SMART PRODUCTION PLANNING FOR SUSTAINABLE PRODUCTION BASED ON FEDERATIVE FACTORY DATA MANAGEMENT

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1 Proceedings of TMCE 2014, May 19-23, 2014, Budapest, Hungary, Edited by I. Horváth, Z. Rusák Organizing Committee of TMCE 2014, ISBN SMART PRODUCTION PLANNING FOR SUSTAINABLE PRODUCTION BASED ON FEDERATIVE FACTORY DATA MANAGEMENT André Picard Department of Computer Integrated Design Technische Universität Darmstadt Germany Reiner Anderl Department of Computer Integrated Design Technische Universität Darmstadt Germany ABSTRACT For sustainable production of highly customized products a holistic understanding of the whole product life cycle and the whole production process is required. Therefore information of all involved products, processes and resources are needed in real-time. Formerly stakeholders within the product development or the production management had rarely access to this real-time information. But nowadays it is made available by the new type of products and their new forms of communication interfaces, so called cyber-physical systems. The product and the production data management have to adopt to support this new type of information exchange. Accordingly within this paper the integration of cyber-physical systems to the federative factory data management is described first. The integration allows real-time information access to all higher situated domain-specific information technology tools. As showcase this paper then focuses on the adoption of methods of production planning. Therefor the new smart production planning is introduced. Differences between the conventional and smart production planning are presented. The impact of smart production planning for different production planning tasks is then illustrated based on a given example. Within this example the use of smart production planning for smart hierarchical route sheets especially within sustainable production is described. KEYWORDS Smart production planning, hierarchic route sheets, federative factory data management, cyber-physical system 1. INTRODUCTION In the past years manufacturing companies experience a dramatic change. Besides cost and time to market individual customer demands are getting most important to successful compete on the global market. Consequently the demand for highly customized products is increasing resulting in a growing variety of products while meantime the batch size decreases. Flexible and agile production is required to adopt to the dynamical changes in manufacturing [1]-[5]. Meantime reasons for potential customers to purchase products become more widely distributed. Additionally to the traditional factors like costs, quality and disposability new factors like energy costs or recycling potential respecting the sustainability of products and production processes become decisive driving criteria for selecting products [3], [4]. For sustainable products and production multiple aspects for example environmental, health, safety, social or economic aspects arisen from different sectors have to be respected [3]. Veleva et al. differs five consecutive levels of indicators for sustainable production [3]: Facility compliance, Facility material use and performance, Facility effects, Supply chain and product life-cycle, and Sustainable systems This paper focuses on the third level Supply chain and product life-cycle. Within this level examples for indicators are extended in regard to the whole supply chain from raw material over the final product to the product end-of-life. Valeva claims that for all 1147

2 indicators a holistic understanding of the product, the environment and the processes is required. Therefore entire, consistent and especially actual information is needed. Otherwise influences or special cases are unknown or unrespected. Inefficient, ineffective or even incorrect decisions are possible - although made to best of stakeholders knowledge - as they are not confirmed by appropriate information [3], [4]. To make available all the required information this paper describes an approach to integrate cyberphysical systems to the federative factory data management. The federative factory data management combined with cyber-physical systems then allows access to requested real-time factory data. Consequently it seems as promising solution to support the management of flexible and agile production while maintaining sustainability. Using the presented approach of integration the resulting smart production planning is then described. An example for the application of smart production planning to hierarchical route sheets for sustainable production planning is presented finally. 2. SMART FACTORY In the past two decades the integration of communication interfaces into products is increasing. These products are called cyber-physical systems. They are a combination of embedded softwareintensive systems, mechanic and electronic components as well as communication interfaces [5]- [11]. Mostly they are connected to global networks like the internet using established internet technologies and protocols like wireless local area network (WLAN) modules and the hypertext transfer protocol (HTTP) [11], [12]. Cyber-physical systems aim to combine the physical and the digital world. Therefor they have a physical and a digital identity. The digital identity fully represents all physical components and all behaviour of the cyber-physical systems. Hence attributes and operations in the cyberspace refer to appropriate elements in the physical world and vice versa. Consequently each cyber-physical system offers its attributes like for example their sensors and actuators states through a predefined communication interface. Available operations are offered in the same manner. Triggering these operations lead to internal status changes as well as to performing digital or physically processes like starting another operation or an engine [5]-[12]. By merging multiple cyber-physical systems to networks a smart environment is created. Within this environment cyber-physical systems get able to combine provided information of all available systems. As a result each cyber-physical system becomes aware of itself and its surrounding environment. It for example gets able to gather its part identification number or its progress within the production process. Cyber-physical systems use these information to take intelligent, autonomous, and context-adaptive decisions. They react on these or other decisions by changing internal or external status, triggering operations or processing events [5]- [12] Application of cyber-physical systems to the production Application of cyber-physical systems to all elements of the conventional production leads to the smart factory [13], [14]. All products and all resources including raw material, intermediate product states, the final product, involved production modules as well as human resources become cyber-physical systems. As they all belong to the smart factory realtime access to their attributes and their operations at any time is provided. Examples for such attributes and operations are the availability of resources, the localization of products or the degree of capacity utilization [12]. At the same time the amount of available factory data is increasing dramatically. To support different stakeholders like the product developer, the factory planer or the production planner to successfully manage the new smart product development and the new smart production process, methods and tools for data management are needed. Required factory data has to be provided by acquiring, processing and filtering all available data Agile and flexible production Application of cyber-physical systems supports production by enabling agile and flexible production as needed for successfully compete in today s global market. Within this paper agile production is understood as the ability of quickly adoption to the environment and flexible is understood as the willingness to change in combination with the ability of being easily modifiable. Due to cyber-physical systems production planning and control is consequently partially reassigned from the production planer to the systems itself. Main 1148 André Picard, Reiner Anderl

3 tasks of these cyber-physical systems in a smart factory are [10], [14]: Self-organized configuration of the production process, Determination of the value added chain, Individual production of customized products, and Tailored adjustments to human resources. Self-organized configuration of the production process includes scheduling the individual production of products, such as the availability of required resources or the working costs of production units [10, 14]. The determination of the ideal value added chain is executed appropriate to requested factors like the duration or production costs by the cyber-physical system itself [10, 14]. Cyber-physical system equally control the individual production of each product. They for example control the logistic, control the quality or reorder production batch sequences [14]. Although cyber-physical systems support the automation of the production process, they also have to support human task where needed. User-tailored interaction respecting for example the knowledge or the work capabilities still are required [10]. By supporting these task cyber-physical systems enable an intelligent, quickly adopting and easily modifiable production. Cyber-physical systems decisions are based on consistent, complete and actual information made accessible by appropriate methods of data management. While the application of cyber-physical systems and smart engineering of these systems [5]-[8], [11] as well as engineering of smart factories and their impact on future production is heavily researched [12]-[14], factory data management for the smart factory still has to be explored. This paper focuses on methods to integrate real-time factory data and to enable access to this data though methods of the factory data management as well as on methods for later decision making. Methods of data warehousing or data mining are not part of interest. Instead knowledge synthesis for sustainable production using smart hierarchical route sheets as part of the smart hierarchic production planning is presented. Although the presented methods can be equally used for product development, this paper focuses on the production process as limiting scenario. 3. FEDERATIVE FACOTORY DATA MANAGEMENT FOR SMART PRODUCTION During the production planning and process different kind of data arises at different sources such as computer aided design (CAD), computer aided manufacturing (CAM), product data management (PDM) or enterprise resource planning (ERP). Human stakeholders scattered over both place and in relation to time aim to collaborate based on this data. They are supported by different, often proprietary and independent information technology (IT) tools. Each tool is selected respecting the involved domainspecific requirements and tasks. As a result the amount of used data formats and IT tools increases in the course of the product development and production management process. But for the command of the complexity of production management, the harmonization and the integration of all specific solutions are required [12], [15]-[17] Integration of IT tools In general three integration strategies of IT tools can be differed: indirect coupling, loosely coupling and direct coupling. Each category respects differently the degree of distribution and the degree of integration resulting in different degrees of flexibility [15], [16]. Indirect coupling preserves autonomy of each IT tool. So each IT tool stays isolated from each other. Hence each application is responsible for the coupling to a central data model. Accordingly a high degree of distribution is ensured meanwhile the degree of integration stays minimized. The weakness of this strategy is recognized during data management processes affecting multiple IT tools. Conversion of information to appropriate formats is then required for each information exchange [15]- [17]. Direct coupling aims at the maximization of integration at the expense to the degree of distribution. Therefor it fully integrates all IT tools to one common product data model. The strategy lacks flexibility. Incompatibility difficulties within other parts of the model have to be determined first before changing or upgrading partial IT tools [15]-[17]. To maximize the degree of flexibility while maintaining the maximum possible degree of distribution and the maximum possible degree of integration the federative factory data management SMART VIRTUAL PRODUCTION PLANNING FOR SUSTAINABLE PRODUCTION 1149

4 uses the strategy of loosely coupling, also called federation. This intermediate solution realizes coupling by establishing standardized communication between the involved, former isolated IT tools. Consistence and actuality of data is preserved while maintaining autonomy of each IT tool. Changes affecting single data models do not affect the rest of the system as long as the communication does not change [15]-[17] Service oriented architecture Within the federative factory data management the communication between the IT tools is realized using a service oriented architecture (SOA). This service oriented architecture consists of four layers (see Figure 1) [2], [16]: Back-end layer, Interface layer, Federation layer, and Front-end layer. In the back-end layer all domain-specific IT tools like CAD, CAM or ERP store their factory data within their specific formats to individual locations like files systems or databases. Every tool can easily be changed or updated only affecting their local storage [2], [16]. Stored data is then published within the interface layer. Using a standardized communication format based on the extensible mark-up language (XML) and the web service definition language (WSDL) web services are promoted allowing access to the published data. Different web services for each IT are present such as parse given CAD data or send CAM data objects [2], [15, [16]. In current implementations the web services use common established internet protocols, mainly HTTP and the hypertext transfer protocol secure (HTTPS) [12]. Within the federation layer the communication and thus the loosely coupling is performed. Therefor a web technology communication format, named SOAP (formerly known as the simple object access protocol), is used. SOAP allows the exchange of data objects using equally XML, WSDL and HTTP/HTTPS [2], [15], [16]. The presentation and visualization of all available data is finally performed within the front-end layer. Available implementations provide multiple functions like requesting, altering or filtering data. These functions also are able to (re-)trigger web services at the federation layer [2], [15], [16]. The layer architecture used in the federative factory data management in combination with common established web technologies based on SOA enables global, efficient and consistent factory data exchange Requirements for the Integration of cyber-physical systems Figure 1 Architecture for cyber-physical systems integration into the federative factory data management [12] 1150 André Picard, Reiner Anderl

5 Using cyber-physical systems data as part of the federative factory data management is promising a huge potential and new possibilities for factory management. But multiple requirements for a seamless integration have to be regarded [12]: Integration without replacing established tools of the federative factory data management, Maintain an open and modular structure to guarantee flexibility and scalability of the cyberphysical systems network, Maintaining the federative communication within the distributed network of cyber-physical systems, Support for the independent development of the data structure of each cyber-physical system to reflect the autonomous character of cyberphysical systems, Ensuring the consistence of data, although allowing to add, alter and remove single, existing data structures to support an adaptive behaviour, Enable real-time access to states and triggering events, Auto-identification of the cyber-physical system, Application of the known methods of data management, and Use standardized communication protocols. An approach for the integration of cyber-physical systems data is presented below. This approach focus on the direct integration via a web application programming interface (API). Another approach using agent systems is not further considered as the additional complexity and the additional overhead is not useful in the context of real-time access to the cyber-physical systems data and in the context of a modular and open structure as promoted by cyber-physical systems [12] Approach for the integration of cyber-physical systems Newer implementations of cyber-physical systems use representational state transfer (REST) endpoints for the communication between each other [18], [19]. These REST endpoints are based on HTTP or HTTPS requests which are pretty similar to SOAP. They exchange information or trigger events by calling specific, predefined and clean uniform resource identifiers (URI). Consequently each attribute and each operation in REST is represented by a set of unique URIs. The entirety of URIs form the so called web API (see Figure 1) [20]. Compared to communication based on SOAP the REST communication is rather lightweight, but fully compatible. Only the automated detection of available web services as well as the definition of appropriate data formats is currently not fully implemented [12], [20]. Consequently under the respect of the restrictions REST endpoints enable the seamless integration of cyber-physical systems into the federative factory data management within the interface layer. Within this layer SOAP requests can still be used for both, conventional web services and REST endpoints. Thus no difference is remarked at the federation and front-end layer. For the purpose of integration of all available information, common information modelling methods are used. Information models are created coupling specific data of the databases of every domain-specific IT tool as well as the separated data in the databases of cyber-physical systems. For the access to specific data an interface of unique URIs is created and described using the Web Application Description Language (WADL). This interface provides all needed information about available services, workflows as well as data Methods of data management For all stored and acquirable factory data the methods of the data management are applicable. These methods include for example [21], [22]: Product structure management, Variant management, Classification of products, and Management of code numbers. These methods are used within the administration modules of the data management, such as [21]: Element, privileges and workflow management, Project management, Product management, and Release, change and variant management. They are essential to manage access as well as to ensure consistence and actuality of the data. Therefor different workflows such as check-out a specific CAD part, grant access to selected items or save new data are provided by data management systems. For the federative factory data management most workflows are processed within the interface layer. As cyber-physical systems are integrated into the federative factory data management at the interface layer, the workflow integration is seamless. But cyber-physical systems must provide appropriate SMART VIRTUAL PRODUCTION PLANNING FOR SUSTAINABLE PRODUCTION 1151

6 data storage and workflows themselves, for example by the usage of special adopted databases. According to VDI 3637 and VDI 499 production data is a subset of factory data. Consequently within this paper focus on production data management as specialization of factory data management is possible. All relevant aspect for production management are covered within the factory data management [23], [24]. Consequently factory data management establishes a basis for relevant functions of the production data management such as: Planning and replanning of production, Sustainable production, and Renaturation and revitalization. In the following as showcase for the usage of federative factory data management combined with cyber-physical systems the smart production planning is described. 4. SMART PRODUCTION PLANNING Production planning includes all processes to successfully plan the production. Production planning is often separated into sequential and simultaneous production planning. Nowadays the consensus is reached insisting that only sequential production planning through incremental production planning steps are applicable [25]. These steps include among others [25], [26]: Production program planning, Material requirements and batch size planning, Lead time scheduling, and Capacity balancing. Production program planning includes medium-term operative tasks like the planning of sales and production volumes and smoothing of sales quantities due to fluctuation of the market. It adjusts strategic planning to real production capacity [25], [26]. Production program planning mostly uses rolling planning as each time period is determined based on data of the previous planning. The minimization of stock and production cost as well as the maximization of contribution margin are main targets of production program planning. Therefor consistent and real-time data is required [25], [26]. Material requirements and batch size planning includes short-term primary and detailed material requirements planning and capacity requirements planning. Therefor products are decomposed to parts. For each part the batch sizes is estimated based on knowledge about former production using infinite loading. Infinite loading means the capacity planning without concrete knowledge of current available capacities. The main tasks of material requirements and batch size planning include the rough-cut scheduling of the production [25], [26]. Lead time scheduling is a short-term task to determine start and end of batch size production. Order release are processed based on former production lot, material procurement, processing time and agreed delivery date [25], [26]. Capacity balancing includes detailed planning activities to balance order releases in respect to available capacities [25], [26] Hierarchical production planning All steps in sequential production planning are successive build op on each other. For an integration of all production planning steps the interdependencies from both direction, top-down and bottom-up, have to be respected. For example the production program planning has to respect production capacities, otherwise material requirements and batch size planning is not able to be performed [25]. Although each task concretizes a partial solution of the production planning, it has to be regarded in the overall process due to the information asymmetry. Higher situated steps decide based on higher aggregated data for fairly long-term problems. Detailed steps decide more detailed problems. They impact planning much heavier but at a shorter period. Detailing takes places based on the given frame conditions combined with more recent data [25]. These frame conditions arise from basic information about task, limitations, possibilities and procedures of the detailed steps. Consequently a strong connection between higher and detailed steps exists, although detailed steps are hierarchically subordinated [25]. While top-down interdependencies are widely supported, the opposite is only poorly implemented due to a lack of information about the current situation within the long-term production program planning. Stakeholders knowledge are therefore the only decisive driving factors for the production planning and the evaluation of results of each production planning step [25] André Picard, Reiner Anderl

7 Hence Söhner lists among others the following lack in hierarchical production planning [25]: Production program planning is based on forecasted and thus stochastic demands not respecting batch sizes or production sequences, Rough-cut capacity planning within the production program planning has to respect all components as this deeply impacts further detailed planning, Rough-cut lead time planning is based on documented waiting time of the current production instead of based on measured or real lead time Simultaneous material requirements planning and rough-cut scheduling is needed to successfully plan needed material for all products and each stage of production, and Batch size planning is an important key for successful production. It directly impacts processing time, setup quantity and thus lead time and stock Smart hierarchical production planning Smart production planning aims at the elimination or at least aims at minimization of all lacks of the hierarchical production planning. Therefore real-time data is provided using the federative factory data management combined with cyber-physical systems as described above. Using REST endpoints and data management methods of the federative factory data management, real-time information about all needed products, resources, processes and intermediate production states are consequently made accessible to all involved stakeholders at any time. This real-time information can be used within all phases of production planning. Scenarios for production planning include rough-cut batch-size planning, lead time simulation, planning of alternative solutions for order releases or evaluation of existing production planning. Every step formerly lacking real-time information is now improved. Consequently all conclusion formerly based on stakeholders experience is now confirmable through real-time data. Brief smart hierarchical production planning supports the transformation of sequential to simultaneous production planning. 5. SMART ROUTE SHEETS Multiple scenarios for the usage of these new knowledge synthesis exist. This paper will present as showcase the usage of smart production planning within smart hierarchical route sheets based on the federative factory data management to improve sustainable production planning Hierarchical route sheets Route sheets form an important base for information in the production planning. They provide different information about for example identification and classification of products, manufacturing steps or quality assurance [27]. Some companies use route sheets as well for price formation or human resource planning [27]. Route sheets therefore are created and detailed along the whole process of production planning. Each step uses transported information to detail and to assure its planning. Different route sheets appropriate to the selected tasks exist, for example [27]: Enumeration route sheet, Sequence route sheet, and Basic operations route sheet. Enumeration route sheets list every operation of a work sequence. They do not provide information about logic order [27]. Sequence route sheets additionally include this logic order [27]. Basic operations route sheets only provide information about manufacturing or assembly operations not routinely conducted [27]. All route sheets are subordinated building a hierarchical structure. Descending along the structure each step adds additional, more detailed information. The detailing has to respect frame conditions of higher situated parent elements Smart hierarchical route sheets As route sheets and hierarchical production planning are strongly linked, they both lack the same restrictions. The application of smart hierarchical production planning therefore has a deep impact on modelling and usage of route sheets. Using new interactive displays like mobile internet devices together with federative factory data management and cyber-physical systems a new form SMART VIRTUAL PRODUCTION PLANNING FOR SUSTAINABLE PRODUCTION 1153

8 of route sheets is created. These so called smart hierarchical route sheets are able to adapt their planning due to real-time factory data. They gather and process real-time information to autonomous and adaptive adopt to surrounding circumstances. Smart hierarchical route sheets are situated within the frontend layer. Using the federative factory data management access to factory data via the federation layer is possible. For example human resource automatically are identified using radio-frequency identification (RFID). The smart hierarchical route sheet gathers information about human resource involved in a specific work sequence. Respecting the physical human abilities like the strength, smart hierarchical route sheets only display operations performable with a low amount of human strength Smart hierarchical route sheets for sustainable production planning Within a more complex scenario the smart product sheets respect real-time factory data of capacity loads of specific resources during the production program planning. Because this data directly impacts all downstream steps of production planning and as consequently this data is transferred in real-time to all these steps, factory planner get able to early synthesize the available information in order to create higher process knowledge. They consequently get able to transform rough-cut to detailed capacity planning already at an early phase of production program planning. The impact of their decisions on different aspect can be evaluated instantly. Possible aspects are for example: The degree of capacity utilisation, Availability of machine resources, and Influence on warehouse stock and intermediate stores Continuous quality of manufacturing resources such as milling machines or numerically controlled lathe is influenced among others by individual warm-up times. To minimize defective goods at start-up the smart production planning optimize their resource capacity. It avoids phases of cool down to reduce the energy demand. Consequently production cost and lead time are reduced meanwhile sustainable production is supported. Cyber-physical systems therefore link available, not fully busy resources to listed operations within the smart hierarchical route sheets originally created by the factory planer. Information about availability and future planned batches are directly gatherable out of the federative factory data management. Forecast and stochastic estimations of capacity loads are eliminated. Lead time planning without smart hierarchical route sheets is based on documented waiting time of the current production. Respecting real instead of estimated past and future production planning, maintenance of machines for example due to tool wear is predicted more precisely. Lead time planning therefore does not longer forecast waiting time for the whole product, instead process time for each operation is planned and captured precisely. Simultaneously processing time for single product parts at each resource is measurable. The production is then optimized in consideration of wear. Wear intensive operations affecting tools can reduce quality for later operations. Route sheets can respect this wear and schedule later batches with lower demand on tolerances to these resources. Subsequent quality assurance can trigger the particular time for maintenance. Hence the exchange of tools is no longer executed based stakeholders experience, but on real wear. Smart hierarchical route sheets equally support warehouse stock and intermediate stores. All parts within each individual operation are tracked. Bottlenecks in production are directly recognized and then escalated along the hierarchical structure. Each involved route sheet is informed. Linked planning steps are retriggered. Because infinite loading is eliminated and capacity balancing is performed without planning delay, factory planner are able to adjust batch sizes in realtime. This approach leads to a material optimization of warehouse stock and intermediate stores by smoothing production fluctuations. Stock size and size of the intermediate stores can be reduced to save material. 6. OUTLOOK Smart hierarchical production planning deeply impacts conventional production planning. Currently the presented method is only illustrated. Concrete implementations are developed within the project Federative Factory Data Management based on Service Oriented Architecture (SOA) and Semantic Model Description on XML and RDF for Manufacturing Products. These implementations 1154 André Picard, Reiner Anderl

9 include standalone applications for personal computers as well as mobile applications for mobile internet devices. Evaluation of the developed applications in the industry have to follow subsequently. Meantime the application of cyber-physical systems to production has to be concretized more. Information models to represent internal and external status of cyber-physical systems as well as an appropriate web API to fully integrate all attributes and operations are currently focus of research. Appropriate workflows for all data management methods have to be implemented or further extended. Smart hierarchical route sheets have been explored inadequately. Their impact on sustainability has to be tested and documented using for example appropriate measures and surveys. Feedback of the efficiency and effectiveness for production management processes by stakeholders are still missing. 7. CONCLUSION Flexible and agile production within smart factories are promising approaches to reduce time and cost as well as maximize quality while producing highly customized products. To satisfy additional customer demands like sustainable products and production, a holistic understanding of products and processes is required. Therefore information about products and processes have to be recorded and made accessible to the stakeholders. Accordingly established methods of production data management have to adopt. Within this paper the integration of cyber-physical systems data to the existing federative factory data management is illustrated. For the integration the common web technologies REST is used. REST endpoints seamless integrate into the existing service oriented architecture. The usage of the methods of this new architecture for smart production planning is described. Opposed to conventional hierarchic production planning a new autonomous and contextadaptive behaviour as well as new possibilities for stakeholders to plan their production are provided. As showcase the transformation of route sheet to smart hierarchical route sheet and their application is shown. These smart hierarchical route sheets are arranged within hierarchical structures. They support sustainable production by the optimization of resources like the exhaustion of machine capacities and energy, by the reducing of wear as well as the minimization of material stock and stored material within intermediate stores. ACKNOWLEDGMENTS Acknowledgments to DFG (Deutsche Forschungsgemeinschaft, Germany) and CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brazil) for funding the FedMan project within the BRAGECRIM (Brazilian - German Collaborative Research Initiative on Manufacturing Technology) Initiative. REFERENCES [1] Lucke, D., Constantinescu, C., and Westkämper, E, 2008, Smart Factory - A Step towards the Next Generation of Manufacturing, Manufacturing Systems and Technologies for the New Frontier, Springer London, pp [2] Mosch, C, Anderl, R., Moura, A., and Schützer, K., 2010, Integrated Process Planning Based On A Federative Factory Data Management, Proceedings of the ASME 2010 International Mechanical Engineering Congress & Exposition, American Society of Mechanical Engineers (ASME), Vancouver, British Columbia, Canada. [3] Veleva V., Hart, M., Greiner, T., Crumbley, C., 2000, Indicators of sustainable production, Journal of Cleaner Production, 9 (2001), pp [4] Linto, J. D., Klassen, R., Jayaraman, V., 2007, Sustainable supply chains: An introduction, Journal of Operations Management, 25 (2007), pp [5] Anderl, R., Eigner, M., Sendler, U., and Stark, R., 2012, Smart Engineering: Interdisziplinäre Produktentstehung, acatech Deutsche Akademie der Technikwissenschaften. [6] Lee E., 2008, Cyber Physical Systems: Design Challenges, International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing (ISORC), Orlando, FL, USA. [7] Broy, M., 2010, Cyber-Physical Systems: Innovation durch Softwareintensive eingebettete Systeme, acatech - Deutsche Akademie der Technikwissenschaften, Germany. [8] Broy, M., Kagermann, H., and Achatz, R , Agenda Cyber Physical System: Outlines Of A New Research Domain, München. [9] acatech, 2011, Cyber-Physical Systems - Driving force for innovation in mobility, health, energy and production, acatech - Deutsche Akademie der Technikwissenschaften. SMART VIRTUAL PRODUCTION PLANNING FOR SUSTAINABLE PRODUCTION 1155

10 [10] Geisberger, E., Cengarle, M., Keil, P., Niehaus, J., Thiel, C., and Thönnißen-Fries, H.-J., 2011, Cyber- Physical Systems - Driving force for innovation in mobility, health, energy and production, acatech - Deutsche Akademie der Technikwissenschaften, Germany. [11] Anderl, R., Picard, A., and Albrecht, K., 2013, Smart Engineering for Smart Products, Smart Product Engineering, M. Abramovici et al., eds., Springer, Heidelberg, Germany, Vol. 5, pp [12] Picard, A., Anderl, R., Schützer, K., Moura, A. Á. A., 2013, Linked Product and Process Monitoring in Smart Factories based on Federative Factory Data Management, Proceedings of the ASME 2013 International Mechanical Engineering Congress & Exposition, San Diego, California, USA. [13] Westkämper, and E., Jendoubi, L., 2003, Smart Factories Manufacturing Environments and Systems of the Future, Proceeding of the 36th CIRP-International Seminar on Manufacturing Systems, Universität des Saarlandes Produktionstechnik, Saarbrücken, Germany, pp [14] Vogel-Heuser, B., Bayrak, G., and Frank, U., 2012, Forschungsfragen in "Produktionsautomatisierung der Zukunft", acatech - Deutsche Akademie der Technikwissenschaften, Germany. [15] Schützer, K., Moura, A., Anderl, R., and Picard, A., 2013, Web Services to Product, Processes and Resources Data Integration: Results and Perspectives of FEDMAN Project, Smart Product Engineering, M. Abramovici et al., eds., Springer, Heidelberg, Germany, Vol. 5, pp [16] Mosch, C., Anderl, R., Moura, A, and Schützer, K., 2011, Prototype of a Federative Factory Data Management for the Support of Factory Planning Processes, Proceedings of the WASET 2010 International Conference on Software and Data Engineering (ICSDE), World Academy of Science, Engineering and Technology, Bangkok, Thailand. [17] Schützer, K., Moura, A., Anderl, R., and Picard, A., 2013, Web Services to Product, Processes and Resources Data Integration: Results and Perspectives of FEDMAN Project, Proceedings of the 46th CIRP Conference Manufacturing Systems, Springer, Setúbal, Portugal. [18] Jennings, E., and Carson, S., 2012, The Pinoccio API, n.d., from [19] Gullickson, J., 2013, RESTduino, n.d., from [20] Pautasso, C., Zimmermann, O., and Leymann, F., 2008, RESTful Web Services vs Big Web Services: Making the Right Architectural Decision, Proceedings of the 17th international conference on World Wide Web, ACM, Beijing, China, pp [21] Anderl, R., Rollmann, T., Völz, D., Nattermann, R., Maltzahn, S., Mosch, C., 2012, Virtuelle Produktentstehung, Handbuch Konstruktion, Hanser Verlag, München, Germany, pp [22] Eigner, M., Stelzer, R., 2009, Product Lifecycle Management: Ein Leitfaden für Product Development and Life Cycle Management, Springer, Berlin, Germany. [23] Verein Deutscher Ingenieure, 2008, Digital factory Fundamentals, Berlin, Germany. [24] Verein Deutscher Ingenieure, 1996, Data collection for long term factory planning, Berlin, Germany. [25] Söhner, V., 1995, Hierarchisch integrierte Produktionsplanung und steuerung, Physica-Verlag, Heidelberg, Germany. [26] Schuh, G., Stich, V., 2012, Produktionsplanung und -steuerung: Evolution der PPS, Springer, Berlin, Heidelberg, Germany. [27] REFA Verband für Arbeitsstudien und Betriebsorganisation. 1991, Methodenlehre der Betriebsorganisation: Planung und Steuerung Teil 3. Hanser, München André Picard, Reiner Anderl