Energy costs in manufacturing have been traditionally classified as overhead costs. Recently, manufacturers ... most relevant industrial nations in 2007 (the circular area indicates .... energy efficiency, industrial environments account for more.
Energy Monitoring in Manufacturing Companies – Generating Energy Awareness through Feedback G. Bogdanski
1, 2
, T. Spiering
1, 2
, W. Li
1, 3
, C. Herrmann
1, 2
, S. Kara
1, 3
1
Joint German-Australian Research Group in Sustainable Manufacturing and Life Cycle Management
2
Technische Universität Braunschweig, Institute of Machine Tools and Production Technology (IWF), Product- and Life-Cycle-Management Research Group, Germany 3
The University of New South Wales, Life Cycle Engineering & Management Research Group, School of Mechanical & Manufacturing Engineering, Australia.
Abstract Energy costs in manufacturing have been traditionally classified as overhead costs. Recently, manufacturers have witnessed a dramatic increase in energy costs. As a result, energy needs to be treated as a manageable, strategic resource. This can only be achieved by including energy flow information in the organizational and operational processes of a manufacturing company. This paper addresses the necessity to include energy monitoring feedback into all layers of the manufacturing management system. A fundamental set of energy-related key performance indicators combined with goal-oriented economic and technical information is proposed to foster energy awareness. Keywords: Manufacturing; Energy Monitoring; Performance Feedback
1
INTRODUCTION
Climate change has forecasted disastrous environmental and economic effects. In the resulting global debate, energy efficiency is promoted as a central flagship initiative creating a resource efficient and low-carbon (low-CO2 emitting) economy [1]. A considerable change in mind set from ‘maximizing profit while minimizing capital’ towards ‘maximizing output with minimum resources’ is necessary to achieve these goals. Figure 1 depicts the current CO2-emissions attributable to the electricity generation of the most relevant industrial nations in 2007. China and India are two of the most carbon intensive countries for electricity generation, whilst also experiencing the highest growth in net electricity generation (by factor 2.6 and 1.6 respectively; 2007 compared to 1999) [2]. Mt of CO2 emissions from electricity generation [circular area]
[Elelctricity generated] TWh
5000 US
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CN
3000
resource to facilitate and produce products in a globalized environment. As obvious as this strategy may appear, fostering energy efficiency is still not common practice throughout society. It is even less common to integrate energy aware management and operation within the industrial context [4][5]. An ‘efficiency gap’ is the observed result of this phenomenon that needs to be overcome [6]. This paper addresses the constraints identified in the efficiency gap discussion; especially, the challenge to cope with the information deficit between various stakeholders inside industrial environments. The objective is to establish a more consistent and persisting effort to act energy aware and foster energy efficiency where technically and economically feasible. Feedback theory has been applied to introduce an energy related KPI (key performance indicator) monitoring system. The proposed energy KPI monitoring system can be implemented into existing communication structures by addressing all stakeholders inside the manufacturing environment. The long-term goal is to narrow down the energy efficiency gap. 2 2.1
2000 JP
1000
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BR
IN AU
0 0
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600
800
1000
1200
[Carbon Intensity of Electricity Production] kgCO2/MWh
Figure 1: CO2-emissions from electrical energy generation of the most relevant industrial nations in 2007 (the circular area indicates the CO2-emissions in million tons) [3]. These striking facts stress the accountability of individuals to alter their behaviour towards a more energy aware and energy efficient way of utilizing valuable sources of energy. Energy cannot simply be treated as an invisible service with unmanageable overhead costs. Instead, energy needs to be perceived as a strategic
ENERGY AWARENESS THROUGH FEEDBACK Importance of the human factor in the efficiency gap
The efficiency gap is the result of various external and internal constraints in companies as shown in Figure 2. Schmid [4] has identified an inadequate assessment of the economic feasibility of efficiency measures as a major constraint between postulated (by external experts) and actual economic potential (evaluated from internal company perspective). Justification for this finding includes internal hidden costs and conservative risk estimation. Additionally, Koopmans and te Velde [6] have categorized the efficiency gap into the top down and the bottom up perspective; highlighting constraints such as the underestimated heterogeneity of company structures and processes that restrict the estimated transferability of efficiency measures. Furthermore, the investor/user-dilemma (split incentives) is addressed as a main market failure inside the organizational structures of a company [4][6]. Several discrepancies between the evaluated efficiency potential
19th CIRP International Conference on Life Cycle Engineering, Berkeley, 2012
Efficiency level
from the bottom up perspective and the realized efficiency level can be represented by informational and physiological constraints. All of the constraints involve the human as the decisive factor. Rohdin, et al. [7][8] have identified these constraints for energy and nonenergy intensive industries. Their findings indicate that insufficient availability of quantitative energy flow information is the most dominant restriction for energy aware decision making.
utilization parameters (e.g. machine type, batch size, production volume, tool type etc.).
Insufficient evaluation of economic feasibility Energy efficiency gap resulting from various constrains
Market failure and organizational constraints Informational and phsycological constraints
Figure 3: Schematic feedback loop (underlined elements are manipulated by energy aware behaviour). Actual efficiency level
Bottom up efficiency potential
Top down efficiency potential
Postulated efficiency potential
Figure 2: Influences and constraints resulting into the efficiency gap (in dependence of [4][6]). Röwenstrunk and Mütze-Niewöhner [9] have identified three main theories to influence awareness and behaviour (attitude) of people in industrial environments: the rational-economic theory, the goalsetting theory and the feedback theory. They have shown that each theory by itself is restricted in its impact, whilst when combined they have catalytic effects on the change of behaviour and awareness. As Kara, et al. [10] have shown, the major enabler for an effective energy feedback lies in metering of energy flows and deriving adequate quantitative information. Thereby, turning seemingly invisible and unquantifiable energy flows into transparent information for the human communication process. 2.2
Feedback via energy related KPIs
This paper focuses on the monitoring aspect of energy flow information, respectively the addressee oriented, continuous provision of direct feedback to the decisive element. The decisive element can directly (e.g. as an operator) or indirectly (e.g. as a manager) interact with the electricity transforming process in a manufacturing environment; all stakeholders have thereby individual requirements on the informational value and interpretation means. Information flows and interdependencies between the stakeholders of the addressed system are presented in Figure 3. The generic diagram can be applied to all application levels of a manufacturing environment (factory, department and unit process). It depicts the central process representing the generic value adding transformation of upstream products (green body, raw material) into downstream products (finished good, assembly) by utilizing energy in diverse forms (operating supplies are neglected in this picture due to the focus on energy). Emissions are for example heat, solid waste and fluidic waste. On unit process level, the transformation process can be understood as a discrete machining process, on department level as a production system and on factory level as an entire production facility. The first group of stakeholders receiving the feedback from the metering points is directly utilizing the transformation process, e.g. operators or machine setters. The second stakeholder group is indirectly utilizing the process (e.g. the production planner), by parameterizing and selecting the process
To overcome the efficiency gap, an evaluation of performance is necessary. In management theory this is done by feedback of key performance indicators (KPIs). Currently there are only few energyrelated KPIs available to be implemented in manufacturing environments. Bunse et al. [11] have presented an overview of several energy efficiency indicators found in scientific literature and have identified the need to provide addressee-oriented KPIs to enable an effective monitoring and assessment of energy efficiency measures. Energy related feedback with KPIs can be communicated through interpersonal communication as well as through technical human machine interfaces (HMI) or existing enterprise resource management (ERP) tools. The usage of information and communication technology (ICT) has already proven to be effective in enabling energy savings in the case of residential buildings and private households [12]. Grønhøj and Thøgerson [13] as well as Darby [14] have come to the conclusion that direct feedback information has made the household’s electricity consumption more visible and salient to their originator. The feedback has empowered actions to actively reduce the overall amount of energy usage while keeping up the standard of living. All authors indicate that the time period between the provision of informational feedback and the event of energy usage is of highest importance. Shortening the idle time between usage and feedback resulted in higher noticeable effects towards energy awareness. The following requirements for the provision of effective feedback can be concluded:
Feedback must be given frequently to be effective [13]
Short idle times between feedback and cause amplify the awareness for cause and effect [13][14]
Long term effects on awareness generation can be sustained by continuous feedback provision (monitoring) [13]
Challenging goals provided with the feedback amplify the effects of changing the energy aware behaviour [13][9][12].
Two theses have been addressed that have proven to amplify energy awareness and change in utilization behaviour through quantitative energy flow feedback. The first thesis is the need to combine energy feedback information with economic reference terms such as ‘per product’ or ‘per unit of turnover’ or with controlling related time spans such as ‘per cycle time’ or ‘per year’. The combination of energy terms with economic references enables addressees of the feedback information to create cost-benefit
analysis’ which consequently alters the rational-economic subsequent behaviour [15]. The second thesis is the creation of energy key performance indicators to be used in goal-setting processes. Goal-oriented energy KPIs can be enriched by ancillary information such as ecological factors (e.g. emission coefficients) to stress the goal of reducing the CO2 intensity [16]. Another goaloriented KPI is the energy share of a unit process during nonproductive times. The accompanying goal for the addressee would be to fulfil a certain reduction objective of the KPI in a given amount of time. In order to sustain the goal-orientation, a monitoring of the KPI in a suitable aggregation time must be ensured to guarantee the commitment and ability to match the set goals by evaluating one’s behaviour [9]. 3
ENERGY MONITORING IN INDUSTRY – FEEDBACK IN MULTI-ADDRESEE ENVIRONMENTS
In contrast to households, where energy monitoring with informational feedback has been found conducive to improving energy efficiency, industrial environments account for more individual stakeholders [13][14]. In order to cluster the necessary fields of action, the proposed feedback KPIs will be presented in three different hierarchical application levels (factory, department, unit process) with similar technical requirements, economic requirements and groups of stakeholders (addressees) [10][11]. The regarded stakeholders are clustered according to the impact and means of their energy related behaviour (Table 1). For specific use cases, the identified generic groups of stakeholders have to be extended in subgroups.
Factory level: KPI
Formula
Aggregation time
Energy Intensity (EI)
factory energy demand/ time span
15 min, hour, shift, day, mgt.board, week, month, year ctrl.plan
factory reactive energy demand/ time span
15 min, hour, shift, day, mgt.board, week, month, year ctrl.plan
energy demand/ turnover
month, year
energy demand/ product
shift, day, week, month, mgt.board, year ctrl.plan
total emission/ energy carrier
month, year
mgt.board, ctrl.plan
production emissions/ product
single unit, yearly production volume
mgt.board, cust
corporate carbon footprint (CCF)
value chain emission inventory
mgt.board, cust
product carbon footprint (PCF)
value chain emissions/ product
mgt.board, cust
CO2 Intensity (CI)
Energy related behaviour
Abbrev. Addressee
KPI target
mgt. board
top management and board, legislation
internal/ indirect goal setting, external investment decisions
ctrl. plan
controlling, planning, design
internal indirect technology alteration, parameterization
op
operator, machine setter
internal direct
tech
maintenance technicians
internal indirect maintenance strategy
sup
supplier of manufacturing external indirect technology, material technology and materials provision
cust
customer
impact
by means of Energy Share
external indirect multi-criteria buying decision
Table 1: Addressee clusters in manufacturing environments.
Factory Level
The factory level is to be addressed as the highest aggregation level of an industrial environment, representing the interface among suppliers, customers and internal departments. Involved stakeholders are usually only indirectly responsible for energy utilization.
share of energy carriers week, month, year within total energy demand
Addressee
mgt.board, ctrl.plan
mgt.board, ctrl.plan
Energy energy demand/ productive shift, day, week, month, mgt.board, Utilization time quarter, year ctrl.plan
command, utilization
The informational value for each individual energy related KPI is depending on the addressee receiving the indicator as well as on its internal attributes. The aggregation level is described as the time span of average determination. KPIs with a low aggregation time allow high levels of detail in evaluation; high aggregation times allow the evaluation of long term effects. Considering the complexity of industrial systems, a differentiation of application fields is necessary in order to come up with sufficient KPIs [11]. KPIs on different hierarchical levels require different aggregation levels to sustain their informational value. For example, aggregation times on factory level can range up to years, while on unit process level the timing can reach down to single seconds. 3.1
For example, on factory level, energy contracts with external suppliers are negotiated. According to the energy contract, the minimal level of aggregation time for energy intensity KPIs (compare Table 2) are to be determined (e.g. 15 minutes for electricity supply). For electrical energy, quality indicators are also relevant for controlling purposes, due to cost impacts. Furthermore, interfaces with downstream customers and competing manufactures also exist. Management on factory level can therefore act as an external KPI communicator in terms of strategically positioning products with external benchmarks (e.g. product carbon footprint (PCF)).
Energy Costs
energy demand/ nonproductive time
shift, day, week, month, mgt.board, quarter, year ctrl.plan
amount of energy peak demand events
shift, day, week, month, mgt.board, quarter, year ctrl.plan
cost (of each specific energy carrier)/ term
15 min, shift, day, week, mgt.board, month, quarter, year ctrl.plan, sup
reactive energy costs (electricity only)/ term
15 min, shift, day, week, mgt.board, month, quarter, year ctrl.plan, sup, tech
cost due to distortions (electricity only)/ term
15 min, shift, day, week, mgt.board, month, quarter, year ctrl.plan, sup, tech
Table 2: Energy related KPIs for feedback on factory level (in dependence to [11]). For internal stakeholders, the factory can be seen as the provider of infrastructure for the departments and as the maintainer of infrastructure (factory shell, technical building services, logistics etc.). On factory level, the long-term energy saving goals are set, thus the aggregation levels can go up to a yearly basis. However, goals on daily or shift basis can be communicated. By passing down goal-oriented KPIs, departments are motivated to foster energy efficiency measures. In order to sufficiently communicate with the top management board, the measured energy information needs to be interpreted with the economic reference indicators of
the factory, such as ‘energy cost per economic term’ or ‘energy demand per turnover’.
of value creation. If appropriate, the level of detail can scale up to the smallest components of technical processes.
3.2
The main goal of a production unit and therefore also of employees in production is to maximize the output without compromising quality [19]. It is important to understand that saving energy might be perceived as being in conflict to output and quality. On unit process level, risk assessment constraints are of major importance. For instance, interferences in production caused by the implementation of energy efficiency measures are often underestimated from top-down perspective (e.g. department level) [5]. In other words, energy efficiency has to be improved in the potential area of conflict with other objectives (e.g. cycle-time, quality of product, material costs, personal costs, etc.) [18]. Therefore, energy monitoring needs to become an integral part of already existent communication structures. This is emphasized by the fact that energy efficiency measures always require resources. A comparison to non-energy related activities thus has to be facilitated. Otherwise those resources will not be granted by decision-makers. A fundamental selection of suitable KPIs is listed in Table 4.
Department level
The department as a substructure module of the factory is an organizational element receiving set goals (internal and external) on economic and ecologic basis. The department covers stakeholders indirectly and directly responsible for utilizing energy (compare Table 1). Department level: KPI
Formula
Aggregation time/subject
Addressee
Energy intensity (EI)
energy demand/ time span
15 min, hour, shift, day, week, month, quarter, year
mgt.board, ctrl.plan
energy demand/ turnover
week, month, quarter, year
mgt.board, ctrl.plan
energy demand/ purchase order
order throughput time
mgt.board, ctrl.plan
energy demand/ product
throughput time
ctrl.plan op
energy demand/ process cell
cycle time
ctrl.plan op
Unit process level:
emissions/ purchase order
order throughput time
mgt.board, ctrl.plan
Energy energy demand/ produced unit intensity (EI)
emissions/ product
throughput time
CO2 Intensity (CI),
Energy Share
ctrl.plan
share of energy
mgt.board
demand/ energy carrier month, quarter, year
Energy energy demand/ Utilization productive time energy demand/ nonproductive time
Energy Costs
ctrl.plan
emissions/ process cell cycle time hour, shift, day, week,
Formula
energy demand of sub-process/ time period for sub-process
cycle time, shift, day, week, mgt.board, month, quarter, year ctrl.plan
Adjusted EI energy demand/ (products rejects)
cycle time, shift, day, week, mgt.board, month, quarter, year ctrl.plan
Specific energy demand (SED)
amount of energy peak shift, day, week, month, demand events quarter, year
ctrl.plan
cost (of each specific energy carrier)/ term
1 min, 15 min, shift, day, week, month
ctrl.plan
reactive energy costs (electricity only)/ term
1 min, 15 min, shift, day, week, month
ctrl.plan, op tech
distortion costs (elec. only)/ term
1 min, 15 min, shift, day, week, month
ctrl.plan, op tech
The department is responsible to match its externally set goals and uses the provided infrastructure of the factory, i.e. utilize their transformation processes in order to fulfil their economic goals (defined value creation). The energy KPIs listed in Table 3 form the basis for a set of monitoring variables. These variables are deduced from energy feedback, ancillary economic and ecologic terms on department level. On basis of the given KPIs, not only can top-down goal setting be applied, but competition across departments can also be encouraged. Furthermore, this turns energy efficiency into an external as well as an internal strategic factor. Unit process level
The unit process level constitutes the level of highest detail and lowest aggregation. On unit process level, in contrast to the aforementioned department and factory level, observed entities are not following organisational structures, but are defined by the way
Aggregation time Addressee cycle time
ctrl.plan, op, sup
share of cycle time
op, sup
energy demand in productive hour, shift, week mgt.board, state/ Energy demand in idle- or op, sup stand-by-state
ctrl.plan
Table 3: Energy related KPIs for feedback on department level (in extension to [11]).
3.3
KPI
shift, day, week
tech, op, sup
energy demand/ processed material
cycle time, hour, mgt.board, shift, week op, sup
energy demand/ operated area
cycle time, hour, mgt.board, shift, week op, sup
Energy cost energy costs/ manufacturing share costs
cycle time, hour, mgt.board, shift, week ctrl.plan, op
Thermodyna energy demand of ideal process/ different mic energy energy demand of real process possibilities efficiency
op, tech
CO2 emissions/ produced unit Intensity (CI)
mgt.board, ctrl.plan, cust
cycle-time
Table 4: Energy related KPIs for feedback on unit process level (in extension to [11][19]). Due to the high level of detail and low aggregation times of the KPIs, the energy related information is most valuable for deriving technical measures. Energy flow data, combined with technical and process knowledge, allows a very deep insight. From the transformation process user’s perspective, it is advisable that KPIs are also designed to serve as a communication basis with external stakeholders such as suppliers of production machines. Furthermore, energy related data can be used to observe deviations (e.g. by thermal efficiency) from normal states and is therefore a valuable source for a maintenance technician. A planner and controller can also use these monitoring results to identify best practice technologies and processes. Issues like over dimensioning of aggregates can be minimized by a higher transparency of energy
flows [5]. If monitoring and KPIs can be standardised, even process alternatives can be benchmarked [11].
KPIs like the CI and SED can be calculated. This procedure and the proposed visualization results are shown in Figure 4. 4.3
4 4.1
USE CASE: INJECTION MOULDING (UNIT PROCESS) Use case description
For the monitoring on unit process level we observe the discrete production of plastic parts via injection moulding from the perspective of a user (group of stakeholders running the process). Injection moulding is a primary shaping production process in which complex geometries can be produced in very high quality and quantity on one single machine and additional equipment [20]. The injection moulding process is understood as one machine and all necessary components (mould, auxiliaries, raw material, etc.) to produce products and is regarded as one unit. Precise energy flow data (low aggregation time) on unit process level is available via ondemand measurement and continuous measurement depending on the age of the machine [10]. Furthermore, data about material flow, production parameters and process variables are available and can be accessed via several databases or directly at the machine. 4.2
Monitoring strategy
As stated earlier, the results obtained by monitoring must be easy to comprehend and should fit into the existent structures of accounting and decision-making. The most important KPI in injection moulding is ‘cost per part’ [21]. Consequently, monitoring results should also be expressed on ‘per part’ basis (refer also to Table 4). As mentioned, metering and monitoring should be standardised in order to benchmark various processes [10]. Standardisation implies to enable comparability, but has increased drawbacks with the heterogeneity of the observed objects. Thus, the level of standardisation has to be selected according to addressee related requirements. In this use case we are targeting to compare a mostly homogeneous set of unit processes. We also target a variety of addressees as shown in Figure 4. Therefore, a high level of standardisation is applied. In case of injection moulding, even on different machines, the sub-process steps as depicted in Figure 4 of one production cycle are usually equal. Therefore, a feasible approach for standardised energy monitoring is to combine the energy profile of one process and its sub-process steps with the corresponding cycle times. As a result, the energy intensity per unit produced and of sub-process steps can be obtained. With further knowledge about the specific CO2 emissions of the used energy carriers and the weight of the product further
Efficiency improvement strategy
With the described monitoring strategy, all direct and indirect addressees can gain an insight into energy flows and consumption of their utilized production system. Now, a detailed KPI based process analysis can be conducted. In the exemplary process shown in Figure 4, the sub-process step of injection as well as plasticisation contributes with a share of 32% to the total cycle time and 36% to the total energy consumption. This pareto-analysis shows that about one third of the demanded energy and production time is allocated to two process steps. Tests have shown that in most cases the energy consumption in the plasticisation phase can be reduced by adjusting speed of the charging screw [22]. Setting the machine accordingly, could be a first efficiency measure initiated by the machine setter (person preparing the machine for production) as a direct utilizer of the process. Another peculiar finding is the comparatively long residual cooling time. During this sub-process step, the machine is in an unproductive state, yet it contributes with 21% to the energy demand per part as shown in Figure 4. This finding can be a trigger to include external stakeholders into an improvement process. For example, the mould designer could be addressed in order to enhance the cooling system of the mould and therewith shorten the residual cooling time. Also, the production planner has the possibility to parameterize the process. He can by himself or in cooperation with the machine manufacturer investigate possibilities to reduce the energy consumption during unproductive machine states. For older hydraulic machines, a reasonable approach is to install frequency inverters in hydraulic systems [19]. As shown, with the obtained KPIs, energy efficiency measures can be derived. Also, the same set of KPIs can be used to assess those measures, which has been identified as a major enabler for energy efficiency improvements as shown in chapter 2. For instance, the enhanced cooling system and the use of frequency inverters are both a parameterisation of the sub-process step ‘residual cooling’. In detail, the efficient cooling system would reduce the time period of residual cooling while the frequency inverter reduces the energy demand during unproductive states (especially during residual cooling). It is therefore important to be able to identify from KPIs that by applying both measures simultaneously the resulting energy savings are lower compared to the sum of the expected absolute savings of both single measures.
Figure 4: Procedure for monitoring energy transformation processes at the example of injection moulding.
In order to derive an economic and ecologically reasonable investment decision, additional information from the accounting department about energy prices, machine costs per hour and product price can be combined with the energy related feedback data (Plan). If proven to satisfy the set goals, measures can be physically implemented (Do) and their impact assessed (Check). Due to the standardised monitoring, a high portability of implemented measures onto other units is directly possible. If measures physically prove to satisfy the set goals, the standardised monitoring allows a potential analysis on all observed units to further increase energy efficiency (Act). In summary, the proposed energy aware KPIs are the enabler to establish a sustainable continuous energy aware improvement process. 5
The paper has identified that one of the major constraints to close the existing efficiency gap is the lack of sufficient information and the lack of proper evaluation of economic feasibilities of efficiency measures. To tackle this deficiencies the feedback theory was applied to introduce a KPI-based energy monitoring system. The monitoring system is proposed to be implemented into existing communication structures and addresses all stakeholders that are directly or indirectly involved in the utilization of energy transforming processes. The resulting energy aware, continuous improvement process has been exemplarily implemented in a use case on unit process level. Further work will focus on the extended development of suitable KPIs. In this context, further investigation on the interoperability and system boundaries between the three levels will be necessary. It is expected that this investigation leads to enhanced measurement strategies, which create a more holistic view without increasing the needed resources. Extensive industry case studies will be performed to verify the applicability of the presented approach on all application levels and its overlapping interfaces. ACKNOWLEDGMENTS
The authors would like to thank the German Federal Ministry of Education and Research (BMBF) for the support of the Joint German-Australian Research Group "Sustainable Manufacturing and Life Cycle Management" (AUS 09/1AP). 7
CARMA, Center for Global Development (2011): Carbon Monitoring, available online: http://www.carma.org/dig/show/ world+country#top. Last accessed on 26th Nov. 2011.
[4]
Schmid, C. (2004): Energieeffizienz in Unternehmen – Eine wissensbasierte Analyse von Einflussfaktoren und Instrumenten, vdf Hochschulverlag, Zürich, 2010, pp. 16-17.
[5]
Seefeldt, F.; Wünsch, M. (2006): Potenziale für Energieeinsparung und Energieeffizienz im Lichte aktueller Preisentwicklungen. Endbericht 18/06. BMWi, Prognos, Berlin.
[6]
Koopmans, C. C.; te Velde, D. W. (2001): Bridging the energy efficiency gap, in Energy Economics, vol. 23, pp. 057-075.
[7]
Rohdin, P.; Thollander, P. (2005): Barriers to and driving forces for energy efficiency in the non-energy intensive manufacturing industry in Sweden, in: Energy, vol. 31, no. 12, pp 1836-1844.
[8]
Rohdin, P.; Thollander, P.; Solding, P. (2006): Barriers to and drivers for energy efficiency in the Swedish foundry industry, in: Energy Policy, vol. 35, no. 1, pp. 672-677.
[9]
Röwenstrunk, M.; Mütze-Niewöhner, S. (2010): Implementation of Energy Awareness in Plants, in Managing CO2 Emissions in the Chemical Industry, edited by Leimkühler, H.-J., Wiley-VCH, pp 89-120.
[10]
Kara, S.;Bogdanski, G.; Li, W. (2011): Electricity Metering and Monitoring in Manufacturing Systems, in: 18th CIRP LCE, Braunschweig, pp 001-010.
[11]
Bunse, K.; Vodicka, M.; Schönsleben, P., Brülhart, M.; Ernst, F. O. (2011) Integrating energy efficiency performance in production management, in Cleaner Production, vol. 19, pp. 667-679.
[12]
Berges, M. E.; Goldman, E.; Matthews, S.; Soibleman, L. (2010): Enhancing Electricity Audits in Residential Buildings with Nonintrusive Load Monitoring, in Industrial Ecology, vol. 14, no. 5, pp. 844-858.
[13]
Grønhøj, A.; Thøgersen, J. (2011): Feedback on household electricity consumption: learning and social influence processes, in Int. J. of Consumer Studies, vol. 35, pp. 13-145.
[14]
Darby, S. (2006): The Effectiveness of Feedback on Energy Consumption, in: Review for DEFRA of the Literature on Metering Billing and direct Displays, vol. 22.
[15]
Archer, D.; Pettigrew, T.; Costanzo, M.; Iritani, B.; Walker, I.; White, L. (1987): Energy conservation and public policy, in Energy Efficiency (Kempton, W., Neiman, M.), pp 69-92.
[16]
Locke, E. A. (1968): Towards the theory of task motivation and incentives, in: Organizational Behaviour and Human Performance, vol. 3, pp. 157-189.
[17]
Michaeli, W. (2006): Einführung in die Kunststoffverarbeitung, 5th Edition, Hanser Verlag, Germany.
[18]
Herrmann, C.; Thiede S. (2009): Towards Energy and Resource Efficient Process Chains. In: 16th CIRP LCE, Kairo, Egypt.
[19]
Pamminger, R.; Wimmer, W.; Winkler, R. (2010): Entwicklung von Kriterien zur Kommunikation der Energieeffizienz von Kunststoff verarbeitenden Maschinen, BMVIT, Wien, Austria.
[20]
Schüle, H. (2008): Verarbeitung von Kunststoffen zu Bauteilen - Urformen, in: Eyerer, P. (ed.) et al.: Polymer Engineering, pp. 211-218, Springer, Germany.
[21]
Rees, H.; Catoen, B. (2006): Selecting Injection Molds – Weighing Cost vs. Productivity, Hanser Verlag, Germany.
[22]
Klamt, K. (2011): Kleine Schritte, große Wirkung, in: Kunststoffe, vol. 2011, no. 10, pp. 150-153.
SUMMARY AND CONCLUSION
In conclusion, the proposed energy monitoring approach is an enabler for the development of energy efficient measures and is furthermore a fundamental resource to evaluate the identified measures. The established monitoring system is a valuable communication hub for various internal and external stakeholders, which is a decisive element to reach beyond energy awareness. Due to the chosen process of standardisation, it is possible to expand the applicability over other unit processes (across department level). As successfully shown, a continuous energy improvement process could be initiated on unit process level. The applied feedback and monitoring system has shown to comply with the stated requirements from chapter 2.2 as frequency of occurrence, continuity and the possibility to include goal-setting. Additional goals from upper levels can enhance and further manifest the continuous improvement, as shown in Figure 4.
6
[3]
REFERENCES
[1]
European Commission (2010): Communication from the commission Europe 2020, COM (2010) 2020 final.
[2]
U.S. Energy Information Administration (2011): International Energy Statistics database extraction, online available on: http://www.eia.gov. Last accessed on 16th October 2010.
