Antônio Sérgio Brejão

Marcos Oliveira Morais

Oduvaldo Vendrametto

Antonio Sérgio Brejão¹

Marcos De

Oliveira Morais¹

Oduvaldo Vendrametto¹

A Macro Sectorial Study of Semiconductor Production

Keywords: Production of Semiconductors, Electronic Components, International Trade

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The conjunctural issues in the semiconductor manufacturing industry can give the companies opportunities of being more competitive. To be competitive, the company requires planning, research, development of new manufacturing processes, primarily in technology, skilled labor, efficiency, profitability, and investment. In Brazil, new production models, macroeconomic policies and the growth of technologies are making the electronic industries more specialized, thus increasing the value added to the product and/or process. The increase in these processes and projects that are improved, well-defined and strategically productive can provide a higher competitive advantage to the country.

Global economies are leading the productive semiconductor industry with significant production, sales and employment generation. These economies have previously defined strategies for all production processes, making them more competitive.

Methodology

The approach will be the quantitative exploratory research with cyclical and macro sectorial analysis of import, export, production and national and international investments in the semiconductor industry.

3 Theoretical Framework

Processes of semiconductor production

The semiconductor fabrication begins with the production of wafers, namely a thin, round slice of a semiconductor material ranging in size from 152.4 mm to 304.8 mm in diameter. The finished wafer is approximately 15mm thick. After the production of wafers, integrated circuits that generate microprocessors and chips are assembled [1; 14].

Semiconductor production in Brazil

Brazilian Company of Semiconductors -CBS was supported by the government through the Program of Technological Development Support for the Semiconductor Industry -PADIS. The unit installed in Minas Gerais was formed with capital from German companies and Brazilian Bank for Economic and Social Development -BNDES. The company will manufacture electronic integrated circuits assembled, unassembled or in the form of discs (wafers) that have not been cut into chips yet.

Incentives for semiconductor plants are a priority of the new industrial policy of the country. Upon qualification, CBS will produce circuits with tax exemptions, and incentives will expire in 2022 [START_REF]Sociedade Brasileira de Microeletrônica: Companhia Brasileira de Semicondutores terá incentivos até 2022[END_REF]. Brazilian government's strategic option for a sustainable development model focused on industry competitiveness was consolidated through the Greater Brazil Plan -PBM. Such plan encourages the productive sector in its efforts to technological development and innovation, including mechanisms to support business expenditure on research and development. The support includes financial, tax and regulatory instruments to encourage investments [START_REF]Brasil Maior: Agendas Estratégicas Setoriais[END_REF].

In 2005 the activity in the chip design center was started at the National Center for Advanced Electronic Technology -CEITEC, with the announcement of CI-Brazil program to support the design of commercial chips in the country. In 2008, federal authorization for the institutionalization of a public company in semiconductors, named CEITEC, was conferred by Federal Law Nº 11759/2008, and such company was operated judicially in the year 2009 in order to encompass the previous operations of the Center Design of CEITEC [START_REF] Bampi | Coord.) Perspectivas do investimento em eletrônica[END_REF].

CEITEC is a public company linked to the Ministry of Science, Technology and Innovation -MCTI that operates in the semiconductor industry by developing solutions for automatic identification (Radio Frequency Identification and smartcards) and specific applications [START_REF]Diário Oficial da União[END_REF]. As an extension of the PBM, law Nº 11484 was approved in 2007, granting incentives to industries of digital TV equipment and semiconductor electronic components and rules on the intellectual property protection of topographies of integrated circuits, thus instituting PADIS, whose purpose is to support technology development for the semiconductor industry in Brazil [START_REF][END_REF].

Also in this context, the Federal Revenue of Brazil -RFB, by Normative Instruction Nº 852, dated June 13, 2008, established procedures to authorize the Program of Technological Development Support for the Semiconductor Industry in the country (PADIS) [START_REF]Brasil: Instrução Normativa RFB nº 852[END_REF].

Brazilian foreign trade

According to Foundation for Research Support of the State of Sao Paulo -FAPESP magazine, the country exports metallurgical silicon at US$ 2/kg. After purified abroad, the silicon is transformed into sheets used in the manufacture of semiconductors or photovoltaic cells, and the cost in that stage ranges between US$ 50 and US$ 1,000, depending on the purity and crystallinity [START_REF]FAPESP: Silício brasileiro para células solares Edição 197[END_REF].

According to Brazilian Association of Electrical and Electronics Industry -ABINEE, the trade balance of the appliances and electronics sector in the period from January to November 2013 showed the following results: exports of US$ 6.69 billion and imports of US$ 40.48 billion, thus generating a deficit of US$ 33.79 billion in the trade balance of electronics products. This result was 11% higher than the occurred in the same period in 2012 (US$ 30.35 billion) [START_REF]ABINEE -Associação Brasileira da Indústria Elétrica e Eletrônica: Balança Comercial[END_REF].

Imports and Exports

For CBS the rate of import duty levied on imported goods was reduced to zero for products such as machinery, apparatus, tools, equipment, computer tools -software for incorporation into their fixed assets, intended for tunneling activities and testing of electronic semiconductor devices [START_REF]Sociedade Brasileira de Microeletrônica: Companhia Brasileira de Semicondutores terá incentivos até 2022[END_REF].

Imports of electrical and electronic components totaled US$ 2.0 billion, being 14.7% higher than those occurring in November 2012, highlighting the components for telecommunications and semiconductors, which together totaled almost US$ 1 billion [START_REF]e Eletrônica: Balança Comercial: do comércio exterior brasileiro[END_REF]. The three most imported items in such industry were: components for telecommunications (US$ 6.2 billion); semiconductors (US$ 4.9 billion) and computer components (US$ 3.0 billion) [START_REF]e Eletrônica: Balança Comercial: do comércio exterior brasileiro[END_REF].

The Trade Balance of the components segment for the period 2002-2008 (Figures 1 and2) had a small participation in the import and export of semiconductors and, in accordance with the BNDES, imports of discrete semiconductors (diodes, transistors, photodetectors and photoemitters) reached US$ 423.1 million and exports totaled US$ 29.6 million data is from September 2008 [START_REF] Bampi | Coord.) Perspectivas do investimento em eletrônica[END_REF].

International trade

The Semiconductor Industry Association -SIA, representing U.S. leadership in semiconductor manufacture and design, announced that worldwide semiconductor sales in 2013 reached $ 305.6 billion, the highest annual total ever in the industry, and an increase of 4.8% in relation to the 2012 total of $ 291.6 billion. Global sales in December 2013 totaled $ 26.6 billion, marking the strongest amount recorded in December, while December sales in the Americas increased 17.3% compared to the same period of the previous year [11; 12].

A research conducted by KPMG U.S. indicated that, worldwide, the semiconductor market is extremely relevant, reaching a turnover of US$ 295 billion in 2010, against US$ 220 billion in 2009, and that the segment generates 200,000 jobs, according to the SIA [START_REF] Kpmg | Enquanto Brasil tenta inserção, mercado de semicondutores[END_REF]. Worldwide, some companies lead the semiconductor production as presented in (Table 1) in the biennium 2007/2008. In Figure 3, BNDES presents a more updated scenario that shows a reduction of global semiconductor production between 2008 and 2011. With respect to investments in the semiconductor industry in the period of 2008-2011 (Figure 4), it is presented a sharp decline with signs of recovery in 2011.

World Market for Semiconductor

Fig. 3 World Market for Semiconductors (Adapted from BNDES apud SAI) [START_REF] Gutierrez | Complexo eletrônico: o projeto em microeletrônica no Brasil[END_REF] Investments in the Semiconductors Industry Fig. 4 Investments in the sector (Adapted from BNDES apud Gamerd; Fabtech) [START_REF] Gutierrez | Complexo eletrônico: o projeto em microeletrônica no Brasil[END_REF] 4

Results and Discussion

In Brazil, implementation processes for semiconductor production occurred in 2005, with the creation of CEITEC and, subsequently, various legal and tax incentives were adopted for the development of semiconductor and hence the creation of new businesses. On the national scenario of companies in the electronics industry subsystem, imports of semiconductors in 2013 grew 10% compared to 2012 with increasing trend.

There was a significant deficit in the trade balance of Brazil in the field of appliances and electronics, which includes semiconductors. Even so, discreetly, Brazil entered the international scenario of semiconductors export. The research shows the importance of the development of semiconductors with an increasing demand in many countries of the world and that, in 2013, the highest sales of semiconductors was recorded, in the order of 4.8%, according to ABINEE [START_REF]e Eletrônica: Balança Comercial: do comércio exterior brasileiro[END_REF]. Semiconductors are among the most imported products in Brazil, with a variation of 10% in the period of 2012/2013. In 2007/2008, the sector had an important sales scenario, where in (Table 1) it is possible to see that the ranking of the three largest companies in US$ billions is positive for the leader.

Despite of the growing sales trend, it is observed that the development of the sector in Brazil needs more technical/commercial diversification to add value to the primary product, which is a relevant question to make the national semiconductor industry more competitive.

In Brazil, the use of semiconductors is growing proportionally to the production of appliances and electronics, and the country is an exporter of primary raw material (silicon) for the production of semiconductors. According to the macro sectorial scenario, it is observed that with R&D investment the domestic industry will add value to the input manufacture process, making it more competitive and less dependent on imports. In the context of technological innovation, it is important to highlight the need of research and development of alternative materials for the production of semiconductor, such as niobium, since Brazil is the largest global producer, with a share of 96% of the market [START_REF]Instituto Brasileiro de Mineração: Nióbio[END_REF]. Brazil exports silicon, which is the main component for the production of semiconductors, which after purification and processing abroad return to the country as finished product, with a much higher aggregate cost, making obvious the need for investments in the sector. However, such export does not bring great benefits to the country because the added value is low, with the trading of product at US$ 2.0/Kg, while after processing abroad it can cost around US$ 1000, depending on the degree of purity, thus obtaining an expressive and attractive market value.

Although speculatively, it is necessary to mention that researches on the quantum computer are growing worldwide. If feasible, the conversion of physical principles into technological equipment with properties where the transistors are not used will have a strong impact on the semiconductor industry, reason why the participants of the semiconductor chain must pay attention to the evolution of the quantum computer research.

Conclusion

As noted in the article, it is a sector in full development. Additionally to investments in technology, the issue of qualification of the workforce should be considered in a broader and strategic project for the country. Upon the growing demand, companies in the electronics sector have been seeking for new technologies and new processes in order to meet their productive and economic needs. Strategies should be defined for optimized production processes and public/private investments, thus generating greater competitiveness. National companies will be more competitive if they start using well-defined strategies developed by the R&D sector of the government and organizations that facilitate investment to meet the demand.

It is observed that there is a structured and measured design over the years for such process, allowing for a guaranteed repeatability, thus favoring the industry. In the macro sectorial environment, the semiconductor industry is booming with a strong tendency to adjust alternative materials.

*

  until September 2008 ** until September 2007 Fig. 1 and Fig. 2 Adapted from Trade Balance of Components Segment Secex Aggregation BNDES[START_REF] Bampi | Coord.) Perspectivas do investimento em eletrônica[END_REF]

Table 1 . Ranking of the top three semiconductor companies

 1

	Ranking 2007/08	Companies	Sales 2007 (US$ Bi) Sales 2008 (US$ Bi)
	1	INTEL	33.9	34.1
	2	SAMSUNG	19.7	17.9
	3	TEXAS INSTRUMENTS	12.3	11.5
	Source: Adapted from BNDES -iSuppli, 2008 [4].


A M N D B Seneviratne
email: dammika.seneviratne@uis.no

R M Chandima Ratnayake
email: chandima.ratnayake@uis.no

Integration of Decision Support Modules to Identify the Priority of Risk of Failure in Topside Piping Equipment: An Industrial Case Study from the NCS

Keywords: In-service inspection planning, topside piping equipment, decision support modules, thickness measurement locations, artificial neural networks, ERP software 1

The identification and prioritization of locations that have potential for failure (also referred to as thickness measurement locations (TMLs)) in the in-service inspection planning of offshore topside piping equipment requires a significant amount of data analysis together with relevant information. In this context, planning personnel analyze data and information retrieved from piping inspection databases through enterprise resource planning (ERP) software to investigate possible degradation trends in order to recognize the TMLs that have reached a critical level. It is observed that suboptimal prioritization occurs due to time restriction vs. amount of data and/or information that has to be evaluated. The suboptimal prioritization omits some of the critical TMLs, increasing the risk of failures whilst also increasing cost due to taking non-critical TMLs into inspection. Therefore, this manuscript illustrates an approach to integrate the decision support modules (DSMs) via an artificial neural network model for the optimum prioritization.

Introduction

DSMs are used in the identification of possible failure locations in offshore topside piping equipment. The base of most DSMs is empirical engineering analysis of systems/equipment. The identification of possible failure location (TMLs) is a major part of inspection planning in offshore oil and gas production and process facilities (OO&G P&PFs) [START_REF] Ratnayake | Maintaining technical integrity of petroleum flow lines on offshore installations: A decision support system for inspection planning[END_REF]. Inspection planning is one of the key elements in maintaining the technical integrity of aging offshore production and process facilities (OP&PFs) (Ratnayake, 2012a). Standards, for instance Norsok Z-008, DNV RP-G101 and API 581, guide the operators and authorities in carrying out and maintaining inspection activities according to the rules and regulations defined by the governing authorities (Norsok Z-008, 2011;DNV RP G101, 2010;API 581, 2008).

Inspection planning is an essential task in maintaining asset integrity at a predefined level in OP&PFs [START_REF] Ratnayake | Modeling of asset integrity management process: A case study for computing operational integrity preference weights[END_REF]. In particular, of the process equipment in the OP&PFs, topside piping equipment, for instance, piping, valves, tanks, etc., undergoes in-service inspections to maintain the required health, safety, environment and quality (HSE&Q) limits imposed by the regulatory authorities on the Norwegian continental shelf (NCS) (Ratnayake, 2012b). The large amount of historical data and information is analyzed during the in-service inspection planning process, mainly to identify the critical TMLs for inspections [START_REF] Ratnayake | Utilization of piping inspection data for continuous improvement: a methodology to visualize coverage and finding rates[END_REF]. The data is analyzed by field experts who have gathered experience in the inspection planning field for numerous years. The data is analyzed considering different scenarios (perspectives). For instance, the corrosion trends, erosion trends, fluid flow variations (turbulences) are considered in identifying the critical TMLs [START_REF] Ratnayake | Maintenance integrity: Managing flange inspections on aging offshore production facilities[END_REF]. Therefore, DSMs developed to identify corrosion trends, flow patterns, etc., are helpful for the inspection planning personnel in identifying the critical TMLs.

Researchers have developed different degradation prediction models for hydrocarbon piping, which use corrosion and erosion empirical formulae. For instance, [START_REF] Nesic | Key issues related to modelling of internal corrosion of oil and gas pipelines -A review[END_REF], [START_REF] Nesic | An open source mechanistic model for CO2 / H2S corrosion of carbon steel[END_REF]Norsok M-506 (2005) have developed a number of corrosion models for the use of hydrocarbon piping equipment for production and process facilities. Furthermore, erosion models developed in DNV RP O501 (2007) are used in the industry to identify erosive locations. The stress identification models are important in the identification of the wall thinning and crack propagation of equipment [START_REF] Alvarado | Decision-making on pipe stress analysis enabled by knowledge-based systems[END_REF]. Flow pattern identification models are also important in recognizing the critical vortexes developed inside the equipment under the multiphase flow, which generate erosion corrosion [START_REF] Valle | Multiphase pipeline flows in hydrocarbon recovery[END_REF][START_REF] Ferre | Method for modelling multiphase flows in pipelines[END_REF]. Moreover, Srinivasan et al. have developed a risk analysis model based on possible corrosion in petrochemical piping equipment [START_REF] Srinivasan | Real time risk-based asset management -A framework for sustainable asset integrity solutions[END_REF]. A DSM, which evaluates the risk level of TMLs based on the technical condition, has been developed by [START_REF] Seneviratne | In-service inspection of static mechanical equipment: Use of a fuzzy inference system for maintaining the quality of an inspection program[END_REF]. However, there is limited use of the above models in the inspection planning field in identification of critical TMLs.

Within the industry, owner/operator specific software modules are used for recording and storing the piping and degradation data and information (Ratnayake, 2012a). Moreover, some of these modules are used for the basic analysis of the degradation rates of TMLs based on the measurements carried out in the field. The linking of the DSMs for the identification of the critical TMLs to the basic ERP software modules, for instance SAP, RIS etc., will improve the quality of recommendation regarding critical equipment for the inspections.

The ERP based software offers flexibility in integrating other software modules [START_REF] Kahkonen | ERP Integration[END_REF]. The use of DSMs is specific to the particular industrial case in which it is used and special attention is needed in integrating into a common ERP system. [START_REF] Kahkonen | ERP Integration[END_REF] have surveyed the development of the integration of DSMs into ERP systems and have stated that the integration must be work specific. Therefore, the manuscript suggests a methodology for integrating the DSMs used in the inspection planning process into the existing ERP systems in the in-service inspection planning of offshore topside piping equipment using artificial neural networks (ANNs).

The ANN is greatly used in data prioritization applications. ANN applications are used by [START_REF] Knapp | Machine fault classification: a neural network approach[END_REF] and [START_REF] Becraft | An integrated neural network/expert system approach for fault diagnosis[END_REF] in manufacturing industries for prioritizing failures. [START_REF] Knapp | Machine fault classification: a neural network approach[END_REF] used the ANNs for the recognition and prioritization of failure propagation by employing fluctuated data. Becraft and Lee (1993) developed a system using ANNs for fault diagnosis in chemical pro-cess plants under high risks. However, ANNs have relatively less use in the O&G industry for prioritizing purposes. The use of an ANN in this study is for the replacement of human judgment. The ANN model prioritizes the critical TMLs according to the criticality given by the DSMs. The ANN models try to imitate the inspection planner and analyze the prioritization like humans. The suggested neural network approach will be a very suitable method for replacing the humans involved in the prioritization system for TMLs. The background of the problem is illustrated in Section 2 of the manuscript, while the methodology is explained in Section 3. System development is explained in Sections 4 and Section 5 presents the concluding remarks.

Background

The current in-service inspection planning processes for OP&PFs involve several techniques, used worldwide, which are standardized by different standardization organizations. One of the widely used methods is risk based inspection (RBI), which is explained in the standards, for instance, API (2002API ( , 2008) ) and DNV (2010).

After the risk assessment (based on RBI) for the equipment in the plant, inspection, test, maintenance and modification activities are planned based on the plants' historical and condition monitoring data. The inspection or maintenance plans reflects the risk (probability and consequence) of the equipment which tends to fail and the philosophy to maintain the fitness of equipment for service by detecting, preventing, controlling and mitigating. The condition of equipment is identified after inspections and necessary actions are taken for maintenance/modifications or re-inspections, if the equipment is not fit for the service.

The inspection planning process consists of several steps. The overall outcome of the inspection planning process is to identify the TMLs for inspections, allocate resources for inspections, and record the measurements and analysis of measurements for further evaluations. Furthermore, a quality inspection program aims to reduce plant downtime, minimize the cost and increase the effectiveness of the inspections.

Annual Budget

Annual activity plan

Evaluate/analyze inspection results

Prepare inspection

Feedback PM plan

Report & filter inspection results

Execute inspection

Summarize technical condition Monitoring

Strategy update

Parameter change Feedback strategy

Planning inspection program

Fig. 1. Inspection planning process

The inspection planning process discussed above consists of the analysis of a vast amount of data with field experience in the identification of possible TMLs for inspections. The data and information are retrieved from condition monitoring systems, field measurements and standards. The TML analysis is performed by considering the historical thickness measurement data, degradation rates, potential degradation locations and potential failure locations. Therefore, the identification and prioritization of critical TMLs in a subsystem is a major part of the inspection planning process, as illustrated in figure 1 (Ratnayake, 2012b).

In handling and recording data, most of the piping inspection data bases (PIDBs) use the tag number or functional location, which is a number given to the specific equipment in a plant subsystem [START_REF] Ratnayake | Application of a fuzzy inference system for functional failure risk rank estimation: RBM of rotating equipment and instrumentation[END_REF]. The system, subsystem and equipment (tag/functional location) numbering is illustrated in the industrial standards, for instance, DNV-RP-G101 (2010). The tag/functional location hierarchy is illustrated in figure 2. The data, for instance measurements from NDE equipment, trend analysis, equipment drawings, pictures, documents, data files and videos, are stored in PIDBs under the tag/functional location number.

Fig. 2. Equipment level hierarchy

In aging OP&PFs, due to the effects of years of operational process cycles, the risk level of the plant equipment varies and the initial RBI assessments change over time. Therefore, inspection planning personnel use their experience and knowledge of the flow conditions and equipment condition to identify and prioritize the critical TMLs for inspection planning. This approach is on an ad hoc basis, and the quality of TML prioritization depends on the expertise of the planning personnel. Therefore, this manuscript discusses the utilization and integration of DSMs, for instance degradation prediction models, flow condition models, technical condition models, etc., using ANN to prioritize the critical TMLs for inspection planning purposes.

Methodology

The OP&PFs have a number of systems which are grouped into subsystems. In a subsystem there are number of pieces of equipment degraded as a result of different degradation mechanisms. The identified degradation mechanisms are empirically modeled by different researchers and industrialists. Most of these degradation models are formed as DSMs to identify the critical TMLs in the subsystem. Each DSM prioritizes the TMLs based on its empirical formula. By applying different DSMs, a different prioritization rank of TMLs is obtained. In current industrial practice, field experts make the final cumulative prioritization using their expertise and knowledge. Therefore, for a proper cumulative prioritization, a deployment of prioritization mechanisms is needed. The artificial intelligence techniques are appropriate in representing human knowledge in the prioritization process. Therefore, in this study an ANN (trained multilayer perceptron) model is proposed to obtain the cumulative prioritization to discover the critical TMLs in a subsystem. The method is shown in figure 3.

Decision Support Modules

DSMs are used in the O&G industry for various purposes, especially the analysis and identification of critical TMLs. By using the DSMs in the inspection planning process, the TMLs are prioritized according to criticality. The prioritization of TMLs in a system by a selected number of DSMs is shown in figure 3. The individual prioritizations of the DSMs are analyzed by the experts to identify the critical TMLs. In this process the experts prioritize the DSMs' outcome, according to the significance of the importance of the decision support module. Then the most significant decision support module outcome is analyzed for TML prioritization. Moreover, by evaluating all the outcomes of the DSMs, a cumulative prioritization of the TMLs is made for the recommendation of the optimum prioritization of possible failure of TMLs. An example of TMLs of a subsystem prioritized according to the individual decision support module and the decision support module prioritization is shown in Table 01.

The DSM prioritization numbers are: P DMS1 , P DMS2 , P DMS3 , .., P DMSi , .., P DMSn. . In an application, the DSM prioritization numbers are to be assigned with real numbers according to the reflection of the field experts. n is the maximum number of DSMs used for the prioritization of TMLs in the subsystem. The TML prioritization numbers are: P 1j , P 2j , P 3j , …., P ij , …, P nj , DSM i = i th DSM number; Inspection point j = j th inspection point (TML) number. The prioritization from the DSM for each inspection point (TML) has been assigned according to the total number of inspection points in the system or on a ranking weight basis (based on the risk levels of TMLs).

P11 P21 P31 Pi1 Pn1 Inspection point 2 P12 P22 P32 Pi2 Pn2 Inspection point 3 P13 P23 P33 Pi3 Pn3 … … … … … … Inspection point j P1j P2j P3j Pij Pnj … … … … … … Inspection point n P1n P2n P3n Pin Pnn

3.2

Use of Artificial Neural Networks in TML Prioritizing

In this proposed methodology, a prediction model is developed using the ANN technique and applied to the prioritized TML ranking from the DSMs to obtain a cumulative prioritization. The literature provides a number of different ANN architectures in the prioritization applications. Different types of neural networks are synthesized and, according to the research objectives, a multilayer feed forward ANN is used in this research study. The input value for each node of the ANN model will be (P ij , P DMSi ).

The output of the ANN model will be PC ANNj where PC ANNj is the cumulative prioritization number for the inspection point (TML) j.

Development of the Proposed Model

A subsystem in one of the matured OP&PFs is selected and the possible degradation mechanisms are identified to select the needed DSMs. The steps for the development, theoretical testing and validation of the proposed model are as follows: 1). Identify the subsystem degradation trends and failure mechanisms to select DSMs for identifying critical TMLs; 2). Obtain the experts' view on prioritization of TMLs and DSMs; 3). Build a priority list of TMLs and the DSMs with the view of the experts; 4.) Define the multilayer feed forward perceptron ANN model with the appropriate number of nodes (similar to DSMs used) and one hidden layer to replace the field experts' decisions; 5.) Define training data for the neural network from the experts' prioritization on TMLs and DSMs; 6.) Generate the test data for the neural network by running each DSM individually on the subsystem to prioritize the TMLs; 7.) Train and test the neural network and validate the results. The total structure of the proposed model is illustrated in figure 4.

Concluding Remarks

The proposed ANN model is suitable for identifying the critical TMLs in a subsystem which is undergoing different degradation trends and flow condition scenarios. The DSMs developed for degradation, flow condition and failure identification of the static mechanical systems under various production and process conditions give different prioritizations. In a real-world situation, field experts analyze the outcome of the different DSMs and identify the critical TMLs for inspections. The neglect of some critical TMLs is common since the field experts have to analyze a vast amount of data from different DSMs. The introduction of artificial neural networks to replace the analysis part of the field experts' work process increases not only the quality of the analysis but also the reliability of the identification of critical TMLs which have a possibility of failure in the operation. Moreover, the neural network system can link with the DSMs and condition monitoring data to generate a system that accurately makes TML prioritization for the inspection planning.

Fig. 3 .

 3 Fig. 3. ANN model for cumulative TML prioritization

Fig. 4 .

 4 Fig. 4. Functional structure of the proposed model The outputs from the DSMs for each inspection point (TML) have been ranked in a local manner (The ranking is based on the risk level of the TML. For instance, Sen-

Table 01 .

 01 TML prioritization by DSM and DSM prioritization

		TML criticality	prioritization	DSM 1	TML criticality	prioritization	DSM 2	TML criticality	prioritization	DSM 3	TML criticality	prioritization	DSM i	TML criticality	prioritization	DSM n
	DSM prioritization	PDMS1	PDMS2	PDMS3	PDMSi	PDMSn
	TML number
	Inspection point 1

Table 02 .

 02 Tentative layout of the comparison of ANN model output and field experts' outcome

	TML number	TML criticality	prioritization	DSM 1	TML criticality	prioritization	DSM 2	TML criticality prioritization DSM 3	TML criticality prioritization DSM i	TML criticality prioritization DSM n	Cumulative prioritization (by field ex-pert)	Cumulative prioritization (by ANN)
	Inspection point 1	P11			P21			P31	Pi1	Pn1	PCFE1	PCANN1
	Inspection point 2	P12			P22			P32	Pi2	Pn2	PCFE2	PCANN2
	Inspection point 3	P13			P23			P33	Pi3	Pn3	PCFE3	PCANN3
	…	…			…			…	…	…	…	…
	Inspection point j	P1j			P2j			P3j	Pij	Pnj	PCFEj	PCANNj
	…	…			…			…	…	…	…	…
	Inspection point n	P1n			P2n			P3n	Pin	Pnn	PCFEn	PCANNn

Acknowledgements

The authors would like to thank Roy Martin Zydeman and the in-service inspection planning team in Aker Solutions Offshore Partner, Norway, for their support in this research study.


Tim Heinemann
email: t.heinemann@tu-braunschweig.de

Alexander Kaluza

Sebastian Thiede

Daniel Ditterich

Johannes Linzbach

Christoph Herrmann

Life Cycle Evaluation of Factories: The Case of a Car Body Welding Line with Pneumatic Actuators

Keywords: life cycle evaluation, total cost of ownership, streamlined LCA, factory evaluation, sustainable manufacturing

During the planning phase of the build up or overhaul of factories a large share of the life cycle spanning impact of such production facilities is determined. Furthermore it is very hard to evaluate the impact of possible measures for improvement, resulting Total Cost of Ownership (TCO) and environmental impacts of factory systems. Against this background this paper presents an integrated life cycle analysis approach for a streamlined economical and environmental life cycle assessment of factory systems. The approach gets applied while using two interacting tools for a) energy efficiency evaluation of pneumatic systems and b) life cycle evaluation of factory systems.

Introduction

When planning projects for new or overhauled factories are started, there is a high degree of freedom about the configuration of the machines, the technical building services (TBS) and the building shell. Also most of the later costs as well as environmental impacts (esp. for the use and disposal phase of the factory) get determined at this stage. However, most of the information for a severe quantification of the financial and environmental impacts of planning alternatives are not available so that wrong decisions can be taken easily. Especially when it comes to add-on measures to reduce the environmental impact, often the budget restrictions impede the implementation as the TCO and financial benefits as well as the environmental improvements cannot be estimated sufficiently. This problem will be solved by applying a Life Cycle Evaluation (LCE) Approach, which becomes manifest in the LCE Tool [START_REF] Heinemann | Life Cycle Evaluation of Factories: Approach, Tool and Case Study[END_REF]. This tool gets applied in combination with a specific tool for the evaluation of pneumatic and electrical actuator systems to calculate the economical and environmental impact of alternative system designs and improvement measures.

Theoretical background

Holistic system comprehension and factory elements

In contrast to many approaches, which divide the factory planning process into sequences and induce a sequential planning of factory elements (e.g. [START_REF]VDI-guideline: VDI 5200 factory planning -planning process/VDI-Richtlinie: VDI 5200 Blatt 1 Fabrikplanung -Planungsvorgehen[END_REF]), this paper introduces a holistic system comprehension of factories, in which all factory elements are of equal importance. This is because in industrial environments all factory elements influence each other regarding the overall resource consumption. The introduced evaluation approach focuses on the factory system and its subsystems [START_REF] Herrmann | Energy Efficiency in Manufacturing -Perspectives from Australia and Europe[END_REF] [4]:  production equipment: processes/machines/value adding process chains  technical building services: responsible for providing production conditions and energy as well as media (e.g. compressed air)  building shell: physical boundary of the factory system and to the outside However, their individual design and control involves diverse disciplines which impedes a synergetic planning of the factory system. Thus, methods to overcome those interdisciplinary challenges are a proper way towards more sustainable solutions.

Life cycle evaluation

Life cycle evaluation (LCE) in the context of this paper addresses the evaluation of financial (Total Cost of Ownership, TCO) and environmental impacts (CO 2 eq.) which result from physical flows during all life cycle phases of the factory system. TCO is a selected perspective on life cycle costing (LCC), which considers all economically relevant monetary flows over the life cycle of products. Therewith, LCC allows analysing trade-offs between life cycle phases and supports to derive solutions from a comprehensive perspective [START_REF] Herrmann | Ganzheitliches Life Cycle Management[END_REF]. Diverse frameworks for LCC in form of standards, norms or guidelines have been developed (e.g. VDMA 34160, VDI 2884, DIN EN 60300). TCO focuses solely on the operator/user perspective of the considered object and all the costs that occur during the course of ownership. To ease application, software based tools which also differ in terms of scope, field of application and general functionalities have been developed (e.g. [START_REF] Thiede | Dynamic Total Cost of Ownership (TCO) Calculation of Injection Moulding Machines[END_REF]). However, those solutions tend to be quite specific for selected applications and less considering systems as a whole [START_REF] Heinemann | Life Cycle Evaluation of Factories: Approach, Tool and Case Study[END_REF]. The calculation of environmental impacts can be done via Life Cycle Assessments (LCA). LCA focuses mainly on environmental impacts throughout a product's life cycle from raw material acquisition through production, use and end-of life treatment. It builds upon Life Cycle Inventories (LCI), which sum up the physical flows along the life cycle of a product [START_REF]Life cycle assessment -requirements and guidelines DIN EN ISO 14044[END_REF]. From a product perspective the incorporated materials are usually well known and easy to assess in LCAs. However, the additional physical (energy/media) flows which occur during the manufacturing phase especially within a factory system are often hard to assess and demand for supporting tools (see also [START_REF] Heinemann | Life Cycle Evaluation of Factories: Approach, Tool and Case Study[END_REF]).

Factory life cycle evaluation approach

Architecture of the approach and tool All calculations and evaluations within the LCE approach are based on life cycle inventories (cumulated physical input and output flows) of the focused system, which can be a system element like an assembly line (see Fig. 1).

Tool interaction for enhanced evaluation options

As the LCE approach demands reliable input data (e.g. technical behaviour of machines), it has to incorporate data from other tools. Especially in planning stages, when real consumption profiles of factory elements are not accessible, tools which simulate element or system behaviour become relevant. Furthermore the LCE Tool only considers factory elements down to the level of detail of machines, TBS or building shells. It cannot consider the behaviour of components (at acceptable effort). E.g. the behaviour of pneumatic components and resulting loads for compressors illustrate the resulting interplay of detailed machine component behaviour and the resource consumption of factory elements. Fig 3 visualizes an exemplary interplay of tools as it will be applied in the case study, which will be described afterwards.

Fig. 3. Tool interplay and information flow

Supporting tool for the evaluation and optimization of pneumatic actuation systems

The LCE Tool benefits from an application of tools like the Life Cycle Cost Calculator (L3C-Tool, Festo AG & Co. KG), which can model actuation systems at a very high level of detail regarding single (pneumatic as well as electrical) actuation operations, resulting compressed air and electricity consumption (see Fig. 3). The L3C-Tool enables the right dimensioning of air compressors and the evaluation of improvement measures to the system of actuators, valves and tubes (see Fig. 4) [START_REF]Planung des effizienten Einsatzes von Druckluft im Karosseriebau[END_REF]. Fig. 4. Dimensioning of pneumatic system and calculation of compressed air demand in L3C-Tool (screenshot) [START_REF]Planung des effizienten Einsatzes von Druckluft im Karosseriebau[END_REF] A major advantage of the L3C-Tool is its ability to conduct transparent and comprehensible energy consumption calculations. This allows a direct cost comparison of different factory design options. Hence, this tool addresses the following main functionalities:

 Modelling of systems on component level and aggregation to machine level  Calculation of energy costs and investment costs  Performance of break-even analyses between different system configurations The L3C tool also aims at identifying energy efficiency improvements, while quantifying energy saving potential, payback time for investments and maintenance costs. Thus, decision makers can carry out an energy diagnosis of configuration alternatives with little effort. Further information about the L3C-Tool can be found in [START_REF]Planung des effizienten Einsatzes von Druckluft im Karosseriebau[END_REF] and [START_REF] Ditterich | Effizienter Drucklufteinsatz im Automobilbau[END_REF]. As main improvement measure an optimization of the pneumatic components reduces the compressed air consumption of the system. Thus, the compressor can be resized compared to the standard setting with a conventional compressor. In order to highlight the broader evaluation potential of the LCE Tool further possible improvement measures will be evaluated exemplarily as well. Due to simplification reasons only the consumption of electricity has an influence on the environmental impact of the factory's use phase. Furthermore, the building shell only creates emissions during its setup and disposal phase. The evaluation time horizon is 30 years of use phase plus a setup and disposal of the factory. Tab. 1 introduces a selection of parameters, which describes the base scenario, and the parameter changes of improvement measures.

Base scenario: Automotive car body welding line

A car body welding line is used as reference. The model consists of a selection of the machines of the manufacturing line, the TBS (compressor station) and a sample building shell. The compressor capacity meets the demand of a car body factory as figured out with the L3C-tool. For the building shell a predefined reference factory has been used [START_REF] Heinemann | Life Cycle Evaluation of Factories: Approach, Tool and Case Study[END_REF].  Sensitivity analysis: +/-10% energy price

Technical measures

Scenario 3: Improved pneumatic components and system design, based on the recommendations from the L3C-Tool

Improving the compressed air system is a lever for decreasing energy cost in production [START_REF] Saidur | A review on compressed-air energy use and energy savings[END_REF] [8].

Overall system design (up to 9%), leakage detection (up to 20%) or more efficient compressors (up to 15%) can reduce the energy consumption significantly [START_REF] Saidur | A review on compressed-air energy use and energy savings[END_REF] [13]. For Sc. 3 a smaller compressor is installed due to improved pneumatic components, a more advanced system design and leakage detection. The total investment is assumed to remain constant as the measure's implementation costs equalize the reduced compressor investment. The impact on the whole factory gets assessed.  Applied changes: Air compressor: 33% less electrical power input (115 kW instead of 171 kW)

 Sensitivity analysis: +/-10% electrical power input of air compressor

Scenario 4: Energy efficient drives

By increasing the energy efficiency of drives (e.g. according to the IEC 60034-30 standard) from class IE1 to IE3 efficiency increases of about 10% can be achieved [START_REF] Abb | IEC 60034-30 Norm zu den Wirkungsgradklassen für Niederspannungsmotoren[END_REF].

 Applied changes: Drives: 10 % less electrical power input (62.2 kW instead of 69.1 kW for the considered section of the body welding line); Machine investment costs: +10%

 Sensitivity analysis: +/-10% electrical power input of production machines

Tab. 1. Scenario description

Results

The car body manufacturing scenario could be modeled successfully within the Festo L3C-tool and the LCE-tool. Even though the LCE tool allows a separate simulation of the factory, the cumulated input data out of the L3C-tool enhanced a flawless application. Four scenarios have been applied in order to test the flexibility of the LCE-tool and the sensitivity of its results. For that matter the TCO and the greenhouse gas emissions (kg CO 2 eq) have been evaluated (see Fig. 6 and Fig. 7.). An additional sensitivity analysis for Sc. 3 has been carried out (see Fig. 8). Environm. impact (1000 t CO 2 eq.)

Building shell

TBS

Machines

The evaluated organizational measures include the change of the shift model (Sc1) and the use of a certified renewable energy mix (Sc2). Whereas the reduced working hours result in lower TCO (-19%) and greenhouse gas emissions (-26%), it has to be considered that the factory output would decrease simultaneously, not regarding additional efforts (e.g. ramp up and shutdown times). The change of the used electricity mix shows slightly higher TCO. The calculated carbon emissions decrease by 99%.

Variations in the installed compressor capacity (Sc3) and the use of more efficient electric drives (Sc4) have been discussed as technical measures. Based on the L3Ctool application and its optimization on component level, the use of a compressor with 33% less power demand becomes possible. This decreases the TCO by 11% and the carbon emissions by 22% (Sc3). Only marginal savings (2% TCO and 3% carbon emissions) could be achieved in Sc4. Based on these results, Sc. 3 has been selected to evaluate the influences of relevant input parameters on the TCO in more detail. A sensitivity analysis varies the compressor's electrical process power, changes energy prices as well as compressor lifetimes (Fig. 8). Increased compressor lifetimes are assumed due to the improved pneumatic system design. Varying process power and energy prices show linear sensitivity effects. A larger influence can be observed for energy prices due to their influence on the factory system. The compressor lifetime shows a staircase-shaped course. This effect is induced by investments that become necessary for replacing compressors after their use phase.

Summary

This paper presented an approach for a rapid life cycle evaluation regarding the economic (TCO) and environmental (CO 2 eq.) impact of factory systems. This approach has been applied to a sample case of a car body welding line. In order also to assess the effect of changes in the configuration of its pneumatic actuation system, the specialized L3C-Tool was used and an optimized dimensioning of the air compressor set could be derived. The life cycle spanning effect of a variation of this factory system element has been evaluated as one improvement scenario via the LCE Tool. The effect of this measure has been compared to other technical as well as organizational improvement measures. By doing so the potential of an interaction of these tools was illustrated. As a result and according to the previously introduced holistic system comprehension of factories factory planning representatives should jointly assess the economical and environmental impacts of factory configuration options already at an early planning stage and under equal consideration of all factory elements.

Fig. 1 .

 1 Fig. 1. Architecture of the life cycle evaluation approachThese flows are based on resource consumption profiles which are taken from data sheets and (simulated) forecasts of the factory's operation modes. During the use phase of the factory also the utilization, shift models, downtimes, etc. are used to calculate the overall physical flows. Every factory element gets described as a set of parameters with physical units. All evaluation is done by transforming the physical flow data into financial or environmental impacts by multiplying it with cost factors as well as environmental impact factors (e.g. CO 2 eq.). This calculation gets supported through an Excel based tool, which supports the main parameterization of the individual factory system model through default templates of factory elements. Therefore the modelling and evaluation of alternative strategic factory settings gets facilitated for quick assessments in an early stage of the planning process. Fig.2shows an overview over the elements of the LCE Tool. Through these elements (user interface, reporting sheets, etc.) the usability gets enhanced. (For a deeper inside in the LCE tool and its data bases e.g. for environmental impacts of the building shell please see[START_REF] Heinemann | Life Cycle Evaluation of Factories: Approach, Tool and Case Study[END_REF].)

Fig. 2 :

 2 Fig. 2: Overview over the elements of the Life Cycle Evaluation Tool

4

  Case study: Optimized planning of car body welding line through improved compressed air systemsCase description and evaluation scenariosThe LCE and the L3C analyze a car body welding line, focussing on pneumatic and electrical actuators synergistically. The case is fictional, but the used data represents a realistic behaviour of a possible manufacturing line. The impacts of sample measures are analyzed, which get translated into financial and environmental KPIs through the LCE Tool. The car body welding line consists of different actuators within the manufacturing application, a compressor station and a building shell (exemplarily: Fig.5).

Fig. 5 .

 5 Fig. 5. Conceptual sketch of the analysed system, regarding the three factory elements (Festo AG & Co. KG)

Fig. 6 .Fig. 7 .

 67 Fig. 6. Scenario evaluation results from LCE Tool, Total Cost of Ownership

Fig. 8 .

 8 Fig. 8. Sensitivity analysis for TCO regarding variations of electricity price, compressor lifetime and compressor power

Guided User Interface Standardized Reporting Sheets

	Economic Evaluation Layer		Ecological Evaluation Layer
	(TCO-calculation)		(Impact-Assessment/Streamlined LCA)
		Cost/Revenue-Factors	Ecol.Impact-Factors
		f or each physical unit	f or each physical unit
		Physical Input-/Output-Flows
		for each year of the observation period
	Factory Life Cycle	Building/ Installation	Usage/ Operation	Update/ Overhaul	Disposal/ Dismantling
		Data sheets of	Data about
		factory elements	operation mode
		Guided Workflow
	General Settings
	Price List / Financial Factors
	CO2eq-List / Ecological Factors			Default data sets
	Total Energy
	Consumption
	Ecological Impact
		Total Cost of
		Ownership
	Aggregated Physical
	Input-/ Output-Flows

  Substitution of the European Electricity mix with certified renewable electricity supplies.

		 Applied changes: Usage of the Swiss renewable energy mix (0.0098715 kg CO2eq/kWh instead
		of 0.559 kg CO2eq/kWh; price increase about 0.0015 €/kWh [10] [11]
		Building Shell	Compressor Station
			Application
			72000 h; maintenance interval/expenses:
		4800h/~30k€; Investment: ~830 k€
	 Compressor: Process power: 171 kW; lifetime: 24000 h; maintenance interval/expenses: 4800h/~19
	k€; investment: ~94 k€  Building shell: Aerated concrete walls, no windows; lifetime: 165000 h; maintenance inter-
		val/expenses: 4800h/~33 k€; investment: ~650 k€
	Orga. measures	Scenario 1: Alternative shift model Application of an alternative shift model that might occur due to changing demands or the introduc-tion of measures for improved working conditions.  Applied parameter changes: 2 shifts (3200 working hours) instead of three shifts  Sensitivity analysis: +/-10% working hours Scenario 2: Alternative energy mix

The following model parameters have been used besides others:  General: Annual operating hours: 4800 h; European electricity mix, price increase 1%/a  Manufacturing line: Process power: 69.1 kW; lifetime:

Acknowledgements

The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 285363. The funded project's title is "Eco Manufactured Transportation Means from Clean and Competitive Factory" [EMC2-Factory]. For further information about the project please visit the website www.emc2-factory.eu.


Gregor Von Cieminski
email: gregor.cieminski@zf.com

Alessandro Rossi
email: alrossi@student.ethz.ch

Integrating aspects of supply chain design into the global sourcing process -insights from the automotive industry

Keywords: Global sourcing, supply chain design, network optimization, simulation

This paper reports on enhancements of the global sourcing process proposed with the purpose of increasing the weight of criteria related to supply chain design in sourcing decisions. In a conceptual study carried out for a German automotive supplier, the global sourcing process proposed by Alard et al. was found to be most suitable for industrial application. From the technical perspective of the supply chain management function, even this comprehensive approach does not address important decision criteria, such as likely structure of global supply chains or the capacity flexibility required of suppliers in global supply chains. The enhancements proposed include new process steps making use of network optimization and simulation techniques to eliminate these shortcomings.

Introduction

In the same way as many other industries, the automotive sector is going through a phase of global expansion. Industry surveys provide evidence that market growth in the industry is shifting from its traditional strongholds in the western industrialized world to the emerging markets, especially in the Asia-Pacific region. The CEOs of automotive companies expect China to remain the specific national market that offers the biggest growth potentials [START_REF]Fit for the Future: 17 th Annual Global CEO Survey[END_REF]. This country alone is predicted to provide around 30 % of the global production of light passenger vehicles in 2020 [START_REF]IHS Global Insight: IHS Automotive Light Vehicle Production Forecast[END_REF]. Among other trends, these market developments are driving two changes in the global structure of the automotive industry. On the one hand, western OEMs (Original Equipment Manufacturers) and suppliers are forced to increase production in emerging markets in order to fulfil their growth objectives. On the other hand, automotive companies entering the market from the emerging nations have increased the cost pressure on their western competitors.

As a consequence, western automotive companies have increased the share of purchasing volume sourced from emerging markets. This is mainly for two reasons:

1. Suppliers in emerging markets (or NSMs, short for "new souring markets") offer purchasing price advantages over the local or regional suppliers in Europe or the United States. 2. In their drive for global growth, western automotive companies aim to avoid the high transaction costs from their traditional production and supply base into the emerging markets. Therefore, new production facilities and a new supply base have to be built up in the emerging markets, i. e. on a global scale.

ZF Friedrichshafen AG (from here on referred to as "ZF"), a German automotive supplier, is also subject to the industry trends described above. In the specific market environment of the organisation, the importance of China as an NSM is growing.

Given the growth of supply volumes sourced in China, the Supply Chain Management (SCM) function in the Asia-Pacific region is facing increasing difficulties in operating ZF's regional supply chains. For example, little attention may be paid to the impact of the location of a newly selected supplier on the structure of ZF's transportation network in China. The experience of the company confirms the findings of the analysis of Ruamsook et al.: SCM objectives are often assigned secondary importance in global sourcing decisions [START_REF] Ruamsook | Sourcing from low-cost countries: Identifying sourcing issues and prioritizing impacts on logistics performance[END_REF].

The SCM function of ZF in the Asia-Pacific region therefore initiated a study into possible improvements of the sourcing process [START_REF] Rossi | Impact of Future Sourcing Decisions on the Supply Chain Concepts for the Asian Region: Integrating Supply Chain Design Aspects in the Sourcing Process of ZF Friedrichshafen AG[END_REF]. This paper reports on the results of this study. First, it presents the case company and the methodology applied in, and the main findings of, the study. Second, it proposes enhancements of the global sourcing process from an SCM perspective in order to eliminate the shortcomings of the current sourcing practice. A set of general conclusions from the study completes the paper.

Initial analysis of global sourcing practices

This section first introduces the case company ZF Friedrichshafen AG. Second, the methodology applied in the analysis of the global sourcing practices of this company is presented and the main findings of the initial analysis are summarized.

Case Company

ZF Friedrichshafen AG is the third biggest German automotive supplier and a leading manufacturer of driveline and chassis technology. In 2013, ZF's total sales amounted to € 16.8 billion and the company employed around 72,500 staff worldwide in its 122 production locations and 33 service centres [START_REF] Zf Friedrichshafen | Annual Report[END_REF]. ZF Group is organized in four divisions defined with an orientation towards customers as well as technologies provided by the company. Additionally, the group encompasses two cross-divisional business units. ZF produces a range of driveline and chassis components -such as transmissions, powertrain modules, axle systems, suspension systems -for the automotive, marine, railway, aviation and wind-power industries. ZF has been operating international production facilities for a number of decades, opening its first plant outside of Europe in Brazil as early as 1958. In recent years, the company has accomplished accelerating growth on a global scale. The biggest growth rates are in the Chinese operations. Here, as in the other global regions, the company has established regional headquarters that support the regional plants by providing corporate services across the main business functions. The regional SCM office initiated the analysis of global sourcing processes illustrated in this paper.

Methodology and findings

The methodology applied in the analysis of global sourcing processes at ZF and in the enhancement of these processes was the "Systems Engineering" approach developed by Haberfellner et al. [START_REF] Haberfellner | Systems Engineering: Methodik und Praxis. 12[END_REF]. The Systems Engineering approach is structured into four phases (corresponding to the system life cycle). This section summarises the different phases with reference to the analysis presented:

1. Trigger: The problem trigger of the analysis of global sourcing processes were the increasing difficulties the Chinese corporate SCM team in the Asia-Pacific region faced in operating ZF's regional supply chains. The purpose of the analysis was to identify possibilities to involve the SCM team earlier in the decision process in order to more systematically and effectively consider the concerns of the function in sourcing decisions.

System development:

The system development phase is sub-divided into a preliminary study, a main study and further detailed studies. Each study uses a defined problem solving cycle. This is described with regards to the global sourcing process in more detail below. 3. System implementation: The subject of this phase is the technical system implementation, i. e. converting the system concept into a tangible object or executable process. This phase was not within the scope of the global sourcing study and is therefore not presented in this paper. [START_REF] Rossi | Impact of Future Sourcing Decisions on the Supply Chain Concepts for the Asian Region: Integrating Supply Chain Design Aspects in the Sourcing Process of ZF Friedrichshafen AG[END_REF]. System use: The system use phase represents the continuous use and improvement of the system under real operating conditions. This phase is also not covered by this paper.

The remainder of the paper focuses on the process steps of the second phase of the Systems Engineering approach, system development. As stated above, the problem solving cycle defined by Haberfellner et al. [START_REF] Haberfellner | Systems Engineering: Methodik und Praxis. 12[END_REF] provided the procedural framework:

1. Situation analysis: The situation analysis mainly examined the global sourcing practices at ZF in the Asia-Pacific region. The analysis included the documentation of the sourcing processes and practices currently in use in the region, challenges and problems associated with regional sourcing decisions and a SWOT analysis of the processes. It was shown that ZF has implemented an advanced global sourcing process in terms of the tasks typically associated with the purchasing function (determining the supply demand, supply market research, supplier identification, supplier evaluation, supplier selection). The analysis also revealed that the process neglects SCM-oriented decision criteria. Thus, sourcing decisions are taken that have a detrimental effect on supply chain performance and supply chain costs.

2. Objective formulation: For engineering the global sourcing process, the ZF SCM team Asia-Pacific defined the clear objective that aspects of supply chain design have to be integrated in the global sourcing process. The results of these analyses have to be considered as additional SCM-oriented criteria in the global sourcing decisions of the company. 3. Solution search: ZF's Asia-Pacific team engaged on a search for a blueprint on which to base improvements of the sourcing process of the company. The search considered reference processes for both, supply chain design and global sourcing, documented in industrial as well as scientific literature. A set of 10 relevant reference processes was thus identified and evaluated. Interestingly, a considerable number of process models from the field of purchasing were excluded because of their solely operational focus. 4. Evaluation: ZF based its evaluation of the 10 relevant reference processes on a set of defined criteria that covered both technical aspects (e.g. "Reference process includes procedure for rough supply chain design.") as well as application-oriented aspects (e.g. "Reference process includes clear documentation of process steps and their building blocks.") [START_REF] Rossi | Impact of Future Sourcing Decisions on the Supply Chain Concepts for the Asian Region: Integrating Supply Chain Design Aspects in the Sourcing Process of ZF Friedrichshafen AG[END_REF]. The criteria were weighted according to their importance and the corresponding scores determined for each of the reference processes under consideration. 5. Decision: As a result of the evaluation, the global sourcing process (GSP) developed Alard et al. [START_REF] Alard | Total Cost of Ownership Considerations in Global Sourcing Processes[END_REF][START_REF] Alard | Reference Process for Global Sourcing[END_REF] was selected as the most suitable process reference for ZF. Figure 1 shows the sequence of fifteen steps Alard et al. have defined for GSP. Its advantages are explained with reference to the figure. The main advantage of GSP is the inclusion of two explicit steps concerned with the design of the production and sourcing network (steps 5 and 6). This ensures that strategic SCM-oriented aspects are considered in the subsequent sourcing decisions.

Also, if relevant, the GSP process steps integrate the total costs of ownership (TCO) as well as aspects of risk management in their analysis (steps 13 and 14, applicable along the chain of process steps). These elements guarantee a balanced consideration of relevant criteria for the sourcing decisions, including supply chain costs and risks.

Enhancing the global sourcing process

Having selected GSP as the relevant process template, the purpose of the main study by ZF was two-fold. On the one hand, ZF matched the GSP process definitions with practices already applied by the company. On the other hand, the company analysed whether GSP matches all its requirements in detail. As this was not completely found to be the case, the process steps "static and dynamic analysis of the sourcing network" were newly defined. These are presented in section 3.1. Subsequent detailed studies were concerned with developing the process steps in more detail. As an example the process step "dynamic analysis of sourcing network" is described in section 3.2

Supply chain design steps in the overall process

Figure 2 depicts the GSP including the enhancements added by ZF.

Fig. 2. Integration of supply chain design and global sourcing

Evidently, ZF did not change the overall structure of GSP. Indeed, steps 9 to 16 remain the same as before.

Step 17 "Project management" was included as an additional cross-process activity to reflect the nature of GSP decisions. Also, in steps 1 to 4 less emphasis was placed on the make-or-buy strategy and other production-related aspects.

The main enhancements are related to steps 5 to 8 (cf. figure 2). First, the process "Sourcing market research" is drawn forward (step 5). This is to reflect the fact that before any specific analyses can meaningfully be carried out, general information about the NSM under investigation ought to be collected. On the basis of this, a first "Sourcing network strategy" (step 6) may be defined that specifies common guidelines for supply chain design in the NSM.

Steps 7 and 8 represent the essential new steps integrated into GSP.

Step 7 entails a "Static analysis of the sourcing network". By means of network optimisation models, the overall structure of the NSM supply chain is analysed. Two types of investigation are conceivable: First, a generic study of the NSM based on probable scenarios of the supply chain structures. This serves the purpose of a general examination of the characteristics and challenges for supply chain designs in the NSM. Overall guidelines for designing the structure of the supply chain network should become apparent. Second, specific studies of the supply chain structures related to a concrete sourcing case. The outcomes of this serve as critical inputs for the TCO analysis with regards to the potential suppliers.

Step 8 provides for a "Dynamic analysis of the sourcing network". On the basis of generic simulation models, the same kind of general as well as specific investigations as for step 7 can be carried out. The objective of the dynamic analysis is to predict the effects of unforeseen or unplanned events on the NSM supply chains. The definition of adequate responses to customer demand variations is of particular interest, namely the requirements for capacity flexibilities and safety stocks. By use of the dynamic analyses, the supply chain costs can be determined even accurately and actions to mitigate supply chain risks can be evaluated in detail.

Use of simulation for dynamic supply chain analysis of sourcing market

Detailed studies for the newly proposed process steps represented the last stage of the system development phase for the global sourcing process. These were carried out for the static analysis and the dynamic analysis of the sourcing network. The ZF SCM reference model [START_REF] Von Cieminski | Adapting and applying the Supply Chain Operations Reference model -a case study from the automotive industry[END_REF] provided a template for the process design. Figure 3 shows the procedure defined for the dynamic analysis of the sourcing network as an example.

Design of process step. The overall process step "Dynamic analysis of sourcing network" is split into 7 sub-process steps. The sequence of these follows that of general simulation studies. The simulation models are built in order to be able to study the NSM supply chains under different operating scenarios. ZF intends to use a supply chain simulation software tool available to the company as a means for the dynamic analysis. As can be deduced from the inputs defined for the sub-process steps (cf. figure 3), the simulation models will be implemented as a comparatively high-level representation of the real supply chains. This allows the simulation studies to focus on a limited set of critical dynamic characteristics.

Designated use of process step.

There are two critical advantages of analysing feasible supply chains in new sourcing markets by means of dynamic events-based simulation. On the one hand, the critical supply chain characteristics -e.g. customer demand, capacity flexibility -may be considered from the perspective of medium-to long-term capacity planning. The Asia-Pacific SCM team can thus clearly state the basic SCM-oriented requirements and make sure that potential suppliers meet these.

On the other hand, similar requirements can be formulated for worst-case scenarios, which also have to be fulfilled across the sourcing network. The impact of a basic set of supply chain risks -e.g. supplier failure, failure of the transport infrastructure -can be accounted for. By means of this, ZF is able to support efforts for supply chain risk management in the sourcing network.

Fig. 3. Procedure for dynamic analysis of supply chains

Further development of process step. By putting the dynamic analysis of the sourcing network -as well as the other new process steps defined -into practice, ZF expects to obtain valuable information about the applicability of the design of the process steps. Most crucially, it should become apparent which characteristics of the NSM supply chains or sourcing networks need to be investigated in detail due to their criticality. Repeated applications of the enhanced GSP will thus lead to a continuously refined process design.

Conclusions

An analysis of the global sourcing processes at ZF Friedrichshafen AG illustrated that the sourcing decision process of the company does not adequately consider SCMoriented aspects. The evaluation of relevant process models available in literature revealed these also to be lacking in this respect. For this reason, ZF enhanced the global sourcing process developed by Alard et al. by two specific steps: a static sourcing network analysis employing network optimisation approaches and a dynamic sourcing analysis on the basis of simulation experiments. As a next step, ZF is planning a pilot application of the enhanced approach for global sourcing led by the corporate SCM team of the Asia-Pacific region. Clear recommendations on the practical design of the supply chains in China as an NSM are sought. At the same time, the practical applicability of the process enhancements presented in this paper will be evaluated. Specifically, the level of detail that is required of the investigation to generate meaningful results should be determined. In the future, cross-industry process benchmarks as well as rigorous action-based research (ensuring a formal scientific input) may be used to further improve the global sourcing practices.

Fig. 1 .

 1 Fig.1. Global sourcing process and its steps[START_REF] Alard | Total Cost of Ownership Considerations in Global Sourcing Processes[END_REF][START_REF] Alard | Reference Process for Global Sourcing[END_REF]


Lorenzo Tiacci

Chiara Paltriccia

Stefano Saetta

Eduardo Martín García

The Supply Chain Design of Biomass Energy Plants: a Simulation Approach

Alternative energies nowadays have to be more competitive than energy derived from fossil fuels. In this study, the supply chain design problem for a biomass plant is considered. In order to reduce the cost per kWh of energy produced, the whole supply chain involving the biomass plant is modeled. A two-steps methodology, consisting in two sequential simulation experiments, is illustrated. The procedure allows to calculate the minimum total surface necessary for wheat production, and to find the size and the number of farms, which minimize the cost per kWh produced, varying different power rating of the plant. Given the general assumptions made, results can be utilized as a decision support tool for the supply chain design of biomass energy plants.

1

Introduction

The need to be more independent from fossil fuels is leading Europe to an increasing use of alternative energy sources. Among them, biomass is still little considered. In particular, in the Mediterranean area, despite it is a mainly agricultural area due to the mild climate, the numerous waste resulting from cereal production could be utilized for energy production from biomass.

In this paper, a support decision tool for the design of the supply chain of a biomass plant is proposed. This study is conducted through a simulation approach, which allows finding the best configuration for a network of farms that supply a biomass energy production plant placed in a central position. In fact, due to the high number of variables that have to be considered, the stochastic nature failures rate of machines and the limited capacity of storages, blockings and starvations phenomena arise along the nodes of the supply chain. For this reason it is very difficult to study the supply chain through analytical models.

The procedure proposed in this paper consists into two sequential steps. In the first one, the aim is to determine the necessary field extension related to different power rating of the plant. In the second step, the goal is to evaluate the optimal number and size of farms required to handle efficiently the resources in order to minimize the cost per kWh produced.

The paper is organized as follows: the next section provides a brief review of the literature on the design of the supply chain in the production of energy from biomass.

In Section 3 and 4 the conceptual and the simulation model are respectively described. In Section 5 the design of experiment is illustrated. Results are discussed in Section 7 and in Section 8 a brief summary is drawn.

2

Literature Review

Alternative energy and Supply Chain (SC) have a central role in social and economic development at different stages [START_REF] Shi | Natural resource based green supply chain management[END_REF]. As outlined in [START_REF] Cucchiella | Issue on supply chain of renewable energy[END_REF], the supply chain capacity is among the main issues for alternative energy development, together with project economics, technical constraints, social effects and environmental impacts .

The management of a biomass plants requires timely supply of feedstock with minimum logistics costs and an optimized biomass logistics system [START_REF] Sokhansanj | Development and implementation of integrated biomass supply analysis and logistics model (IBSAL)[END_REF]. Several methods have been used to model and analyze different aspects of agricultural biomass supply chains. In [START_REF] De Carlo | Sustainable choice of the location of a biomass plant: an application in Tuscany[END_REF] a location model for a biomass plant, based on an AHP approach, is proposed. Different type and issues on biomass supply chains were studied through static spreadsheet models in order to calculate the costs related to storage and transportations [START_REF] Allen | Logistics management and costs of biomass fuel supply[END_REF][START_REF] Brundin | Fuels from agriculture. Cost calculations of systems based on straw and energy grass[END_REF][START_REF] Clegg | A cost model of straw conservation systems[END_REF]. Indeed, as a further study, mathematical optimization-modelling tools were proposed as another methodology to optimize logistical decisions [START_REF] Eksioglu | Analyzing the design and management of biomass-to-biorefinery supply chain[END_REF][START_REF] Rentizelas | An optimization model for multi-biomass tri-generation energy supply[END_REF][START_REF] Rentizelas | Logistics issues of biomass: The storage problem and the multi-biomass supply chain[END_REF].

These static models are not able to describe the dynamic behavior and the complexity related to a biomass supply chain, that can be achieved only adopting simulation approaches [START_REF] Ebadian | A new simulation model for multi-agricultural biomass logistics system in bioenergy production[END_REF]. Simulation approaches have been also utilized to validate analytical models developed for multi-echelon supply chains of bulk materials such as grain [START_REF] Saetta | A decomposition approach for the performance analysis of a serial multi-echelon supply chain[END_REF]. Among the simulation frameworks, the Integrated Biomass Supply Analysis and Logistics (IBSAL) model [START_REF] Ebadian | A new simulation model for multi-agricultural biomass logistics system in bioenergy production[END_REF] considers various stages of the agricultural biomass logistics system. This model has been further enhanced considering the demand fulfilment and in-farm and at-plant storage management, considered in the IBSAL Multi Crop (IBSALMC) model [START_REF] Ebadian | Modeling and analysing storage systems in agricultural biomass supply chain for cellulosic ethanol production[END_REF].

In our work we developed a conceptual model inspired by IBSALMC, and a corresponding simulation model implemented in Arena. These models have been utilized to propose a design methodology and a decision support tool for the supply chain design of biomass plants.

Conceptual Model

A biomass plant, which produces energy from wheat biomass, is considered in this work. To provide this plant whit a sufficient quantity of wheat biomass it is necessary a specific area around the plant in which a certain number of farms is distributed. Each farm manages a different field surface cultivated with one cereal farming. Farms are allocated on an approximately circular area (Fig. 1).

Wheat is harvested during the summer. After harvesting, wheat is left on the field for a certain period, in order to dry. When the yield is ready, it is baled by a square baler, a machine used to compact yield into square bales of 1.2 m x 1.2 m x 2.4 m and 500 kg of weight each. During the processes involved in the supply chain, while the bales stay in the field waiting to be stored, they lose weight due to the natural drying process. This mass loss causes a reduction of the total energy that can be produced by the plant.

The biomass plant owns a truck that collects bales from each farm to feed its grinder. When in the farm storage there are enough bales to fulfil a truck (at least 8 bales), the truck is called from the biomass plant, bales are loaded onto the truck by a tele-handler and then the truck drives bales to the plant, where they are stocked and then used to feed the grinder and to produce biomass energy. Farms that claim for the truck simultaneously are served following a FIFO policy. It is important to highlight that in this RS a sufficient number of bales should always be present in order to ensure a continuous feeding of the plant grinder.

The annual costs considered are divided into two main categories: logistics and storage costs. In detail, costs considered are: cost per hour of each machine, cost of failures, cost of transportations, and holding costs.

The processes that forerun the storing of bales at the plant are very important, because they are responsible of the continuous production of the plant. Blocking and starvation phenomena along the nodes of the described SC arise due to the following reasons:

1. Limited capacity of resources (balers, stingers stacker, tele-handlers, truck, RS, plant storage, grinder) 2. Stochastic failures of machines (balers, stingers stacker, tele-handlers, truck, grinder).

Due to space limitation it is not possible to report in detail all the features characterizing the model, which will appear in an extended version of the paper.

Simulation Model

The simulation model, built using Arena by Rockwell Software, is an indexed model. This feature of Arena is very useful when repetitive components are modelled, as the farms in our conceptual model. Through an 'ID', which is an integer number within a specific class, each object can be viewed as an object arrays. The index identifies the object in the array. In this way, resources, queues, expressions, processes can be represented by vectors or matrices [START_REF] Altiok | Simulation modelin and analysis with Arena[END_REF]. An indexed model is a parametric model, and this is fundamental to perform scenario analysis, because it allows avoiding to reprogram the model varying the number of farms.

The hypotheses considered in the model are: times related to each process are deterministic; times related to failures of machines are stochastic and exponentially distributed. We validated our model through two widely adopted techniques [START_REF] Law | Simulation Modeling and Analisys[END_REF]. The first one consists in computing exactly, when it is possible and for some measures of the input parameters, some measures of outputs, and using it for comparison. The second one, an extension of the first one, is to run the model under simplifying assumptions for which is the true characteristics are known and, again, can easily be computed.

The processes involved in the SC are modelled as seven blocks (Fig. 2).

Fig. 2: Diagram representation of the main blocks of the simulation model

The simulation starts with the harvest. The first block is the creation of bales, which corresponds to the bailing process. Bales are created in according to the bailer capacity. The second block is related to the operations over the field, where the stinger stacker collects bales and forms lots, each containing 8 bales, that become the basic entities. When the number of lot waiting in the RS is reached, the truck is claimed. Truck availability depends on the state of the processes of the other farms. The third block represents the process related to the truck seizing and the bales loading. This process involves the stinger stacker and the plant truck and it is followed by the transportation to the plant and the bales unloading process, which involves the plant truck and the stinger stacker. The last two blocks are related to the at-plant operations, in particular to the storage and to the bales processing. Details related to resources involved and entities considered are described in Fig. 2.

The model is completed with three independent sub-models, which make use of dummy entities. The first one is realized for indexing motivations, and is related to the assignment of specific attributes to each farm (f.e. size of the farm, distance from the plant, etc.) varying the number of farms generated. The second and the third submodels are essentially built to record statistics in specific moments of the simulation.

Design of Experiment and Results

In order to explain a methodology to design the optimal configuration of the supply chain that provides wheat to a biomass plant for energy production, two different experiments were built. The first experiment is aimed at evaluating which is the optimal extension of the total surface involved in the wheat production for a biomass plant of a given power rating. The second experiment is performed to evaluate the optimal number of farms, and therefore their optimal size, in order to reduce the unitary cost of energy produced. Each experiment is conducted for four different power rating of the plant: 200kW, 400 kW, 600 kW and 800kW, and has been replicated ten times. All the results refer to the average values obtained from the simulations.

Experiment 1

As before mentioned, the aim is to determine the extension of the total surface needed in the wheat production for a biomass plant, given the power rating of the plant. A specific area shall be managed by a high number of little farm or by a low number of big farms. To calculate the total surface for each power rating of the plant, a scenarios analysis have been performed. In each scenario the number of farms NF varies in an interval of [START_REF] Rentizelas | Logistics issues of biomass: The storage problem and the multi-biomass supply chain[END_REF]70], with NF multiple of 5. The size of each farm has been generated randomly from a uniform distribution with a range [START_REF] Rentizelas | Logistics issues of biomass: The storage problem and the multi-biomass supply chain[END_REF]30] ha. The minimum number of farms which guarantees at least the 90% of utilization rate of the plant is considered for calculating the total surface required. By multiplying the number of farms NF and their average extension, it has been possible to calculate the total surface required by the plant. This procedure has been replicated for different power rating of the plant.

In Table 1 the results are reported for each scenario. The total surface found are: 300 ha for a 200kW power plant, 600 ha for a 400kW power plant, 900 ha for a 600kW power plant and 1200 ha for a 800kW power plant.

% Demand fulfillment

Experiment 2

Experiment 2 represents the second stage of the design methodology. Aim of this second stage is to evaluate the optimal number of farms, and therefore their size, in order to reduce the unitary cost of energy produced. Indeed the same surface can be managed or by a great number of small farms or by a small number of large farms (Fig. 3). The total surface necessary to guarantee a sufficient supply of wheat to the plant represents the fundamental input data.

Fig. 3. Schematic representation about two possible opposite configuration of the network

The scenario analysis performed for each power rating is developed in the following way. A certain number of scenario are generated varying the number of farms NF from 10 to 70. For each scenario, the average surface mk of each farm is calculated as the ratio between the total surface required by the plant and the number of farms con-sidered. Then the surface of each farm is randomly generated from a uniform distribution with range [mk-5, mk+5] ha.

The optimal combination of number/size of the farms, which allows to minimize the cost per kWh of energy produced, is the one in which the appropriate number of machines and resources operates with reduced queues and idle times. In fact, it is not only necessary to own a sufficient area ensuring a sufficient total quantity of wheat, but is also important to ensure a continuous flow of material from the farms to the plant. Results related to Experiment 2 are reported in Fig. 4. It is possible to highlight that the functions that describe the costs per kWh, varying the number of farms, are always convex. The optimal number of farms, as expected, increases as the power of the system increase.

Summary

This research study analyzed the SC of a biomass plant evaluating costs for different power rating of the plant. This study was conducted adopting a simulation approach. The results achieved are valid under the assumptions made in the conceptual model. These assumptions are applicable to a large set of wet biomass supply chains. Thus, the relations found among the power rating of the plant and the optimal number and size of farms can be utilized as a supporting decision tool for the preliminary design of this type of supply chains. This model can be further developed considering other realistic aspects, such as weather conditions, the rotting process of the bales, and different variety of crops. Even other resource management policies could be considered, such as the sharing of resources like balers and stinger stackers among farmers.

Fig. 1 .

 1 Fig. 1. Schematic representation of the plant and the network of farms

Fig. 4 .

 4 Fig. 4. Cost per kWh as a function of the number of farms

Table 1 .

 1 Results of Experiment 1

	Farms Total surface	200kW	400kW	600kW	800kW
		[ha]
	10	200	68.11	-	-	-
	15	300	95.17	-	-	-
	20	400	99.72	-	-	-
	25	500	99.83	81.04	-	-
	30	600	99.86	93.58	-	-
	35	700	-	99.63	-	-
	40	800	-	99.33	84.49	-
	45	900	-	99.89	92.97	-
	50	1000	-	-	98.73	79.47
	55	1100	-	-	99.90	86.21
	60	1200	-	-	99.93	92.46
	65	1300	-	-	-	97.64
	70	1400	-	-	-	99.85

© Springer-Verlag Berlin Heidelberg 2014