A Multidimensional Assessment and Selection

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Procedia Manufacturing 21 (2018) 281–288 Procedia Manufacturing 00 (2017) 000–000 www.elsevier.com/locate/procedia

15th Global Conference on Sustainable Manufacturing 15th Global Conference on Sustainable Manufacturing

A Multidimensional Assessment and Selection Methodology: A Multidimensional Assessment and Selection Methodology: Optimized Decision-making of Joining Technologies in2017, Automobile ManufacturingDecision-making Engineering Society International Conference 2017, MESIC 28-30 June Optimized of Joining Technologies in Automobile 2017, Vigo (Pontevedra), Spain Body Development a Saphir A. Choudrya*, SteffenBody MüllerbbDevelopment , Uwe Alberbb, Frank Riedelcc, Dirk Landgrebeaa Saphir A.models Choudryfor *, Steffen Müller , Uwe Alber , Frank Riedel , Dirk Costing capacity optimization in Industry 4.0:Landgrebe Trade-off Chemnitz University of Technology, Reichenhainer Str. 70, 09126 Chemnitz, Germany AG, Development of Technology, 74148 GermanyGermany ChemnitzAUDI University of Technology, Reichenhainer Str. Neckarsulm, 70, 09126 Chemnitz, between used capacity andStr.operational efficiency Franhofer-Institut - IWU, Reichenhainer 88, 09126 Chemnitz, Germany AUDI AG, Development of Technology, 74148 Neckarsulm, Germany a

b

a

c c

b

Franhofer-Institut - IWU, Reichenhainer Str. 88, 09126 Chemnitz, Germany

A. Santanaa, P. Afonsoa,*, A. Zaninb, R. Wernkeb a University of Minho, 4800-058 Guimarães, Portugal Abstract b Unochapecó, 89809-000 Chapecó, SC, Brazil Abstract The research deals with the specification of a methodical approach for the systematic selection of the optimum joining The research withapplications the specification a methodical approach systematic selection technology fordeals different in car of body development. To date,for thethe decision-making is basedofonthe theoptimum expertisejoining of the technology aforstandardized different applications in car body development. To date, the decision-making based on of the developer; selection procedure does not exist. Thus, an assessment about the ispotential of the the expertise several joining Abstract developer; a cannot standardized selection does not Thus, an assessment about the potential of thecompared several joining technologies be made. Withinprocedure the framework of aexist. benchmark study various research approaches were to the technologies cannot be from made.a Within the stakeholder framework of a benchmark various research approaches compared to the requirements identified conducted analysis. Except study the economic criteria, none of thesewere models consider Under the concept offrom "Industry 4.0", production processes will be pushed to be increasingly interconnected, requirements identified acriteria. conducted stakeholder analysis. Except the economic criteria, none of these models considerThe the technological and ecological Hence, a new approach is required which analyzes the multidimensional criteria. information based onmethodology, a realcriteria. time basis and,athe necessarily, much more thisthe context, capacity technological and ecological Hence, new approach is required which criteria. developed assessment including different dimensions, canefficient. be usedanalyzes toIncreate an multidimensional optimum balanceoptimization amongstThe the goes beyond the traditional aim of including capacity maximization, contributing also foroptimum profitability and value. developed assessment the different dimensions, be used toorganization’s create solution an optimum balance the criteria concerning cost,methodology, technological value, and sustainability-impact to can identify the already in theamongst early stage criteria concerning cost, technological value, and sustainability-impact to identifysuggest the optimum solution already in theinstead early stage of the car body development. Indeed, lean management and continuous improvement approaches capacity optimization of of the car body development. maximization. The study of capacity optimization and costing models is an important research topic that deserves © 2017 The Authors. Published by Elsevier contributions from both the practical andB.V. theoretical perspectives. This paper presents and discusses a mathematical © 2018 The Authors. Published by Elsevier B.V. © 2017for Thecapacity Authors. Published by B.V. Peer-review under responsibility of Elsevier the scientific committee of the models 15th Global Conference on Sustainable Manufacturing. model management based on different costing and TDABC). A generic model(GCSM). has been Peer-review under responsibility of the scientific committee of the 15th Global(ABC Conference on Sustainable Manufacturing Peer-reviewand under responsibility of the scientific committee ofdesign the 15thstrategies Global Conference on Sustainable Manufacturing. developed it was used to analyze idle capacity and to towards the maximization of organization’s

Keywords: Multidimensional assessment; Joining technologies; Sustainable manufacturing, Car body development

value. The trade-off capacity maximization vs operational efficiency is highlighted and it is shown that capacity Keywords: Multidimensional assessment; Joining technologies; Sustainable manufacturing, Car body development optimization might hide operational inefficiency. © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 2017. Keywords: Cost Models; ABC; TDABC; Capacity Management; Idle Capacity; Operational Efficiency

1. Introduction * Corresponding author. Tel.: +49-1573-2434-132;

address:author. [email protected] * E-mail Corresponding Tel.: +49-1573-2434-132; The cost of idle capacity is a fundamental information for companies and their management of extreme importance E-mail address: [email protected] 2351-9789 2017 The Authors. Published by Elsevier in modern©production systems. In general, it isB.V. defined as unused capacity or production potential and can be measured Peer-review of the scientific committee of the 15th Global Conference on Sustainable Manufacturing. 2351-9789 2017responsibility The Authors. Published by Elsevier B.V.hours in several©under ways: tons of production, available of manufacturing, etc. The management of the idle capacity Peer-review underTel.: responsibility the761; scientific committee the 15th Global Conference on Sustainable Manufacturing. * Paulo Afonso. +351 253 of 510 fax: +351 253 604of741 E-mail address: [email protected]

2351-9789 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 2017. 2351-9789 © 2018 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 15th Global Conference on Sustainable Manufacturing (GCSM). 10.1016/j.promfg.2018.02.122

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1. Introduction Car body development, one of the most important steps in the automobile value chain, is facing considerable challenges such as growing competition, stricter environmental regulations, as well as rising production costs. Nowadays, various materials and configurations are being used in vehicle bodies to manufacture modern car body and lightweight structures [1]. However, the specific characteristics of individual materials are presenting new challenges. These challenges include, for example, the joining technology, where technologies face numerous restrictions. Thus, there is a need to develop new joining technologies for different materials (e.g., for joining aluminum and carbon fiber) [2]. This necessitates investigating when a technology is economically, technologically, and ecologically beneficial for component-specific applications. To date, the selection process in the automotive sector is mostly based on the expertise of the developer, a standardized approach for decision-making on the individual assignments does not exist. The extant literature focuses mainly on cost or technological value. Due to the non-transparency of the overall expenditure and the benefit of the respective joining technologies for the specific applications, no statement can be made about the assessment dimensions economy, technological value, or ecology of a joining technology. The essential part of this research work is the specification of an approach that enables optimized decision-making during the selection process of joining technologies. Therefore, a multidimensional assessment methodology based on Ashby's methodology for the selection of materials in construction has been developed. The new approach, including the criteria for the different dimensions, can be used to identify the most efficient joining technology, allowing for overall transparency concerning cost, technological value, and sustainability-impact already in the early stage of the car body development. 2. Literature Research Within the framework of a benchmark study relevant research models dealing with the selection and assessment of manufacturing technologies were analyzed. The reviewed models were verified with the basic and performance requirements gathered from a conducted stakeholder analysis. Basic requirements are very essential for the assessment, where performance requirements are more supportive to improve the accuracy and practical feasibility [3]. The consideration of the dimensions economy, technology and sustainability represent the basic requirements. Besides the basic requirements, various performance requirements exist for the assessment model. Fig. 1 compares the requirements with the selected assessment models in order to identify the potential of the research models. The overview shows that none of the reviewed models completely meets all basic requirements. Particularly the

Ahnert; Hübner; Riedel, 2014 [4] Prüß; Stechert; Vietor, 2010 [5] Pieverling, 2002 [6] Goecke; Krautwald, 2012 [7] Reinhart; Mosandl; Gartner, 2001 [8] Gausemeier; Brandis; Kaiser, 2010 [9]

Fig. 1. Benchmark-Analysis: Excerpt of essential Assessment-Models

Consideration of uncertainties

Weighting system

Consideration of lingual criteria

Performance requirements

Transferability

not fulfilled

Ecological view

partially fulfilled

Technological view

fulfilled

Economical view

Basic requirements



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dimension sustainability impact has rarely been analyzed for the assessment of joining technologies. So far, the focus has been on cost and technological value, which amongst others is evaluated in the model of Riedel [4]. In the framework of the economic assessment, the author limits the cost calculation to investment costs. The environmental aspect is taken into consideration over the energy usage; a complete life cycle assessment (LCA) similar to, for example, ISO EN 14040, has not been performed [10, 11]. Also, the technological assessment is limited to the joint strength. The models of Prüß et al. and Pieverling have an approach to characterize technologies by considering qualitative and quantitative criteria in order to identify the technological value [5, 6]. Besides Goecke et al., the assessment method of Reinhart et al. can convince within the economic evaluation by a comprehensive cost calculation considering also the upstream and downstream processes [7, 8]. Similar to Riedel the ecological dimension is evaluated exclusively by energy consumption [4]. Within the stakeholder-analysis, the transferability, the consideration of lingual (subjective) criteria, the weighting of criteria, and the consideration of uncertainties were identified as performance requirements. Transferability requires a general validity of the basic approach, due to the individuality of components. In order to indicate a realistic value of the impact of non-monetary criteria, a consideration of lingual criteria as well as a weighting model is necessary. The consideration of quantitative uncertainties allows to identify risks already in the early phase of development. The benchmark shows that only the selection methodology for joining technologies of Prüß et al. as well as the selection model for production processes of Pieverling can partially satisfy through their multidimensional approach [5, 6]. Hence, a new approach has to be developed for the assessment and selection of joining technologies in the body development where all derived basic and preferably performance requirements can be satisfied completely. 3. Assessment and Selection Methodology In order to meet the requirements mentioned in section two for the development of an assessment and selection methodology for joining technologies a modular design has been chosen. Based on Ashby's methodology for the selection of materials in the construction, the first step of the method involves the description of the joining problem within the translation module [12]. The following isolated analysis of the different dimensions within the modules cost, technology, and sustainability allows a transparent decision-making. To identify the optimum solution, however, the findings of the individual modules are consolidated in the assessment module. Fig. 2 shows the modular structure of the developed assessment methodology. Translation-Module (Chapter 3.1)

Cost-Module (Chapter 3.2)

Technology-Module (Chapter 3.3)

Sustainability-Module (Chapter 3.4)

Assessment-Module (Chapter 3.5)

Optimum Solution (Chapter 3.6)

Fig. 2. Modular structure of Assessment and selection methodology

3.1. Translation Module The translation module is required to describe the joining problem, which identifies the requirements for the joining technologies. By screening the defined requirements with the characteristics of the joining technologies, the

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technological feasibility of different alternatives can already be determined in the early development phase. For the description of the requirements one can distinguish between four categories: function, objectives, binding constraints and soft constraints, as well as free variables [12]. 3.2. Cost Module Existing cost models for car body development are often limited to the initial investment as a decision-making basis for the selection of a joining technology. However, investment costs represent only a fractional amount of the total cost [13]. The designed assessment model allows to include the entire life-cycle-costs (LCC), i.e., all costs from the creation to the utilization phase within the cost analysis. There already exist several standards dealing with the research matter highlighting the procedure and relevant cost variables [14–16]. Table 1 shows the essential cost drivers which are used for a LCC-calculation. The total cost for the joining technology (j) are calculated according to (1) by the sum of the individual cost drivers (i) over the time (t). Table 1. Life Cycle Costing: Main cost drivers of joining technologies. Main cost factor

Notation

Occurrence

Initial Investment

I

One-time

Operation Cost

O

Continuous

Utilization Cost

U

One-time

n

n

i 1

i 1

n

T

LCC j   I i    Oi (t )dt  U i t 0

(1)

i 1

3.3. Technology Module While the cost module includes the monetary factors, all non-monetary factors are analyzed and evaluated in the technology module. Basically, according to Breiing, a transformation of non-monetary in monetary factors is possible, but the aggregation of the factors would lead to an additional fuzziness [17]. Therefore non-monetary factors, which are either qualitative or quantitative, are considered isolated. Qualitative factors take lingual criteria into account, requiring a high evaluation effort due to their subjectivity. Quantitative factors on the other hand are objective, hence, associated with less effort. Table 2 shows an excerpt of criteria to be considered during the selection process of joining technologies as well as exemplary characteristics of those in car body development. The characteristics must be evaluated individually for each technology using a consistent scaling (e.g., 0–9 points). Table 2. Excerpt of exemplary impact criteria of joining technologies. Criteria (ci)

Nature

Weight (wi)

Value (vj,i)

Accessibility

Quantitative

e.g. 100%

e.g. 9

Flange width

Quantitative

e.g. 50%

e.g. 5

Complexity

Qualitative

e.g. 10%

e.g. 0

Due to the individual importance of the criteria in car body development, a weighting of the influencing criteria is necessary for the technological analysis. The weighting allows a realistic depiction of preferences. The general calculation of the technological value (TV) is described in (2).



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n

TV j   wi * v j ,i

(2)

i 1

3.4. Sustainability Module The Sustainability Module allows to analyze the environmental impact of a joining technology over his entire life cycle. The increase of energy and raw material prices as well as stricter legal requirements have increased the importance of energy and resource efficiency in the industry [4]. For evaluating and optimizing the resource efficiency several methods have been developed. The Life Cycle Assessment is one of the most established methods, which is carried out in four phases [11]. The first phase is the goal and scope definition where functional unit, system boundaries and limitations are determined. In the second phase, the Life Cycle Inventory, the in- and output from and to nature is collected, which is translated in the third phase, the Life Cycle Impact Assessment, into a number of aggregated metrics. The metric is the Global Warming Potential (GWP = kgCO2equiv.) that has also been applied to the Sustainability-Model. The last phase is for discussion and comparison of the results for a benchmark. Table 3 shows an excerpt of environmental aspects to be considered for a LCA-calculation. The total GWP for a joining technology (j) is calculated according to (3) by the sum of the individual energy (Ei) and resource factors (Ri), which are required for the joining process and multiplied with the corresponding impact indicator (s, ri). Table 3. Life Cycle Assessment: Exemplary input for a LCA of a joining technology. Environmental aspects (input)

Units

GWP [kg CO2 equiv.]

Electrical Power

kWh

180.000

Compressed air flow

m³/s

20.000

Coolant flow

m³/s

70.000

n

n

i 1

i 1

GWPj  (s *  Ei )   ( Ri * ri )

(3)

3.5. Assessment Module The Assessment Module carries out an overall assessment of all dimensions through the component information obtained from the translation modules as well as the consolidation of the modules Cost, Technology, and Sustainability. In order to maintain the transparency within the assessment, the aggregation of the dimensions is purposely foregone. Thus, the normalized key figures Cost-Index (CI), Technological-Value-Index (TVI) and Sustainability-Impact-Index (SII) were developed for the modules. These show the ratio to the optimum joining technology within the dimension to be examined. The indices can have a solution set between 0 and 1, while a value of 1 represents the optimum. The calculation for the module indices is shown in (4).

CI j 

min (LCC j ) LCC j

TVI j 

TV j max (TV j )

SII j 

min( GWPj ) GWPj

(4)

3.6. Optimum Solution In order to determine the recommended technology, the key figures determined within the assessment module are merged into a performance indicator (PI). The basic calculation of the performance indicator is described in (5). The optimum alternative is the technology with the highest PI.

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2

PI j  CI j  TVI j  SII j

2

 Optimum Solution  max( PI j )

(5)

The calculation of the PI assumes an identical importance of the individual dimensions. However, this often does not correlate with the reality due to individual company target figures. Hence, with the strategic performance indicator (sPI) taking into account, the strategic emphasis of the dimensions cost, technology, and sustainability impact through the preference coefficients pC, pTV, and pSI the figure was extended. Here, the sum of all preference coefficients must be one. The calculation of the strategic performance indicator is shown in (6). Similar to the PI, the technology with the highest sPI is the optimum solution.

sPI j  (CI j * pC ) 2  (TVI j * pTv ) 2  ( SII j * pSI ) 2  Optimum Solution  max( sPI j )

(6)

For visualizing the individual key figures and pointing out the potential of each alternative, a multidimensional assessment portfolio was developed which allows a comparison of the joining technologies from different viewpoints. Quantitative uncertainties can be expressed by the standard deviation. Fig. 3 shows the structure of the portfolio as part of the case study using a fictional scenario. 4. Case Study In the following, the developed approach for the assessment of joining technologies is applied to a fictitious scenario in order to demonstrate the potential of the methodology. The calculation is based on a life cycle of seven years. Furthermore five technologies are available in the starting position. In Table 4, an excerpt of the requirements derived from the component-specific information, such as flange width, is defined within the translation model. Table 4. Excerpt of impact criteria given from the translation module. Criteria (ci)

Type

Specification

Flange width

Binding Constraint

16 mm

Composite materials

Binding Constraint

Aluminum (Al) with Steel (St)

Density

Free variable

Positive

The subsequent screening in Table 5 shows that technologies B and D do not meet the requirements provided by the translation model. While the flange width of alternative B exceeds the binding constraint of 16 mm, alternative D shows a restricted material spectrum with solely aluminum-combinations. Therefore, the further evaluation is limited to the alternatives A, C, and E, which can satisfy all requirements to assure the technological feasibility. Table 5. Screening: Joining technologies with no technological feasibility. Joining Technology (j)

Cause of no technological Feasibility

Technology B

Flange width 20 mm

Technology D

Only Al-Al joinable

The calculation of the individual dimension indices is carried out in Table 6 according to (3). Apparently, alternative A represents economically the most advantageous technology, while alternative C indicates three times higher costs. In return, technology E shows a high ecological advantage as the GWP of technology A is significantly higher. Alternative C, however, can offer the highest technological value.



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Table 6. Calculation of indices for cost, technological value and sustainability-impact. Joining Technology (j)

LCCj

CIj

TVj

TVIj

GWPj

SIIj

Technology A

400.000 €

1

185

0.8

600.000

0.5

Technology C

800.000 €

0.5

240

1

400.000

0.8

Technology E

1.200.000 €

0.3

200

0.8

300.000

1

Table 7 shows the calculated Performance Indicator as well as the strategic Performance Indicator according to (4) and (5). Within this case study, the strategic characteristic of the operator is exemplary assumed to express a technologic and ecologic orientation with the given preference coefficients pC = 0.2, pTV = 0.35, and pSI = 0.45 for the calculation of the sPI. Table 7. Calculation of the Performance Indicator and the strategic Performance Indicator. Joining Technology (j)

PIj

Rank

sPIj

Rank

Technology A

1.36

1

0.40

3

Technology C

1.35

2

0.50

2

Technology E

1.34

3

0.54

1

Apparently, while evaluating the joining technologies without the strategic influence, technology A can demonstrate the highest Performance Indicator, and therefore represents the optimum solution. Respectively, technology C and E are ranked second or last due to the comparatively high costs. However, considering the strategic orientation with the strategic Performance Indicator, the ranking is vice versa. Technology E can demonstrate the highest sPI, thus represents the optimum solution. Technology A is ranked last due to the comparatively low Sustainability-Impact-Index and the high weighting of the dimension sustainability. Fig. 3 shows the findings of the case study in the multidimensional assessment portfolio.

1

Technology-Value-I ndex

0.9 0.8

Joining Technology (j)

0.7 0.6 0.5

Performance Indicator

Technology A

0.4

Technology C

0.3

Technology E

0.2 0.1 0

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1/ 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Fig. 3. Multidimensional Assessment Portfolio: Classification of reviewed technologies .

Strategic Performance Indicator

288 8

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5. Conclusion and future work To enable sustainable planning and manufacturing processes, as well as maintaining or establishing certain competitiveness, a multidimensional assessment of joining technologies including economic, technological, and ecological factors is crucial. The presented approach allows a transparency to be created for monetary and nonmonetary criteria to determine the optimum balance between the different dimensions. Furthermore, the modular design of the method ensures an optimized decision-making for joining technologies due to the consolidation of the different indices and the strategic orientation in the last step through the strategic performance indicator. The developed approach can satisfy all basic requirements conducted from the stakeholder-analysis. Further challenges for the implementation of the method in the body development are the specification of the individual modules, shown in Fig. 2, as well as generating reliable data on life-cycle cost as well as technological benefit or sustainable impact for the evaluation. For example, the modules cost and sustainability necessitate a definition of boundaries, determining which parameter should be included for the assessment. For the technology module, the identification of relevant impact criteria as well as the development of a weighting model is required. To validate the presented approach and identify the potential finally, the transfer from the theoretical methodology to case scenarios in the automotive industry is essential. References [1] Braess, H., & Seiffert, U. (2003). Vieweg Handbuch Kraftfahrzeugtechnik. Wiesbaden: Vieweg+Teubner. [2] Friedrich, H. E. (2013). Leichtbau in der Fahrzeugtechnik. Wiesbaden: Springer Verlag. [3] Arash S., Masoud P., Jiju A., & Park, S. (2013). Typology of Kano models: a critical review of literature and proposition of a revised model. International Journal of Quality & Reliability Management, Vol. 30 Issue: 3, 341-358. [4] Riedel, F. (2013). Selection of joining technologies for the car body manufacturing depending on the energy and resource efficiency Automotive Engineering Congress 2013. Nürnberg: Automotive Engineering Expo. [5] Prüß, H., Stechert, C., & Vietor, T. (2010). Methodik zur Auswahl von Fügetechnologien in Multimaterialsystemen. Design for X - Beiträge zum 21. DfX-Symposium. [6] Pieverling, J. V. (2002). Ein Vorgehensmodell zur Auswahl von Konturfertigungsverfahren fü r das Rapid Tooling. Mü nchen: H. Utz. [7] Goecke, S.-F., & Krautwald, A. (2012). Effiziente thermische Fü geverfahren (1st ed.). Brandenburg: Fachhochsch., Fertigungs- und Produktionstechnik, Maschinenbau im Fachbereich Technik. [8] Reinhart, G., Mosandl, T., & Gartner, J. (2001). Fügeverfahren für die marktnahe Produktion. Werkstatttechnik, (WT08004), 4-8. [9] Gausemeier, J., Brandis, R., & Kaiser, L. (2000). Auswahl von Montageverfahren auf Basis der Produktkonzeption. 7. Paderborner Workshop Entwurf mechatronischer Systeme. Paderborn: Heinz Nixdorf Institut. [10] University, C. M. (2008). Economic Input-Output Life Cycle Assessment - Carnegie Mellon University. Retrieved March 28, 2017, from http://www.eiolca.net/ [11] EN ISO 14040:2006: Environmental management - life cycle assessment. (2009). Berlin: Beuth. [12] Ashby, M. F. (2005). Materials selection in mechanical design. Oxford (Conn.): Elsevier/Butterworth Heinemann. [13] Geissdö rfer, K. (2009). Total cost of ownership (TCO) und life cycle costing (LCC): Einsatz und Modelle: ein Vergleich zwischen Deutschland und USA. Berlin: Lit. [14] DIN EN 60300-3-3:2005-03 (2005). Dependability management – Part 3-3: Application guide - Life cycle costing (IEC60300-3-3:2004); German version EN 60300-3-3:2004, Beuth, Berlin. [15] Forecasting model for lifecycle costs of machines and plants. (2006). Berlin: Beuth. [16] VDI 2884 (2005). Purchase, operating and maintenance of production equipment using Life Cycle Costing (LCC), Beuth, Berlin. [17] Breiing, A., & Knosala, R. (2013). Bewerten technischer Systeme. Theoretische und methodische Grundlagen bewertungstechnischer Entscheidungshilfen. Berlin: Springer Berlin.