Fredy Huaylla
email: huayllfr@univ-savoie.fr

Lamia Berrah
email: lamia.berrah@univ-savoie.fr

Vincent Cliville
email: vincent.cliville@univ-savoie.fr

A decision-aiding approach for residential PhotoVoltaic System choice: an application to the French context

Keywords: PhotoVoltaic System, sustainability, energy efficiency, Decision-Aiding, ELECTRE

In the last decade the interest for electric production using PhotoVoltaic System (PVS) has strongly increased; namely for the citizen who is able to use roofs or personal surfaces to put in place such systems. For instance, the installed residential PVS capacity has grown to 550 MW (Megawatt) in metropolitan France during the last years. The same trend can be noticed in many developed countries at the same time. However, in light of this investment, people would like to be informed of the different aspects, especially the economic and environmental ones, in order to make their choice. In this sense, this paper deals with the decision-making problem for the residential PVS investment. Different alternatives of module surfaces and PV technology installation are considered and a procedure founded on the ELECTRE approach to aid the citizen in his choice is proposed.

Introduction

Nowadays in different countries, concerns about the environment are taken into account increasingly through energy policies that aim to reduce in the future the effects of the global warming generated by the emission of GreenHouse Gases (GHG). In Europe for example, the European Union (EU) has set as a goal for 2020 the production of 20% of its total consumed energy using Renewable Energy Systems (RES), which would imply a 33% electricity production coming from these kinds of systems [START_REF] Haurant | Multicriteria selection aiding related to photovoltaic plants on farming fields on Corsica island: A real case study using the ELECTRE outranking framework[END_REF]. In France, the goals mentioned above would imply a 23% total energy consumed production coming from RES [START_REF] Haurant | Multicriteria selection aiding related to photovoltaic plants on farming fields on Corsica island: A real case study using the ELECTRE outranking framework[END_REF]. To achieve this, during the last years French energy policies have promoted the installation of PhotoVoltaic Systems (PVS) mainly through solar plants and residential PVS. As a result, at the end of March 2011, 1146 MW had been installed in metropolitan France, where about half corresponded to residential PVS (integrated into the buildings) [START_REF] Leloux | Review of the performance of residential PV systems in France[END_REF]. From a general point of view, a PVS is a system composed of PhotoVoltaic (PV) modules and other complementary components called Balance of System (BOS). BOS includes: structures for mounting the modules and the power-conditioning equipment for converting the generated direct current electricity (DC) into alternating current electricity (AC), with the required form and magnitude for its consumption or insertion in the power grid [START_REF] Fthenakis | Photovoltaics: Life-cycle analyses[END_REF]. PV modules are usually made of mono-crystalline silicon (sc-Si), multi-crystalline silicon (mc-Si) and amorphous silicon (a-Si). The solar energy to electricity conversion yield of each module usually goes in the following order, starting from the bigger: sc-Si, mc-Si and a-Si [START_REF] Sumper | Life-cycle assessment of a photovoltaic system in Catalonia (Spain)[END_REF].

Since the installation of a PVS implies an investment, it is logical to compare these kinds of projects with other options. Different criteria are associated to PVS, such as energy, economics, environmental and social criteria, where each criterion has different indicators that can permit an assessment of the performance of the whole system.

To cope with such a decision problem, studies have already been made to find the best renewable energy system including PVS. For example, Hauran et al. [START_REF] Haurant | Multicriteria selection aiding related to photovoltaic plants on farming fields on Corsica island: A real case study using the ELECTRE outranking framework[END_REF] used a multicriteria-aiding decision method to select the 4 best PVS projects among 16 projects that were presented to the local government of the island of Corsica in France. For that assessment, 7 criteria have been considered, related to energy, environmental, social and economic aspects, such as: total energy produced, ecological degradation, economic and financial benefits to the community, visual impact, etc. In general, the point of view of the evaluation was one that looked for the benefit to the whole community, so economic aspects related to the profitability of the project were not taken explicitly into account [START_REF] Haurant | Multicriteria selection aiding related to photovoltaic plants on farming fields on Corsica island: A real case study using the ELECTRE outranking framework[END_REF]. Athanasios et al. [START_REF] Athanasios | Technological, economic and sustainability evaluation of power plants using the Analytic Hierarchy Process[END_REF] assessed 10 power plants considering technological, economic and sustainability aspects through 9 criteria. The ranking of the project is obtained after the application of the AHP -Analytical Hierarchy Process -method. In their study, an emphasis was put on technology and sustainability criteria, giving the conclusion that renewable energy plants were at the top of the evaluation ranking. Again, economic criteria did not consider the profitability of each plant; instead, investment and operational costs were taken into account [START_REF] Athanasios | Technological, economic and sustainability evaluation of power plants using the Analytic Hierarchy Process[END_REF].

If the previous works concern public organisations decisions, they do not have an equivalent for the private investors and particularly for residential investment. In this study, a decision-making problem related to French residential PVS is studied considering energy, economic, environment and esthetic criteria. As a difference from cited works, the profitability of the PVS was taken into account in the economic criteria since, in our case, the considered point of view is the one of an investor (the owner of the dwelling), who, logically, would like to recover his investment. More precisely, for a given location, people of residential sectors willing to produce electric energy by investing in a PVS are faced with the following problem: Which kind of technology to choose among the three available on the market and what is the total surface to put in place? Knowing the involved criteria for choosing the PVS, this is a typical multicriteria decision problem of potential alternatives ranking. So using MultiCriteria Decision Aiding (MCDA) methods, namely the outranking ones, can supply interesting information to aid residential people in their choice.

This paper is organised as follows. In Section 2 the French context related to residential PVS systems is summarised. In Section 3 the decision problem is described by the considered alternatives and criteria. Then the ELECTRE III (ELimination and Choice expressing The Reality) outranking method is used to rank the alternatives. Results are discussed before concluding remarks.

French policy for residential PVS

Since 2004, the number of PVS installations in France has grown thanks to the implementation of a tax credit for the material costs of the systems. Since 2006 this increase is higher because of the policy implementation in tariffs that obligates the French electricity company, Électricité de France (EDF), to buy and to pay for the electricity produced by PVS. As a consequence, at the end of March 2011, residential PVS represented more than 550 MW distributed in more than 160 000 installations of less than 10kWp, where 98% had a nominal power of less than 3kWp [START_REF] Leloux | Review of the performance of residential PV systems in France[END_REF].

Nowadays, French PV policy is maintained although the level of subsidies has been reduced compared to previous years. The policy for residential PVS can be summarised in the following points [6] [7].

• Residential PVS installations of less than 9kWp can enjoy of a tax credit of 11% for the material cost of the system.

• EDF company signs a contract with the owner to buy the electricity produced by the PVS. The tariff is fixed for 20 years at the moment of the contact signing. At the second trimester of 2012, this tariff was 0.3706 €/kWh for installations of a peak power under 9 kWp.

• A Value Added Tax (VAT) of 7% is applied for the material and installation costs for residential PVS of less than 3kWp; although, the TVA rises up to 19.6% for PVS installations of over 3kWp.

• Local governments usually give financial aids for the PVS installation.

Our studied residential PVS are located in the Rhône-Alpes area in France. We assume the valid PV policy in 2012 and the following considerations.

• The PVS accomplishes all the technical constraints required to benefit from the subsidies.

• The solar irradiation over the PV modules, with a slope of 45° and a South orientation, was set to 1408 kWh/m²/year.

• The life time of the installation was set for 20 years.

• The discount rate over the investment was set at 3%.

• The maintenance costs of the PVS were considered 0.

• The dwelling surface was equal to 80 m2 and the final annual energy consumption of a standard French dwelling equal to 137 kWh/m2/year [8]. So a citizen who is interested in installing a PVS on his dwelling roof can describe a given PVS, identified as its module technology and the total modules surface, through the considered criteria (energy, economic, environmental and esthetic) for the assessment study. Thus the possible alternatives can be compared in order to give information for the final choice. But this comparison can be difficult because there is often no Pareto dominance: how to know if a more economic PVS is more attractive than another which is better according to the environmental criteria? So for residential people, a ranking which gives a synthesising view of the potential PVS represents an interesting progress. In this way the MCDA outranking methods supply partial preor-der of the potential alternatives [START_REF] Roy | Paradigms and Challenges, in Multiple Criteria Decision Analysis[END_REF], i.e. a ranking of the considered PVSs according to the preferences of the dwelling owner.

Obviously, aggregation methods like MAUT, AHP, MACBETH or UTA could be used, each of which allows decision-maker to supply total order information [START_REF] Guitouni | Tentative guidelines to help choosing an appropriate MCDA method[END_REF], [START_REF] Greco | Preferences and Decisions: Models and Applications[END_REF]. However, such methods need more knowledge to build the utility functions and so remain less understandable for residential investors than the outranking methods that directly use criteria values. The main outranking methods are the ELECTRE family [START_REF] Roy | The outranking approach and the foundations of ELECTRE methods[END_REF] and the PROMETHEE family [START_REF] Brans | How to select and how to rank projects: The PROMETHEE method[END_REF]. Interested readers can find the description of these methods in [START_REF] Martel | Other outranking approaches, in Multiple Criteria Decision Analysis[END_REF]. The ELECTRE III method which is well adapted to the real life company decision problems as shown by the numerous studies already published, is used in this study [START_REF] Figueira | ELECTRE METHODS, in Multiple Criteria Decision Analysis[END_REF].

3

Decision-aiding for residential PVS

Problem definition

The considered problem is viewed as a ranking problem, i.e. the residential investor would like to know what the best alternatives are. In this way it is necessary to define:

• the set of considered alternatives,

• the retained decision criteria,

• the description of each alternative according to the criteria.

Twelve alternative scenarios are considered for our decision problem. For working purposes, each alternative is labelled by the combination of the considered technology ((sc-Si, mc-Si and a-Si) and the total module surfaces (6.25, 12.5, 18.75 and 25 m²).

For example: Alternative one = ALT1 = "sc-Si; 6.25 m 2 ". Six decision criteria are retained, which are:

• II, the Initial Investment, i.e. the amount of money required at the beginning for installing the PVS.

• NPV PI II =
, the Profitability of the Investment at the end of the system's life cycle, defined as, with NPV: Net Present Value [START_REF] Chabot | TEC : an analysis economics method (La méthode TEC d'analyse économique)[END_REF]. • Esthetic, related to the visual impact of the PVS.

• AEP ER AEC = ,
The values associated to each criterion for the considered alternatives are given in table 1. They are computed using data from previous works and from current legislation and costs. In this sense for example, values for EPBT and GHG equivalent emissions are computed from studies made by Fthenakis and Alsema for different PV module technologies [3] [17]. Economic values are computed from current legislation [6] [7] and esthetic values are assigned considering a point of view of a selected potential investor. a-Si, 25 9530 0.49 29.9 1.9 50. 4 5 It appears that there is no alternative which is better or less equal to the others according the whole set of criteria (no Pareto dominance). Thus, it is not possible to make a choice from this information. The ELECTRE III method is hence deployed to give more meaningful information.

ELECTRE III method

From the alternatives description, ELECTRE III ranks the set of alternatives by considering pairwise comparisons according to the Condorcet principle which announces that an alternative outranks another one if it is at least as good according to a majority of criteria without being clearly worst according to the other criteria [START_REF] Condorcet | Essai sur l'application de l'analyse la probabilité des décisions rendues. la pluralité des voix[END_REF]. The preference relation definition between two alternatives is based on the concordance index which reflects the arguments to favour a given action instead of another one and the discordance index which reflects the argument against this action instead of this other action. The computation of these indexes involves the definition of both criterion preference thresholds and criterion weights representing the criterion's relative importance in the decision-making process. The comparison of the whole pairwise comparisons thanks to mathematical rules allows to give as a final result a partial preorder of the alternatives. Interested readers can find more detailed information in [9] [15].

Note that complementary sensitivity analysis of the description of actions or criteria (thresholds, weight) can be made to reinforce the information for decision-aid.

ELECTRE III parameters definition

To proceed with the ELECTRE III software which supports the method, the preference thresholds of indifference and strict preference have to be expressed for each criterion. These values are assigned by the dwelling owner thanks to a dialogue with the ELECTRE method expert who has to explain the meaning of the parameters. The weight definitions of the 6 considered criteria are obtained by the SIMOS method [START_REF] Figueira | Determining the weights of criteria in the ELECTRE type methods with a revised Simos' procedure[END_REF]. Both declared thresholds and weights are found in table 2.

In addition, ELECTRE III deployment requires quantitative values for all the criteria, which is not the case for the esthetic criterion. So, linguistic terms must be converted into quantitative values. This transformation leads to integer values that increase with the satisfaction level (a scale from 1 to 7 is retained). Two integers associated to two successive linguistic terms must be different according to the considered strict preference threshold, so the concordance index computation can be consistent.

Results and discussion

The ranking of the alternatives is given in figure 1. The alternative "ALT12" is considered as better than all the other ones. The alternative "ALT5" is better than the other ones except "ALT12" and is incomparable with "ALT6. So a given alternative can be ranked with possibilities of ex-aequo (AlT4, ALT 9) or incomparability (ALT8, ALT 4). Hence, residential people can choose the "ALT12" alternative. To better assess this choice, they can also make a sensitivity analysis regarding:

• the ELECTRE parameters definition (weights and threshold),

• the values describing the alternatives according to the criteria.

The first sensitivity analysis must be carried out when the residential people have hesitated during the questionnaire allowing the ELECTRE deployment. It can also be interesting to simulate different types of people behaviour such as "mainly economic" with the high weight for PI, "mainly environmental" with high weights for EPBT and GHG or for "limited investment capacity" with a high weight for II.

Figure 1: Alternative partial pre-order obtained with ELECTRE

The second analysis is useful to take into account the potential uncertainties concerning the alternative description through the six criteria, namely the technical and economic ones. Indeed, it is well known that energy production depends on environmental factors and that the governmental tax policy can change during the twenty five years of PVS exploitation [START_REF] Roque | Les sytèmes photovoltaïques : caractéristiques et performance[END_REF]. Besides the traditional indifference and strict preference thresholds definition, current works are in progress to deal with this point which can modify the initial ranking given in fig. 1. Main contributions of this work were the application of an outranking method to a PVS residential context and the consideration of direct economic benefits, such as the PI.

Conclusions

This study concerns the residential PVS choice. After a description of the PV module which is the main component of the PVS, the French PVS installation context is presented. From the review of criteria for PVS choice, the study focuses on relevant ones for residential installation. Then the decision-aiding ELECTRE method is briefly described before its application for the PVS residential choice among twelve alternative PVS. The obtained partial preorder is presented and discussed namely through the two complementary sensitivity analyses. Further works for taking into account uncertainties about technical and economic aspects are finally envisaged. Further works for taking into account uncertainties about technical and economic aspects are envisaged as well as the adaptation of the method for the use of PVS installers and resellers.

Table 1

 1

		: criteria values
		Alternatives				Criteria
	Name	Technology, Surface (m 2 )	II (€)	PI	ER (%)	EBPT (years)	GHG (gCO 2 /kWh)	ESTH (1 -7)
	ALT1	sc-Si, 6.25	3719 -0.33 9.1	3.0	75.6	7
	ALT2	mc-Si, 6.25	3315 -0.23 9.2	2.3	58.1	6
	ALT3	a-Si, 6.25	2701 -0.43 7.5	1.9	50.4	5
	ALT4	sc-Si, 12.5	7187	0.05 18.2	3.0	75.6	7
	ALT5	mc-Si, 12.5	6380	0.2	18.3	2.3	58.1	6
	ALT6	a-Si, 12.5	5152	0.09 14.9	1.9	50.4	5
	ALT7	sc-Si, 18.75	10656 0.18 27.3	3.0	75.6	7
	ALT8	mc-Si, 18.75	9446	0.35 27.5	2.3	58.1	6
	ALT9	a-Si, 18.75	7603	0.27 22.4	1.9	50.4	5
	ALT10	sc-Si, 25	15759 0.11 36.3	3.0	75.6	7
	ALT11	mc-Si, 25	13955 0.27 36.7	2.3	58.1	6
	ALT12

Table 2 :

 2

	weights and thresholds values
		II (€)	PI	ER	EPBT	GHG	EST
				(%)	(years)	(gCO 2 /kWh)	H (1-
							7)
	Weight	2.6	1.7	2.6	0.7	0.7	1.7
	Indifference threshold	5%*II	0.0	5%	0.5	1	1
			5
	Strict pref. threshold	10%*II	0.2	15%	1	5	1


Nico J Vandaele
email: nico.vandaele@kuleuven.becatherine.decouttere@kuleuven.be

Catherine J Decouttere

The Multiple Faces of Mass Customization: Product Design, Process Design and Supply Chain Design

Keywords: Mass customization, business model innovation, supply chain design 1

Mass Customization is one of the buzz words of the last decade. However, the purpose of efficiently unfolding multiple variants of a product or service has deep grounded consequences for the business processes and the underlying system. Subsequently, the supply chain structures supporting these processes face many challenges. In this paper we expose an integrated view on mass customization from a design perspective, rooted in a user-oriented design paradigm. First, we want to find an answer to whether mass customization is a feasible business model to guarantee sustainability in the Flemish textile industry. Second, how can the long tail business model be designed in an integrated way, relating product, process and supply chain design. The mass customized product/service design framework is based on field data gathered from a technological innovation supporting programme operational in Flanders' textile industry. Based on these data, some preliminary benchmarking observations can be made which relate to product, process and supply chain design.

Introduction

Mass customization and "the Long Tail" business model, as defined and described by [START_REF] Anderson | The Long Tail: Why the future of business is selling less of more[END_REF], give an answer to the trend of personalization intended at the cost and effort of a standard product or service. "Tailor made" was the traditional way of presenting a perfect fit to a very specific customer need. More specifically for the textile industry, what started off as mainly ergonomic fit (for instance in terms of size), evolved to tailored functionality and individual emotional benefits such as self-expression and uniqueness. The design aspect in the broad sense became an asset creating a willingness to pay. However, companies applying the mass customization business model, need to find a new balance between unique product/service performance and the related cost, while keeping the process and supply chain complexity within feasible limits. Essential elements in this exercise are the co-creation experience, product modularity, and enabling technologies, allowing unique products which are producible in small lot sizes or even in a one-by-one mode. In the context of the Technological Support Programme for Mass Customization issued by the Flemish Government, we observed and supported the textile and confection industry in Flanders in its transition to mass customization. With an objective of creating sustainable added value and rejuvenating a traditional but struggling Flemish industry in a global context (suffering from delocalisation), the viewpoints from both the customer and the producer are investigated.

2

Customization for a sustainable textile industry Elements of sustainability. Sustainability for the Flemish textile industry is translated in to objectives in terms of social, ecological and economical dimensions. On the social level, it was brought down to "keeping jobs in Flanders". Knowing that large parts of the production in the textile industry has been delocalized from Flanders, we see new activities arising in personalized design, research in technical textiles with new functionalities, automated production of small batches or unique one-of-a-kind products based on efficient technology and experience etc. A lot of these new activities deliver goods and services with a high level of customization.

On the ecological level, the overall carbon footprint of the industry is considered. Key elements driving sustainability in a mass customized textile industry are lower material use due to less scrap, lower transportation and extension of life time of products.

The economic benefits of MC in the textile industry are the result of the transition from a make to stock to a make to order approach. Supply chain related costs such as inventory cost and scrap should be significantly lower. This will depend on the accepted order lead time by the customer of the personalized goods.

The textile industry is known to be highly sensitive to fashion and trends, and each season, this "fashion risk" can lead to significant scrap costs due to unsellable goods.

Market environment. Companies who initially started up with mass customization in a highly customized market, for instance functional sportswear, seem to have a competitive advantage compared to companies who want to make the transition to customization in a market were standard or low customized products and services are the main reference. This is the case with for instance the confection industry or the interior textile industry (curtains and carpets).

However, the theoretical opportunities in the mass production confection industry sector is also high as fashion risk and inventory risks can be minimised by converting away from a mass production business model towards a mass customization business model [START_REF] Piller | Handbook of Research in Mass Customization and Personalization[END_REF].

Company profile. Reality reveals that the integration of the voice of the customer and the conversion of the sales approach is yet a hard learning curve for traditional companies, as it usually involves a turnaround of both their innovation and processing approach as well as their distribution and supply network. Moreover, the financial benefits of more lean and sustainable operations are not immediately felt as the turnaround to a mass customization network involves the influx of financial resources.

Business opportunities from customization

The literature on Mass Customization is vast and elaborate and traces back to Davis [START_REF] Davis | From future perfect: Mass customizing[END_REF]. We refer to [START_REF] Da Silveira | Mass customization: Literature review and research directions[END_REF], [START_REF] Zipkin | The Limits of Mass Customization[END_REF] for a comprehensive review. The added value of addressing individual needs, whether functional or not, need to be reflected in a positive cocreation experience, which guides the customer in a pleasant way from the understanding of his individual needs through the design process and placing the order of the perfectly matching product. In this way, the negative effect on the purchasing process of an overwhelming range of possibilities can be overcome [START_REF] Schwartz | The paradox of choice: why more is less[END_REF]. The degree to which the product and service should be customized in order to obtain optimal added value, is a key question with implications for the entire business model design. A MC company needs to adopt new skills allowing it to gain and maintain a profound insight in its customer's individual needs and expectations. A highly performing customer knowledge system becomes a necessity.

Already from the conception and development of the products and services, the customization approach will be applied. The product, service and system architecture will be in line with the configuration steps the customer will be invited to take part in. The role of designers and product developers changes towards more openness to the voice of the customer, they will balance between reflecting the company identity and brand essence in the customized products and services and providing guidance and reassurance during the co-creation process where it is expected by the customer.

Accordingly, the processing and the delivery of the product/service has to be performed in an efficient and cost effective manner. In one way or another, multiple technological possibilities are there. The Flemish textile industry has a leading position in technical textiles (used for special purposes like fire protection, healthcare applications, etc.), in implementing three-dimensional body-scanning techniques, in digital printing techniques and many more.

Our double entry research question is as follows:

1. Is mass customization a feasible business model to guarantee sustainability in the Flemish textile industry? 2. How can the long tail business model for a Flemish textile company be designed in an integrated way: product, process and supply chain?

In order to answer these questions, an integrated approach amongst product design, process design and supply chain design is at stake.

The MC product, process and business model design framework

Against this framework, we give an overview of the dimensions which positively and negatively influence the success of mass customization in the Flemish textile industry, using a multi-dimensional approach based on industry data from 2011. These data were collected based on structured interviews, company audits and group assessments. Important elements from the data collection were grouped into product, process and business model categories. Subsequently we measured some core KPI's from the companies. At this point we only give an overview of some important key value dimensions.

4.1

Product/service design Perceived Usefulness (PU) is here measured as a product attribute with a considerable user involvement and is in this context defined as "the value of customization, i.e. the increment of utility a customer gains from a product that fits better to her needs than the best standard product attainable as perceived by the customer [START_REF] Kaplan | Factors influencing the Adoption of Mass Customization: The Impact of Base Category Consumption Frequency and Need Satisfaction[END_REF]. Examples can be found in sportswear where individual physical characteristics may largely dictate the willingness to deviate from an industrial standard. Perceived Ease Of Use (PEOU) is the degree to which the co-creation step is pleasant or free of effort for the user. It refers to customer friendly toolkits and configurator applications where the user is led comfortably through his decision process and where positive emotions are elicited during the customizing process.

I Designed It Myself Effect (IDME). Here the feeling of being responsible for the outcome and authoring the design is key. It resembles the 'good' feeling stemming from the actions and the opinions of the customer which turn the product into an element of self-esteem. It is a value-generating effect that arises merely from the fact that the customer is the originator of the product [START_REF] Franke | The "I Designed It Myself" Effect in Mass Customization[END_REF]

Process design

From the process side the degree of Mass Customization (MC) is measured as the degree to which products can be produced, i.e. the ability, to achieve customization, ranging from a solely standard product to a fully customized product. Process Technology Adaptation (PTA) is the degree to which the company has adapted or employs the adequate process technology to be able to deliver mass customization. Industry known benchmark technologies are used to produce an overall assessment.

4.3

Business Model design

The Point-Of-Sale Effectiveness (POSE) is the degree to which the point of sale is successful in reaching the customer and explaining the benefits of the personalized product offer in an easy way.

Also the organization of the supply chain plays a role and is measured by Supply Chain Network Responsiveness (SCNR). This is measured by the degree to which a company is able to control all the elements leading to responsiveness for mass customization. If many of these supply chain nodes are independent, the harder it is for an independent player herein to deliver a mass customized product.

Key Performance Indicators

In this part we opted to represent all categories of sustainability elements : Social Gain, defined as the number of jobs kept in Flanders thanks to mass customization.

Ecology Gain, measured by the difference in carbon footprint between mass customization and standard production.

Economic Gain, via the turnover realized by the Mass Customized product families related to the total turnover of all products combined with the growth rate of the company.

The measurement of social gain and ecology gain is not easy but feasible using realistic alternative scenarios and past experiences as benchmarks.

Preliminary observations

The measurements on these dimensions have been used to assess the relative position of 17 individual textile companies, who are interested to go along on the road of mass customization. In general, our findings are that there are quite some differences among the MC business models applied and among the mass customization performance of these companies

Natives and Hybrid companies

More specific, we noticed that companies who started up applying the mass customization business model, "MC natives" have a higher probability of success than established companies who convert to mass customization ("hybrid" group, Fig 1). The latter evolved to a hybrid business model offering both standard goods and mass customized products. Some of these companies are in a transition phase to a business model fully based on customization, while others consider "customization projects" as a type of innovation next to the core business of mass production of standard products and services. Their intention is not to convert fully to MC but to be present in it. MC natives seem to be slightly better equipped for MC with their process technology and point of sales effectiveness. On average, they have a more profound insight in the user needs and some of these companies were established and are being led by usersinnovators. In this group, we find mainly products which are performing functionally better when they fit better, like sportswear. However there is also a group of products which are not perceived as being highly user-centric but which are produced with highly performing technology allowing small lot sizes, avoiding large stocks and waste, e.g. in the interior textile business. The hybrid group has not made a final choice for MC yet, and this is observed in many business elements: the culture and employer's mind-set is not equally committed to personalization as it is the case for the MC natives. The business processes, partners and supporting software are not designed for MC, they need to be adapted for it and evolve. Optimal degree of customization is key

We also observed that the degree of customization is a crucial decision to be made by the company. It determines the balance between fulfilling user needs and costs. Once it is fixed, it determines nearly all other performance dimensions of the mass customization business model in the company. It also has a certain effect on the PU of competitor's products as it influences the customer expectations towards customization levels of products and services.

In the confection industry, we still observe a certain resistance to maximize personalization. Mainstream customers are not yet massively prepared to share their detailed biometric data from 3D scanning technology in order to obtain better fitting apparel and save time on shopping. On the other hand, e.g. cyclists and other professional athletes do appreciate the additional functionality and are very much willing to go through a personalization process and accept the higher costs involved. The same elevated PU is sensed by mainstream customers in for instance the bedding industry: a highly effective POS, including for instance an "experience module" in the store and additional expert advice, succeeds in creating a much higher willingness to pay for the customized products.

An uncontrollable and quite unpredictable dimension is the IDME. If it is present, it can have a huge impact on willingness to pay. However, it is a factor which seems very hard to initiate or promote. It is likely to be triggered by hypes or trends but we have no evidence hereof yet.

Application of the model

Additionally to natives and hybrids, some interesting clusters can be delineated where companies can use the information in a peer based setting. A more advanced use of the results is where the assessment leads to a gap analysis with subsequent roadmap construction. The assessment of less controllable pull factors, such as PU and IDME and less controllable cost factors such as PTA (based on state of the art technology) give insight in a theoretical opportunity of a customization. When a certain degree of MC is fixed, it allows to design possible business models with directions for the approach of the customer (PEOU) and the Supply Chain (SCNR, POSE and also PTA) We relate this to the seminal article of Fisher [START_REF] Fisher | What is the Right Supply Chain for your Product[END_REF], posting that there is a right supply chain for every product. This boils down to the distinction between efficient versus responsive supply chains, serving functional and innovative products respectively. In this way we intend to give additional insight to the textile industry and help them to setup plans to go into the direction of mass customization as a path towards rejuvenation of a suffering industry. We experienced that the representation of the business model building blocks using the business model canvas [START_REF] Osterwalder | Business Model Generation[END_REF] facilitates company insights in the transition path. We also experienced that introducing sustainability, in the broad sense, and implementing it in decision making, is still a very tough issue.

Conclusions and further research

In short, if we trace back to our research questions posted earlier, we can conclude that: Mass customization is a viable business model for the Flemish textile industry The integration of product, process and supply chain design is a way to materialize mass customization.

Future research challenges include the quest for more refined metrics and data collection methods, the inclusion of more companies in to the database and the development of an adequate visualization for better insight and decision making.

Aknowledgments. This research is based on a data which was gathered through the technological innovation supporting programme of the Flemish government "Mass customization for the Textile industry" (2012-2014, IWT095011)

Fig. 1 .

 1 Fig. 1. Mass customization natives versus hybrids in terms of key value dimensions


Olga Willner
email: owillner@ethz.ch

Manuel Rippel

Matthias Wandfluh

Paul Schönsleben

Development of a Business Process Matrix for structuring the implications of using configurators in an engineer-toorder environment

Keywords: Engineer-To-Order, Customization, Product Configurator, Business Process, Value Chain

A methodological application of mass customization principles in engineer-to-order (ETO) processes is expected to lead to shorter lead times, increased quality as well as cost reductions. Product and process configurators, commonly used in mass customization processes, have to be adjusted according to ETO product and process requirements for their successful application in an ETO environment. In this paper the organizational requirements for a successful adaption of configurators to ETO processes are identified and structured. A Business Process Matrix capable of categorizing the implications of using product and process configurators in an ETO environment is developed.

Introduction

The publication "From Future Perfect" by Stan Davis in 1987 brought about the advent of mass customization. Mass customization was defined as the capability to produce a wide range of product variants close to mass production prices through flexible and agile processes [START_REF] Davis | Future perfect[END_REF]. Pine [START_REF] Pine | Mass customization the new frontier in business competition[END_REF] operationalized the concept for broad practical application through identifying the core technical enablers for mass customization such as a modular product design or the application of advanced manufacturing and information technologies. At present, in a vast number of industries mass customization production processes can be taken for granted [START_REF] Piller | Does mass customization pay? An economic approach to evaluate customer integration[END_REF]. A technical as well as organizational realization of a broad range of variants is well advanced and solidly based on standardized processes. In particular, a deployment of product and process configurators contributes substantially to ensure efficient and standardized business processes in a mass customization environment [START_REF] Forza | Managing for variety in the order acquisition and fulfilment process: The contribution of product configuration systems[END_REF].

Recent academic research in the field of mass customization almost exclusively focuses on the transition from being a mass producer to becoming a mass customizer. The transition in the opposite direction, the move from being an engineer-to-order (ETO) company to becoming a mass customizer is often neglected [START_REF] Haug | From engineer-to-order to mass customization[END_REF]. ETO companies are defined as companies delivering products which are engineered or optionally reengineered according to the specific requirements of a customer [START_REF] Caron | Engineer to order' companies: how to integrate manufacturing and innovative processes[END_REF]. ETO business processes can be characterized to be highly order specific, knowledge-intensive and of high complexity [START_REF] Hicks | Product life cycle management in engineer-toorder industries[END_REF]. A methodological application of mass customization principles in ETO processes is expected to lead to shorter lead times, increased quality as well as cost reductions.

Product and process configurators, commonly used in mass customization processes, have to be adjusted according to ETO product and process requirements for their successful application in an ETO environment. Product structures as well as organizational processes may have to be aligned to novel conditions. A deployment of configurators, originally designed for mass customization purposes, in an ETO environment has a multitude of implications on the business processes along the value chain. The objective of this paper is to develop a Business Process Matrix for categorizing the implications of using configurator solutions in an engineer-to-order environment. This is expected to lead to a structured overview of the most relevant action fields for the implementation of an efficient ETO process along the value chain.

Research Methodology

The presented results are based on literature analysis and face-to-face interviews. First, literature research was carried out. Academic papers with the words "Engineer-To-Order", "Design-To-Order", or "Mass Customization" used in conjunction with "Product Configurator", "Product Structure" or "Processes" in title, key words or abstract were collected and reviewed. Second, interviews with decision-makers of six Swiss-based production companies from various manufacturing sectors (e.g. production equipment, elevators, pumps) were conducted. To produce robust results, representatives of small and medium enterprises (SMEs) as well as large enterprises were interviewed. The investigated companies varied in maturity levels regarding the deployment of product configurators in ETO processes. The interviews centered on the current state of integrating configurator solutions in ETO processes; both product structure and organizational issues were discussed in detail.

Results

In the following, a Business Process Matrix for identifying and structuring the organizational implications of using product and process configurators in an engineerto-order environment is developed. First, the structural setup of the matrix is determined. Then, the core enablers for a successful application of configurators in ETO processes are introduced. Finally, the content of the matrix is derived from literature research and interviews conducted.

Derivation of Business Process Matrix

The solution space of the Business Process Matrix is spanned by a horizontal axis, depicting the business processes, and by a vertical axis, depicting the functional areas of a business organization. Business processes are defined as "a set of logically related tasks or activities performed to achieve a defined business outcome" [START_REF]APICS: Dictionary 13th Edition[END_REF]. In this Business Process Matrix the ETO business processes "Sales", "Design" "Make" and "After Sales" are applied on the horizontal axis [START_REF] Hicks | Product life cycle management in engineer-toorder industries[END_REF][START_REF] Schönsleben | Methods and tools that support a fast and efficient designto-order process for parameterized product families[END_REF]. Functional areas are the basis for a functional organizational structure [START_REF]APICS: Dictionary 13th Edition[END_REF]. For this Business Process Matrix the functional areas "Marketing & Sales", "Procurement", "Engineering" and "Production and Logistics" have been identified as being significantly involved in ETO processes. The objective of this matrix is to depict (a) how the tasks along the ETO business process chain are divided between the different functional areas; (b) which information in the form of data is required by whom; (c) which software tools are the enablers for sharing the required information; and (d) which benefits for the business process are achieved through the use of a configurator. To achieve this, the categories "Tasks", "Input data", "Software tools" and "Benefits of configurator" are discussed within the fields of the matrix. The varying impact of the application of configurator solutions on the individual matrix fields is depicted by a color gradation.

Enablers for configurators in ETO processes

In this section the core enablers for an application of product and process configurators in ETO processes are introduced and illustrated.

Product Modeling

An essential prerequisite for an efficient application of configurator solutions in an ETO environment is the description of product structures in product family models. Product structures can be modeled by the application of adaptive or generative methods. In case of an adaptive approach, a suitable parent version is determined from the existing variants to then be adapted to the requirements of the new variant by either developing plus/minus bill of materials (BOM) and operational routings or including dummy positions in product structure templates [START_REF] Schönsleben | Methods and tools that support a fast and efficient designto-order process for parameterized product families[END_REF]. A modeling of product structures based on a generative approach takes place through a rule-based configuration of product variants. The definition of new product variants is achieved by the change of parameter values, expressing rules and constraints, that exist in a product configurator [START_REF] Schönsleben | Methods and tools that support a fast and efficient designto-order process for parameterized product families[END_REF]. The development of a product family logic, based on modularity and hierarchy principles, contributes to an increased reuse of parts [START_REF] Schönsleben | Methods and tools that support a fast and efficient designto-order process for parameterized product families[END_REF][START_REF] Brière-Côté | Adaptive generic product structure modelling for design reuse in engineer-to-order products[END_REF]. The definition of a stringent product family logic provides the backbone for leveraging the full benefit of a configurator solution.

Integrated Information Technologies

Information technologies (IT) are one of the core enablers of mass customization [START_REF] Piller | Mass customization and SAP R/3TM -business solutions like SAP R/3 as an enabler of mass customization[END_REF], and even more so of engineer-to-order processes. A diverse range of software products is required for an effective management of ETO business processes, e.g. the sales and after sales phases are often backed up by Customer Relationship Management (CRM) software, the design phase by Computer Aided Design (CAD) systems, the make phase by Enterprise Resource Planning (ERP) systems, Computer Aided Manufacturing (CAM) systems and Manufacturing Execution Systems (MES) [START_REF] Flynn | Empirical research methods in operations management[END_REF],

and Product Data Management (PDM) and Product Lifecycle Management (PLM) provide support through the complete business process chain [START_REF] Warschat | Enabling IT for mass customization: the IT architecture to support an extended enterprise offering mass-customized products[END_REF].

In configurator solutions (here consisting of the modules "sales configurator", "product configurator" and "process configurator") a variety of functionalities are embedded which support different phases of ETO business processes. In the sales phase, the application of a sales configurator supports the generation of valid offer documents. This includes the conversion of customer requirements into commercial specifications (parameterized definition of sales configuration). To be of further use in the design and make phase, commercial specifications have to be converted into technical (dimensioned commercial specifications) and production (including bill of materials and operational routings) specifications, as required by CAD and ERP systems [START_REF] Forza | Managing for variety in the order acquisition and fulfilment process: The contribution of product configuration systems[END_REF]. Whereas the main output of the product configurator is the generation of BOMs based on parameterized product structures, the process configurator provides the appropriate process plans and operational routings.

Applying configurators as stand-alone solutions does not provide a considerable added value. An integration of configurator solutions into the existing IT landscape is crucial to enable an automation of processes, a close collaboration between departments and a proficient exchange of data.

Business Process Matrix

The Business Process Matrix provides a structured approach for decoding the interrelations between business processes and functional areas caused by the deployment of configurators in ETO processes. The matrix is designed with a practical application in mind. In the following, the principal requirements of the different ETO business processes on the configurator solution are described:

Sales

A potential customer order in the ETO segment is often initiated by an invitation to tender [START_REF] Hicks | Product life cycle management in engineer-toorder industries[END_REF]. The generation of a valid offer document, including mapping of customer requirements, definition of commercial characteristics as well as quotation of prices and lead times, is the general objective within the tender stage. Since the success rate for winning a tender is a mere 30% in the ETO sector, fast and cost-efficient processes are required during the tender stage [START_REF] Konijnendijk | Coordinating marketing and manufacturing in ETO companies[END_REF]. Therefore, the Marketing & Sales department, usually the interface to the customer during the entire business process chain, has to collaborate closely with various functional areas, e.g. estimated prices and lead times have to be provided by Procurement and Production, the conceptual design has to be developed by Design, the feasibility of the proposed product has to be verified by Production & Logistics.

The integrated application of a configurator solution is disposed to support the collaboration on a technical as well as organizational level. First, a parameterized definition of commercial as well as technical specifications becomes feasible through its deployment. Second, rule-based feasibility and validity checks can be automatized leading to shorter process times and a prevention of manual errors. Third, historical project data systemized and structured by the configurator solution can be consulted for reference.

Design

In the ETO segment, products are designed or altered according to order-specific requirements. This tasks reaches from the development of a conceptual design and selection of major components and systems [START_REF] Hicks | Product life cycle management in engineer-toorder industries[END_REF] to the concise definition of BOMs and operational routings. On the way, feasibility checks and evaluation of consequences of redesign on functionality and prices might have to be performed. The application of a configurator solution, backed up by a stringent product family logic can contribute to a systemic reuse of standard and pre-defined components [START_REF] Schönsleben | Methods and tools that support a fast and efficient designto-order process for parameterized product families[END_REF][START_REF] Brière-Côté | Adaptive generic product structure modelling for design reuse in engineer-to-order products[END_REF]. An increased reuse of parts and components is beneficial for both consistency of business processes and workload of functional areas, e.g. the feasibility of standardized price calculations increases, BOMs and operational routings for subassemblies can be reused, fewer inventories to achieve the same service level required.

Make

Production processes in the ETO sector are often highly unpredictable and of low controllability [START_REF] Konijnendijk | Coordinating marketing and manufacturing in ETO companies[END_REF][START_REF] Bertrand | Production control in engineer-to-order firms[END_REF]. Bertrand [START_REF] Bertrand | Production control in engineer-to-order firms[END_REF] characterizes the ETO market situation as dynamic (strong fluctuations in mix and sales volume in shot and medium term), uncertain (unknown product parts, uncertain lead times, prices and capacities, process uncertainties) and complex (multi-project character of the situation, complex capacity situation). On the other hand, ERP systems are designed to only support production of a limited number of product variants [START_REF] Aslan | Enterprise Resource Planning systems: An assessment of applicability to Make-To-Order companies[END_REF][START_REF] Van Veen | New developments in generative BOM processing systems[END_REF] and presume BOMs and operational routings to be static and accurate during production.

A rule-based and consistent preparation of product and process data with the aid of the configurator solutions contributes to a generation of BOMs and operational routings easier processable by ERP systems. Consequently, a preparation of product and process data with the aid of the configurator solutions contributes substantially to process standardization, reduction in errors and decrease in complexity.

After Sales

In many markets, after sales is a business process elementary for success [START_REF] Hicks | Product life cycle management in engineer-toorder industries[END_REF]. For the provisioning of a proficient after sales service, it is indispensable to know which parts are installed in specific machineries and to have quick access to the right spare parts. Product knowledge aggregated in configurator solutions should therefore be made available to software used in after sales service (e.g. CRM, PDM, PLM systems). Also knowledge relating to operations and maintenance should be fed back from After Sales to Engineering to be used for future improvements [START_REF] Hicks | Product life cycle management in engineer-toorder industries[END_REF].

Implications

The structural deduction of the matrix and its contents illustrate that the Business Process Matrix developed in this chapter is suitable for structuring and contextualizing the organizational adjustments required for the implementation of configurators. Hence, the matrix can be understood as methodological reference in the context of adjusting organizational structures for the successful adaption of configurators to an ETO setting. The matrix depicts that a multitude of fields of action have to be considered when introducing product and process configurators to an ETO environment.

A concise specification of parameterized product structures and, possibly, modular component families is obligatory for the set-up of a fast and efficient product configuration process suitable for ETO products. Furthermore, the selected configuration system has to be fully integrated in the present IT infrastructure and interfaces to existing IT solutions (e.g. CAD, ERP, PLM systems) along the business process chain have to be provided. In this context, a common logic for the conversion and enrichment of data along the dimensions commercial, technical and production characteristics has to be determined. And above all, a close collaboration between all functional areas along the business process chain has to be ensured. Without doubt, an integrated setup of IT systems can contribute considerably to the design of fast and efficient ETO processes. However, due to the immense complexity and low predictability of ETO processes this alone is not sufficient. Only through a company-wide, or even preferable along the whole supply chain, incorporation of skills such as an integrated knowledge management, intercultural competencies and a comprehensive technicalorganizational understanding the creation of a competitive ETO process becomes feasible.

Conclusion

The literature analysis and interviews conducted reveal that a standardized application of configurators in ETO processes still provides a multitude of organizational and technical challenges. This paper contributes to the development and dissemination of a methodology suitable for identifying and structuring the organizational challenges resulting from the deployment of configurators in an ETO environment. A Business Process Matrix for categorizing the implications of using product and process configurators in an engineer-to-order environment is devised. The matrix highlights the most relevant action fields for the implementation of an efficient ETO process along the value chain. Both literature analysis as well as input from practitioners contributed to the design of the Business Process Matrix.

In future research, the authors intend to test and strengthen the Business Process Matrix in practical use cases in various manufacturing companies. This case-based approach is expected to reinforce the practical relevance of the model developed in this paper. The insights gained from the use cases will be applied to refine the Business Process Matrix at hand.


Dimitris Mourtzis
email: mourtzis@lms.mech.upatras.gr

Michalis Doukas
email: mdoukas@lms.mech.upatras.gr

Foivos Psarommatis

Simulation-based design of production networks for manufacturing of personalised products

Keywords: Simulation, Planning, Decentralisation, Personalisation

This paper presents a method for the design of manufacturing networks focused on the production of personalised goods. The method, which is implemented to a software tool, comprises of a mechanism for the generation and evaluation of manufacturing network alternative configurations. An exhaustive search and an intelligent search algorithm are used, for the identification of efficient configurations. Multiple conflicting user-defined criteria are used in the evaluation, including cost, time, CO2 emissions, energy consumption and quality. Discrete Event Simulation models of manufacturing networks are simulated for the calculation of performance indicators of flexibility, throughput and work-inprocess, and are used for assessing the performance of centralised and decentralised networks. The results obtained through the exhaustive and intelligent search methods are compared. The applicability of the method is tested on a real-life industrial pilot case utilising data from an automotive manufacturer.

Introduction and State of the Art

The market globalisation trend causes the decentralisation and internationalisation of supply chains and of manufacturing activities [START_REF] Ueda | Value creation and decision-making in sustainable society[END_REF]. Increased outsourcing is realised by Original Equipment Manufacturers (OEMs) leading to the formation of strong cooperation bonds with their suppliers that have to be coordinated and aligned towards achieving common goals. Innovative production concepts replaced traditional network structures [2]. Moreover, customers demanded personalised products, available at low prices and high quality, at the right time [START_REF] Thirumalai | Customisation of the online purchase process in electronic retailing and customer satisfaction: An online field study[END_REF]. The demand volatility calls for systems that efficiently deal with uncertainty in inventory planning [START_REF] Radke | A risk management-based approach for inventory planning of engineering-to-order production[END_REF]. Also, globalisation increased CO2 emissions primarily due to production electricity generation methods used [START_REF] Herrmann | Effects of globalisation on carbon footprints of products[END_REF].

The need for designing and planning efficient manufacturing networks in today's landscape is evident [START_REF] Tolio | A Rolling Horizon Approach to Plan Outsourcing in Manufacturing-to-Order Environments Affected by Uncertainty[END_REF]. Current approaches tackle the manufacturing network configuration problem using mathematical programming, bound computation, heuristics, meta-heuristics and sensitivity/stability analysis [START_REF] Michalos | An intelligent search algorithm-based method to derive assembly line design alternatives[END_REF]. Computer simulation, nevertheless, has been indispensable for evaluating what-if scenarios for the design and planning problem of manufacturing networks. Discrete Event Simulation (DES) is a necessary tool in order to assess performance indices of dynamic system behaviour [START_REF] Labarthe | Toward a methodological framework for agent-based modelling and simulation of supply chains in a mass customisation context[END_REF] [START_REF] Persson | The development of a dynamic supply chain analysis tool: Integration of SCOR and discrete event simulation[END_REF]. Many approaches have been proposed for dynamic manufacturing network management using simulation techniques. A continuous modelling approach for supply chain simulation was applied in the automotive industry and depicted that initial inventory levels and demand fluctuation can create delivery shortages and increased lead times [START_REF] Pierreval | A continuous simulation approach for supply chains in the automotive industry[END_REF]. A DES approach that included a decision-making mechanism was presented in [START_REF] Longo | An advanced supply chain management tool based on modelling and simulation[END_REF]. However, it did not indicate the best configurations of the supply chain but was utilised for identifying potential solutions. A simulation approach that uses meta-heuristics was tested on a newspaper production and distribution problem. The applicability of the method however, was not validated through a real manufacturing problem [START_REF] Chiang | A simulation / metaheuristic approach to newspaper production and distribution supply chain problems[END_REF]. The proposed approach in this paper includes a macroscopic investigation of the performance of manufacturing network measured in terms of flexibility, annual throughout and Work-in-Process (WIP) for the identification of efficient configurations for the manufacturing and transportation of personalised products. Concluding, the proven NPhard problem of identifying efficient manufacturing network configurations [START_REF] Garey | Computers and Intractability -A Guide to the Theory of NP-Completeness[END_REF] requires intelligent methods in combination with simulation [START_REF] Yoo | Hybrid algorithm for discrete event simulation based supply chain optimisation[END_REF].

The contribution of the suggested approach can be found in the following. The decision-making mechanism is tightly integrated with the simulation engine because the best alternative schemes that derive from the first comprise the input to the latter. Moreover, the Intelligent Search Algorithm (ISA) that uses three adjustable control parameters is presented and compared to an exhaustive method. ISA is an artificial intelligent search method that is utilised for identifying high quality solutions in a timely manner. ISA can be valuable in cases when exhaustive search is non-feasible due to the required computational effort introduced by the magnitude of the search space [START_REF] Michalos | An intelligent search algorithm-based method to derive assembly line design alternatives[END_REF] [START_REF] Mourtzis | A multi-criteria evaluation of centralised and decentralised production networks in a highly customer-driven environment[END_REF]. Additionally, centralised and decentralised manufacturing networks are compared regarding their performance under highly personalised product demand. Finally, the method is tested on a real-life industrial case utilising data from an automotive manufacturer.

Manufacturing Network and Product Modelling

The manufacturing network models under investigation consist of traditional centralised manufacturing network (CMN) structures, where assembly tasks can only be performed by the OEM at specific plants and decentralised manufacturing networks (DMN), where, a set of suppliers and dealers (partners) can perform personalisation tasks (e.g. application of the wrap cast carbon) (Fig. 1) [START_REF] Mourtzis | A multi-criteria evaluation of centralised and decentralised production networks in a highly customer-driven environment[END_REF]. The personalised product under investigation is a hood and a door of a commercial car. The personalisation options are the addition of a custom sticker, a personalised image and a cast carbon wrap.

Design and Planning Method

The decision-making method includes resource-task assignments. The algorithms used in this procedure are either an Exhaustive Search (EXS) or the ISA. EXS generates the entire search space and identifies the best alternative. Thus, EXS is computationally intensive. ISA is an artificial intelligence search method that utilises three adjustable control parameters [START_REF] Mourtzis | A multi-criteria evaluation of centralised and decentralised production networks in a highly customer-driven environment[END_REF], namely: SNA (Selected Number of Alternatives) that controls the breadth of the search, DH (Decision Horizon) that controls the depth of the search and SR (Sampling Rate) that guides the search towards high quality paths.

The steps of the decision-making method are: a) form a set of alternatives, b) determine the decision-making criteria, c) calculate the utility value of alternatives, and d) select the best alternative [16][17]. Multiple conflicting criteria are considered simultaneously, based on the design and planning objectives. The best identified schemes are fed into the simulator. Fig. 3 depicts the input-output data and the workflow of the method. -Basic Hood (HL1)

..

-Customization Components

..

-Wrap cast carbon (HL2a)

..

- Ornament (HL2b) ….……..

-Personalization Components

..

-Personalized text (HL2c)

..

-Personalized image (HL2d) -Door

Criteria

The quality of the schemes is quantified by the means of the following criteria:

1. Cost ( 1) is the sum of the production and transportation cost (in €) [START_REF] Mourtzis | A multi-criteria evaluation of centralised and decentralised production networks in a highly customer-driven environment[END_REF][18]: 2. Lead time ( 2) is calculated from the point that an order is placed to the point that it is actually available for satisfying customer demand [START_REF] Mourtzis | A multi-criteria evaluation of centralised and decentralised production networks in a highly customer-driven environment[END_REF][19]: 3. Energy Consumption (3) takes into consideration the Watt specifications and the processing time of each manufacturing resource [START_REF] Mourtzis | Design and planning of decentralised production networks under high product variety demand[END_REF][20]: 4. CO2 Emissions ( 4) are calculated taking into consideration the distance travelled and the emitted grams of CO2 per kilometre [START_REF] Mourtzis | A multi-criteria evaluation of centralised and decentralised production networks in a highly customer-driven environment[END_REF][18] [START_REF] Mourtzis | Environmental impact of centralised and decentralised production networks in the era of personalisation[END_REF][21]: 5. Quality ( 5) is an indicator that takes into account the mean quality of the parts, services that the manufacturing network partner provides and on their respect of due dates and takes values between, calculated based on empirical data [0-100]. 6. Annual Production Rate ( 6) is expressed as the mean value of annual production volumes over the complete simulation period [START_REF] Michalos | An intelligent search algorithm-based method to derive assembly line design alternatives[END_REF][19] [START_REF] Alexopoulos | Quantifying the flexibility of a manufacturing system by applying the transfer function[END_REF]. 7. FLEXIMAC (7) is a quantification of flexibility, using the processing and flow time of the parts produced and is used to compare two similar networks. It is calculated using the system eigenvalues Ωi and computing the amplitude Qi on those Ω frequencies. It is then calculated as the average value of the ten largest Qi [START_REF] Michalos | An intelligent search algorithm-based method to derive assembly line design alternatives[END_REF][15] [START_REF] Alexopoulos | Quantifying the flexibility of a manufacturing system by applying the transfer function[END_REF]. 8. Work-In-Process (WIP) is the inventory between the start and end points of the manufacturing system without including raw materials and finished products [START_REF] Hopp | Factory Physics: Foundations of Manufacturing Management[END_REF].

Additionally, the following indicators are used to express the resource availability:

Mean Time Between Failures (MTBF) is defined for each one of manufacturing network partners. MTBF is based on previous manufacturing knowledge and historical data and takes into account the observed frequency that a supplier fails to deliver the ordered batch, because of resource break-downs or capacity constraints.

Mean Time To Repair (MTTR):

The MTTR is calculated for each one of the manufacturing network partners as the time required for them to resume production.

𝐶 = ∑ 𝑃𝑐 𝑘 𝐾 𝑘=1

+ ∑ 𝑇𝑟 𝐶𝑟 𝑅 𝑟=1

(1) (7) where: Pck: cost of task k (k=1,2,…K), TrCr: cost of route r (r=1,2…R), Pt: processing time, Tt: transportation time, St: setup time, ECT: the sum of the energy due to trans-portation (J), D: transportation distance (km), TC: energy consumption per kilometre (J/km) 21, ECP: the sum of the energy for all the processes (J), RW: the Watts of the resource (J/s), grams of CO2 emissions/Km 21, N: number of products in one truck, QLk: the quality of the supplier that performs the task k, APi: the annual production volume for the i th year of simulation and ny: the number of years (simulation period), Qi: the eigenvalues of the system.

𝐿 = ∑ 𝑃𝑡 𝑘 𝐾 𝑘=1 + ∑ 𝑇𝑡 𝑟 + ∑ 𝑆𝑡 𝑘 𝐾 𝑘=1 𝑅 𝑟=1 (2

Results and Discussion

As a resource, a selection of machines that are responsible for performing a task (e.g. production of the basic hood) is assumed. The volatile demand profile, as provided by the automotive manufacturer for the simulation experiments, is depicted in Table 1. The computer simulation experiments were performed on an Intel TM i7 3.4GHz computer with 8GB of RAM. Fig. 4 depicts that there is a strong correlation between the Total Number of Alternatives (TNA) and the required Computation Time (CT). The TNA especially in the case of L10 has a difference of 3 orders of magnitude from the L9 case. In the case of L8, the EXS execution was non-feasible due to computational constraints i.e. depletion of the available system memory. It is obvious that the ISA is the preferred method is cases with very large TNA. It is also noted that the CT for L9 and L10 is a projection based on the calculated TNA for these cases.

Fig. 4. TNA and CT for the CMN and DMN configurations for the different LoPs

The values of the criteria the derived from the experiments are included in Fig. 5. Especially for the production of highly personalised products (L8), the Decentralised Manufacturing Network (DMN) depicts significantly reduced criteria values compared to the Centralised Manufacturing Network (CMN). In addition, the ISA yields results that belong to the 10% of the best solutions, in terms of utility value (derived from the EXS). Indicatively, the ISA yielded a high quality solution with 23.3% more cost, requiring however, one 1,026 times less CT than the EXS (pie-chart in Fig. 5). In realistic manufacturing cases, EXS is highly ineffective because TNA may be calculated in the The best configurations identified by the EXS were modelled in a simulator for the calculation of the flexibility, annual throughput and WIP (Fig. 6). The physical "as-is" manufacturing network was first modelled as a digital DES model. This initial model was verified through simulation and any bottlenecks were identified. Afterwards, the truck capacity as well as the buffer sizes were adjusted in all DES models, considering the volume of products and weight constraints. As a result, the transportation and storing activities were optimised in order to minimise idle times and improve machine utilisation. Finally, the initial stock was zero, thus, a ramp-up phase was included in all simulation experiments for the DMN and CMN and for the various LoPs. The simulation results depict that the flexibility indices of the CMN configurations are higher for non-customised products, whereas as the LoP increases, DMN displays higher flexibility. The annual throughput follows the same trend. For non-customised products, the CMN is more productive than the DMN ones and as the LoP increases the latter are more productive. The WIP is increasing as the LoP increases for both the CMN and DMN, due to the fact that the number of required processes increases for the production of the final assembly.

1E+00 1E+01 1E+02 1E+03 1E+04 1E+05 1E+06 1E+07 1E+08 1E+09 1E+10 1E+00 1E+01 1E+02 1E+03 1E+04 1E+05 1E+06 1E+07 1E+08 1E+09 1E+10 1E+11 1E+12 L5 L6 L7 L8 L9

Conclusions and Future Work

The work presented in this paper can support decision-makers during the design of efficient manufacturing network configurations focused on the production of personalised products. The computer simulation experiments depicted the advantages of the decentralised configurations, especially as the LoP increased. The calculated performance indicators of Annual Throughput, Flexibility and Work-in-Process revealed the superiority of the DMN configurations over the CMN under the same personalised product demand. For the case of non-customised products, the CMN configuration yielded better results, as was expected, because traditional CMN configuration were formed to satisfy the needs of mass production, where predictions regarding product demand were reasonably accurate. Moreover, the ISA supported the identification of high quality solutions in cases were an EXS was not feasible to be performed due to the required computation burden. More specifically, the EXS, in the case of the fully personalised product (L8 variant), yielded a result in 2.340 minutes, a non-optimum scenario for real-life manufacturing terms. On the other hand, the ISA yielded high quality results with a deviation of 10,39% in the utility value from the best solution obtained from EXS in 3 minutes, a difference of 3 orders of magnitude. Future work will focus on extending the capabilities of the method. At first the procedure of feeding alternative manufacturing network configurations (DES models) in the simulation engine will be automated. Moreover, additional flexibility indicators will be used for assessing the alternative network configurations. A web-based mechanism for the automatic calculation of the distances between the autonomous manufacturing network entities will be incorporated. Finally, the method will be deployed into a web-based tool.

Fig. 1 .

 1 Fig.1. Models of centralised and decentralised manufacturing networks[START_REF] Mourtzis | A multi-criteria evaluation of centralised and decentralised production networks in a highly customer-driven environment[END_REF]

Fig. 2

 2 contains the Bill of Materials (BoM), the Bill of Processes (BoP) and the different Levels of Personalisation (LoP) used in the experiments.

Fig. 2 .

 2 Fig. 2. BoM, BoP of the personalised car and LoPs

Fig. 3 .

 3 Fig. 3. Input-Output and Workflow of the presented approach

6

  Personalised image printing 7 Apply wrap cast carbon on hood 8 Apply wrap cast carbon on door 9 Apply ornament 10 Apply personalised text 11 Apply personalised image *LoP: Level of Personalisation

  )𝐸𝐶 = 𝐸𝐶 𝑇 + 𝐸𝐶 𝑃 = ∑ 𝐷 𝑟 * 𝑇𝐶 + ∑ 𝑃𝑡 𝑘

Fig. 5 .

 5 Fig. 5. Criteria Values and Utility Value of the EXS and ISA for the CMN and DMN and LoPs

Fig. 6 .

 6 Fig. 6. Calculated Indicators for CMN and DMN configurations for the different LoPs

Table 1 .

 1 Demand profile of the car model of the case study

	Month 1	2	3	4	5	6	7	8	9	10	11	12	Total
	Orders 25.200 36.399 22.399 22.399 19.601 16.800 11.200 5.601 28.000 39.200 30.800 22.399	280x10 3

Acknowledgments

This work has been partially supported by the EC funded project "e-CUSTOM -A Web-based Collaboration System for Mass Customisation" (NMP2-SL-2010-260067).


Tao Peng

Xun Xu
email: xun.xu@auckland.ac.nz

Juhani Heilala
email: juhani.heilala@vtt.fi

Energy-efficient Machining via Energy Data Integration

Keywords: Energy efficiency, STEP-NC, Data integration, CNC Machining. 1

Energy-efficient machining strategies are required to be implemented in daily practice to advance the competitiveness of the enterprises on global scale. Energy information, which currently not considered as an integral part of production data, is studied. A need is identified to integrate energy information into production program and solidify the knowledge for extensive reference. To effectively represent and share the data, standardized format is regarded as one promising approach, thus STEP-NC is adopted. The proposed data models are grouped into four, i.e. automated energy monitoring and recording, energy estimation and labeling, energy optimization, and machine tool energy performance. The developed schema is compliant and harmonized with other parts in STEP-NC standards. A case study is presented to add energy information to STEP-NC file. It can be concluded that standardized data format enables the integration of energy information into the production process and enhances its sustainable performance.

Introduction

The world is well-connected and better-informed nowadays than ever before. It is also true to manufacturing domain. New requirements of modern manufacturing industry are placed, such as sustainable performance, minimum supply chain management cost, and optimal product lifecycle performance. Energy-efficient machining system is focused in this study, as it is the key area in metalworking industry for the future. To keep energy efficiently used, energy information from machining process shall be available to assist process planning or lifecycle analysis [START_REF] Heilala | Energy efficient enhancement in discrete manufacturing with energy use parameters[END_REF]. A series of simulationbased analysis and decision making systems are developed, focusing on energy usage and performance on product lifecycle level [START_REF] Heilala | Simulation-based sustainable manufacturing system design[END_REF]. However, the ways to actually improve the daily practice in factories is still lacking.

The separated parties along the production chain desire to seamlessly share energy knowledge and data in a systematic manner. Data representation and exchange activities demand to be universal and consistent. This also contributes to significant savings on supply chain management cost, which is considered as the top priority of the enterprises for the next decade. With the rapid development in CAx, data communica-tion problem exists between different activities, known as "Island of Automation". Even within each activity, the data exchange between different applications cannot be done without sacrificing accuracy or completeness. Energy-efficient machining system inherits the same problem. Therefore, how to standardize the energy consumption data and make it accessible and interoperable to different parties is focused in this research. ISO 14955 envisions establishing a method to evaluate environmental impact of machine tools, including eco-design and testing on metal cutting machines [START_REF]International Organisation for Standardisation: ISO/DIS 14955-1, Machine tools -Environmental evaluation of machine tools -Part 1: Design methodology for energyefficient machine tools[END_REF]. ISO 20140 aims to standardize evaluation procedure and data model for environmental performance of manufacturing systems [START_REF]International Organisation for Standardisation: ISO/DIS 20140-1, Automation systems and integration -Evaluating energy efficiency and other factors of manufacturing systems that influence the environment -Part 1: Overview and general principles[END_REF]. Though both standards are still at the draft stage, it can be seen that standards are regarded as a good solution to remedy this problem. In this paper, existing STandard for Exchange of Produce model data (STEP [START_REF]International Organisation for Standardisation: ISO 10303-1, Industrial Automation Systems and Integration -Product Data Representation and Exchange -Part 1: Overview and Fundamental Principles[END_REF]) is adopted to connect energy data model developed. The proposed data models are likewise open to be integrated with ISO 14955/20140 if it fits better.

2

Literature review

Energy-efficient machining covers the principle metalworking operations, e.g. milling, turning, and drilling. Computerized numerical control (CNC) machines, as the key players, are designed and widely adopted to perform these types of operation. Up till now, more than 70% of the jobs are done using CNC machines world-wide. Over the years, researchers in this area endeavor to figure out how energy consumption behaves in machining, and what are the influential factors. Energy modeling research started with examining cutter-workpiece interaction [START_REF] Munoz | An analytical approach for determining the environmental impact of machining processes[END_REF]. The theoretical analysis discovered some critical factors that affect the energy consumption, such as tool properties, materials [START_REF] Shao | A cutting power model for tool wear monitoring in milling[END_REF]. Later, with the development of computing technology, statistic modeling based on rich data became popular [START_REF] Draganescu | Models of machine tool efficiency and specific consumed energy[END_REF]. These so-called empirical models use the production data to establish the relationship between the main variables and the energy usage. It allows the analysts to select only the interested/controllable factors. More factors yield to have impact on energy consumption, e.g. machining environment [START_REF] Aggarwal | Optimizing power consumption for CNC turned parts using response surface methodology and Taguchi's technique-A comparative analysis[END_REF]. The factors that influence energy consumption are summarized in [START_REF] Peng | The state of the art in energy consumption model -the key to sustainable machining[END_REF]. However, the studies are focused on one part of energy usage, i.e. cutting energy.

Researchers dealing with sophisticated CNC machine tools nowadays realize the actual cutting energy is only account for 15%-25% of the total energy consumed [START_REF] Dahmus | An environmental analysis of machining[END_REF]. Thus, discrete event modeling is introduced to model energy usage [START_REF] Dietmair | A generic energy consumption model for decision making and energy efficiency optimisation in manufacturing[END_REF]. More components are being considered in this new approach, so a more complete energy profile can be obtained. Moreover, advanced energy signal monitoring system is requested to collect and record data of influential factors. It is crucial to create adequate awareness of the energy efficiency information, but available system is still lacking. One application of energy efficiency monitoring and optimization system was reported in [START_REF] Heilala | Ambient Intelligence based monitoring and energy efficiency optimisation system[END_REF]. Automated monitoring process enables online energy optimization for a process. To apply the energy information collected during machining to other applications, data model should be able to hold the basic energy information and detailed energy report for reference. Databases are essential to sustain the analysis, as energy is related to di-verse factors. A well-organized database should contain energy reference. Even the representative data from previous operations can help. All these aspects mentioned should be well considered to make the energy data integration successful. Figure 1 shows the IDEF0 diagram on how energy information acts to enhance energyefficient machining processes. [START_REF]International Organisation for Standardisation: ISO 14649-1, Data Model for Computerized Numerical Controllers -Part 1: Overview and Fundamental Principles[END_REF] and ISO 10303-238 [START_REF]Industrial Automation Systems and Integration -Product Data Representation and Exchange -Part 238: Application Protocal: Application Interpreted Model For Computerized Numerical Controllers[END_REF]. ISO 14649 is used in this research for data modeling purposes, considering its straightforward data structure and adequate manufacturing data. The proposed energy data models are developed using EXPRESS language (standardized in Part 11 of STEP standard [START_REF]Industrial Automation Systems and Integration -Product Data Representation and Exchange -Part 11: Description Methods: The EXPRESS Language Reference Manual[END_REF]) and presented using EXPRESS-G (graphical representation of EXPRESS) in a more understandable manner. Four groups of data models are proposed to hold energy information, i.e. data models for automated energy monitoring and recording, energy estimation and labeling, energy optimization, and machine tool energy performance. The suggested data model is connected to and harmonized with other parts in ISO 14649. Detailed diagrams are omitted due to page limits.

Automated energy monitoring and recording

Before the actual machining happens, energy data collection can be planned. Inspired by the traceability data model developed by Campos et al. [START_REF] Campos | Standard process monitoring and traceability programming in collaborative CAD/CAM/CNC manufacturing scenarios[END_REF], new functions are modeled and connected to nc_function in Part 10 to automate the monitoring process.

A holistic view of this group is illustrated in Figure 2, with proposed nc_functions.

All the entities can be Sub-Grouped (SG) into three. SG I functions are designed to hold information of current system, including Obtain_time, obtain_sensor_data, and obtain_machine_tool_data. SG II functions aim for holding energy monitoring data. They can be inserted in the NC program, and enables appropriate data acquisition automatically. Start_energy_monitoring_sensor_data, stop_energy_monitoring_sensor_data, and signal_source_identified belong to this group. SG III defines the data structure of energy report for reviewing or auditing. Enable_energy_report, energy_report_format, and data_for_generate_energy_report are core functions.

Energy estimation and labeling

The influential factors are considered to develop this part of data model. Figure 3 illustrated the major entities proposed. As one attribute of ENTITY ener-gy_usage_label, ENTITY machining_energy_model is abstract supertype of ma-chine_tool_energy_model and main_cutting_energy_model. The former is used for systematic energy profile, while the latter is focused on actual cutting energy. Attach the estimated energy usage to NC program is called energy labeling. The main purpose is to indicate the energy used for the current project and its constituent elements, offering a brief view to the process reviewer or process planner. Utilizing the proposed data model, energy labeling can be done in two ways, i.e. energy estimation for selected workplan/workingstep and energy summary for the project.

Energy optimization

Currently, energy optimization is not regarded as major or even one concern of most machining. Online energy optimization is rarely supported. Realizing the need, ener-gy_optimization_data is proposed to hold data for machining. It allows the controller to adapt the settings autonomously for following steps based on specified energy model within the max value permitted and other availability at the shop floor. It enhances the smart controller performance in an energy-efficient fashion.

Machine tool energy performance

A well-organized database is critical for a comprehensive energy analysis. It should contain energy reference as a part of machine tool performance. To include this piece of information, it is best to attach it to machine tool database. This is because generally energy performance of a machining process is related to the machine tool in use, its structure, capability and condition. Produce the same part on different machine tools often results in different energy usage pattern. Hence, we proposed ENTITY ener-gy_performance_reference (Figure 5) to store the energy history of the machine tool, connected to standard_machining_process of machine tool data model.

Fig. 5. ENTITY energy_performance_reference

A machining energy model will be specified for an energy performance record, as well as its ratings. How to quantify the energy efficiency index in a meaningful and standardized manner can be further explored incorporate with ISO 20140. This is included as future research work.

its_energy_model its_energy_rating its_energy_usage energy_performance_reference

To sum up, tighter data flow and integration is highly required. The energy information data model proposed in this section enhances data integration for energyefficient CNC machining processes, and fully harmonized with existing parts in STEP-NC standards. The EXPRESS-G diagrams are developed using ST-Developer version 14, STEP Tools. Inc., USA.

A case study

A case study is conducted to demonstrate the use of energy data model in Example 1 of ISO 14649-11 [START_REF]International Organisation for Standardisation: ISO 14649-11, Data Model for Computerized Numerical Controllers -Part 11: Process Data for Milling[END_REF]. To produce this part, five workingsteps are required, i.e. planar face finishing, hole drilling, hole reaming, pocket roughing, and pocket finishing.

Originally, there is no energy information in the sample NC program. We implement the developed data models in section 3.2 to modify sample STEP-NC file. Energy information is added. The calculated energy summary of the production project is attached at the end of the file. It estimates how much energy is expected to be consumed before it happens. Segments of modified part 21 file are shown below. One can easily locate the basic energy consumption summary at line #200.

Discussion and future work

The drive for increased competitiveness, reduced energy consumption and overall cost, pushes for energy-efficient machining operations. It has become evident that machine tool vendors and CNC users are making great efforts to improve the energy efficiency, both on machine tool itself and operation strategies. However, it is observed that insufficient energy data interoperability from CNC machining to other applications remains as one of the key issues in limiting energy-efficient machining processes. Thus it yields a necessary and crucial step to include energy information into the data sharing. The proposed data models for covering energy information purposes, augments the existing STEP-NC data model. It is important to collaborative machining environment in which energy monitoring, analysis and optimization can be exercised based on the developed data model.

For the next generation of intelligent CNC machines. The possible applications enabled by this data model are beyond the case study. For example, automated energy monitoring can be achieved by inserting standardized energy monitoring functions into the production file, and while executing the file, the STEP-compliant smart CNC controller can automatically activate requisite sensor to capture the data, and store the data in a consistent manner. The planning system can select the most satisfactory signal and effective sensor if there is more than one sensor on the CNC. The recorded energy profile can be recorded in a separate file, which is ready for post machining analysis. With the energy footprint, one can re-evaluate the energy consumption planned at the beginning, and update the energy pattern. Other areas that may benefit from the energy information data model includes but not limited to,  Design for manufacturing  Energy-efficient process and production planning  Resource management  Enterprise investment guidance Though attentions are drawn in this field, research results are relatively preliminary. Future work will focus on optimizing energy consumption for machining practices, and explore the possible approach to control the CNC machines adaptively based on the data model. Development of software tools in assisting energy data monitoring, analysis and optimization is another useful work planned in the near future.

Fig. 1 .

 1 Fig. 1. IDEF0 diagram of integrating energy information into machining processes

Fig. 2 .

 2 Fig. 2. Data models for automated energy monitoring and recording

Fig. 3 .

 3 Fig. 3. Data models of machining_energy_model in EXPRESS-G

Fig. 4 .

 4 Fig. 4. Proposed energy_optimization_data and its relationship with machining_operation

  WITH ENERGY INFORMATION'),'1'); FILE_NAME('EXP1_E.STP','25-06-2012',('TAO PENG'),('UoA'),'','',''); FILE_SCHEMA((('COMBINED_SCHEMA','ENERGY_INFO_FOR_MACHINING_SCHEMA')); 'PLANAR FACE1',#18,(#27),#31,#36,#40,#42,$,()); #44=MACHINING_WORKINGSTEP('WS FINISH PLANAR FACE1',#5,#43,#27,$,#198); ... #108=WORKPLAN('MAIN WORKPLAN',(#44,#64,#65,#93,#94),$,#107,$,#199); #109=PROJECT('EXECUTE EXAMPLE1',#108,(#18),$,$,$,#200); ... #194=CUTTING_FORCE(0.04000000, 12.00000000, 2.50000000, 20.00000000, $); #195=MAIN_CUTTING_POWER(#194, $); #196=ENERGY_EFFICIENT_MACHINING_PARAMETERS(#195,100.00000000,"DRY"); #197=MAIN_CUTTING_ENERGY_MODEL('ENERGY WS FINISH PLANAR FACE1',#20,'STEEL',.MILLING_CAPABILITY.,#196); #198=ENERGY_USAGE_LABEL(2789.500000000, #197); #199=ENERGY_USAGE_LABEL(8425.00000000, $); #200=BASIC_ENERGY_CONSUMPTION_DATA($, 8425.00000,(2789.500000,$,$,3975.500000,1660.00000),$,'REPORT EXP1',$); ENDSEC; END-ISO-10303-21;