Hendrik Hopf
email: hendrik.hopf@mb.tu-chemnitz.de

Egon Müller
email: egon.mueller@mb.tu-chemnitz.de

Modeling of Energy-Efficient Factories with Flow System Theory

Keywords: Energy Efficiency, Factory Modeling, Energy Flow Systems. 1

Energy and resource efficiency have become important objectives for industrial processes. They are taking more and more impact on the competitiveness of companies. Therefore, energy-efficient factory systems are needed in terms of sustainable production. Thus, new or improved models, methods and tools focusing energy efficiency are necessary. In this paper, the "Flow System Theory", as a tool for modeling technical systems and processes, is presented. It is extended in order to describe the energy flow systems of factories. This is supposed to be the basis for systematic modeling of energy-efficient factory systems.

Introduction

Increasing demand for raw materials and the depletion of non-renewable resources lead to scarcity and uncertain availability of resources. Raw material and energy prices continue to rise in the future. Climate policy objectives such as the reduction of global CO 2 emissions and the expansion of renewable energy should counteract these developments. Besides cost, time and quality, energy and resource efficiency are more and more considered in business activities. It is not only an environmental contribution due to political, social and ecological challenges [START_REF] Nyhuis | Ressourceneffizienz im Fokus[END_REF]. These objectives influence increasingly the competitiveness. The industrial sector causes about 30% of the energy consumption in Germany, whereas the most of it is used for process heat and mechanical energy [START_REF]Federal Ministry of Economics and Technology: Energiedatennationale und internationale Entwicklung[END_REF]. By improving energy efficiency, direct cost savings and indirect environmental benefit can be achieved [START_REF] Jeon | Modeling Green Fabs -A Queuing Theory Approach for Evaluating Energy Performance[END_REF]. The saving potential through more efficient use of energy is estimated in different studies with 20 -30% for industry, but it varies from company to company [START_REF] Hesselbach | Energie-und klimaeffiziente Produktion -Grundlagen, Leitlinien und Praxisbeispiele[END_REF]. It should be noted that energy efficiency is recognized as an essential tool for energy savings. However, for instance, the "World Energy Outlook 2012" emphasizes that this potential is not yet exhausted [START_REF]Executive Summary[END_REF].

In consequence, more research and development are necessary to design energyefficient systems and processes.

Methodical Planning of Energy-Efficient Factories

The energy-efficient factory has become a new type of factory. It focuses on the manufacture of goods and services by minimal energy use and it emphasizes the energy flow [START_REF] Wirth | Fabrikarten, Fabriktypen und ihre Entwicklungsetappen[END_REF]. Therefore, it includes energy-optimized systems and processes which are treated in their entirety with the complex coherences. Closed energy cycles of the factory contribute to the minimization of energy losses [START_REF] Neugebauer | Forschung für die Produktion in Deutschland -Maximale Wertschöpfung aus minimalen Ressourceneinsatz[END_REF]. The energy-efficient factory is an active participant of the energy conversion chain. In addition, this factory integrates holistically the objective energy efficiency in the structural and procedural organization through Energy Management.

For the above-mentioned reasons, there are new requirements which lead to numerous new energy-related planning contents and volumes in different fields of action (e.g. building, building services or manufacturing) [START_REF] Müller | Energieeffiziente Fabriken planen und betreiben[END_REF]. In this way, the complexity of this discipline rises. Therefore, there is a demand for new or improved models, methods and tools to support the planning participants.

For designing energy-efficient factories, a few scientific approaches have been developed in the last time.

The planning method for manufacturing processes in automobile industry by Engelmann concentrates the factory planning process on energy-related activities and includes action approaches (efficiency factor, reduced losses, recovery, substitution, dimensioning and mode of operation) for improving energy efficiency [START_REF] Engelmann | Methoden und Werkzeuge zur Planung und Gestaltung energieeffizienter Fabriken[END_REF]. These approaches are expanded by including "sensitization" by Reinema et al. [START_REF] Reinema | Energieeffiziente Fabriken -Ein Vorgehen zur integralen Gestaltung[END_REF]. The "EnergyBlocks" planning methodology by Weinert et al. predicts the energy consumption of production systems basing on energy profiles of different operating states [START_REF] Weinert | Methodology for planning and operating energyefficient production systems[END_REF]. The "Peripheral Model" structures the systems of a factory according to the dependence on the production program, whereby the energy consumption can be traced to its origin backwards from the peripheral processes to the main processes [START_REF] Müller | Energieeffiziente Fabriken planen und betreiben[END_REF]. The model for sustainable factories by Despeisse et al. aims at the interactions between manufacturing operations, supporting facilities and building with the help of material, energy and waste flows [START_REF] Despeisse | Industrial ecology at factory levela conceptual model[END_REF].

Besides, there are further methods and tools such as "Cumulative Energy Demand" [START_REF]VDI 4600: Cumulative energy demand (KEA) -Terms, definitions, methods of calculation[END_REF], "Life Cycle Assessment" [START_REF]International Organization for Standardization: ISO 14040: Environmental management -Life cycle assessment -Principles and framework[END_REF], "Material Flow Analysis" [START_REF] Tschandl | Integriertes Umweltcontrolling -Von der Stoffstromanalyse zum Bewertungs-und Informationssystem[END_REF] or "Material Flow Cost Accounting" [START_REF]Environmental management -Material flow cost accounting -General framework[END_REF] which are not originally developed for factory planning, but can be used for specific activities (e.g. for analysis or evaluation). Energy flow visualization tools like Sankey diagrams support planning tasks, too. A popular example is also the "Energy Value Stream Mapping" (EVSM). It integrates energy aspects like energy key figures and design recommendations in the primary Value Stream Mapping [START_REF] Erlach | Energiewertstrom -Der Weg zur energieeffizienten Fabrik[END_REF][START_REF] Reinhart | Energiewertstromdesign -Ein wichtiger Bestandteil zum Erhöhen der Energieproduktivität[END_REF][START_REF] Bogdanski | An Extended Energy Value Stream Approach Applied on the Electronics Industry[END_REF][START_REF] Schillig | Energiewertstromanalyse -Eine Methode zur Optimierung von Wertströmen in Bezug auf den Zeit-und den Energieeinsatz[END_REF]. The EVSM is a tool for energy-oriented analyzing and optimization of business processes and chains. However, the EVSM is not suitable for designing systems because of the specific principles of the Value Stream Mapping (e.g. process analysis based on a snapshot).

As a result, it was found that it is hardly possible to model factory systems with its elements, relations, items etc. considering energy efficiency with the help of existing methods and tools.

Modeling with Flow System Theory

In factory planning the factory is interpreted as factory system through system theory approaches. Complex totalities and coherences are described with the help of systems thinking [START_REF] De Weck | Systems Engineering -Grundlagen und Anwendung[END_REF]. In general, the factory is a sociotechnical system including humans and technology. In this paper, the technical systems are focused. These systems are artificial constructions that are created to fulfill a specific purpose [START_REF] Ropohl | Allgemeine Technologie -Eine Systemtheorie der Technik[END_REF]. In the case of factories, this means the production of goods.

The "Flow System Theory" is a tool for modeling technical systems and processes [START_REF] Wirth | Flexible Fertigungssysteme -Gestaltung u. Anwendung in d. Teilefertigung[END_REF][START_REF] Wirth | Vom Fertigungsplatz zur Kompetenzzelle -Voraussetzung für den Aufbau kompetenzzellenbasierter Netze[END_REF][START_REF] Schenk | Fabrikplanung und Fabrikbetrieb -Methoden für die wandlungsfähige und vernetzte Fabrik[END_REF]. Hereby, the factory as an entire system combines particular flow systems. They are mainly separated by the flow items material, information, energy as well as persons and capital. The flow systems consist of individual flow system elements (e.g. machines or facilities) which are passed by the flow items. Meanwhile, flow functions such as the basic functions transform, transport and store are executed as well as changes of property or state of the flow items or flow system elements are caused [START_REF] Schenk | Fabrikplanung und Fabrikbetrieb -Methoden für die wandlungsfähige und vernetzte Fabrik[END_REF]. Thus, the Flow System Theory offers a suitable basis for describing the factory system with its elements, functions, relations and items. Therefore, in the following, this approach will be extended in order to describe energy flow systems of factories.

Extension of Energy Flow Systems

Energy Flow Items, Functions, System Elements and Structures

In general, terms of energy are specified by physical (e.g. anergy, exergy or forms of energy) technical (e.g. energy consumption, energy losses or waste heat) or economical (e.g. primary or secondary energy) aspects [START_REF]The Association of German Engineers (VDI): VDI 4661: Energetic characteristics -Definitionstermsmethodology[END_REF]. On the one hand, flow items of energy flow systems can be described by the energy forms such as mechanical energy or heat energy. On the other hand, energy carriers such as electricity, compressed air or water can be used. This means that these mediums are allocated to the energy flow although they belong to the material flow by system theory approaches. However, due to the good measurability of the energy carriers, it is an acceptable solution which is also often used in practice (e.g. metering of consumption by Energy Management).

The basic functions of system theory are usable for energy flow systems, too. Transform converts qualitatively and / or quantitatively the input into the output, while transport and store do not change the objects themselves, but moves them through space and time [START_REF] Ropohl | Allgemeine Technologie -Eine Systemtheorie der Technik[END_REF]. Based on that, specific technical functions of energy flows such as energy provision / input, generation, conversion, storage, transportation / distribution, utilization, recovery and emission / output are derived (based on [START_REF]The Association of German Engineers (VDI): VDI 4661: Energetic characteristics -Definitionstermsmethodology[END_REF][START_REF] Crastan | Elektrische Energieversorgung 1 -Netzelemente, Modellierung, stationäres Verhalten, Bemessung, Schalt-und Schutztechnik[END_REF][START_REF] Rebhan | Energiehandbuch -Gewinnung, Wandlung und Nutzung von Energie[END_REF][START_REF] Rudolph | Energieanwendungstechnik -Wege und Techniken zur effizienteren Energienutzung[END_REF][START_REF]The Association of German Engineers (VDI): VDI 4602 Part 1: Energy management -Terms and definitions[END_REF]). The flow functions are connected as chains or cycles.

The necessary technologies (e.g. generation or storage system) are derived from the flow items and the flow functions. The energy-relevant systems of the factory are classified in these types of technologies. In addition, these systems act as the so called flow system elements of the energy flow. It should be noted that the elements belong to different flow systems. This condition allows describing the object with its different functions. For example, primarily, a manufacturing machine is a part of the material flow, but it is also an energy consumer in the energy flow and an informationprocessing computer in the information flow. Thus, the system includes several major and minor functions. Furthermore, it is important to remember that each process and each flow system element needs energy for its performance. Therefore the material and information flows are always connected to energy flows [START_REF] Schenk | Fabrikplanung und Fabrikbetrieb -Methoden für die wandlungsfähige und vernetzte Fabrik[END_REF]. Thus, the different flow systems are linked with each other through the flow system elements. The flow items pass the flow system elements one after another which results in a chain of functions (processes). By this, the flow system elements are connected with each other in space and time. Different structures of flow systems are derived from that fact. In general, an energy conversion chain starts with the input of a source and ends with the output of a drain. Meanwhile, various transformation, transportation and storage steps are processed. The energy chains differ in the energy carrier and the structure of the factory system. For example, compressed air is usually generated inhouse whereas electricity comes mainly from outside. In context of energy efficiency, closed energy cycles (e.g. by energy recovery) are preferred. By this, the energy losses are minimized.

Energy Flow Systems

The classification of the energy flow systems belongs to the flow items. Therefore, basing on the necessary energy forms, the energy carriers of the processes are determined. The principle flow system from provision to emission can be set up for every energy carrier. However, it is also possible to separate the energy flow systems by energy forms (for physical description) or classes such as solids, liquids or gases. Figure 1 summarizes the energy flow functions, system elements and systems.

Use Case

The application of the Flow System Theory for modeling energy flow systems of a factory is illustrated in this section. The "Experimental and Digital Factory" (EDF) [START_REF] Horbach | Building Blocks for Adaptable Factory Systems[END_REF] is chosen as a simplified example. The EDF consists of various manufacturing, logistics and information systems of an item-based production. For example, figure 2 shows an assembly machine which needs electricity and compressed air and generates waste heat as byproduct. Therefore, different facilities such as the supply of electricity and compressed air as well as room lighting and heating are necessary for the operation of the machine and the other production systems. The structures of the flow systems are derived from the connection between the output of sources (e.g. air compressor) and the input of drains (e.g. tank) of the several energy flows.

The electricity is delivered in the building and distributed in several areas by distribution boxes. The compressed air and heat generation is in the same building but outside the production area. The room heat is provided by wall and ceiling radiators. The waste heat of machines and facilities has to be added, too. The interchange of warm and cold air between the building and its environment happens through the building envelope as well as through doors, gates and windows. In this case, the room is declared as energy user as drain of the generation of heat. However, it is also a heat converter, transporter and storage.

As an extension, by adding quantitative key figures (e.g. energy consumption per time period), inputs and outputs (balancing) as well as capacities and demands can be compared (not shown in figure 2). Hereby, weak spots such as bulk consumers as well as under-/overcapacities can be identified.

For example, facilities such as building services are often oversized because of multiple safety factors. Moreover, in practice, there is often no adaption of the energy infrastructure after changing the production processes. However, this is very important to optimize the energy infrastructure like reducing energy losses by avoiding converting processes or oversized systems (e.g. transformers or compressors). These facts are important indicators for the energy efficiency-oriented determination of functions, dimensioning, structuring and designing of factory systems.

Fig. 1 .

 1 Fig. 1. Energy Flow Systems as Extension to[START_REF] Schenk | Fabrikplanung und Fabrikbetrieb -Methoden für die wandlungsfähige und vernetzte Fabrik[END_REF]

Fig. 2 .

 2 Fig. 2. Qualitative Model of the Energy Flow Systems of the Experimental and Digital Factory As a result, the modeled factory creates transparency of the energy flow systems. It is the basis for the energy efficiency-oriented planning, analyzing and optimizing of the factory. Furthermore, other planning tools and methods such as energy flow visualizations with Sankey diagrams can build on it.

Acknowledgements. The Cluster of Excellence "Energy-Efficient Product and Process Innovation in Production Engineering" (eniPROD ® ) is funded by the European Union (European Regional Development Fund) and the Free State of Saxony.

Summary

Sustainable production depends on energy and resource-efficient factory systems. Therefore, adequate models, methods and tools are necessary for factory planning. In this paper, the extension of the "Flow System Theory" and the qualitative description of energy flow systems are presented. As a result, the presented approach offers a suitable basis for the description of factory systems with their elements, relations and items. This is supposed to be the starting point for systematic modeling of energyefficient factory systems and for further planning, analyzing and optimization activities. Currently, the energy-relevant key figures are specified and evaluated for the quantitative modeling of factory systems (see also [START_REF] Hopf | Visualization of Energy -Energy Cards create Transparency for Energy-Efficient Factories and Processes[END_REF]). Further on, the approach is tested and validated in other practical use cases.


Ralph Riedel
email: ralph.riedel@mb.tu-chemnitz.de

David Jentsch
email: david.jentsch@mb.tu-chemnitz.de

Sebastian Horbach
email: sebastian.horbach@mb.tu-chemnitz.de

Joerg Ackermann
email: joerg.ackermann@mb.tu-chemnitz.de

Egon Müller

Egon Müller Agile

Egon Mueller
email: egon.mueller@mb.tu-chemnitz.de

Agile Planning Processes

Keywords: Agility, component-based planning, planning methods

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Production systems are decisive for the competitiveness of industrial enterprises and major characteristics of these systems are determined during their planning phase. Both, production systems and production system planning are embedded in a dynamic and complex environment, which is shaped by tremendous changes like higher market dynamics and the awareness for scarce resources. New system characteristics like modularity and adaptability [START_REF] Zaeh | Symbiosis of Changeable and Virtual Production -The Emperor's New Clothes or Key Factor for Future Success?[END_REF] are needed in response. Together with further trends e.g. in IT, resulting production systems become more and more complex and less predictable.

Resulting challenges cannot be met with the traditional planning approaches. New methods are required to cope with uncertainty, dynamics and complexity [START_REF] Riedel | Integrating Planning And Operations, Technology And People In Industrial Engineering[END_REF]. Agile principles were developed for software development to meet exactly these challenges. This paper absorbs these agile principles and transfers them to the process of production system planning. The remainder of the paper is organized as follows: The following chapter briefly summarizes production systems in general, the particularities of production system planning, as well as agile principles. Concluding requirements for new planning approaches are derived. The third chapter provides the novel agile planning model. This is followed by the description of a concrete idea of realization, the modular planning methodology and of a supportive tool, the interactive planning manual. The paper concludes with directions for further research.

2

Theoretical Background

Production Systems

The notion of production systems is based on general systems theory [START_REF] Von Bertalanffy | An Outline of General Systems Theory[END_REF] and the fundamental views on systems [START_REF] Ropohl | Allgemeine Technologie -Eine Systemtheorie der Technik[END_REF]: the structural, hierarchical and functional perspective [START_REF] Bellgran | Production Development[END_REF]. The structure includes people, technology and organization, while the hierarchy denotes several layers from the individual workplace up to the network of production facilities [START_REF] Jentsch | Strategic Capabilities as Enablers for Innovation in Production Systems[END_REF]. The function highlights that the system transforms inputs into value added outputs due to an open system border and processes (operational, management and supportive processes).

Production System Planning

The core task of production system planning, namely factory planning [START_REF] Schenk | Factory Planning Manual: Situation-Driven Production Facility Planning[END_REF], is the design, dimensioning, realization and ramp-up of production sites [START_REF] Hernández Morales | Systematik der Wandlungsfähigkeit in der Fabrikplanung[END_REF]. Due to its uniqueness, production system planning is usually executed as a project and is primarily oriented on the business objective of ensuring a strong competitiveness [START_REF] Schmigalla | Fabrikplanung: Begriffe und Zusammenhänge[END_REF]. There is a high degree of uncertainty in early stages of the planning process whereas this decreases throughout the planning project. Therefore, a systematic procedure is recommended in order to cope with the existing complexity [START_REF] Hernández Morales | Systematik der Wandlungsfähigkeit in der Fabrikplanung[END_REF]. A common generic procedure for problem solving is the systems engineering method, which consists of the steps (1) searching for and setting goals, (2) searching for solutions and (3) selecting a suitable solution [START_REF] Haberfellner | Systems Engineering[END_REF]. Most of the phase models for production system planning are based on this logic. The majority of methods used in production system planning can be characterized as analytical, which qualifies them for deterministic and static situations as well as for long-term planning cycles [START_REF] Dombrowski | Methoden-und Werkzeug-Matching zur Planung innovativer Fabriken: Von Betriebszielen zu innovativen Fabrikstrukturen[END_REF]. The usage of these methods follows a simple stimulus-response scheme (problem -method -solution) which does not reflect the actual understanding of a problem [START_REF] Dörner | Problemlösen als Informationsverarbeitung[END_REF]. Therefore, conventional planning principles are more and more limited in a complex and turbulent environment. Production systems (and also planning systems) can be described with typical characteristics from general systems theory: They are emergent, i.e. the behavior of the system can be predicted from the behavior of the single elements, which are the different protagonists and stakeholders in a planning project. They are cross-linked, i.e. the particular elements (protagonists, activities, decisions) are dependent on each other. They are contingent, i.e. the relations between the particular elements cannot be determined a priori but emerge in the course of execution. They are open, i.e. there is an exchange with the relevant environment, which in turn can be characterized as fuzzy and dynamic. They are complex, i.e. a huge amount of elements and relations and their dynamics lead to a high amount of possible alternatives for behavior (high variety). They are self-referential, i.e. the behavior of the system has effects on the system itself. They are (partly) autonomous, i.e. the system can independently make decisions regarding its own activities.

Agility and Advanced Planning Methods

Agile principles emerged first in software development projects and were established to overcome the traditional plan driven methods, e.g. the waterfall model or the spiral model [START_REF] Augustine | Agile project management[END_REF][START_REF] Lindvall | Empirical Findings in Agile Methods[END_REF]. The agile principles are documented in the Agile Manifesto [START_REF]Manifesto for Agile Software Development[END_REF] (see also [START_REF] Lindvall | Empirical Findings in Agile Methods[END_REF][START_REF] Boehm | Get ready for agile methods, with care[END_REF]) and comprise values like "Individuals and interactions are more important than processes and tools" or "Responding to change is more important than following a plan". Agile methods can be characterized as follows [START_REF] Lindvall | Empirical Findings in Agile Methods[END_REF][START_REF] Pfleeger | Software Engineering: Theory and Practice[END_REF]:

• Iterative: they deliver full functionality from the very beginning and then change it with each new release • Incremental: the system is partitioned into small subsystems by functionality and new functionality is added with each new release • Self-organizing: the development team has the autonomy to organize itself • Emergent: technology and requirements emerge throughout the development process Augustine et al. [START_REF] Augustine | Agile project management[END_REF] characterize the agile approach as an "overall humanistic problem solving approach", which assumes that all members are skilled and valuable stakeholders relying on the collective ability of autonomous teams as the basic problem-solving mechanism and minimizing up-front planning. The adaptability to changing conditions is stressed instead. Agility does not mean that there is no planning. Conversely, there is no detailed pre-determination of the whole project activities at the beginning. Planning is done phase by phase and is understood as a continuous process, which adapts to the ever changing situation [START_REF] Gernert | Agiles Projektmanagement: Risikogesteuerte Softwareentwicklung[END_REF].

Implications and Requirements for Planning Processes

The process of production system planning can be defined as a problem solving process for complex, ill-defined problems. Typical characteristics of such problem situations are a small amount of transparency, multiple and contradictory objectives, a vast number of interrelated influencing factors, fuzziness and uncertainty. As a result, it is hardly possible to oversee all relevant factors, interrelations and the problem as a whole from the very beginning. A clear picture can be developed only when working on the problem and when gaining more and more knowledge about the situation and about the system. A central requirement from this discussion is that planning processes and the underlying activity sequences must be able to cope with insecure and incomplete information and goal definitions. Further information is gained in the course of working on the planning project. With more and more detailed information, the activity plan as well as used methods and tools can be concretized. Furthermore, a common issue is the unreflected usage of methods and tools ("methodism"). Solving this problem requires the evaluation of the suitability of methods for each situation and to allow necessary adoptions.

3

Agile Planning Model

Process Model

For the subject area of production systems planning, which is characterized by a high degree of complexity and dynamics, it is barely possible to determine all necessary steps and activities in advance in a detailed manner. It is rather necessary to adapt the planning procedure to the actual situation. Therefore, the planning of activities becomes more and more accurate when eligible information becomes available in the course of the project execution. The activity plan itself cannot be considered as finalized but needs to be adapted during the whole project. This procedure is known as "planning on the road" or as "planning construction site" [START_REF] Von Der Weth | Die Sinnlichkeit des Wissens und die Weisheit der Dinge[END_REF][START_REF] Von Der Weth | Management der Komplexität[END_REF]. Transferring this principle to production system planning relates back to the agile approaches and the resulting procedure is displayed in figure 1 [cf. 21].

Fig. 1. "Planning on the road"-principle

Based on a problem definition, objectives and a vision for the planning project are developed. Hereby it has to be considered that objectives may change during project execution due to external developments or due to knowledge gained as the project progresses. Then a framework is defined based on objectives and identified boundary conditions. The expected result is specified as far as possible. For the execution, this framework needs to be detailed -but only as far as reliable information allows. Activities which are close to the point of time when the planning is carried out can be specified better than activities with a greater temporal distance. This is followed by executing the project, whereas -following the procedure of a continuous improvement process -the following steps are processed in terms of several iterations:

• Analysis of the situation, i.e. of environment, requirements, goals, actual results, system behavior and system performance etc. The advantage of this procedure is that intermediate results can be gained, which are tangible, and the results can be evaluated against initially defined goals. At the same time, internal and external dynamics are considered due to the iterations. Therefore, the project planning as well as the whole planning project can be labeled as evolutionary. The effects of the proposed procedure are shown in figure 2.

Fig. 2. Project and solution maturity development

On the one hand the uncertainty might be rather high at the beginning of a planning project. On the other hand the degrees of freedom for the later solution and the possibilities for variations are high as well. This is equivalent to a high degree of complexity [22]. Therefore, the concreteness and specificity of goals needs to be low in order to allow enough degrees of freedom for planning activities and for the later solution. This corresponds to a high degree of internal variety which fulfills the "law of requisite variety" by Ashby [START_REF] Ashby | An introduction to cybernetics[END_REF]. With an increasing degree of maturity, i.e. progress in the planning project, uncertainty is reduced, and the complexity of the task and internal variety are decreasing. As a consequence, the degrees of freedom for activities and for the solution are reduced. However, the complexity of the solution increases because intermediate solutions and sub-systems as well as their interrelations are fixed.

The described model could be summarized as reference on how agile planning processes are designed. This leaves a gap on the actual planning content which is further elaborated in the next section.

Component-based Planning as Content Model

One possibility to model the planning content can be found in component-based planning, dating back to the 1970s, when Wirth and Zeidler [START_REF] Wirth | Untersuchungen zur Entwicklung und Anwendung von Bausteinen in der technologischen Betriebsprojektierung unter besonderer Berücksichtigung ihrer Schnittparameter[END_REF] developed the vision of functional elements (components) in a modular system allowing for combination of components and their reuse across planning projects. This line of thought yielded two analytical classes: planning components and the particular elements of the production system: the object components (e.g. technology). Jentsch et al. [START_REF] Jentsch | Towards an Interactive Planning Manual for Production Networks[END_REF] showed in this respect a strong dominance of object components in the planning literature, while planning components are less developed. We will therefore focus on planning components further on. The concept of planning components was significantly enhanced by King-Kordi and Näser [START_REF] King-Kordi | Methodik zur bausteinbasierten Planung und Organisation von verfahrenstechnischen Produktionssystemen[END_REF][START_REF] Näser | Component based planning of manufacturing plants[END_REF] with the perspective of socio-technical systems and yielded the following views: The target view pertains to specific and measurable goals of a component or its deployment. Tasks or operations are represented in the functional view. A function utilizes tangible and intagible resources (people, knowledge, machinery etc.) summarized with the resource view. The structural view refers to different hierarchical layers of the production system. Finally, the yield view summarizes the (potential) output of a system in terms of products or services. Planning components can be further detailed based on this view concept (see figure 3). This generic model of a planning component distinguishes further kinds of resources such as methods and particular knowledge, which is necessary to fulfill the planning function.

The outlined component model enables the modularization of the planning process. Hence, planning components provide the generic building block to fill agile planning processes with required tasks. This could be done either with a blank-sheet-approach in every project or building upon the documentation of prior experience. The next section follows the second suggestion and introduces a way to access experience structured in a component-oriented manner.

Interactive Planning Manual

The general idea of the interactive planning manual is to support on the one hand inexperienced project members with a pool of completed planning processes. Here, the user may check e. g. how to use a specific planning tool. On the other hand, the component oriented structure allows for a simple reuse of particular building blocks by integrating them into a new planning project. Users receive consequently a bridge between task specific knowledge and its application during the planning process.

The manual follows a modular structure enabling for example the classification of the planning problem in order to find in a library suitable building blocks or combinations of them as planning processes. Further modules allow for definition of new components and processes as well as the tracking of project progress. Further details are given by Jentsch et al. [START_REF] Jentsch | Towards an Interactive Planning Manual for Production Networks[END_REF]. The manual was implemented in a standard enterprise content management system and is currently being prepared for a laboratory test with students utilizing the manual during a long-term case study on production system planning. The test is intended to yield further insights in system acceptance and its suitability for agile planning processes.

Conclusion and Outlook

The dynamics of the environment and the complexity of modern production systems induce the requirement to reconsider planning methods. The combination of these observations with a modern understanding of human problem solving yields a new approach towards planning the production systems of the future. However, the conceptual work on planning processes does not stand alone. There are conceptual synergies to component-based planning and those synergies will be further investigated by means of laboratory tests in the near future.

  • Fixation of an appropriate procedure/ development of an activity plan for the next steps; hereby the procedure is concretized stepwise while executing the planning project • Definition of sub-goals consistent with the overall goals of the project • Execution of the activities, i.e. of particular planning steps • Evaluation of results, based on pre-defined success criteria • Restructuring of the entire solution/ concept, based on the insights gained whilst performing the activities • Actualization of the overall planning procedure

Fig. 3 .

 3 Fig. 3. General model of a planning component displayed as a function diagram (based on [26])


Matteo Mario Savino
email: matteo.savino@unisannio.it

Antonio Mazza
email: antonio.mazza@unisannio.it

Sustainability Enhancement through Environmental Impacts Evaluation

Keywords: Sustainability, Environmental impacts, Fuzzy techniques

The work is aimed to propose an approach toward the evaluation of environmental impacts enhancing sustainability and the process of environmental assessment in an optic of sustainable development. A set of environmental aspects applicable to a firm activity to define the overall environmental impact are selected and processed through a fuzzy approach aimed to reduce subjective judgments. The procedure is based on a vectorial graph allowing to organize data and define a structured data analysis. An action research conducted in a genetic research center is conducted to evaluate the effectiveness of the proposed approach.

Introduction

Environmental Impact Assessment (EIA) is an efficient method for preserving natural resources and protecting the environment. Therefore, most developed countries have introduced EIA into their regulations and the consequent approval of all projects [START_REF]Environmental Impact Assessment Guidelines[END_REF]. An EIA can be a useful tool to promote the goals of sustainable development as the process includes assessments of the effects of a project development and includes local opinion and knowledge [START_REF] Lee | Environmental assessment in developing and transitional countries[END_REF]. Too often in the past, development projects have taken place in developing countries without EIA studies or conscious efforts to predict and mitigate adverse environmental impacts [START_REF] Appiah-Opoku | Environmental impact assessment in developing countries: the case of Ghana[END_REF]. Nowadays, the trend toward industrial sustainability is observed in many matters requiring a shift in managerial and methodological approaches [START_REF] Garetti | Sustainable manufacturing: trends and research challenges[END_REF]. Despite criticism, the influence on decision making still maintains its importance as a criterion of effectiveness of the EIA system because of its valuable attributes -quantifiability with descriptive statistics and understandability to involved parties [START_REF] Heinma | Effectiveness of Environmental Impact Assessment system in Estonia[END_REF].

The work presents a methodical approach to enhance sustainability and environmental assessment process. It selects a set of environmental aspects which can be applied to a firm activity, defining its overall environmental impact. This is done thorough a fuzzy approach aimed to reduce subjective judgments rising along multi criteria processes. The procedure is based on a vectorial graph allowing to organize data and define a structured data analysis.

The work is organized in five sections: after a literature review on sustainability, environmental management approaches and application fields of fuzzy techniques, the third section shows the methodical proposal for the sustainability environmental as-sessment, the fourth section describes the action research conducted in a genetic research center and its main results while the last section deals with conclusions.

2

Literature Review

-Sustainability and Environmental Management Systems

The term "sustainable development" was coined by the IUCŃs 1980 World Conservation Strategy stating that "for development to be sustainable it must take account of social and ecological factors, as well as economic ones" [START_REF] Iucn | World Conservation Strategy[END_REF]. In this sense, environmental sustainability can be considered one of the three pillars of sustainable development, together with social and economic one [START_REF]UN, Report of the World Summit on Sustainable Development[END_REF]. White and Nobel [START_REF] White | Strategic environmental assessment for sustainability: A review of a decade of academic research[END_REF] examined the strategic environmental assessment (SEA) sustainability relationship over the past decade, focusing in particular on the incorporation of sustainability in SEA while Apolloni and Savino [START_REF] Apolloni | Environmental plant optimization in small sized enterprises through an operative framework[END_REF] analyzed the motivations that can lead Small and Medium Enterprises to implement an Environmental Management System.

The progress of improving sustainability can be evaluated by a set of appropriate environmental sustainability indicators with reasonable target values [START_REF] Walmsley | Framework for measuring sustainable development in catchment system[END_REF]. Fischer and Gazzola [START_REF] Fischer | SEA effectiveness criteria -equally valid in all countries? The case of Italy[END_REF] or Bond et al. [START_REF] Bond | Framework for comparing and evaluating sustainability assessment practice, Sustainability Assessment: pluralism, practice and progress[END_REF] identified effective sustainability assessments involving procedural, substantive, transactive and normative elements. Manzini et al. [START_REF] Manzini | Model for evaluating the environmental sustainability of energy projects[END_REF] developed a model for assessing the environmental sustainability of energy projects not only integrating environmental sustainability indicators over the lifetime of the project but also taking into account the influenced area by the local energy project. Moldan et al. [START_REF] Moldan | How to understand and measure environmental sustainability: Indicators and targets[END_REF] pointed out the attention toward different approaches and types of indicators developed for the assessment of environmental sustainability to set targets and then "measuring" the distance to a target to get the appropriate information on the current state or trend. On the same line, Caniato et al. [START_REF] Caniato | Environmental sustainability in fashion supply chains: An exploratory case based research[END_REF] developed a research aimed at identifying the drivers that push companies to adopt "green" practices, the different practices that can be used to improve environmental sustainability, and the environmental Key Performance Indicators (KPI) measured by fashion companies. Cai et al. [START_REF] Cai | Exploring the role of IT for environmental sustainability in China: An empirical analysis[END_REF] explored the role of information technology (IT) for energy and environmental sustainability for its crucial role in the energy consumption and environmental related issues while Petrini and Pozzebon [START_REF] Petrini | Managing sustainability with the support of business intelligence: Integrating socio-environmental indicators and organizational context[END_REF] managed sustainability with the support of business intelligence.

-Fuzzy approaches for environmental assessment

Over the last years several approaches based on fuzzy logic have been developed to assess environmental impacts, indicating the potential of fuzzy logic in this field [START_REF] Peche | Environmental impact assessment by means of a procedure based on fuzzy logic: A practical application[END_REF]. As an example, Peche and Rodriguez [START_REF] Peche | Environmental impact assessment by means of a procedure based on fuzzy logic: A practical application[END_REF] presented a fuzzy procedure specifically developed to control and minimize the inconsistencies that arise from the available information on environmental impacts while Blanco et al. [START_REF] Blanco | AIEIA: software for fuzzy environmental impact assessment[END_REF] developed an EIA computational application based on fuzzy logic, which takes into account either the quantitative or the qualitative assessments of each environmental impact. Similarly, Larimian et al. [START_REF] Larimian | Developing a fuzzy AHP model to evaluate environmental sustainability from the perspective of Secured by Design scheme -A case study[END_REF] proposed a model to achieve environmental sustainability using a fuzzy Analytic Hierarchy Process (AHP) to prioritize environmental factors. A fuzzy decision aid model for environmental performance assessment in waste recycling is developed by Nasiri and Huang [START_REF] Nasiri | A fuzzy decision aid model for environmental performance assessment in waste recycling[END_REF] while Kaya and Kahraman [START_REF] Kaya | An integrated fuzzy AHP-ELECTRE methodology for environmental impact assessment[END_REF] proposed an environmental impact assessment methodology based on an integrated fuzzy AHP-ELECTRE approach where weights of the assessment criteria are determined by a fuzzy AHP procedure. In general terms, applications of fuzzy techniques can also be found with reference to maintenance [START_REF] Savino | A model for the optimization of maintenance costs through fuzzy techniques[END_REF], [START_REF] Brun | Integrated system for maintenance and safety management through FMECA principles and fuzzy inference engine[END_REF] or economy [START_REF] Yadav | Effects of learning on optimal lot size and profit in fuzzy environment[END_REF].

Methodological proposal

A multi decision problem is featured by different and relevant aspects where multiple objectives can be involved. A new methodology has been proposed aimed to overcome the limits of common techniques in order to obtain a fair and objective evaluation of such goals. Fuzzy logic has been applied to a vectorial graph which represents a tool of simple applicability providing at the same time useful information in visual form. Such method allows the auditor to identify from a qualitative and quantitative point of view the most critical environmental aspect for the organization and together the industrial activities involved in such aspect.

As shown in Fig. 1, the graph is organized in two levels of nodes: the first level represents the firm activities which can be in [1,N] interval while the second one represents the environmental aspects in [1,M] interval. Eight environmental impacts has been selected: (i) fuel consumption, (ii) water consumption, (iii) power consumption, (iv) air pollution, (v) special wastes, (vi) smell, (vii) noise and (viii) dangerous materials.

Arches link the two levels of nodes indicating that a given activity generates an impact on one or more environmental aspects. In Fig. 1, the third activity generates impact over the fifth, sixth and eight environmental aspect. In addition, a four position vector can be associated to each arch indicating the (i) legislation compliance -LC, (ii) impact entity -IE on the basis of detectability, dangerousness and importance, (iii) control degree -CD according to type of control and reaction capability, and (iv) territorial sensitivity -TS considering territorial context and claims frequency: {LC, IE, CD, TS}.

Fig. 1. Vectorial graph

As an example, Impact Entity is based on the following three parameters: (1) Consistency of the impact-C, (2) Dangerousness -D and (3) Degree of Detectability -DD. C assumes the following values according to some conditions related to consistency: 1 -negligible, 2 -low, 3 -medium, 4 -high. D is the dangerousness toward environment and health assuming values: 1 -no dangerousness, 2 -dangerous , 3 -very dangerous, 4 -extremely dangerous. Finally, DD can assume values 1 in case of immediate and simple detectability, 2 in case of detectability performed by proper instruments, 3 in case of detectability through bio-chemical analysis and 4 in case of impossible detectability. The value IE is obtained by the average of these three parameters.

The four position vector is filled for each activity and its corresponding environmental aspects; for this reason, to each second level node, i.e. the environmental impact, an impact matrix [nx4] can be associated according to the number of n (n<=N) activities linked to the environmental impact. The matrix is elaborated to obtain an overall vector referred to the environmental impact, i.e.

{max[LC],

[ ] [ ] , max[CD], max[TS]}. Elevating to its maximum value the average of IE favors the accountability of dangerousness within the process of impact evaluation. LC, CD and TS are evaluated on a [START_REF] Apolloni | Environmental plant optimization in small sized enterprises through an operative framework[END_REF][START_REF] Bond | Framework for comparing and evaluating sustainability assessment practice, Sustainability Assessment: pluralism, practice and progress[END_REF] scale for IE a fuzzy set on a [START_REF] Apolloni | Environmental plant optimization in small sized enterprises through an operative framework[END_REF][START_REF] Bond | Framework for comparing and evaluating sustainability assessment practice, Sustainability Assessment: pluralism, practice and progress[END_REF] scale is used.

The final evaluation of the environmental impact is performed considering the overall vector. Values of the first, third and last column are compared to a [START_REF] Apolloni | Environmental plant optimization in small sized enterprises through an operative framework[END_REF][START_REF] Bond | Framework for comparing and evaluating sustainability assessment practice, Sustainability Assessment: pluralism, practice and progress[END_REF] scale indicating possible corrective actions to be planned and the respective timing: 1) Not necessary actions, 2) Long/Medium term actions, 3) Short term actions, 4) Urgent actions. On the other hand, the second value of the overall vector is evaluated according to a triangular or trapezoidal fuzzy function by the use of Matlab, a mathematical software. A flow diagram summarizing the methodology is shown in Fig. 2.

Fig. 2. Methodology flow diagram

According to the selected environmental aspects and the firm activities to be investigated, the corresponding vectorial graph is built through whom it is possible to obtain the impact matrix. Values of relative overall impact vector are processed by a classical fuzzy process made of a fuzzyfication step, an inference engine according to fuzzy rules and a de-fuzzyfication step. Such process allows to define the criticality of the selected activity with respect to the environmental aspects.

Application case and results

Proposed methodology has been validated on field in a genetic research center. For simplicity and according to environmental assessment, in the present work the analysis has been focused on two firm activities, i.e. (1) animal breeding and (2) laboratories, and six environemenatal impacts, as shown in Fig. 3.

Fig. 3. Vectorial graph -Application case

Each activity has been related to the environment aspect filling the respective impact vector to define values LC, IE CD and TS. The vector has been processed by a fuzzy analysis through the use of fuzzy rules and functions, as shown in Fig. 4. Values have been set through a tuning performed in accordance to firm management on the basis of the possible outcomes. More in details, short term and urgent interventions have been preferred defining not balanced value intervals for IE.

For the specific case study, the analysis of the de-fuzzyfied overall impact vector allowed to state the following:

Conclusions

The present work defines a methodological proposal for the evaluation of environmental impacts with the objective to improve firm sustainability. Such goal is achieved with reference to resource usage and pollution reduction in order to prevent environmental impacts such usage of dangerous material or waste production.

A set of environmental aspects have been selected which can be taken as a reference while conducting an environmental assessment; by the use of such aspects it is possible to define the impacts that a given firm activity generates. A method matching the activities and the relative environmental impacts is proposed through a vectorial graph providing useful information about the entity of each impact. A further analysis of these multiple impacts provides a value indicating the overall environmental impact of each activity. To overcome subjectivity in such multi criteria process, a fuzzy technique has been proposed and developed through the use of a mathematical solver.

The methodology has been validated in a real test case considering a genetic research center and focusing on two main activities and six environmental impacts. The analysis has allowed to define intervention area of the firm with respect to some lacks in terms of environmental aspects which the procedure has allowed to highlight. Together with intervention area, the methodology has provided a timing of corrective actions to be performed to front weak aspects emerged during the analysis. The support provided by fuzzy set theory application allows to reduce and overcome uncertainties and subjectivity in the values assignment procedure which can be found in other models.

In general terms, such methodology is well suited to be used in a wider environmental management system where in accordance to actual regulations a certification process is sometimes necessary. It can provide a strong support to a criticality analysis of environmental aspects, focusing on intervention area and defining corrective actions ad respective timing through a fair and objective analysis.

Fig. 4 .

 4 Fig. 4. Fuzzy rules implemented thorough a mathematical solver

•

  Legislation compliance necessitates of short term interventions for power and fuel consumption, special wastes and dangerous materials while of long/medium term interventions for water consumption and air pollution; • Impact Entity necessitates of short term interventions for power and fuel consumption, dangerous materials, special wastes and air pollution while no interventions are needed for water consumption; • Control Degree necessitates of short term interventions for fuel consumption and of long/medium term interventions for power and water consumption, special wastes, dangerous materials and air pollution; • Territorial Sensitivity necessitates of long/medium interventions for power consumption while no interventions are needed for the remaining environmental aspects.


Lucio Zavanella
email: zavanell@ing.unibs.it

Ivan Ferretti
email: ivan.ferretti@ing.unibs.it

Simone Zanoni
email: zanoni@ing.unibs.it

Laura Bettoni
email: laura.bettoni@ing.unibs.it

A Queuing Approach for Energy supply in manufacturing facilities

Keywords:

Nowadays increasing energy efficiency is one of the main objectives of manufacturing systems so as to remain competitive despite of the foreseen increase in energy prices for the next years. We propose a novel queuing-based model for the appreciation of the energy consumption on a company base, so as to optimize the total energy costs due to electricity utilization. We propose an analytical model based on the extension of the M [x] /M/∞ model where arrivals of the queuing model represent the statistical distribution of switch-on of a generic set of machines and departures represent statistical distribution of the resources switch-off. This models can be easily used to assess and establish the contract with the energy supplier under optimal parameters of contractual power, based on its tariff components. Numerical examples are offered to show the applicability of the proposed model.

Introduction

Manufacturing systems are usually organized in multiple departments and in many of them there are multiple machines and each machine has its own electricity demand pattern over time. This energy requirement is usually different from machine to machine and it depends on the power of the machine and from the relative duration of different state (idle, standby, load level, machining parameters, maximum speed) produced by different product routings which encompass it. The practical problem lies on the fact that a company does not have access to endless amounts of electricity, or that it is already subject to a contract with the supplier, which generally provides a maximum level of supply which, when exceeded, determine substantial penalties to the user. For this reason, the objective is to create a model, of simple use, able to assist energy managers who, thanks to the model itself, may simulate different production scenarios under different electricity supply contracts, so as to minimize the expected costs. In particular, the application of the model implies the calculation of the following performance indices:

• probability of exceeding a specific level of power requirement;

• expected average-power requirement;

• economic evaluation and comparison of different electricity supply contracts.

Dietmair and Verl [START_REF] Dietmair | A generic energy consumption model for decision making and energy efficiency optimisation in manufacturing[END_REF] introduced a generic method so as to model the energy consumption behavior of machines based on a statistical discrete event formulation. The parameter information required to characterize the discrete events can be obtained by a small number of simple measurements or by a degree of uncertainty from the machine and component documentation. Bruzzone et al. [START_REF] Bruzzone | Energy-aware scheduling for improving manufacturing process sustainability: A mathematical model for flexible flow shops[END_REF] proposes the integration of an EAS module (energy-aware scheduling), within an advanced planning and scheduling (APS) system, incorporating a model to control the shop floor power's peak for a given detailed schedule; unavailability of the actual machine tools power profile and idle energy, requires to assume constant mean power demand for each job. The goal of EAS is to optimise the given schedule from the viewpoint of the energy consumption, while keeping the given assignment and sequencing fixed. The problem is discussed by minimizing the shop floor power's peak, while limiting the possible worsening of the two scheduling objectives (tardiness and makespan minimisation), and modeling it as a Mixed Integer Programming (MIP) problem. He et al. [START_REF] He | A modeling method of task-oriented energy consumption for machining manufacturing system[END_REF] proposed a modeling method of task-oriented energy consumption for machining manufacturing system. The energy consumption characteristics, driven by task flow in machining manufacturing system, are analyzed, thus describing how energy consumption dynamically depends on the flexibility and variability of task flow in production processes. The results show a valuable insight of energy consumption in machining manufacturing system, so as to make robust decisions on the potential for improving energy efficiency. Prabhu et al. [START_REF] Prabhu | Modeling Green Factory Physics -An Analytical Approach 8[END_REF] proposed a queuing models to predict energy savings in serial production lines where idling machines are switched to a lower power state in serial production lines consisting of machines with Poisson arrival and exponential service time.

Prabhu and Jeon [START_REF] Prabhu | Modeling Green Fabs -An Queuing Theory Approach for Evaluating Energy Performances[END_REF] extended their previous model and generalize the energy aware queuing model to a re-entrant structure. Moreover, an application to a semiconductor factory is presented. The paper is organized as follows: section 2 presents the system and the main assumptions. In Section 3, a model is introduced and the energy cost function are presented. In Section 4 a numerical example is offered. Section 5 summarizes the paper content, describing possible future research directions.

The system

One of the most important managerial actions to reduce energy costs is the proper fitting of the energy supply contracts with the plant requirements. In particular, these contracts generally provide the maximum thresholds that, when exceeded, require payment of penalties. For example, in the electricity contract there is a threshold for the maximum power contemporary required by the loads, also defined as "contractual power". The problem faced in this paper is to determine the appropriate contractual power considering the variability in electricity usage (given by stochastic distribution of loads switch on and switch off), which is generally not predictable a priori. A simple model is then proposed, derived from queuing theory, which allows to model adequately the variability of the electrical energy consumption and to suggest the contractual power more correct adopting a probabilistic approach to the economic optimisation.

Queuing theory is capable of providing models (and consequent insights) to predict the systems behavior when they asked for providing services at randomly occurring demand. In a queuing system, entities (data, parts, jobs, etc.) arrive in the system and require some form of service (operations, machining, assembly processes, etc.). When more demand for service occurs at a level larger than resource available for service, a queue is formed. The idea is to model the variability in electricity usage by a queuing system, where the entities are represented by the power devices in the department considered. In particular, the distribution of arrivals describes devices activation, while the distribution of services describes their shutdown. Given the random nature of the two distributions, two exponential distributions are taken. Obviously, this assumption is valid in departments where it is not predictable a priori the consumption of these devices. The arrival and the service rates are thus expressed as power per unit of time. In such a queuing system, the average number of entities represents the average power required useful for determining the contractual power. The queuing system defined is depicted in the following figure.

Fig. 1. System considered in the analysis

In order to obtain tractable models, it is assumed that the arrival process and the service process are stationary. Figure 2 shows how the electric power required by a generic machine is variable (for reference see He et al, 2012) because, in general, power varies with respect to the states in which the machine is (state of heating, pressing state, stand-by, etc..). In order to simplify the problem, it may be useful to define the required power constant as shown below. In the next section we present the queuing model proposed.

Fig. 2. Variation of the required power in the machine states 3 Model

The model of the queuing theory proposed for the solution of the problem invesigated is [ ] / /∞. In particular, we assume, in addition to the assumption that the arrival stream fits a Poisson process, that the actual number of entities in any arriving module is a random variable , which takes on the value with probability , where is a positive integer, 0 < < ∞. Because the Poisson process models the arrival of oneand only one -entity per arrival event, we consider a bulk input. This assumption models the simultaneous activation of multiple devices or the activation of a device at different power levels. Moreover, we assume infinite services, because every device is activated instantly (i.e., no queue are permitted).

After the definition of the birth-death process and the equations governing the system, using a generating function approach (Gross and al., 2008), it is possible calculate the average number of entities in the system = ∑ ( . ) . This performance is the average power required. Following, we show the rate balance equations related to the states 0-1 and the states 1-2:

= → = (1)
+ (1 -) = # (2)
Using the Little's law and assuming infinite services, the average waiting time in the system is:

$ = 1 (3)
In the industrial context, the power required by the department, or company, is always satisfied by the supplier of the electric power, even if this exceeds the predetermined contractual power. Whenever the power required exceeds the threshold, the consumer will be subjected to a penalty. The objective of this analysis is to verify, in which cases it may be convenient to pay penalties instead of having a greater contractual power and vice versa.

In order to compare the cost of energy in case of exceeding or not the power threshold, we define the following objective function. The model is not based on energy consumption, but the probability of being in a given state and thus requiring power at a given level, which is why the formulae that follow are based on these probability distributions. In particular, the objective function is composed by two parts: the first is the evaluation of the cost (expressed as a rate €/h) below the threshold, while the second is the evaluation of the cost (expressed as a rate €/h) above the threshold. The penalty is paid only for the delta of power exceeding the threshold.

%%& = ∑ . ' . ( + ∑ ()* (+ . , . (-)* -) (4)
where:

• ' is the energy cost below the threshold

[€/h] • , is energy cost above the threshold [€/h] • -is the threshold [kW]
• -)* is the maximum power beyond the threshold [kW]

Numerical Analysis

So as to study the behavior of the model, we performed a numerical analysis to investigate how the model parameters influence the optimal solution of the energy model. The scenario considered consists of a production system with machine tools with a required power that can be represented by 4 different batch sizes with linearly increasing probability distribution, the rate of arrivals is equal to 10 kW/h, which is equal to 10 events of 1kW per hour, while the service rate is 35 kW/h. The average power required L is equal to 12.99 kW. Given a particular energy supply contract two options can be possible:

• adopt a supply contract with 20 kW of contractual power and pay all consumption without incurring any penalty • adopt a supply contract with 10 kW of contractual power, thus paying at a base fee the power used up to 10 kW and pay a penalty for the exceeding.

According to the formulae shown in the previous section, the expected energy cost for the first case is equal to:

Conclusion

This work faced the problem of the electrical energy supply contract in an industrial environment. The main objective is to properly adjust the contractual power in a manufacturing system composed by several electrical resources and analyze the power consumption probability distribution applying queuing theory. The electrical power demand has been viewed as a physical user (client) that requests access to a system (electrical network) for a certain period of time (thus resulting in the consumption).

The mathematical model of a [ ] / /∞ queue fits this case and it helps in capturing the dynamics of the system. Moreover, using synthetic formulae with probability functions it is possible to look for the most convenient electrical supply contract, given a set of offer available from the suppliers.

  Thus, in this particular case it is more convenient to apply a lower power contract and pay the penalties due to the probability of exceeding the contract power. Assuming the same scenario above, but at an arrival rate equal to 11 kW/h instead of 10 kW/h, the result changes as follows. Unlike the previous case, the average power becomes very high (35.04 kW instead of 12.99 kW), despite of the value of the arrival rate increased only by one unit (from 10kW/h to 11kW/h). The contractual powers assume the default values of 30kW and 40kW. In the first case the cost per hour is equal to 3.89 €/h, while with a greater contractual power the expected cost is equal to 4.43 €/h.

	%%&(10 $ /0 123/1) = + ;	9	: . 0,2 ( < . 1,5 € $ℎ € . 10 $ $ℎ . (12,99 -10) $ = 1,46	€ ℎ
	%%&(20 $ /0 123/1) =	9	. 0,2	€ $ℎ	. 13 $ = 1,61	€ ℎ
	While, if the second option is applied, the result is as follows:


Hendro Wicaksono
email: hendro.wicaksono@kit.edu

Tim Belzner
email: tim.belzner@students.kit.edu

Jivka Ovtcharova
email: jivka.ovtcharova@kit.edu

Efficient Energy Performance Indicators for Different Level of Production Organizations in Manufacturing Companies

Demands for lower CO2 emissions due to the climate change and the rising of energy prices force manufacturing companies to deal with the energy issue. Energy management, where one of the tasks is energy efficiency evaluation, can help the companies to overcome the issue. This paper presents holistic metric to evaluate the energy efficiency in manufacturing companies, which considers the different organization level of production, such as machine or equipment level, production line level, and factory level. As the size of the scope and the number of observed factors vary, the metric provide flexible criteria to select relevant variables. The developed metric could be used to simulate and to compare energy efficiency of different production facilities, lines, and factories in a single company. The metric is an instrument to recognize how energy (in) efficient is a production system, so that adjustments may be made in the planning and management to achieve the energy savings.

Introduction

The global climate change, which is caused by the increase of CO2-emmisions, is a crucial issue in 21 st century. Every sector including manufacturing is demanded to reduce the CO2-emissions. One of the important measures to achieve this is to use the energy more efficiently. However this is not an easy task. Demands on various and customized products have made the manufacturing processes more complex. The processes are often very energy intensive and therefore expensive. In order to minimize expenses in production, energy consumption has to be regulated. The corporate energy management is designed to support companies in this task. Energy management defines the sum of all processes and measures which are developed and implemented to ensure minimal energy consumption by given demand [START_REF]Energiemanagement[END_REF]. An energy management system (EnMS) is a systematic way to define the energy flows and as a basis for decision for investments to improve energy efficiency. Through an EnMS, the energy policy, planning, implementation and operation, monitoring and measurement, control and correction, internal audits, as well as a regular management review are designed and executed [START_REF] Kahlenborn | DIN EN 16001: Energy Management Systems in Practice -A Guide for Companies and Organisations[END_REF]. The standard ISO 50001 describes the requirements for EnMS for industrial companies. Energy management systems that are built according to this standard can either be integrated into existing systems or implemented independently. The standard is based on the PDCA cycle (Plan-Do-Check-Act), where continuous improvement is possible [START_REF] Milgram | Plan, Do, Check, Act: The Deming or Shewhart Cycle[END_REF]. This paper presents the development of energy performance indicators (EPI) to help the manufacturing companies in "Check" phase of the cycle. The indicators are used to measure energy efficiency degree of different levels of production organization in a single manufacturing company, namely machine or production facility, production line, and factory. The indicators provide a flexibility that allows the selection of relevant variables and definition of the observation system.

In section 2 we present our analysis result on the existing energy efficiency measurement concepts and identify their advantages and deficiencies. Section 3 describes the first necessary step to calculate the energy efficiency metric namely the modeling the system boundaries and components. In section 4 we list the factor influencing the figure calculations. And then, we demonstrate the calculation of the figures for different levels of production organization in section 5.

Related Work

Figures to measure energy efficiency can be classified into absolute and relative figures. Absolute figures consider only the output values, whereas relative figures take into account the dependency of the output value with the given input. Further categories of energy efficiency figures are classification and relational figures. Classification figures represent the energy consumption portion of smaller part to the bigger part of a system. Relational figures describe the energy consumption based on cause-effect relation, for instance, energy consumption per produced product piece [START_REF] Kals | Betriebliches Energiemanagement -Eine Einführung[END_REF]. German Engineer Association (VDI) introduced technical figures related to energy evaluation namely energetic efficiency degree, utilization ratio, and specific energy demand [START_REF]Verein Deutscher Ingenieure (VDI): Energetic characteristics: definitions -terms -methodology[END_REF].

The energy requirements relative to the amount of product depends very much on the type and quality of the product. For example production of a car requires more energy than a lamp. An approach that solves this problem is energy efficiency figure presented with the equation (1) [START_REF] Müller | Energieeffiziente Fabriken planen und betreiben[END_REF].

(

This energy-efficiency measure also allows a comparison of systems that produce completely different products and can be applied in the machine level as well as in the factory level, even if different products with different quantities or qualities are produced. Some metric systems have been developed to evaluate the energy efficiency for entire industry sectors. The Energy Performance Indicator (EPI) from Energy Star considers the energy efficiency at the factory level. The data are reported by the participating companies each year and then can be compared in a tool. It is not possible to evaluate energy efficiency in operation level, since production process and equipment change very quickly. To ensure comparability between different factories, separated benchmarking for different industrial sectors is created, for instance, for automotive industry [START_REF] Boyd | Development of a Performance-Based Industrial Energy Efficiency Indicator for Automobile Assembly Plants[END_REF], glass manufacturing [START_REF] Boyd | Development of a Performance-based Industrial Energy Efficiency Indicator for Glass Manufacturing Plants[END_REF], and cement industry [START_REF] Boyd | Development of a Performance-based Industrial Energy Efficiency Indicator for Cement Manufacturing Plants[END_REF][START_REF] Boyd | Measuring Improvement in the Energy Performance of the U[END_REF]. The Energy Star EPI is a good tool to evaluate the energy efficiency to compare different factories in the same industry sector. However, in order to determine cause-effect relation, the concept is not suitable. The Odyssee Energy Efficiency Index (ODEX) is an index that determines energy efficiency progress in the main sectors (industry, transport, households) and the overall economy (all consumers) [START_REF]European Energy Efficiency: Analysis of ODYSSEE indicators[END_REF]. ODEX is designed to track developments of energy efficiency over time in the industry. It is not able to compare individual companies or factories. The International Energy Agency (IEA) developed MEEP (Measure(s) of Energy Efficiency Performance) consisting four types of figures that differ in their potential applications [START_REF] Tanaka | Assessing Measures of Energy Efficiency Performance and their Application in Industry[END_REF]. The fourth MEEP figure is interesting, since it can be estimated, how energy efficient is a factory or an entire industry sector. However, this figure cannot be applied at the machine level.

System Modeling

To develop energy efficiency metric for a single manufacturing company, first it is important to define the system boundaries and a model that covers the different organization levels of production. In our work we extend the UPN model [START_REF] Fünfgeld | Energiekosten im Betrieb[END_REF] to UPNT model and consider the machine, production line and factory level. The basic elements of the UPNT model are energy conversion (U), production (P), ancillary (N), and transport (T) facilities. Only through the interaction of these facilities the production processes are conducted. Energy conversion facilities convert energy and deliver it in a processed form to the other systems (N, T, P). Examples of energy conversion facilities include tranformators, air compressors, steam generators, power plants etc. If an energy conversion facility is connected to internal energy network, where other sources including the ones from utility companies are connected, mix-costs from different sources is resulted in the network. Fig. 1 illustrates the method to calculate the mix-costs in the company's internal energy network.

Ancillary facilities provide the necessary conditions to achieve an unobstructed production. They serve the production directly or operating condition generally. Ancillary facilities include lighting, HVAC systems, coolant pumps, exhaust systems, etc. Facilities that are not located directly in production, such as kitchen appliances in the canteen are also considered as ancillary facilities. In principle, the transport facilities are included as ancillary facilities. However, they differ, because instead of being directly used by production facilities, they transport products to the next process step. It is very important, in particular, if the manufacturing company has different sites.

Production facilities are the most important entities in the production, because they are used to manufacture the product. Based on the efforts in production process that executed by production facilities, the customers pay the production costs and the manufacturers earn the money. However, the production facilities depend on functioning conversion, ancillary and transport facilities to work. As the basic in calculating energy efficiency degree, we use the ratio of output-input (effort) that is expressed with equation ( 2):

(2)

There are many factors that are considered influencing the input and output in our energy flow model. We consider the following factors influencing the production outputs that are used as the numerator in calculating the efficiency.

x Product quantity and characteristics. The influencing factors are not only product quantity, but also the qualitative characteristics of the products. Both of the influencing factors should be integrated as a unified parameter.

x Generated added value. The generated added value may not be known for each stage of production directly. It can be calculated by considering the values of incoming to outgoing products. x Output Energy. The output of conversion facility is directly the amount of generated energy that is delivered in the production. x Distance traveled between production facilities. Transport facilities are needed to carry the products or materials between production facilities. It is not an adding value, since it does not change the product value.

The following list presents the factors that are taken into account in our work affecting the energy effort to manufacture products and determine the denominator in calculating the energy efficiency.

x Energy demand or consumption. The energy demand corresponds to the energy effort in executing a production process. It also can be easily measured. It is calculated through a certain time as integral of power over time. x Energy form. Various energy forms have different characteristics, application areas, and costs. x Energy price. The energy price takes into account all costs incurred in the generation of every form of energy.

x Energy peak load. The energy peak load plays role in the costs incurred in providing the energy to the company. It corresponds to which amount of power should be provided to the company in a certain time, in order to ensure that the production activities are still running properly. x Emission certificate efforts. Companies are demanded to get emission certificate because of the pollution and climate change issue. The costs to achieve emission certificate is considered in the calculation.

Calculation Model and Results

In the section we describe the developed methods to calculate the energy efficiency figures or Energy Performance Indicators (EPI) on machine, production line, and factory level. The calculations are based on the main principal as described by the equation ( 2).

The main function of production facilities or machines is to create a new product or product part from source materials. As mentioned before, it is not appropriate to consider only product for evaluating energy efficiency. The solution is through considering also values generated by the production facilities. For example, if particleboard manufactured from waste wood, the value is greater than when they are produced from fresh wood. Fig. 2 depicts the energy flow model for machine level. From it can be seen, that the inputs of a production facility is made up from direct and an indirect ones. The direct inputs are from the energy and load supplied by utility companies or conversion facilities. The indirect inputs are generated due to the fact that the production facility receives the outputs of ancillary and transportation facility. The energy cost of a production facility is also ascribed from the output of the connected ancillary and transport facilities. The inputs of a production facility include proportion of used energy and load cost from ancillary and transport facilities, energy provided by conversion facilities, and energy form. Equation (3) calculates the energy performance index on machine/production facility level. The ratio D i can be defined by the energy modeler or estimated for some kinds of facilities such as lighting and heating.

On the production line level, the efficiency calculation is in principle the same as at the machine level, but with extended system boundary. It includes other ancillary and conversion facilities, which are not considered at machine level. Fig. 3 illustrates this relationship. Other facilities that are taken into account cannot be defined in general way. They must be decided individually. A question that leads to the decision, for example: Which facilities should be shut down if the entire production line is shut down? Such as facilities are for instance lighting systems or IT facilities for production line monitoring. Analogue to the equation (3), equation ( 4) computes the energy performance index for production line level.

Similar to machine and production line level, the energy performance indicator on factory level is calculated with expanded system boundary. Only ancillary facilities that are used by production processes are considered in the calculation. The energy efforts of facilities belonging to other business departments such as sales, logistics is not taken into account. But social rooms, canteen etc. are taken in to consideration, since they are used by the production personnel. Similar to line level, for the factory level the system boundary is widened again including those considered conversion and ancillary facilities. The EPI is applied in a stainless steel manufacturer. Table 1 shows examples of the calculation results in machine and production line levels. The entities having EPI less than one means that energy costs exceed the supply and therefore should not occur. Otherwise, it would be an alarm signal, because the process is extremely inefficient. Higher values are conceivable when a process generates a lot of values and /or require very little energy. A value greater than one does not automatically make a positive contribution margin, as there are other costs in addition to the energy costs. However, this is not the objective of the EPIs. Rather, they should provide a comparison of different alternatives in the choice of equipment to be used. As shown in table 1, the EPI can benchmark the energy efficiency of different machines, e.g. ovens, pressers, turning machines. It also shows the comparison of three production lines L1, L2, L3, which L3 is the most energy efficient production line.

Conclusions

So far, institutions and researchers has been developing energy performance indicators (EPI), which are used to measure and benchmark energy performance of different companies belonging to particular branch. This paper presents an approach to calculate EPI in a single manufacturing company. The EPI represents the ratio of total energy usage to the generated production value in different organization levels in production system, such as machine, production line, and factory level. The energy flow involving the production, ancillary, transport, and conversion facilities in the company should be defined first to perform EPI calculation. The energy flow is modeled based on extended UPN-Model. It considers also the external energy sources from utility companies. The energy flow model is mainly used to calculate the costs of input and output energy. We demonstrated the energy flow model for different levels of production organization. In this paper, we list the factors influencing the energy inputs and outputs. The formulas to calculate the EPI for different levels are also illustrated. Furthermore we present some results of the EPI application in a stainless steel manufacturer.

By using the developed EPI, production planners or facility managers are able to simulate and compare different production and facility configurations, in order to find the most energy efficient ones. However, the developed EPI is exclusively used for evaluating energy efficiencies within the companies. The EPI itself cannot be used directly for measuring the economic performance. Nevertheless, there is a strong relation of the EPI to other conventional metrics. The EPI may use the conventional metrics, such as total cost and cycle time to calculate the net production value and energy effort.

Fig. 1 .

 1 Fig. 1. Method for calculating energy costs in company's internal energy network

Fig. 2 .

 2 Fig. 2. Energy flow model for machine level

Fig. 3 .

 3 Fig. 3. Energy flow model for production line level

Table 1 .

 1 EPI calculation results in stainless steel production Description EPI M: Oven 21,0 --30,3 -49,6 M: Presser (1x) and Drop hammer (2x) 11,4 -21,0 -21,9 M: Turning machine (3x) 10,5 -20,9 -32,6 M: Boring Machine, CNC machine (1x) 15,7-17,7 M: Ring roller(2x) 15,5 -18,9 L1: Oven (EPI = 21,0), Drop hammer (EPI = 21,9), Ringrolling (EPI = 15,5) 19,5 L2: Oven (EPI = 49,6), Presser (EPI = 11,4), Ring-roller (EPI = 18,9), Rolling-machine (EPI = 21,7) 19,3 L3: Oven (EPI = 30, 3), Drop hammer (EPI = 21,0), Rolling machine (EPI = 20,9), 21,3