reference platform for robotics research and manufacturing[END_REF] and ABB YuMi [START_REF] Kirschner | YuMi, Come and Play with Me! A Collaborative Robot for Piecing Together a Tangram Puzzle[END_REF]. However, these robots have limitations in terms of payload, velocity, and strength that prevent their use in several industrial contexts. Some of these limits are related to the necessity of respecting the Technical Specifications ISO 15066 in order to assure human worker safety.

Differently from such architectures, present study focuses on the subset of HRC that exploits standard industrial robots and allows for a safe interaction with the robot, paying a reasonable fee in reduced flexibility. The field of study is the industrial manual and automatic assembly by welding. Small factories usually already have robotic welding cells next to their manual workstations. The aim of this work is to show that it is possible to redesign existing industrial robotic cells for executing a number of collaborative actions, respecting safety requirements.

The rest of the paper is organized as follows. Section 2 revises the state of the art in literature. Section 3 describes the two ontologies necessary in this study, used to classify the kind of information that must be made available within the cell, and the possible methods of acquisition and communication of such information. In Section 4, the design and setup of a HRC working cell is described, specifically for an industrial use case. Finally Section 5 draws conclusions and states future works.

Related Works

The collaborative execution of manufacturing tasks between humans and robots aims to improve the efficiency of industrial processes, for an high adaptability and robustness of the cell Several works are present in literature that address the evaluation of robotic cells, especially in automotive industry [START_REF] Papakostas | Industrial applications with cooperating robots for the flexible assembly[END_REF][START_REF] Pedrocchi | Safe human-robot cooperation in an industrial environment[END_REF].

Some efforts have been devoted to investigate the safety of human-robot collaboration [START_REF] Harper | Towards the development of international safety standards for human robot interaction[END_REF][START_REF] Matthias | Safety of collaborative industrial robots: Certification possibilities for a collaborative assembly robot concept[END_REF], and also the problem of define the optimal assignment of tasks between workers and robots was studied in some use-cases [START_REF] Ding | Optimized task distribution for industrial assembly in mixed human-robot environments -case study on IO module assembly[END_REF][START_REF] Tan | Extending task analysis in HTA to model man-machine collaboration in cell production[END_REF]. Other problems investigated in literature address the psychological acceptation of the robot as a reliable team member [START_REF] Hinds | Whose job is it anyway? A study of human-robot interaction in a collaborative task[END_REF][START_REF] Freedy | Measurement of trust in human-robot collaboration[END_REF].

Some studies are related to the reduction of programming time of robots by exploiting Programming by Demonstration or Learning by Demonstration techniques [START_REF] Argall | A survey of robot learning from demonstration[END_REF][START_REF] Antonelli | Qualification of a collaborative human-robot welding cell[END_REF][START_REF] Antonelli | FREE: Flexible and Safe Interactive Human-Robot Environment for Small Batch Exacting Applications[END_REF]. Even if several human capabilities cannot be fully replaced by robots, it is possible to achieve a solution by combining the capabilities of both. Moving the collaborative robot from laboratory demonstrations to current production cells arises new orders of issues that have not been adequately considered in the past.

What's missing is a solid methodology to configure a HRC cell, based on the manufacturing process under consideration and the kind of collaboration to implement.

HRC Ontologies

Ontologies plays a fundamental role in knowledge management because they formally specifies the key concepts, properties, relationships, and axioms of a given domain [START_REF] Bruno | Exploitation of a Semantic Platform to Store and Reuse PLM Knowledge[END_REF].Two ontologies are necessary in this study. The first one, named CCORA, is an extension of a standard ontology for robotics and automation (CORA) by adding concepts related to collaborative cells. The second one, named MPRO, is a manufacturing process ontology, which contains concepts related to manufacturing processes and related machines, tools and parameters. The relationships between CCORA and MPRO with the other existing ontologies in the domain is represented in Fig. 1. The Ontologies for Robotics and Automation Working Group (ORA WG) defined a core ontology for robotics and automation (CORA), which specifies the general concepts in this context [START_REF] Prestes | Towards a core ontology for robotics and automation[END_REF]. CORA extends SUMO, the Suggested Upper Merged Ontology, which is an open source upper ontology, widely used in several domains [START_REF] Niles | Toward a Standard Upper Ontology[END_REF]. Between CORA and SUMO, there is the CORAX ontology, which represents concepts and relations commonly found in subdomains but that are too general to be included in CORA [START_REF] Fiorini | Extensions to the core ontology for robotics and automation[END_REF]. We further extended the CORA ontology in the Collaborative CORA (CCORA) by inserting the concepts related to human-robot collaboration. CCORA inherits from both CORA and SUMO, because some CCORA entities are specifications of more general SUMO concepts. The MPRO ontology cover the knowledge related to manufacturing operations, thus is inherits directly from the SUMO concepts.

Collaborative CORA (CCORA) Ontology

The most general SUMO category is Entity, given as a disjoint partition of Physical and Abstract entities. The first one represents entities with spatio-temporal extension, while the second one is for entities that do not need have spatio-temporal extension.

Both entities are further specialized: Physical into Object and Process, while Abstract into Quantity, Attribute, SetOrClass, Relation and Proposition. SUMO has a total of more than 500 entities, covering a broad spectrum of concepts.

The CORAX ontology defines concepts that are too general to be in CORA, which cover aspects of reality that are necessary for modelling, but are not explicitly or completely covered by SUMO. Examples of CORAX entities are physical environment, interaction, artificial system, processing device, robot motion, human robot communication, robot robot communication. We further extended the CORA ontology in the Collaborative CORA (CCORA) by inserting the concepts related to human-robot collaboration, as shown in Fig. 3. We defined a collaborative robotic system an entity formed by robots, human workers, and a series of devices. Particularly, four kinds of devices are need: observing devices, pointing devices, gripping devices and holding devices. A collaborative robotic environment, i.e., a collaborative cell, is an environment equipped with a collaborative robotic system.

Three types of collaborative environments exist, the spatial collaboration environment, the temporal collaboration environment, and the spatiotemporal coloration environment. Depending on the kind of collaborative environment, different devices are needed. For example, in the case of a spatial collaboration, where humans and robots work in the same space but in different times, observing devices are mandatory (e.g., laser scanner) to reveal the human presence, to grant that the human worker is not in the cell while the robot is working.

CCORA also extends the SUMO Process with the transport processes, which are not covered by the MPRO ontology.

Manufacturing Process (MPRO) Ontology

The MPRO ontology cover the knowledge related to manufacturing operations. The main classes of the MPRO ontology are Manufacturing process, Machine, Tool and Parameter [START_REF] Bruno | Semantic organization of product lifecycle information through a modular ontology[END_REF]. A portion of the MPRO ontology related to the SUMO ontology is shown in Fig. 4.

Assembly operations are further divided in Permanent joining processes and Mechanical fastening. Permanent joining processes are Welding, Brazing and soldering, and Adhesive bonding. Welding processes are further divided between Fusion welding and Solid state welding. To Fusion welding belong Arc Welding, Resistance Welding, Oxyfuel gas welding, and others.

Tools are the elements that are used during processes or directly perform the work on the workpiece. For example, Welding tools are used in different welding processes and include Welding molds, Solid state tools, and Fusion welding tools. Fusion welding tools are mostly related with Electrodes, which can be Consumables, if they are consumed during the process or Non-consumables. Consumables electrodes are Coated electrodes, Electrogas welding electrode, Flux cored electrodes, Wire electrodes, and GMAW guns. The Parameter class represents the parameters of the manufacturing processes that have to be stored, such as the speed of the drill, the temperature, the water pressure, etc.

Ontology-based HRC Cell Design

The considered industrial process is the assembly of a two-stage cutter (shown in Fig. 5 left), which is used to shred snow and send it to the rear turbine, which throws it out from the roadway. This process was originally executed manually by two operators, but the company wanted to evaluate the introduction of a robot to perform a subset of tasks. By mapping the industrial process on the MPRO ontology, we were able to define the processes and the corresponding tools that are needed in the cell, i.e., a welding torch for the arc welding and a grinder for the grinding process. These elements were inserted as instances of the corresponding entities in the ontology.

Assuming a task assignment was done (the assignment method is out of the scope of this work), by analysing the tasks assigned to the robot, and knowing that the kind of collaboration of interest in this case is a spatial collaboration, it was possible to exploit the CCORA ontology to select among the vast amount of possible kinds of robots, devices, etc., the set of elements needed in the cell. The following entities were considered: Industrial Robot, Observing device, Gripping device, and Holding device. Among the available industrial robots, the selection was for a KUKA robot KR 300 R2500 ultra C, due to the load and distance capacity. The holding devices are a tool plan to allow the robot executing the operations, a rotating platform so that the robot can easily reach all the positions required for the operations, and a component storage, where the final components are arranged according to a precise order in such a way that the robot can identify and pick them. The gripping device is a blocking gripper, so that the robot can move disks and spiders. The Observing devices are two laser scanners, one for verifying the correct positioning of tools and items, and another one to identify the human worker position. The resulting ontology corresponding to the HRC of such process is shown in Fig. 5 (right).

Conclusion

The paper proposed a method to support the design of a human-robotic cell based on the industrial process of interest and the kind of HRC needed. Both the knowledge related to the machines and tools needed for the manufacturing process and the relationships between each kind of collaborative environment and the devices needed in the corresponding cell are stored in the ontology. From this knowledge base it is possible to select the elements needed for the full configuration of the cell.

Fig. 1 .

 1 Fig. 1. Relationships between the two defined ontologies (CCORA and MPRO) with respect to previous ontologies SUMO, CORA and CORAX.

Fig. 2 .

 2 Fig. 2. Relationships among concepts of SUMO, CORAX and CORA. CORA focuses on defining a robot, along with the specification of other related entities. The entities defined in the CORA ontology are Robot part, Robot interface, Robot group, Robotic system (further divided into Single Robotic System and Collective Robotic System) and Robotic environment. The relationships among concepts of SUMO, CORAX and CORA are represented in Fig.2.We further extended the CORA ontology in the Collaborative CORA (CCORA) by inserting the concepts related to human-robot collaboration, as shown in Fig.3. We defined a collaborative robotic system an entity formed by robots, human workers, and a series of devices. Particularly, four kinds of devices are need: observing devices, pointing devices, gripping devices and holding devices. A collaborative robotic environment, i.e., a collaborative cell, is an environment equipped with a collaborative robotic system.Three types of collaborative environments exist, the spatial collaboration environment, the temporal collaboration environment, and the spatiotemporal coloration environment. Depending on the kind of collaborative environment, different devices are needed. For example, in the case of a spatial collaboration, where humans and robots work in the same space but in different times, observing devices are mandatory (e.g., laser scanner) to reveal the human presence, to grant that the human worker is not in the cell while the robot is working.CCORA also extends the SUMO Process with the transport processes, which are not covered by the MPRO ontology.

Fig. 3 .

 3 Fig. 3. Relationships among concepts of CCORA, SUMO and CORA.

Fig. 4 .

 4 Fig. 4. Relationships between MPRO and SUMO concepts.

Fig. 5 .

 5 Fig. 5. Image of the cutter considered as use case (left) and the developed ontology with instances (right) shown in the Protégé (http://protege.stanford.edu)


Andrea Ballarino
email: andrea.ballarino@itia.cnr.it

Carlo Brondi
email: carlo.brondi@itia.cnr.it

Alessandro Brusaferri
email: alessandro.brusaferri@itia.cnr.it

Guido Chizzoli
email: guido.chizzoli@itia.cnr.it

The CPS and LCA Modelling: An Integrated Approach in the Environmental Sustainability Perspective

Keywords: CPS, LCA, Industrial Sustainability, Modular LCA, Industry 4.0

The present paper clarifies the methodological framework to implement the integration of Cyber Physical Systems (CPS) in the perspective of environmental sustainability. Firstly, authors identify main methodological, software and hardware pre-requisites enabling the use of CPS systems in accordance with the literature and environmental standards. Then, the paper focuses on the main barriers in the case the information is distributed along a flexible supply chain and identifies a methodology to coordinate environmental information from ubiquitous sources by the use of modularization. This methodology is applied in the field of energy management for the steel sector for different production lines. In particular, it is highlighted as the consumption tracking may be linked to the real-time management of suppliers and processing cycles in order to pursue strategies for environmental impact minimization. Results emphasized as the development of the CPS systems that are designed for sustainability can produce relevant results for the effective restrain of industrial environmental impacts.

Application of CPS for Environmental Sustainability

The Cyber Physical Systems (CPS) are identified by many authors as key technology for the development of manufacturing industry in the next future [START_REF] Schuh | Increasing collaboration productivity for sustainable production systems[END_REF] [2] [3] [START_REF] Dumitrache | The Enterprise of Future as a Cyber-Physical System[END_REF]. Such technology in fact introduces a number of significant potential for production systems such as the ability to process a large amount of data in real time, the ability to optimize operations by flexible and automatic solutions, and finally the ability to enhance collaboration between different levels of the factory and suppliers.

In particular, some authors identify the features that are obtained through the CPS systems such as a prodromal condition for the actual sustainability of production systems [START_REF] Waibel | Investigating the effects of Smart Production Systems on sustainability elements[END_REF] [4] [START_REF] Bonci | A database-centric approach for the modeling, simulation and control of cyber-physiscal system in the factory of the future[END_REF] [START_REF] Estevez | Green Cyber-Physical systems[END_REF]. Some authors emphasize also that the development of the CPS systems could lead further barriers to sustainability by massive production of devices and by causing significant social effects on employment market [START_REF] Waibel | Investigating the effects of Smart Production Systems on sustainability elements[END_REF]. Despite such remarks, various beneficial contributions to sustainable manufacturing paradigm can be identified.

In the first place, the support of the CPS systems to Sustainable manufacturing can be performed through new instruments for sustainability policies. The Sustainable Manufacturing in fact requires the implementation of strategic objectives such as zero waste, the benign emissions, the use of renewable resources, the circular economy, the process efficiency, social investment, and the modification of trade criteria goods. Each of these objectives entails increasing complexity, operation intelligence and a growing integration between the operations along the supply chain and the facility [START_REF] Esmaeilian | The evolution and future of manufacturing: A review[END_REF] [8] [START_REF] Haapala | A review of engineering research in sustainable manufacturing[END_REF]. The development of the CPS majorly affects these areas by increasing monitoring, collaboration and efficiency across several areas [START_REF] Schuh | Increasing collaboration productivity for sustainable production systems[END_REF] [START_REF] Waibel | Investigating the effects of Smart Production Systems on sustainability elements[END_REF] while in other cases the CPS constitutes the bulk of new sustainability-oriented technologies [START_REF] Estevez | Green Cyber-Physical systems[END_REF].

The second contribution of CPS regards the building of an infrastructure to ease the calculation of Key Performance Indicators (KPIs) for the sustainability of industrial practices. The environmental impact assessment of production operations, in fact, involves interdisciplinary areas and requires a data-intensive process. Such assessment increases in complexity with the concurrent increase of the production constrains. The development of CPS allows to reduce this convolution in calculation by capturing latent properties of ubiquitous systems. Furthermore, the independent behavior of CPS also enables to manage this exponential complexity in relation to response actions and decisional support [START_REF] Dumitrache | The Enterprise of Future as a Cyber-Physical System[END_REF]. Through such support, CPS makes possible to develop integrated environmental management systems that are constantly connected to the analytical values and environmental accounting and create a continuous stream of verifiable environmental information.

From another point of view, the implementation of CPS and industry paradigm 4.0 introduces a series of constraints that are needed to fully implement this potential capability. The main involved issues seem to concern the information role, its transfer, and the complexity management [START_REF] Bonci | A database-centric approach for the modeling, simulation and control of cyber-physiscal system in the factory of the future[END_REF] as inevitable problems to be treated. Another important aspect is the reliability of data that are collected through sensor networks as well as their reuse [START_REF] Tang | Trustworthiness analysis of sensor data in cyber-physical systems[END_REF] [11] [START_REF] Desmit | An approach to cyber-physical vulnerability assessment for intelligent manufacturing systems[END_REF].

CPS applications for sustainable manufacturing, by literature, include applications for circular economy in the modelling of Industrial Park [START_REF] Pan | Applying Industry 4.0 to the Jurong Island Eco-industrial Park[END_REF], green computing to increase efficiency in energy consumption [1] [14] and other [START_REF] Estevez | Green Cyber-Physical systems[END_REF] applications.

Requirements for the LCA Integration within the CPS Framework

The intended purpose of such work is to sequentially clarify pre-requisites, current barriers and potentialities for LCA integration within CPS. The use of CPS in the environmental perspective, may allow a real time quantification of the environmental impact of a set of productive activities that are subject to a certain variability. From one side, this usage enables most reliable Life Cycle Assessment (LCA) calculation in comparison to traditional ex-post LCAs. According to CPS framework, LCA seems a suitable methodology to be integrated within CPS to quantify environmental implication of a certain activity by virtue of certain characteristics: LCA is designed to provide quantitative data; LCA is directly connected to business areas that monitor the business flows and which provide monitoring data for Life Cycle Inventory (LCI) phase; taking a perspective on the entire life cycle, the LCA methodology manage data that are external to company boundaries; finally, the LCA methodology is well supported in standardization by various ISO standards in the international arena, and then the generation and verification of the environmental information tend to be more congruent between different countries. The expected progression CPS functions is characterized by five hierarchical levels [START_REF] Kao | A Cyber Physical Interface for automation Systems-Methodology and Examples[END_REF], each level corresponds to a stage of CPS functional development. Equally, it is possible to define a progression of functions for real-time analysis of environmental impact that can be linked with the LCA. Each of these layers includes a number of prerequisites regarding the different elements involved, such as the sensors, the data management systems, the simulation methods, and the standardization and optimization criteria (fig. 1).

Fig. 1. Requirements for CPS use in the LCA framework

Smart connection level. At this level, the challenge to create a reliable LCA from distributed data depends on the chance to record data on the environmental aspects and to bring them to a higher level.

Therefore, it is necessary that CPS glean data from a distributed network of data sources regarding direct tracking trough sensors or indirect tracking through Information Systems (IS). Such data should be attributed to local mass and energy balances for a set of activities. The data collection system should track consumption of materials, energy or items as well as solid, liquid or gaseous emissions. The sequence of data collection assumes different trends depending on the characteristics of the flow to be monitored.

As an example, the tracking of a homogeneous gaseous flow over the time will require an instantaneous flow rate measurement system while in the case the composition changes over time an extra measurement device is needed (e.g. a mass spectrograph) in order to identify the chemical composition. The data used by CPS systems is not necessarily derived from field sensors but can also be obtained through IS such as Enterprise Resource Planning (ERP) or Supply Chain Management (SCM) tools. CPS should recognize and capture these data from remote systems through the use of complementary information that must travel together to the data on physical features. In this sense, methodologies such as the semantic web could support the identification of standard protocols for data transmission. The data collection system also influences the temporal trend of its tracking (e.g. solid waste can be monitored on a monthly basis while electric power consumption can be monitored on a shorter basis). It would therefore be important to understand through a preventive analysis which are the critical flows and the related measurement policies.

Another key issue concerns the monitoring cost. The calculation of a time-variant mass/energy balance can be expensive for a company. Therefore, the development of "smart inventory" systems allows to produce more information through a single measure or a measures combination. Such indirect measurement can require inference methods and data combination in order to extrapolate the investigated environmental aspects.

Data-to-information connection level. At this level, the data collected from the field are transformed into meaningful information in environmental terms. As an example, it becomes possible to trace the carbon dioxide produced by a single task.

In terms of LCA this kind of transposition requires that the local impact is defined through a modification of traditional LCA calculation criteria. The Goal and Scope phases, for example, can become reconfigurable at an upper level depending on the end-user needs. The information storage becomes no longer a remote activity, instead the information is continuously available. Such a transformation requires a modularization of environmental information that no longer refers to the entire life cycle but can be considered as congruent assembly of gate-to-gate modules.

The modular approach has already been applied in the manufacturing environment [START_REF] Brondi | A modular Framework for the LCA-Based simulation of production systems[END_REF]. A second important aspect concerns the final assessment quality that must be suitable for a major precision in comparison to traditional LCAs.

Data to be processed should be attributable to the same functional units and should be supplied with standard information to support upper level calculation as allocation and quality assessment. As a fundamental requirement, the application of the LCA modularization requires connecting the material and energy flow monitoring, namely the inventory of the environmental aspects, with the environmental profile of individual flows. Therefore data are associated with database that are external to the physical CPS. The databases roles include both data storing for long-term strategies and active capabilities in supporting CPS to govern the production systems operations [START_REF] Bonci | A database-centric approach for the modeling, simulation and control of cyber-physiscal system in the factory of the future[END_REF]. These stored data, which are updated by a different rate in comparison to physical flow data, requires also an intermediate step for respective association in order to be referred to the same activity (e.g. an absorbed current should be referred both to a certain operation and to an incoming energy flow from a specific supplier).

Cyber level. At this level, it becomes possible to express the aggregate environmental impact and to simulate the LCA-based environmental efficiency of the CPS through the mirroring of the behaviour of a production unit in a virtual environment. The CPS should express a function for instantaneous and predictive impact on the environment for the cumulated activities under its control. Simulation is the essential step to perform such activity. In environmental terms, environmental profile function can be calculated as modular variable environmental profile constituted by the instantaneous value of the different impact categories.

At this level, the CPS also coordinates with other analogous units to exchange information on other activities that have influence on its environmental impact. Also in this case, the communication should be supported by protocols aiming to coordinate and address information. Such aspect concerns the automatization of LCA procedures such as allocation of field measurements and information alignment. The CPS unit should report together with the final data, information on the overall quality of the calculation, complementary information on calculation criteria (allocations, the system boundaries, geography, period sampling etc.). In the absence of these complementary information, the environmental impact function risk to be inferred by incongruent modules leading to a worse result than the traditional LCA. Some examples of protocols for standardization of environmentally-related information has been studied for the UML language for the Manufacturing Execution System (MES) [START_REF] Lee | Capturing Green Information and Mapping with MES Functions for Increasing Manufacturing Sustainability[END_REF] and the Product Life Cycle Management (PLM) [START_REF] Zhao | Design and Implementation of a PLM System for Sustainable Manufacturing[END_REF].

In terms of simulation criteria, different criteria can be linked to LCA. Discrete event simulation [START_REF] Logfren | Relating manufacturing system configuration to life-cycle environmental performance: discrete-event simulation supplemented with LCA[END_REF] [START_REF] Sproedt | A simulation-based decision support for eco-efficiency improvements in production systems[END_REF] has been applied to model the environmental impact for different production scenarios. According to these criteria, modelling of the behaviour of a production unit is separated from its actual field operation, the same impact tends to depend on general purpose database.

The application of a simulation that is connected with the real time operational value can introduce a direct link with industry-driven data within simulation both in term of inventory data and performed assessment. In addition, the introduction of dynamic simulation methods allows to extend the LCA beyond its normal scope of and to create the basis for the application of the optimization criteria.

On the other hand, the creation of a CPS "virtual twin" requires the application of calibration methods. An initial calibration relates to the explicit link between environmental drivers and environmental effects within the simulation model. The LCA methodology is based on "black box" structure while the simulation requires a certain degree of explication for the links between the environmental drivers and their effects according to a logic of "grey box". In this sense, the modularization should be designed in such a way that the information might be cut out in the perspective use for the simulation. A second type of calibration regards the temporal correlation between the field measurements and simulation of an activity. If the sampling of the mass flows and energy are delayed in time with respect to the range of the simulation calculation system may inherit an implicit inaccuracy.

As an example, a certain input value function (e.g. a function for a processing material) may report constant value between two sampling moments while other input values (e.g. compressed air functions) are varying independently from the source behaviour. It appears therefore necessary to partially align the sampling times and simulation times. Finally, the time trend of some driver value that comes from remote sources can be unpredictable. It will be necessary therefore to use regression methods to reduce uncertainty in "time machine" simulation.

Cognition level. At this level data from CPS level are further elaborated in order to provide key performance indicators that are compliant with existing environmental standards. At this level, it is also possible to link CPS data with other sustainability accounting systems (e.g. cost accounting or social accounting) or company accounting system (fig. 2). The existence of misaligned industrial simulation areas can limit the transition from an accounting system to another due to the fact such areas are still not integrated (e.g. product design, process simulation, ergonomics, etc.). Further integration of simulation areas can create a holistic simulation area that affects different decision areas. In such sense, knowledge domain integration performed by ISA 95 and other standards can also foster CPS development.

Fig. 2. Data elaboration in the environmental perspective through different CPS levels

In terms of environmental performance CPSs can pave the way to new generation of Environmental Management Systems (EMS) within companies through the creation of a continuous digitized assessment of environmental information. The modular and flexible structure of environmental data from CPSs should allow to quantify the environmental performance of different business areas as well as of different stages of the product life cycle.

Configuration level. At this last stage, CPS can self-configure itself based on real time LCA outcomes, system constraints and Objective Functions (OF) related to sustainability targets or other targets. The proper development of this function requires that environmental optimization criteria are clearly addressed. LCA can be already applied for process optimization according to an environmental sustainability perspective. The general problem for optimization has a multi-objective nature cause to the distinctive features of the LCA characterization indicators, the key performance representation according to international standards, and the perspective integration of these indicators with other non-environmental performance indicators. In mathematical terms, the branch of the Sustainability Decision Support System includes a range of analytical support methods that are based on artificial intelligence (Expert System-ES, Genetic Algorithm-GA, Artificial Neural, Network-ANN, Intelligent Agents-IA, Case Based Reasoning-CBR), but also other Geographic Information System-GIS-based methodologies, Internet-based services, and Business Intelligence BI. In the presence of different criteria and priorities, effective automatic self-configuration by the CPSs exist in case multi-objective optimization system is transparently and congruently addressed.

Application of CPS to Energy Grids for LCA based Optimization

The potential application of CPS systems has been explored in the case of an energy supply system for a European process industry company. The application of CPS framework was implemented in a smart grid in order to manage real-time data to optimize energy consumption in the sustainability perspective from the point of view of the final consumer industry. These CPSs can exchange real-time information on energy attributes and real-time demand in a coordinate way (fig. 3).

Fig. 3. Application of CPS for Environmental optimization\ of energy grid

The user company has already implemented a system of sensors that can record energy consumption from electrical grid and the CPS system is able to estimate the energy consumed for each work cycle and, prospectively, the energy required for subsequent scheduling through a Time-machine system. User can gain more insight on the system variation and self-adjust consumption according to prediction of everchanging attributes of energy production pattern (costs, environmental impacts etc.).

Similarly, the energy supplier is able to transfer a series of real-time information regarding the average hourly energy price and, at the same time, the LCA-based environmental information impact. Both information are based on regression analysis when CPS simulate the future energy supply. Information on price is based on opensource database for energy market while the Environmental impact is based on combined information by the national energy authority on the actual allocation between renewable energy and fossil-based energy production. The system thus incorporates the ideal features for the implementation of CPS systems since the monitoring of environmental aspects (energy consumption) and energy consumptions present performance that are time dependent. In this case, the CPS allows to represent the environmental performance through equation 1:

𝑰 𝑤 𝑡 + 1 = 𝐴 • 𝒌 𝑡 * 𝑡+1 𝑡 𝑤(𝑡) (1)
In such equation, I represents the energy performance indicator at a given moment t+1 for a number of monitored operations, k(t) represents the vector of unit flow performance, w(t) represents the instantaneous absorbed power by the company at the moment t for the monitored operations while A is a normalization coefficient.

The variable k(t) depends on the monitoring of the CPS on energy network conditions and is partly based on the LCA databases support, while w(t) depends on the instantaneous monitoring of consumption within the company through sensor system.

𝒌 𝒆 𝑡 = 𝒌 𝒔 (𝑡) • 𝑝 𝑠 (𝑡) 𝑃 𝑡𝑜𝑡 (𝑡) 𝑆 𝑠=1 (2)
In equation 2, ke(t) represent the environmental impact vector for the supply of energy at time t from the network. ks(t) corresponds to the unitary energy impact vector for a given energy source s for the total of S energy production technologies (e.g. carbon dioxide equivalent for one kwh that is produced by solar) while ps and Ptot represent respectively the provided energy power on the energy grid at instant t by using the energy generation technology s and the total energy power available on the grid.

Fig. 4. Daily regression energy analysis according to CPS real-time monitoring system

The objective function to be optimized in this case coincides with a multi-objective function that concurrently minimizes Iw as a set of indicators. Outcome for such applications regards the identification of time spans for focusing production in correspondence of minimal energy cost or minimal environmental impact. Sustainability profile resulting from operations arrangement of the examined factory can be improved simply by introducing time constrains to single operations. Open issues still remain in identifying methodologies for concurrent optimization of cost and environment that are not based on arbitrary weighting of such categories. At the same time, the formalization of production constrains in the optimization model could better emphasize margins for green manufacturing policies.

Conclusion and Remarks

The implementation of CPS can have positive and remarkable effect for the implementation of Sustainable Manufacturing policies. In particular, the implementation of LCA tools can introduce a quantitative basis to provide effective environmental improvements in virtue of its intrinsic characteristic. The proper implementation of CPS systems integrating LCA features can concurrently provide real-time management systems integrating factory and supply chain level, efficient EMS and provide effective tools to adapt operational management to high-level sustainability targets. Such progresses introduce the possibility of intelligent production unit that are able to adjust and record their environmental impact. CPS can in fact include the ubiquity of information in the production chain and manage the complexity of the criteria to calculate the environmental impact. However, its implementation poses a number of challenges and constrains to industry automation at semantic, physical, operational and decisional levels. In particular, protocol development for data integration appears as an indispensable key area to foster CPS application for eco-efficiency. Apparently, the use of CPS seems to imply major effects in presence of certain operational characteristics as the congruency between variance of different operational drivers, the cost and availability of field data and margins for company to address such indications within decisional areas. According to the presented case, the application of CPS to smart energy grids seems to meet all these features. Improvements in real-time and foresight LCA assessments introduce measurable improvements on specific sustainability target. Perspective work can be oriented on the further formalization of production constrains as well as to reliability of regression analysis by a comparison of an ex-ante optimization policies and ex-post gate-to-gate analysis of effective environmental impacts. In parallel, further research on multi-criteria optimization can enforce CPS intelligence for such purpose.


Ben Luetkehoff

Matthias Blum

Moritz Schroeter

Self-Learning Production Control using Algorithms of Artificial Intelligence

Keywords: production control, self-learning algorithms, data analytics

Manufacturing companies are facing an increasingly turbulent market -a market defined by products growing in complexity and shrinking product life cycles. This leads to a boost in planning complexity accompanied by higher error sensitivity. In practice, IT systems and sensors integrated into the shop floor in the context of Industry 4.0 are used to deal with these challenges. However, while existing research provides solutions in the field of pattern recognition or recommended actions, a combination of the two approaches is neglected. This leads to an overwhelming amount of data without contributing to an improvement of processes. To address this problem, this study presents a new platform-based concept to collect and analyze the high-resolution data with the use of self-learning algorithms. Herby, patterns can be identified and reproduced, allowing an exact prediction of the future system behavior. Artificial intelligence maximizes the automation of the reduction and compensation of disruptive factors.

Introduction

Driven by the Internet of Things and countless initiatives in the context of Industry 4.0, the shop floor level is steeped by IT. This poses the question as to which extent these approaches can be used to reduce and control the increasing production complexity. The collection of real-time data and its interpretation with data analytics are supposed to enable improved planning decisions. The research project "Intelligent Production Control" (iProd) takes exactly this point into account: technologically and theoretically the progress towards completion, machine condition and the current flow of material can be captured by installing sensor systems and identification technology. The difficulty lies in, on the one hand, the connection of heterogeneous data to form an overall picture to generate recommended actions [START_REF] Kropp | Entwicklung eines Ereignismodells als Grundlage der Produktionsregelung[END_REF][START_REF] Schuh | Design of a data structure for the order processing as a basis for data analytics methods[END_REF]. On the other hand, the reaction speed of implemented planning and control solutions is limited by the detection and evaluation of deviations by the employee [START_REF] Meier | Echtzeitfähige Produktionsplanung und -regelung in der Auftragsabwicklung des Maschinen-und Anlagenbaus[END_REF]. Moreover, current algorithms are based on inflexible heuristic optimizations and static analyses. Even though, simulation can be based on this kind of information, the reality is still far away from a self-learning production control that, to a certain degree, compensates deviations and disturbances independently [START_REF] Hamann | Lernfähige intelligente Produktionsregelung[END_REF][START_REF] Hauptvogel | Bewertung und Gestaltung von cyber-physischer Feinplanung, 1st edn. Produktionssystematik[END_REF]. Therefore, the goal of this project is the application of existing technologies, architectures and algorithms from the field of artificial intelligence to the problems of production, so that the open loop production control is expanded to an advanced closed loop production control.

Before the project is described in detail, the motivation of this paper is outlined and a short outline of the necessary basics and state of the art is given. Additionally, the requirements for an intelligent closed loop production control are presented. Afterwards, the concept itself is introduced and discussed before a conclusion is given and necessary further research is outline

Motivation

Data analytics is a scientific process of mathematical-logical transformation of data to improve decision making [START_REF]Towards agile BI: applying in-memory technology to data warehouse architectures[END_REF]. Four stages of the decision-making process exist: descriptive, diagnostic, predictive and prescriptive analytics. The decision making process assisted by data analytics can be described as a four-stage model. The first stage is called descriptive analytics and deals with the question "What happened?". This stage aims at analyzing large amounts of data in order to find out what happened in the past. The next stage, called diagnostic analytics, deals with the question "Why did it happen?" by analyzing interactions in the first stage. The third and fourth stage also allow proactive optimization. Predictive analytics focusses on the question "What will happen?" and predicts future behavior by applying methods of pattern recognition as well as the use of statistics. Prescriptive analytics focusses on the question "What should I do?". The assistance of data analytics either serves as a support mechanism for the decision process or implements decisions autonomously. [START_REF] Stich | Daten und Software als entscheidender Wettbewerbsfaktor. Industrie 4.0 Magazin[END_REF] 3

State of the Art

In this chapter, the basic principles deviation management, information modelling, Artificial Intelligence, machine learning and their application in production planning and control are discussed.

A multitude of software solutions has been developed in the past in order to manage the dynamics of production planning and control with the use of IT. In context of production control, especially Enterprise Resource Planning and Manufacturing Execution Systems have been used for detailed production planning and control [START_REF] Kletti | Die perfekte Produktion[END_REF]. Approaches that focus on closed loop production, i.e. an additional feedback loop to regular open loop control, can be found in several dissertations [START_REF] Hamann | Lernfähige intelligente Produktionsregelung[END_REF][START_REF] Simon | Fertigungsregelung durch zielgrößenorientierte Planung und logistisches Störungsmanagement[END_REF][START_REF] Zetlmayer | Verfahren zur simulationsgestützten Produktionsregelung in der Einzel-und Kleinserienproduktion[END_REF]. However, these are not based on artificially intelligent methods but instead on procedures of discrete-event simulation. These require exact models of influencing variables, which come along with manual effort. Even the automatic generation of models has only been theoretically discussed within this field [START_REF] Selke | Entwicklung von Methoden zur automatischen Simulationsmodellgenerierung[END_REF].

Intelligent, self-learning systems are aware of their current performance. They are therefore able to evaluate it in comparison to the ideal performance, enabling them to develop strategies to improve the solution. Machine learning (ML) methods enable intelligent systems to complete tasks in unexpected situations [START_REF] Giese | Multi-Agent System Design for Safety-Critical Self-Optimizing Mechatronic Systems with UML[END_REF]. Learning the behavior strategies of a technical system with the use of ML has already been used for many purely virtual scenarios and for a few scenarios in robotics. The best-known examples for this come from the technology company Google, e.g. the latest application example for learning gripping operations [START_REF] Levine | Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection[END_REF]. Recently, data-driven ML has become progressively more important in the context of Industry 4.0. However, significant research results have not yet been presented.

Research for the application of Artificial Intelligence in the context of Production Planning and Control is nothing new. In the 90ies, the application of artificial neuronal networks in business has already been researched by Schneider, who identified different adequate network types and parameters [START_REF] Schneider | Neuronale Netze für betriebliche Anwendungen: Anwendungspotentiale und existierende Systeme[END_REF]. Huang analyzed the suitability and weaknesses of existing approaches for the application of neuronal networks in production systems [START_REF] Huang | Artificial neural networks in manufacturing. Concepts, applications, and perspectives[END_REF]. In the following years, approaches to build groups for flexible manufacturing systems were developed [START_REF] Kulkarni | Dynamic grouping of parts in flexible manufacturing systems -a self-organizing neural networks approach[END_REF] for capacity planning and sequencing. Reddy et al. used artificial neuronal networks for pattern recognition on the production process level [START_REF] Reddy | Identification and interpretation of manufacturing process patterns through neural networks[END_REF]. Scholz-Reiter et al. introduced a system with cascaded control loops for a neuronal closed loop production control. Implicit knowledge is integrated into the network with the help of case based reasoning. The training and selection of the suitable artificial neuronal network remains a task for experts [START_REF] Scholz-Reiter | Fallbasierte neuronale Produktionsregelung : Nutzung des Case-Based Reasoning zur Produktionsregelung mit neuronalen Netzen[END_REF]. Hamann developed a control concept for production systems that is based on "Computational Intelligence". Neuronal networks control the stock of work systems by determining the optimal target stock heuristically. Next, the quality of the model is shown with theoretical observations and practical examples. The quality can be increased continuously by training the neuronal networks with new examples [START_REF] Hamann | Lernfähige intelligente Produktionsregelung[END_REF]. This approach is suited mainly for bottlenecks due to the high implementation complexity and does not represent a universal concept for the entire production control. Di Orio et al. discuss a paradigm called Self-Learning Production System (SLPS) to ensure the evolvability of industrial systems over time. This is achieved with context-awareness and data mining techniques in order to ensure the adaption of a system to changing processes. The discussed approach focusses on the systemization of technical and theoretical developments to provide a baseline for the SLPS solution [START_REF] Di Orio | Self-Learning Production Systems: A New Production Paradigm[END_REF].

The State of the Art shows that existing approaches for the rapid elimination of disturbances and deviations often focus solely on reproducing interdependencies in simulation models or do not offer the feedback of real-time data. The implementation and adaption of these models is despite extensive research effort in the context of automated model generation linked to manual input [START_REF] Selke | Entwicklung von Methoden zur automatischen Simulationsmodellgenerierung[END_REF][START_REF] Bergmann | Automatische Generierung adaptiver Modelle zur Simulation von Produktionssystemen[END_REF]. In the framework of simulation models, forecasts can be calculated for different scenarios, the exact reproduction of real interdependencies in the form of these scenarios is linked to high manual effort. Methods of pattern recognition allow the formalization of knowledge. Because of increasing computing capacities, promising approaches can be found in the field of artificial intelligence -especially in ML. The high requirements for reliable and robust processes as well as an efficient use of resources complicate the application of complex trial and error methods parallel to the ongoing production. Extended data driven solutions are needed that can be applied purposefully and allow efficient learning of new tasks. Here, a combination of ML and model driven approaches seems imaginable in order to achieve a broad field of application [START_REF] Schaal | Learning Movement Primitives[END_REF].

Requirements

The research project has five requirements in order to achieve an intelligent closed loop production control: 1) a real-time capability, 2) a pattern recognition, 3) an analysis of deviations, 4) a derivation of response strategies and 5) a feedback.

Real-time representation of data is required and the essential element for handling with deviations. Decisions are based on data and therefore the provision is one of the main challenges. The earlier data are available and presented in a useful way the better the response time of the system. Furthermore, the real-time representation serves to a better overview of the production processes. With the help of data, the production is presented in a transparent manner, which leads to a better process understanding and can help to stabilize the production processes.

Another condition is the pattern recognition. Machine learning methods evaluate a database to detect anomalies. A basic prerequisite for machine learning methods is the reliability and real time availability of data. Through this, machine-learning methods are able to discover patterns early and just in time. Pattern recognition support the analysis of error causes and error sequences. This draws on specifically designed algorithms. If there is a deviation in the production process, the deviation needs to be captured completely.

Afterwards the deviation has to be analyzed, which is the next main requirement of the system. It is very important to understand the causes and effects of the deviation. The analysis is the fundament of the classification process, in which deviations allocated to clusters.

The next step is to create a link between a type of deviation and a response strategy. For this allocation, different response strategies have to be defined. According to this, response strategies are another requirement for a working system. A distinction is made between automated, semi-automated and manual response strategies. One aspect of a response strategy is the development of a morphology with characteristic attributes and properties captured by sensors and IT. In order to prioritize the reaction strategies, the aspect of time needs to be regarded as well.

iProd -A Concept for Data Driven Closed-Loop Production Control

The research project iProd intends to develop a robust and intelligent closed loop production control that continuously reacts to deviations within the production system by target-performance comparison. It configures the parameters of the production order accordingly, based on model-based forecast scenarios. With a basis of real-time, high-resolution data from the direct production environment, the optimization model applies algorithms of artificial intelligence in order to deduce effective recommended actions. These recommendations are supposed to contain (partly) autonomous decisions, made by a pattern recognition system. The system analyses the observations, assesses them with reaction strategies and links them to a course of action. A feedback into the control loop rates the result of the measures. The research project reproduces the application of a closed loop production control very precisely and describes one of the relevant problems of manufacturing companies -the simple control and laying-up of the production with regard to a turbulent environment. All of the addressed processes are collected on a data platform. The concept is visualized in the following Fig. 1. The result is an increased capacity of the personnel that allows the search, analysis and removal of causes. This leads to a sustainable stabilization of the entire and relieves employees by reducing the amount of short time, highly critical decisions. A positive impact on cycle times and work in progress affect delivery dates positively. The degree of capacity utilization -for employees and machines -is harmonized and sudden surge in workload can be averted. Thus, the capacity can be planned better without risking the flexibility of the company.

Flow of material

The proposed concept is based on four elements: A) the collection and aggregation of real-time data, B) the ability to recognize patterns with self-learning algorithms, C) the deduction of response strategies and D) the implementation of a data platform for production control.

A) Real-time feedback data: The basis for the concept is the identification and virtual representation of real-time data along the material flow. The analysis and evaluation of structured and unstructured real-time data enables an optimization of complex production systems. The digital shadow of the production is the basis for a databased predictability of future situations within a real system and for the realization of potentials for innovation and optimization [START_REF] Schuh | Der Digitale Schatten in der Auftragsabwicklung[END_REF].

The overarching goal of iProd covers the development and validation of an Industry 4.0 suitable solution concept, which exploits technical possibilities in digitalization. Therefore, the digital image of a production forms the basis for a holistic analysis an evaluation of the automatized captured data.

B) Pattern The collected data in form of a digital shadow allows recognizing patterns that would otherwise stay hidden. This is achieved by descriptive, predictive or prescriptive analysis of the data. Different methods can be applied for data analytics. For example, there classical data mining and machine learning tools that can be used on structured data and other tools that can be used for unstructured data [START_REF] Gröger | The Data-driven Factory[END_REF]. In this case, there is only structured data from the production environment. Classical tools for the analysis of structured data are, for example, Online Analytical Processing (OLAP), querying and reporting [START_REF] Kassner | Product Life Cycle Analytics -Next Generation Data Analytics on Structured and Unstructured Data[END_REF] and will mainly be used in this project. Based on patterns from the production system, the project enables the accurate forecast of the future system behavior in consideration of influencing disruptive factors. This is the basis for the implementation of an intelligent closed loop production control.

C) Response strategies: The intelligence of the production control results from the ability to implement response measures in a systematical and partly autonomous way. The goal is a systematic allocation of response strategies, which can be either automatic, or manual, in order to react to deviations in production. This way, the achievement of the logistical targets can be ensured through the focused feedback of measured values and self-learning control units, in spite of dynamic influencing factors. Transferred to industry partners participating in the project, this means that manufacturing companies from different kinds of industries become capable to design their production reactively and efficiently with the implementation of machine learning methods. D) Data platform: The production control is the fundamental condition for capturing and comparing the relevant control variables with the planning guidelines (reference variables) continuously and in real-time. For the approximation of the control variable, the action alternatives are rated in the data platform of iProd. For the determination of the regulating variables, for example work-and machine usage plans, the optimal configuration can be defined. This way, adequate measures for the parameterization of the order-generation, the order-authorization, the sequencing and the capacity-coordination of the production can be derived.

In summary, iProd intends the substitution of classical control logic, in which for example set-up times, process times and transition times are based on average values or even on subjective evaluation. Instead of this, iProd allows the forecast of future disruptive factors and resulting deviations besides the identification of current deviations, to fix the control difference in a proactive way by an optimized parameter configuration.

Conclusion and Further Research

Manufacturing companies are facing an increasingly turbulent market defined by products growing in complexity and shrinking product life cycles. This leads to an increase in planning complexity and higher error sensitivity. In practice, IT systems and sensors integrated into the shop floor in the context of Industry 4.0 are used to deal with such challenges. However, while existing research provides solutions in the field of pattern recognition or recommended actions, a combination of the two approaches is neglected. This leads to an overwhelming amount of data without contributing to an improvement of processes. Therefore, this paper presents a new platform-based concept that enables an increased logistical capability. This is achieved by collecting and analyzing high-resolution data with the use of self-learning algorithms. Thereby, patterns can be identified and reproduced, allowing an exact prediction of the future system behavior influenced by interfering factors. The use of artificial intelligence maximizes the automation of the reduction and compensation of the identified disruptive factors.

However, the presented paper also has a few shortcomings. First, further research is needed to enable and improve the proposed concept. It is necessary to elaborate the discussed approach to solve the discussed problem of production control. Moreover, the concept still only focusses on the manufacturing industry while other industries are neglected. Furthermore, use cases need to be found for a possible application and validation of the proposed concept. In addition, it is important to improve the manmachine interaction, e.g. visualization, to enable a broad dissemination of the concept.

Fig. 1 :

 1 Fig. 1: The concept of the research project


Volker Stich
email: volker.stich@fir.rwth-aachen.de

Sebastian Schmitz
email: sebastian.schmitz@fir.rwth-aachen.de

Violett Zeller
email: violett.zeller@fir.rwth-aachen.de

Relevant Capabilities for Information Management to Achieve Industrie 4.0 Maturity

Keywords: Industrie 4.0, agile company, information management, manufacturing companies 1

Industrie 4.0 is changing the industrial landscape in an unanticipated way. The vision for manufacturing industries is to transform to an agile company, in order to react on occurring events in real-time and make data based decisions. The realization requires also new capabilities for the information management. To achieve this goal agile companies require taking measured data, analyzing it, deriving knowledge out of this and support with the knowledge their employees. This is crucial for a successful Industrie 4.0 implementation, but many manufacturing companies struggling with these requirements. This paper identifies the required capabilities for the information management to achieve a successful Industrie 4.0 implementation.

Introduction

The term "Industrie 4.0" -or labelled with different terms as "Industrial Internet of Things" in USA -describes the widespread integration of information and communication technology in industrial manufacturing [START_REF] Gudergan | The Global Evolution of The Industrial Internet of Things. A Cross Country Comparison Based on an International Study on Industrie 4.0 for Asset Efficiency Management[END_REF]. Industrie 4.0 can be defined scientifically as real-time, multilateral communication and data transmission between cyber-physical devices with high data volumes rates [START_REF] Schuh | Industrie 4.0 Maturity Index. Managing the digital transformation[END_REF]. The main benefit in realizing Industrie 4.0 concepts is the transformation of companies to an agile and learning company in order to be competitive in a growing dynamic business market. Many studies have shown that manufacturing companies are highly interested in capturing the named benefit within a targeted timeline of five years [3; 4]. However, the actual implementation speed is too slow to achieve this goal. Use cases are dominant, but an end-to-end implementation is necessary to realize the presented potentials. A systematic implementation in companies has not been conducted and companies need a precise development path for a holistic implementation of Industrie 4.0.

Due to the Industrie 4.0 definition, one action field is information management. An efficient information management is the key for successful companies to ensuring that available data and information can be used to make decisions. The relevant task in the information management and its influence on production processes are not transparent for manufacturing companies [START_REF] Mckinsey | Industry 4.0. How to navigate digitalization of the manufacturing sector[END_REF][START_REF] Zühlke | Die Cloud ist Voraussetzung für Industrie 4.0. Präsentation. VDI. VDI-Pressegespräch anlässlich des Kongresses "AUTOMATION 2013[END_REF].

2

Vision of Industrie 4.0 for Manufacturing Companies

The overall objective for producing companies is continuous, long-term enhancement of liquidation and stakeholder value. Quality and time objectives replace traditional objectives like a substantial margin especially in high-wage countries [START_REF] Schuh | Prozessmanagement[END_REF]. Flexibility is the key for manufacturing companies to produce and deliver products in high quality and adapt to customer requirements fast. New market entrants, ever-shorter product lifecycles and customized solutions increase the required agility of companies. Industrie 4.0 enables flexibility and agility as two success factors for producing companies [START_REF] Bauernhansl | WGP-Standpunkt Industrie 4.0[END_REF].

With a better availability of data and information, companies can learn how things are related to each other and can make faster decisions. A faster reaction to events achieve agility, as one key capability required by companies in Industrie 4.0 [START_REF] Schuh | Industrie 4.0 Maturity Index. Managing the digital transformation[END_REF]. Derived from this vision, four corresponding Industrie 4.0 levels describe the business value of Industrie 4.0 for manufacturing companies. The Figure 1 shows Industrie 4.0 levels defined by the acatech -the national academy of science and engineering [START_REF] Schuh | Industrie 4.0 Maturity Index. Managing the digital transformation[END_REF]. The definition of these levels represent following Industrie 4.0 maturity levels [START_REF] Schuh | Industrie 4.0 Maturity Index. Managing the digital transformation[END_REF]: The starting point is the digital visibility of events in the company. This means that all processes and events leave a digital trace. In order to achieve this goal, all kinds of processes, such as management, business or supporting processes have to be captured digitally and be available in real-time. All data are processed and displayed in an appropriate level of detail for the given use-case. Having achieved visibility, decisions are information-based rather than assumption-based.

Transparency

Predictive capacity Adaptability

What is happening? "Seeing"

Visibility

Why is it happening?

What will happen?

How can an autonomous response be achieved?

"Understanding" "Being prepared" "Self-optimising" Fig. 1: Industrie 4.0 Maturity Levels [START_REF] Schuh | Industrie 4.0 Maturity Index. Managing the digital transformation[END_REF] Given the real-time availability of all relevant data, companies can reach the next maturity level of transparency resulting from understanding cause-and-effect relationships in the obtained data. Created process knowledge derived from context specific data combination and aggregation granted decision support. Big Data applications are used in environments where traditional methods of data analysis fail due to size and scope of obtained data. With the help of stochastic methods, these applications reveal unknown cause-and-effect relationships in producing companies. Big data applications fed systems, like enterprise resource planning (ERP) and manufacturing execution systems (MES), with the aggregated data. The configuration of these applications allows that data are transmitted to the right software automatically.

The next target level predictability bases on identified cause-and-effect relationships. The conducted measures grant a comprehensive and reliable input in order to make better forecasts and predictions. Probabilistic methods forecast events in the future and developed strategies face them in advance. Hence, best possible reactions can be determined more reliably and initiated faster. With these new capabilities in predicting the company's market environment, the number of unexpected events decreases. Thus, production planning achieves a new level of reliability. The quality of the prognoses is highly dependent to the preliminary work done in the described former stages. Information quality is resulting from a comprehensive digital signature and defined cause-and-effect relationships. Quantifying information quality is crucial in order to make valid prognoses yet remains a challenge. Both practical and expert knowledge are key factors in order to generate sound prognoses.

Companies can accomplish the final level self-optimization by continuously adapting insights given by transparency and visibility. In manufacturing, selfoptimization controls automatically controlling the manufacturing process. All factors essential to the company's success are included in such a system (e.g. production planning, production control). In order to decide which steps to automate, companies have to determine and evaluate costs and benefits for all manufacturing steps. Repeating manufacturing steps should always be considered when examining the capability to run autonomously. When communicating with suppliers and customers, approval and confirmation notifications have to be supervised critically. Companies reach the target of self-optimization when they able to use the digital signature in a way that the system is able to make decisions quickly and put their measures into practice fast with the best possible outcome for the company.

Industrie 4.0 Requirements to the Information Management

All four Industrie 4.0 levels have in common, that relevant information in right time, place and quality are required to enable these databased decisions and achieve business value. Providing time-critical information in the right quality to the right decision maker is the main task of information management and enables companies to achieve targets like flexibility, quality and time objectives. Therefore, this business function handles processes and information systems in order to provide, process, save, generate and transfer data and information to all business processes. This includes data processing to generate knowledge out of data [START_REF] Mangiapane | Modernes IT-Management. Methodische Kombination von IT-Strategie und IT-Reifegradmodell[END_REF]. Information systems are sociotechnical systems in which information is provided based on economic criteria by both people and information and communication technology [START_REF] Krcmar | Einführung in das Informationsmanagement[END_REF]. In order to provide decision-relevant information, access and confidentiality of all required data has to be ensured and a detailed digital picture of the production system has to be enabled. Only providing is not enough. Information have to be filtered, clustered and showing relationships, so that data analysis is a required capability for manufacturing companies. Data processing and interpretation transform raw data into valuable information and knowledge [START_REF] Geisberger | Integrierte Forschungsagenda Cyber-Physical Systems[END_REF]. Data preparation and visualizations reduces for users the complexity of relations. Furthermore, the communication between users and systems has to be bidirectional to enable the user to feed information back.

Providing data and information for decisions, companies' data have to be available throughout different IT systems like Enterprise resource planning (ERP). To do this, it is necessary to create an information system architecture for agile companies with a central platform. This requires horizontal and vertical integration, standardized exchange formats and interfaces as well as appropriate data quality.

Methodical Approach of this Paper

The overall target of the research activity is to identify relevant Industrie 4.0 capabilities of the information management and integrate these capabilities in an Derived from the requirement, this chapter describes the Industrie 4.0 capabilities to the information management. For a successful Industrie 4.0 implementation, two principles are relevant. First, available data should be prepared and processed in a manner that supports decision-making. In order for the data to be usable, the organization must meet technical requirements for real-time access and possess an infrastructure that enables the necessary data processing and seamless information delivery. Second, manufacturing companies require an IT integration in order to enhance data use and increase agility [START_REF] Schuh | Industrie 4.0 Maturity Index. Managing the digital transformation[END_REF]. To realize both principles, producing companies have to possess a level of capabilities. It is the task for the information management to ensure that capabilities and develop a roadmap to realize these capabilities. Figure 3 shows an overview of the required capabilities. An explanation of these capabilities are followed in detail.

Data for decision making

Data analysis

Information provision

User interface

Resilient IT infrastructure

IT integration

Horizontal integration

Vertical integration Data quality

Standard data interface IT-Security Fig. 3: Required Industrie 4.0 capabilities for a successful implementation

Data analysis

Data analysis defines the transformation process from data into information to use the information for valuable decisions [START_REF] Chen | Business Intelligence and analytics. From Big Data to Big Impact[END_REF]. The degree of digitization and interdependence of production plants is continuously increasing. This results directly in an increasing amount of data. Literature describes data analysis along four levels basing on each other: The descriptive analysis describes the evolvement from data to information by putting data into context. In the next step, cause-effect relationships are revealed by conducting correlation analyses (diagnostic analyses). Within the predictive data analysis, events in the future are forecasted by methods of simulation or regression. Last, prescriptive data analysis provides recommendations for action by using optimization algorithms and simulation approaches [START_REF] Shi-Nash | Data Analytics and Predictive Analytics in the era of Big Data[END_REF]. Within a digital environment, commonly referred to as "Industrie 4.0", a large and poly-structured amount of data is available and exceed traditional analysis methods ("Big Data") [START_REF] Krumpe | XaaS und Big-Data-Technologien. cloudoptimiertes Datenmanagement für Open-Government-Data[END_REF].

New technology enable an efficient processing of these data. Use-cases for data analysis in an "Industrie 4.0"-environment are forecasting machine failures and an optimized production planning process.

Information provision

Information provision includes the suitable provision of information [START_REF] Hermann | Design Principles for Industrie 4.0 Scenarios[END_REF]. Due to increasing amount of information, companies focusing on an efficient information provision [START_REF] Fiebig | Informationen auf dem Shopfloor[END_REF]. Delivering contextualized information to employees allows that they use the results of the data analysis to support their decision-makings. Companies have to make sure, that the provided information are the right information in that situation.

Methods like information modelling or information logistic concepts identify the right information [START_REF] Krcmar | Informationsmanagement. 6[END_REF].

Instead of searching for the information across several different IT systems and processes, sorting or interpreting it themselves, the IT system delivered the right information in accordance of the specific context of the actual task and the right content. The term system-of-engagement describes an efficient information provision. Systems of engagement focus on the employees instead of daily processes, like systems of enrichment. These system of engagement works similar to apps and collect all required information from available IT-Systems and show these in the right relation to the employees task [START_REF] Schuh | Steigerung der Kollaborationsproduktivität durch cyber-physische Systeme[END_REF].

Furthermore, companies have to ensure, that employees use the provided information and information system for their tasks and decisions. Not used information or systems leads to redundant data and missing feedback to former processes [START_REF] Aier | Flexibilisierung von Organisations-und IT-Architekturen durch EAI[END_REF].

User interface

User interfaces describe the interface and interaction modus between IT systems and users [START_REF] Calvary | A Unifying Reference Framework for multitarget user interfaces[END_REF]. IT systems can deliver information in form of tables, animations, Augmented Reality or voice. The better the information are displayed and corresponding to the actual process, companies reach potentials like increasing productivity or quality. Easily understandable visualisation (e.g. 3D-animations) reduces the decision complexity. Its content and presentation should be adapted to the task being performed and the employee's skill level. The used technology must be mobile, highly versatile and easily usable [START_REF] Fallenbeck | IT-Sicherheit und Cloud Computing[END_REF]. Users need intuitive possibilities to react on events and communicate with the IT systems. Depending on the actual task though, gesture or voice control are used [START_REF] Vogel-Heuser | Herausforderungen und Anforderungen aus Sicht der IT und der Automatisierungstechnik[END_REF].

Resilient IT infrastructure

Resilience defines how an IT system reacts under changes circumstances. Resilient IT systems are stable within foreseeable circumstances [START_REF] Laprie | From dependability to resilience[END_REF]. Data analysis and delivery require a resilient IT infrastructure that fulfils the relevant technical data capture, transfer, storage and processing requirements and guarantees the IT system's functionality. A common problem for manufacturing companies is the actual IT infrastructure, which is not designed for the big amount of data [START_REF] Bartel | Big Data im Praxiseinsatz. Szenarien, Beispiele, Effekte[END_REF]. Backups or specialised software prevent threats to people and material assets and guarantee the system's long-term usability. Situation-based data storage ensures that applications can access the data within an appropriate timeframe. In-memory databases allow frequent accessing of the data, so that it can be used to provide rapid and stable decision-making support [START_REF] Porter | How Smart, Connected Products Are Transforming Companies[END_REF].

Horizontal IT system integration

The horizontal integration describes the integration of different process steps within a company. The integration contains operational, executional and administrational processes and IT systems. The horizontal integration abolish different version status over the value stream and enable a single source of truth [START_REF] Kaufmann | Die horinzontale Integration der Wertschöpfungskette in der Halbleiterindustrie. Chancen und Herausforderungen[END_REF]. A complete horizontal integration along the value stream and without media disruption enables companies to link the order information to product, work and process instructions [26; 2]. Companies can react flexible and data based due to an interconnection between engineering, planning and production data [START_REF] Bloching | Die digitale Transformation der Industrie[END_REF]. This interconnection include also an information feedback, e.g. the feedback of production parameter back to the production planning to adapt the planned production time. To implement a horizontal integration of the IT-systems local data storages have to opened and interfaces between IT-systems be connected. Common data storage is the basis for a companywide single source of truth. All users access the same set of information [START_REF] Schuh | Industrie 4.0 Maturity Index. Managing the digital transformation[END_REF].

Vertical IT-system integration

A big amount of data is available throughout the value chain. To analyse these data and identify interdependencies between them, a vertical integration of IT systems is required. The vertical integration is one of the Industrie 4.0 key aspects [START_REF] Kaufmann | Die horinzontale Integration der Wertschöpfungskette in der Halbleiterindustrie. Chancen und Herausforderungen[END_REF]. IT systems and the machines on the field level exchange information continuously between them. It is necessary to create an information system architecture for agile companies with a central platform that connects existing IT systems to each other and provides contextualized information. The vertical IT integration focus on the integration of IT systems on different levels and the dissolution of the automation pyramid [25; 26; 28]. This means the dissolution of the IT systems structures and hierarchies.

Standardize data interfaces

Data interfaces describe the transition between two IT systems. Standardized data interfaces is the required communication basis in Industrie 4.0 [START_REF] Hoppe | Standardisierte horizontale und vertikale Kommunikation: Status und Ausblick[END_REF][START_REF]ebusiness w@tch. e-Business Interoperability and Standards. A Cross-Sector Perspective and Outlook[END_REF][START_REF] Sindermann | Schnittstellen und Datenaustauschformate[END_REF]. A continuous information flow between the IT systems and the access for all users on the same set of information requires standardized data interfaces. Data interfaces facilitate the exchange of data and information from individual IT systems. Nowadays many interfaces are proprietary, which means that the interface works only in that individual use case [START_REF] Sindermann | Schnittstellen und Datenaustauschformate[END_REF]. To react agilely on changing IT-systems and information flows a flexible IT-landscape is required. Neutral or standard interfaces and data exchange formats across all the relevant systems are necessary for this flexible IT-landscape [START_REF] Schuh | Steigerung der Kollaborationsproduktivität durch cyber-physische Systeme[END_REF].

Data quality

Data quality means the degree of data usability for the individual purpose [START_REF] Heinrich | Informationsmanagement. Grundlagen, Aufgaben, Methoden. 11[END_REF]. IT systems integration relies on sufficiently high data quality. Poor data quality in the IT systems results in incorrect aggregated data and inaccurate feedback, ultimately undermining confidence in both the IT systems and their contents [START_REF] Schuh | Steigerung der Kollaborationsproduktivität durch cyber-physische Systeme[END_REF]. This makes it impossible to achieve the goal of databased decision-making. Data governance policies provide organisations with guidance for the processing, storage, management and presentation of high-quality data within the company. Even a perfect data quality is impossible to reach, goal within the Industrie 4.0 vision is a "fit for use" [START_REF] Strong | Data quality in context[END_REF]. Technical capabilities for improving data quality include automated data cleansing (identification, standardisation, duplicate removal, consolidation and enhancement of data) and master data management systems.

Upgrade IT security

The increasing integration of information systems as well as human factors and other contributors bear the risk of criminal attacks. The potential damage that these attacks can cause increases in proportion to the degree of integration. IT security encompasses different strategies for identifying and implementing security measures. Compliance with standards such as IEC 62443 can help to contain the risks. Such standards include proactive measures to maintain IT security and adapt it in response to changing circumstances [START_REF]Network and system security[END_REF].

Conclusion and Outlook

This paper describes possible capabilities for the information management in order to achieve the four maturity levels of Industrie 4.0 successfully. Companies have to master these capabilities to reach the overall goals of Industrie 4.0, like flexibility, quality and time objectives. All capabilities base on a literature research analysis. This paper defines the capabilities and presents the requirement for an Industrie 4.0 implementation.

The following research activities match these capabilities to the described Industrie 4.0 maturity levels. The maturity levels will be described by the configuration of each capability.

Figure 4 shows exemplarily the matching for the capability of data analysis. Literature separates data analysis into descriptive, diagnostic, predictive and prescriptive analysis. Target of the first maturity level is that data are processed and displayed in an appropriate level of detail for the given use-case. To reach this goal, data have to transfer to information by putting data into context. These requirements are given by descriptive analysis. The maturity level "transparency" is described by cause-and-effect relationships in the obtained data. Diagnostic analysis include correlations and identifies these cause-and-effect relationships. Predictive data analysis forecasts events in the future by methods of simulation or regression. This forecast is the central issue in the third maturity level predictability. Prescriptive data analysis provides recommendations for action and optimization algorithms. The fourth maturity level adaptability requires recommendations for action and optimization algorithms to react autonomously. Following research activities will split and match all identified to the maturity levels. The allocation is used for a maturity model that assess the maturity degree of the information management. Furthermore, the validation of the presented approach will include interviews and assessments in producing companies in order to prove that the identified information management capabilities support the achievement of each Industrie 4.0 level.

Fig. 2 :

 2 Fig. 2: Methodical approach of this paper

Fig. 4 :

 4 Fig. 4: Example for Matching of Capabilities to Maturity Levels