Elisa D'avolio
email: elisa.davolio@unifi.itromeo.bandinelli@unifi.itrinaldo.rinaldi@unifi.it

Romeo Bandinelli

Rinaldo Rinaldi

Product Development and PLM performance measures: a multiple-case study in the fashion industry

Keywords: Performance Measurement, Product Development process, Product Lifecycle Management, PLM, Fashion industry

Performance measurement represents one of the key lever to increase business competitiveness. Fashion companies are characterized by the centrality of products, so development and engineering should be monitored and controlled through proper performance measures. The goal of the entire study is to discuss how performance measurement is able to address and sustain the product development process within the fashion supply chain, considering also the involvement of PLM. A case study analysis has been conducted to select and validate the main measures related to product development and PLM.

Introduction

The importance of performance measurement was first acknowledged a long time ago but, as the years have gone by, Supply Chain (SC) complexity has taken over. The aim of the present study is to highlight the importance of performance monitoring with a special focus on the product development process.

Performance measurement is a cross department practice, which should support and control the overall set of processes within the SC: product development (PD) is just one of them, but it becomes strategic for particular industries as in the Fashion business environment.

The product and its progress are key features for a fashion company that is trying to compete on quality and time-to-market.

From concept to production, the leading role is played by products: each season several items are proposed; depending on the market segment that the company choose to satisfy, the number of new products compared to the "carry over" may vary. Lots of collection, departments and people are involved so that measuring performance is a strategic imperative.

Nevertheless, the growing amount of information related to the product is triggering the importance of proper Information and Communication Technologies (ICTs) supporting PD. The Product Lifecycle Management (PLM), well known in the manufacturing industry from almost two decades, is spreading also in the fashion industry. An inner circle of PLM vendors is proposing ad hoc solutions: they are fitting the requirements of fashion companies, more and more complex in terms of data management.

Performance measurement is also including PLM on its evaluations, as a system that has to be reliable in order to allow users to develop collections and that is producing measurable benefits.

The goal of the entire study is to discuss how performance measurement is able to address and sustain the PD process within the fashion SC, considering also the involvement of PLM.

A case study analysis has been performed to compare literature review results, mostly related to the manufacturing industry, with empirical evidences.

The paper is organized as follows: the second section describes the main literature about PD Key Performance Indicators (KPIs); in the third section, the methodology is presented and in the fourth section the main results are analyzed. The paper concludes with several remarks and future challenges.

2

Literature review

Performance measurement is a largely debated topic within scientific literature, since the 90's, but the number of papers which deal with PD, as a specific process within SC, is just a small subset. The latter becomes smaller and smaller as the focus shifts to the fashion industry.

In order to study the large number of performance measures available, researchers have categorized them. A framework has been proposed for performance measurement based on three main components to manufacturing SC success: resources, output and flexibility [START_REF] Beamon | Measuring supply chain performance[END_REF]. Another classification based on the main SC performance measures distinguishes between innovativeness, information accuracy and timeliness [START_REF] Cai | Improving supply chain performance management: a systematic approach to analyze iterative KPI accomplishment[END_REF]. Other approaches include SC management models ( [START_REF] Gunasekaran | A framework for supply chain performance measurement[END_REF], [START_REF] Sheperd | Measuring supply chain performance: current research and future directions[END_REF]) or Balanced Scorecard for SC management evaluation [START_REF] Bhagwat | Performance measurement of supply chain management: a balance scorecard approach[END_REF]. Just a little part of the analyzed literature ([6], [START_REF] Moore | Markerting capabilities and firm performance in fashion retailing[END_REF], [START_REF] De Brito | Towards a sustainable fashion retail supply chain in Europe: Organisation and performance[END_REF]) is focused on the fashion industry: the authors analyze particular measures, mostly related to Marketing and Retail needs or to sustainability issues.

The topic of PD performance measures is still underexplored in literature. PD activity is intrinsically intangible, non-routine, uncertain and organizationally complex. These special characteristics combine to make PD performance measurement especially challenging.

According to [START_REF] Tatikonda | Product development performance measurement[END_REF], a given performance measure is characterized by the combination of four aspects: its managerial purpose, object of interest, measurement forms and linkages with other metrics. The dimensions and elements of these four characteristics make up a formative framework defining the space of conceivable PD metrics.

More industry-specific papers ( [START_REF] Driva | Measuring product development performance in manufacturing organisations[END_REF], [START_REF] Tsu-Ming | Performance improvement in new product development with effective tools and techniques adoption for high-tech industries[END_REF]) describe how PD can be better controlled for manufacturing and high tech companies. A research [START_REF] Driva | Measuring product development performance in manufacturing organisations[END_REF] has led to the development of the performance measurement for product development (PMPD)

3

Research approach

The research has been inspired by several projects carried out in fashion companies that are recognizing the importance of performance measurement and its role in the PD process.

The starting point has been a literature review, that has allowed the authors to learn from other studies and compare metrics and viewpoints about KPIs classifications.

Consequently, the research has focused on two main areas: PD KPIs and PLM KPIs; the first one is more process-oriented and the second one more IT-oriented, but both are strictly linked to product development.

Given the different approaches to performance measurement and involvement of information technologies, two parallel case studies have been conducted.

Case study has been chosen to investigate the choices in terms of approach to performance management in Italian fashion companies. This methodology is adopted to gain a more in-depth understanding of the dynamics present within single settings [START_REF] Yin | Case study research: Design and methods[END_REF]. In order to increase confidence in the findings, multiple-case sampling has been used [START_REF] Miles | Qualitative data analysis[END_REF].

For the first area of interest, i.e. PD KPIs, a questionnaire has been designed and administrated to six fashion companies, as shown in Table 1. The sample has been classified basing on:

• The activities conducted in-house or outsourced: it is a driver of the importance that the business assigns to several tasks.

•

The main product represents the core business and reveals the companies' critical success factors (CSFs): quality seems to be more strategic for leather goods and timing for ready-to-wear (RTW).

• The companies' sizes in terms of stock keeping units (SKUs) •

The market segment the companies belong to The firms are international brands with at least a business unit in Italy. The half part of the sample is composed of big high fashion (luxury) companies, producing leather goods (shoes, bags, accessories) or made to measure garments. While, other three companies are medium lines selling ready to wear (pants, skirts, sweaters, dresses) and outerwear (jackets, trench, winter coats). The authors have decided to involve companies paying huge attention to the PD process, which is always conducted in-house. The interviewees were Managers of the finance, product development and production departments. For the second area of interest, i.e. PLM KPIs, only IT and Data Managers have been involved because of their higher knowledge about enterprise systems, configurations and interfaces. This analysis required a more detailed study, given the lack of literature researches and the complexity of the topic.

Two big high fashion companies, representing internationally iconic brands and selling mainly leather goods, participated to this second stage of the research (Table 2). They manage in-house the great part of the business processes and have implemented the same fashion-specific PLM solution, which is properly integrated with other systems. In Figures 1 and2 the different methodologies have been represented. The research that has led to the acknowledgement of PD KPIs and performance measurement approach has been a validation of already known, but more industry-specific, performance measures.

Instead, the research that has led to the acknowledgement of PLM KPIs has been introduced by a generic examination of PLM KPIs but the biggest contribution has come from meetings dedicated to IT issues.

Discussion

In order to investigate about PD performance measures in the fashion industry, a "draft" list of KPIs has been identified from the literature review. The interviewees were asked to examine this list and to specify the degree of importance of the indicator, according to a 1-4 scale (1=Not important; 2=Low importance; 3=Important; 4=Very important).

Moreover, interviewees detailed for each measure the typology of ICT tool (e.g. PDM, PLM, Business Intelligence, ERP) adopted in order to store and monitor KPIs.

In table 3 the validated list of PD KPIs is detailed and classified basing on resource, cost and time measures. The last column of the table, counts the number of cases adopting each KPI: resource and cost KPIs are the most adopted by the companies interviewed, in particular prototype and sample annual costs and HR employment in different departments.

Each case study described in Table 1, has its own way to manage those KPIs. The first case is a high fashion company that prefers to monitor production KPIs more than PD ones. Just few PD measures are controlled and they have a low importance. PLM is still not implemented, but a PDM provides the information to manage KPIs.

The second case is a RTW company that pays huge attention to PD, also for time KPIs. A Business Intelligence solution is able to trace data about performance measures, but the firm is not ready to a full PLM implementation.

The third case is a RTW diffusion company that controls other SC KPIs and has a holistic approach to performance measurement. It monitors all the KPIs listed in Table 3 and is using a PDM solution.

The fourth case is an outerwear company that is not measuring lots of KPIs among the PD measures: it prefers to monitor just cost KPIs, so it has a financial approach to performance measurement. These are controlled through a Business Intelligence solution.

The fifth and the sixth cases have in common the strategic goal to sell premium quality products: leather goods (fifth case) and ties, scarves and made-to-measure garments for men (sixth case). PD is definitely their core business, so they measure many of the listed KPIs, more than measures related to other SC processes (sourcing, production, distribution, etc.). They strongly believe in handcrafted tradition and are still not able to innovate through PLM solutions.

This first step of the research has been particularly interesting because interviewees have validated the list of performance measures and acknowledged the importance of proper tools supporting PD management. Coming to the second part of this research, i.e. investigating PLM performance measures in the fashion industry, the main results are listed in the tables 4-8.

KPIs have been assessed basing on: • a performance measure classification: time, cost, quality, flexibility and infrastructure KPIs. The majority of PLM KPIs are time-based measure because one of the main challenges in PLM implementation is the reduction of time-to-market and time to develop products.

• a process classification: data management, data configuration, printing, import/export configuration. PLM solutions allow to manage data from the user interface (create styles, colors, materials. etc.) or to configure data in proper settings (define templates, attributes, behaviors on copy, validation rules, etc.).

Other IT solutions and layers could be interfaced to PLM, as printing layers, ERP, MRP and PDM. When PLM is the master data for PD, an import/export job scheduling is available through stored procedures or proper tables.

During business meetings, the following KPIs have been identified and validated, explaining the general meaning and the background.

Time measures are mainly related to actions taken by users in day-by-day data management, developing styles and materials. The latter two represent the core business objects (BOs) for a fashion PLM solutions. Users usually manage properties within single BOs or aggregated information in table views, including data of more departments (shoes, bags, accessories, etc.) and more collections. One of the key features of a flexible PLM is the opportunity to introduce expressions calculating costs or ensuring data validation: the elapsed processing time may vary depending on the number of objects involved and on the complexity of the expression. Users also need to export data from PLM and to send it to suppliers: data packages, bill of materials (BOMs), quotes reports have to be printed in pdf files. The performances of the printing process have been also included because sending product information to factories is fundamental for companies which outsource production, as long as printing massive data (e.g. all the BOMs by collection) may take long times. Export batches are the protagonists of data exchange with business enterprise tools. They are scheduled through proper jobs and have to be as fast as possible to ensure data updates and real time collaboration. Other information are imported in PLM, as material codes, and their availability is also remarkable.

Cost measures refers to issues noticed by users, that could be bugs or simple needs for training, requiring an application maintenance service. Costs related to the upgraded release and to an additional customization should be taken into account.

Quality measures represent authentic drivers to select a business specific PLM, given the enterprise architecture structure. The number of aggregated data to be exported to ERP and the frequency of export are strictly dependent on the business environment needs: from four times to once in a day, a company should need to export all the information of more than two seasons.

Flexibility measures could refer to the capability of PLM to support the majority of business processes linked to products. When agile deployment is feasible, the PLM vendor is also able to introduce proper changes to the present configuration and this is a measure of flexibility too.

Finally, infrastructure measures could be seen from the business viewpoint or from the PLM vendor perspective. In the first case, the business needs to understand if it is compliant to PLM system requirements and hardware configuration. In the second case, the PLM has to guarantee an adequate number of out of the box (OOTB) business objects and several upgrades, at least for bug fixing reasons.

Fig. 1 .Fig. 2 .

 12 Fig. 1. Methodology adopted for PD KPIs

Table 1 .

 1 Case studies conducted for PD KPIs

	Cases	In-house activities	Main Product	N° SKU	Market segment
	Case 1 All	Leather goods	>1000	Luxury
	Case 2 Distribution, product	Ready-to-wear	>1000	Diffusion
		development
	Case 3 Distribution, retail, product	Ready-to-wear	500-1000	Diffusion
		development
	Case 4 All but production	Outerwear	100	Diffusion
	Case 5 All	Leather goods	>1000	Prêt-à-porter
	Case 6 All	Made to measure	>1000	Luxury

Table 2 .

 2 Case studies conducted for PLM KPIs

	Cases	In-house activities	Main Product N° SKU	Market	PLM
					segment	implementation
	Case 1 All	Leather goods	>1000	Luxury	Since 3 years
	Case 2 All but production	Leather goods	>1000	Luxury	Since 2 years

Table 3 .

 3 Classification of PD KPIs

	KPI ID	KPI	PD KPIs			Description	KPIs
		classification					adoption
							(0-6 cases)
	PD-1	Resource Human	resources	Average number of HR employed in the	4
			(FTE) -Design		Design Department
	PD-2	Resource Human	resources	Average number of HR employed in the	5
			(FTE)	-	Product	PD Department
			Development
	PD-3	Resource Human	resources	Average number of HR employed in the	5
			(FTE) -Modelling	Modeling Department
	PD-4	Resource Human	resources	Average number of HR employed in the	5
			(FTE) -Prototyping	Prototyping Department
	PD-5	Resource Number of fabrics	Average number of fabrics adopted	3
			(FW)			during the fall-winter season
	PD-6	Resource Number of fabrics (SS) Average number of fabrics adopted	3
						during the spring-summer season
	PD-7	Resource Number of colors (FW) Average number of colors developed	3
						during the fall-winter season
	PD-8	Resource Number of colors (SS) Average number of colors developed	3
						during the spring-summer season
	PD-9	Resource Number of planned	Average number of models planned	3
			models (briefing)	during the briefing
	PD-10	Resource Number of final models	Average number of actual models	4
			(briefing)			calculated during the briefing
	PD-11	Resource Product typology (%	Percentage of carry over and new	3
			carry over and new	products within a season
			models)
	PD-12	Cost	Prototypes annual cost Average annual cost to produce	6
						prototypes
	PD-13	Cost	Samples annual cost	Average annual cost to produce samples	6
	PD-14	Cost	Prototype			Ratio between the average prototype cost	3
			Cost/Production Cost	and the average production cost
	PD-15	Cost	Sample			Ratio between the average sample cost	2
			Cost/Production Cost	and the average production cost
	PD-16	Cost	Fitting		costs/	Ratio between the average fitting cost and	1
			Production cost		the average production cost
	PD-17	Time	Compliance	with	Ratio between the average number of	3
			Marketing Brief	models planned and the average number
						of actual models calculated during the
						briefing
	PD-18	Time	Compliance with the	Ratio between the actual time and the	2
			product	engineering	planned time to engineer products during
			schedule (FW)		the fall-winter season
	PD-19	Time	Compliance with the	Ratio between the actual time and the	2
			product	engineering	planned time to engineer products during
			schedule (SS)		the spring-summer season
	PD-20	Time	Number of fitting	Average number of fitting session during	1
			sessions (FW)		the fall-winter season
	PD-21	Time	Number of fitting	Average number of fitting session during
			sessions (SS)		the spring-summer season

Table 4 .

 4 PLM Time KPIs

	KPI ID PLM KPIs			Description	Process
	PLM-1	Time to create/copy a	Average time to create a new	Data management
		style/material			style/material or a carry over
	PLM-2	Time to create a new BOM	Average time to create a new BOM	Data management
	PLM-3	Time to create/copy a new	Average time to create a new BOM	Data management
		BOM item			item
	PLM-4	Time to create a color library Average time to create a seasonal	Data management
					color library
	PLM-5	Time to issue a supplier	Average time to send to suppliers a	Data management
		request			technical	sheet	containing
					information	for	product
					prototyping/sampling
	PLM-6	Time to massively issue	Average time to send to suppliers	Data management
		supplier requests		more technical sheets containing
					information	for	products
					prototyping/sampling
	PLM-7	Time to load aggregated	Average time required to display	Data management
		information in table views	information related to more styles in
					a table view
	PLM-8	Processing time for costs	Average elapsed processing time to	Data management
		calculations			execute an expression related to cost
					calculation
	PLM-9	Time to print a table view	Average time to print the	Printing
					information contained in a table view
	PLM-10 Time to print a set of BOMs	Average time to print the	Printing
					information contained in a (BOM)
	PLM-11 Time to print Data Packages	Average time to generate and print a	Printing
					Data Package containing more sheets
	PLM-12 Time to export the report of	Average time to run the scheduled	Import/Export configuration
		BOM items to ERP		export batch containing information
					about styles and the related materials
	PLM-13 Time to export the report of	Average time to run the scheduled	Import/Export configuration
		supplier quotes to ERP		export batch containing information
					about styles and the related quotes
	PLM-14 Time	to	import	Average time to run the scheduled	Import/Export configuration
		style/material/suppliers codes	import job containing information
		from PDM			about styles/materials/suppliers
	PLM-15 Time to download reports	Average time to download a single	Import/Export configuration
		imported in PLM		report imported into PLM

Table 5 .

 5 PLM Cost KPIs

Table 6 .

 6 PLM Quality KPIs

Table 7 .

 7 PLM Flexibility KPIs

	KPI ID PLM KPIs	Description	Process
	PLM-16 Number of PLM incidents	Average number of system issues	Data management
		(monthly)	noticed by users
	PLM-17 Cost to implement a custom	Average cost to implement medium-	Data configuration
		configuration	high configuration based on business
			requirements
	PLM-18 Data searching time	Refers to the capability of the user	Data management
			interface to be simple and friendly
	PLM-19 Cost to upgrade release version Average cost to upgrade the system	Data configuration
			to the new release
	KPI ID PLM KPIs	Description	Process
	PLM-20 Number of seasonal data to be	Average number of data exportable	Import/Export
		exported to ERP	to ERP related to a season (including	configuration
			several collections and styles)
	PLM-21 Frequency of master data	Average frequency of master data	Import/Export
		export to ERP	export from PLM to ERP	configuration
	PLM-22 Loop monitoring & controlling Availability of a tool to control and	Data configuration
			remove potential loops
	PLM-23 PLM system scalability	Refers to the PLM capability to	Data configuration
			increase	its	performance	to
			accommodate the resources growth
	PLM-24 Information tracking	Refers to the PLM capability to	Data configuration
			allow	product	information
			traceability and history
	KPI ID	PLM KPIs	Description	Process
	PLM-25	Number of user profiles	Average number of users configurable	Data
			in PLM		configuration
	PLM-26	PLM footprint in the fashion	Percentage of business processes	Data management
		company	supported by PLM
	PLM-27	PLM system flexibility	Refers to the PLM ability to adapt to
			possible or future changes in business
			requirements

Data configuration

Table 8. PLM Infrastructure KPIs

The case studies related to PLM performance measures are particularly meaningful because through the listed KPIs it is possible to finalize an all-embracing PLM assessment and, in detail, to:

compare different PLM solutions compare different versions of the same PLM solution compare the data management before/after PLM compare different configurations of the same PLM solution (in different companies).

While time KPIs are more industry specific, the remaining are more generic performance measures that could be monitored in any PLM project.

This analysis about PLM performance measures differs from the one related to PD KPIs because an evaluation of the measures within the case studies is still missing. Nevertheless, the companies interviewed have demonstrated how strategic business alignment, process-based PLM design and reduction of customizations are critical success factors for fashion firms implementing a PLM solution.

One of the main improvements consists in changing the way to and PLM enables this change. To better perform, a company has to focus on process enhancement and then on system change, avoiding to customize the solution to support old processes.

Software customizations entail an effort in terms of costs and time; PLM is also more expensive to maintain and less flexible for future integrations. Choosing OOTB configuration is one the right ways to improve the overall set of PLM performances.

Conclusions and future work

This research has the objective to underline the importance of PD performance measures for fashion companies. A preliminary literature review has introduced the authors to KPIs analysis and classification, concerning both PD process and PLM. A lack of studies in the fashion industry has been noticed and a case study analysis has been performed.

The authors have tried to provide a comprehensive view of the measures related to product lifecycle, starting from process to data management. PD KPIs has been validated with each company interviewed and a qualitative estimation of the measures has demonstrated the chosen approach to performance measurement. PLM KPIs has been identified through a more complex and deep analysis with process owners and ICT managers. This analysis represents a preliminary stage to evaluate PLM performance within fashion companies, through proper industryspecific KPIs.

The interviews have allowed the authors to describe in a qualitative manner the best way to perform PD process and PLM implementation. Future researches will be conducted to achieve a quantitative assessment of PD and PLM measures, in order to ensure generalizability of results.


Conrad Boton

Louis Rivest

Daniel Forgues

Julie Jupp

Comparing PLM and BIM from the Product Structure Standpoint

Keywords: BIM, PLM, Product Structure, Bill of Material, BOM, Construction

The increasing use of Building Information Modelling (BIM) across the construction industry highlights the potential for a common endpoint with manufacturing industries. Previous research work has shown that it is possible to improve BIM with the features and the best practices from Product Lifecycle Management (PLM) approach. This article provides a comparison between the PLM and BIM approaches from the standpoint of the Product Structure (PS) and the Bill of Material (BOM). It discusses the need to explicit a structuring concept in the BIM approach in order be able to switch to an informationcentric management approach in construction projects instead of the current activity-based approach.

Introduction

Considered to be refractory to information technology, the construction industry has suffered a great delay in terms of productivity compared to other industries such as aerospace and automotive. The sector is characterized by high fragmentation, heterogeneous project teams and a lack of interoperability. For decades, several studies have explored the role of information technology as an integrating element and enabler of productivity without much success until recently [START_REF] Howard | Computer integration: reducing fragmentation in AEC industry[END_REF][START_REF] Sacks | Impact of three-dimensional parametric modeling of buildings on productivity in structural engineering practice[END_REF][START_REF] Arayici | Technology adoption in the BIM implementation for lean architectural practice[END_REF].

Yet with the rise of BIM [START_REF] Eastman | BIM Handbook: A Guide to Building Information Modeling for Owners, Managers, Designers[END_REF], the industry seems at a crossroads. Indeed, BIM appears to have the potential to solve a number of persistent problems in the sector (interoperability, optimization of information flows, etc.) that will lead to improvements in productivity [START_REF] Sacks | Impact of three-dimensional parametric modeling of buildings on productivity in structural engineering practice[END_REF][START_REF] Eadie | BIM implementation throughout the UK construction project lifecycle: An analysis[END_REF]. While early studies focused on interoperability issues and other technological improvements, it appeared quite soon that the great value of BIM lay in collaboration and optimization of information flows throughout the project life cycle: unfortunately the source for most of the current limitations of the BIM approach. Recent research [START_REF] Jupp | Incomplete BIM Implementation : Exploring Challenges and the Role of Product Lifecycle Management Functions[END_REF][START_REF] Jupp | BIM and PLM : Comparing and Learning from Changes to Professional Practice Across Sectors[END_REF][START_REF] Aram | Integration of PLM solutions and BIM systems for the AEC industry[END_REF] suggests improving BIM based on best practices from PLM.

Jupp [START_REF] Jupp | Incomplete BIM Implementation : Exploring Challenges and the Role of Product Lifecycle Management Functions[END_REF] recently studied the consequences of incomplete BIM implementation in construction projects. The results suggest that the PLM approach actually provides interesting features to solve many of the problems currently encountered in the BIM approach. Indeed, similarities exist between the current upheavals in construction and the changes observed in complex manufacturing industries a few years ago with the arrival of the PLM approach [START_REF] Jupp | BIM and PLM : Comparing and Learning from Changes to Professional Practice Across Sectors[END_REF]. In addition, the philosophy and overall objectives of BIM are similar to the PLM approach.

Recent studies suggest that as a methodology, instead of evolving into a construction-dedicated PLM approach, that BIM and the structured product data stemming from its modelling processes be used to create an effective connection between the application of PLM and ERP systems to construction projects [START_REF] Holzer | Fostering the Link from PLM to ERP via BIM The AEC Industry in Transition[END_REF]. Holzer presents the Bill-Of-Materials (BOM) as the missing link between BIM and existing (and largely disconnected), feasibility, design, construction and operational processes [START_REF] Holzer | Fostering the Link from PLM to ERP via BIM The AEC Industry in Transition[END_REF].

The concept of the BOM is widely used in other industrial sectors, and the Product Structure is central to PLM systems. Moreover, "unlike product structures, a BOM cannot store a complete customized products family" [START_REF] Pinquié | An illustrated glossary of ambiguous PLM terms used in discrete manufacturing[END_REF] and "does not consider the product data that is associated with the technical objects" [START_REF] Pinquié | An illustrated glossary of ambiguous PLM terms used in discrete manufacturing[END_REF]. However, these two concepts are useful and practical in manufacturing industries to convey information throughout the entire lifecycle of a project.

Since the construction industry is comparable to the other discrete manufacturing industries, despite some notable differences, these concepts then appear to be very interesting directions with good potential in enabling an information-centric management approach in construction projects in the age of BIM.

We could reasonably argue that PLM systems would not be the same without a Product Structure to organize data. However, while the construction industry uses product breakdown structures, comparing PLM and BIM through the way product structure is defined and exploited could bring valuable insight on the differences between these sectors and the tools they use. Ultimately, this comparison could even provide a way to transpose some PLM successes to BIM. This article explores the concepts of BOM and Product Structure in the light of current BIM practices and provides a discussion on how PLM and BIM compare with respect to the notion of the Product Structure as conceived of in discrete manufacturing industries. It first introduces the background through the peculiarities or the construction industry and the related works. It then defines the different concepts and discusses how they can be seen as structuring concepts in the BIM approach.

Background

Particularities of the construction industry

It seems important, before going further, to present a comparison between the construction industry and other manufacturing industries. In a study by Green et al. [START_REF] Green | Making sense of supply chain management: a comparative study of aerospace and construction[END_REF] a comparison between the construction and aerospace industries provides an accurate representation of the differences of context between these industries. They identify two major elements to analyze these differences: the structural differences and the relationship with government. It is generally accepted that the construction sector is larger but still very fragmented and localized, while the aerospace industry has highly consolidated in recent decades due to considerable competitive pressure. In previous research on construction industry fragmentation, Howard et al. [START_REF] Howard | Computer integration: reducing fragmentation in AEC industry[END_REF] distinguish vertical fragmentation from horizontal fragmentation. Vertical fragmentation concerns the fact that a construction project is divided into several more or less short phases. Horizontal fragmentation relates to the fact that multiple different specialist interactions occur during the same phase. The combination of vertical and horizontal fragmentation therefore gives rise to small specialist firms operating on small local markets.

If the last decades have seen the merger of several major players in the aerospace industry to form large blocks, mergers observed in construction remain comparatively low [START_REF] Green | Making sense of supply chain management: a comparative study of aerospace and construction[END_REF]. The structure of the construction industry can be illustrated by a large-based pyramid dominated by small firms. While the dominance in the aerospace industry is made by large companies with a much more widespread technological expertise throughout the supply chain. Competition between firms in construction is done more on cost than on technical expertise. In addition, the combination of technological requirements and the complex network of interdependences in aerospace is a significant barrier for new entrants, which is far from the case in construction [START_REF] Green | Making sense of supply chain management: a comparative study of aerospace and construction[END_REF]. The last structural difference identified by Green et al. [START_REF] Green | Making sense of supply chain management: a comparative study of aerospace and construction[END_REF] is related to the customer base that seems highly diversified in construction but very narrow in aerospace, which is characterized by long-term collaborative relationships between a small number of highly sophisticated clients.

Moreover, unlike aerospace, the construction sector is marked by high flexibility and a strong sense of "laissez-faire" that governments prefer to see as an advantage for the sector [START_REF] Green | Making sense of supply chain management: a comparative study of aerospace and construction[END_REF]. This need for flexibility was confirmed by Kubicki [START_REF] Kubicki | Assister la coordination flexible de l'activité de construction de bâtiments, Une approche par les modèles pour la proposition d'outils de visualisation du contexte de coopération[END_REF] who finds that it occupies a central place especially during the construction phase where mutual adjustment is important. A very recent study showed empirically that there is significant difference between the as-planned activities and the actually-performed activities [START_REF] De Blois | Iterative Project Processes Within Temporary Multi-Organizations in Construction: The Self-, Eco-, Re-Organizing Projects[END_REF]. In reality, the work processes are usually not documented and they are "voluntarily" informal. This makes traceability of accurate information very difficult. Despite these differences, the two industries remain altogether comparable for many reasons. Patrick [START_REF] Patrick | Construction project planning and scheduling[END_REF] highlights the fact that both try to manufacture a product using appropriate resources (materials, equipment, labor). The product is manufactured using a specific process, and its implementation takes place on a particular site. The quality/cost ratio is very important and there is a need for optimization.

Related works

Based on an empirical research, Jupp [START_REF] Jupp | Incomplete BIM Implementation : Exploring Challenges and the Role of Product Lifecycle Management Functions[END_REF] studied the consequences of incomplete implementation of BIM as is currently the case in most applications of the methodology. The study identifies three types of problems: process-based issues, technology-based issues and policy-based issues. The study then identifies the basic features of PLM in order to demonstrate that there are a range of established solutions that cover a large part of these problems. The results show that PLM can actually be an opportunity to expand existing applications of BIM. However the study also suggests that the transverse application of a BIM-PLM solution that is based on discrete manufacturing processes might lead to other types of problems due to the complexity of interfaces observed in construction projects. Indeed, the problems observed in the use of BIM in construction projects are often specific to the client and the project's principal requirements, while the PLM approach is based on generic features. Moreover, the complexity and the large uncertainties of the collaborative environment in the construction industry suggest great caution. In the same period, Aram & Eastman [START_REF] Aram | Integration of PLM solutions and BIM systems for the AEC industry[END_REF] proposed a discussion on the improvement of BIM with PLM functionalities. They noted that it should be necessary to modify and adapt many aspects of current PLM technology before being able to apply it in construction due to the major differences between the construction and the PLM's traditional target industries [START_REF] Aram | Integration of PLM solutions and BIM systems for the AEC industry[END_REF]. Jupp and Nepal [START_REF] Jupp | BIM and PLM : Comparing and Learning from Changes to Professional Practice Across Sectors[END_REF] explored how BIM and PLM have impacted the professional practices in construction and manufacturing industries. For each industry, they explored the way BIM or PLM change the working practices through the new activities they come with, the new responsibilities and roles, the competencies needed and the relations in the supply chain. The study highlighted the unique characteristics of each industry and PLM and BIM contrasts. They concluded that the level of BIM maturity across the construction industry is improving, increasing the possibility to reach a "common endpoint with manufacturing industries". In the same spirit, Holzer [START_REF] Holzer | Fostering the Link from PLM to ERP via BIM The AEC Industry in Transition[END_REF] noted that the full potential of the BIM approach has reached a maturity at a level that is possible to consider its integration, through the definition of BOM, with PLM systems, and moreover with the production line. According to this research, the efforts undertaken in the past to link the manufacturing data with construction information in the frame of ERP systems have failed due to the use of 2D CAD that is not best suited for this purpose. With BIM, it is now possible to have the necessary information-centric project delivery approach. So it can be easier to integrate construction processes with product information.

In complement to these researches, many technology-centered works attempted to merge BIM and PLM capabilities into a single technological environment [START_REF] Reefman | A Controlled Integral Product Model(IPM®) in Building and Construction[END_REF][START_REF] Lebègue | Lascom AEC BIM Edition, un couplage BIM IFC et PLM[END_REF][START_REF] Popov | Conference on Digital Construction[END_REF]. If the effectiveness of these solutions remains to be seen, they have the merit of showing that a sum of features cannot be the solution to issues unresolved in the adoption of PLM, nor of BIM.

3

Product Structure: the missing link in the BIM approach?

What is it?

Jansen-Vullers et al. [START_REF] Jansen-Vullers | Managing traceability information in manufacture[END_REF] defined the Bill of Materials as "a list of components required for the production of a parent item". In the form of a network the BOM "goes-into relationships", and usually stores the number and type of relationships between component units necessary for the parent unit [START_REF] Schuh | Process oriented framework to support PLM implementation[END_REF]. The structure of a BOM differs according to the production environment it is demanded by. Maull et al. [START_REF] Maull | The role of the bill-of-materials as a CAD/CAPM interface and the key importance of engineering change control[END_REF] distinguish five different production environments (demanding different BOM structures) including make-to-stock, assemble-to-order, make-to-order, engineer-toorder, and selling capacity. In a make-to-stock environment, a BOM is prepared for each product and a percentage BOM regroups similar final products. In an assembleto-order environment, modular BOMs are used because it is impractical to define a BOM for each product due to the large variety of products. In make-to-order environments, four different BOM types are used: a planning BOM for forecasting purposes (relationships between product families and components), a standard BOM (semi-finished products released from engineering), a reference BOM (kinds of the product), and an order BOM (used in the case of particular orders from customers). In engineer-to-order environments, because customers change their requirements throughout the life of the product, the BOM is developed gradually. BOMs usually begin in the design-engineering department but are found in the inventory control, procurement, shipping, marketing, manufacturing, field services, and even in the accounting department [START_REF] Saaksvuori | Product lifecycle management[END_REF].

There should be no confusion with the Product Structure even if in colloquial language, a BOM is often referred to as a Product Structure [START_REF] Saaksvuori | Product lifecycle management[END_REF]. According to Brière-Côté et al. [START_REF] Brière-Côté | Adaptive generic product structure modelling for design reuse in engineer-to-order products[END_REF], these two concepts are different despite their similarity. As a singlelevel part list, a BOM, is considered as a simple filtered Product Structure snapshot at some point during the life of the product development [START_REF] Pinquié | An illustrated glossary of ambiguous PLM terms used in discrete manufacturing[END_REF], and is not identical to a Product Structure [START_REF] Saaksvuori | Product lifecycle management[END_REF], which is defined as "an organized hierarchical collection of technical objects that are linked via 'part-of' relationships" [START_REF] Pinquié | An illustrated glossary of ambiguous PLM terms used in discrete manufacturing[END_REF]. The Product Structure then "describes hierarchically, using items, how a product can be generated from assemblies, sub-assemblies, and components" [START_REF] Saaksvuori | Product lifecycle management[END_REF]. Resulting from a logical breakdown technique with particular concerns, the Product Structure stores not only technical objects but also associated product data and customized product families in a dynamic way and according to different views (e.g., a structural view, filtered view, cognitive view) [START_REF] Pinquié | An illustrated glossary of ambiguous PLM terms used in discrete manufacturing[END_REF]. Figure 1 illustrates a structured view of as-specified, asdesigned and as-planned technical objects, product data and customized product families. Eynard et al. [START_REF] Eynard | UML based specifications of PDM product structure and workflow[END_REF] noted that all instantiated data are managed and stored in the Product Structure.

In PLM tools, these data are classified using metadata and appropriate links are established between the parts and related files through metadata configurable links [START_REF] Eynard | UML based specifications of PDM product structure and workflow[END_REF]. In practice the Product Structure can encompass various data types. Trappey et al. [START_REF] Trappey | An object-oriented bill of materials system for dynamic product management[END_REF] identified at least 10 major data types and functions including product definition, service parts support, material purchase planning, assembly sequence, order entry facility, resource analysis, pricing, cost analysis, manufacturing instruction, and engineering change control. For product design and manufacturing management purposes, PLM systems require, in addition to the product structure manager, a workflow engine which "according to the product structure, sends the right available data at the right time to the right user" [START_REF] Eynard | UML based specifications of PDM product structure and workflow[END_REF]. This constitutes an important aspect in optimizing the information flow in the PLM approach.

The need to explicit a Product Structure concept in the BIM approach

To ensure that the appropriate information is contained within the model, current BIM practices suggest identifying the function of the model and specify the related information requirements prior to implementation so as to develop the model accordingly. Kreider and Messner defined a BIM use as "a method or strategy of applying Building Information Modeling during a facility's lifecycle to achieve one or more specific objectives" [START_REF] Kreider | The Uses of BIM: Classifying and Selecting BIM Uses[END_REF]. Twenty-five BIM uses have been identified (e.g., 3D coordination, design review, cost estimation, phase planning, site analysis, mechanical analysis, etc.) with various use frequencies and different impacts in the industry [START_REF] Kreider | Determining the frequency and impact of applying BIM for different purposes on building projects[END_REF]. To define information requirements, a Model Element Table (MET) is used. The MET structure in construction is quite similar to the BOM in manufacture.

It summarizes the list of the model elements but also "indicates the LOD [Level of Development] to which each Model Element Author (MEA) is required to develop the content of the Model Element at the conclusion of each phase of the Project" [START_REF]E202-2008 Building Information Modeling protocol exhibit[END_REF] (See Fig. 2). Thus despite their similarity in terms of structure, unlike the BOM which is extracted from the model, the MET serves as base for the construction of the model. The BIM model is then developed according to the requirements defined in the MET. In current BIM tools, it is possible to generate a model elements hierarchical list and many concepts are used that could be similar to Product Structure, including the Product Breakdown Structure (PBS) and the Model Element Breakdown (MEB). [START_REF]E202-2008 Building Information Modeling protocol exhibit[END_REF] According to Gijezen et al. [START_REF] Gijezen | Organizing 3D Building Information Models With the Help of Work Breakdown Structures To Improve the Clash Detection Process[END_REF], the PBS "divides the final object in physical systems, components, and elements". The PBS concept is well known in the BIM approach. For example, it plays an important role in 4D simulation where the model creation consists of linking it with the Work Breakdown Structure (WBS) from the schedule [START_REF] Zhou | An interactive approach to collaborative 4D construction planning[END_REF]. Moreover Gijezen et al. [START_REF] Gijezen | Organizing 3D Building Information Models With the Help of Work Breakdown Structures To Improve the Clash Detection Process[END_REF] showed how the PBS could be used to improve the clash detection process in 3D coordination. In BIM software, it is also known as the Model Element Breakdown (MEB), as described by Saluja [START_REF] Saluja | A Process Mapping Procedure for Planning Building Information Modeling (BIM): Execution on a Building Construction Project[END_REF], that can be used to create information exchange worksheets. Figure 3 shows an example of a model elements hierarchy list as presented in Autodesk Revit.

Fig. 2. Excerpt of a Model Element Table (MET)

Some BIM software dedicated plugins (BOM to Excel1, SysQue BOM2, etc.) are emerging, suggesting that the way the MEB is managed in BIM tools seems insufficient to cover practitioners' needs. For instance, if it is possible to extract detailed "materials and quantities takeoffs" from current BIM software such as Revit, it seems not possible to manage the breakdown structure "according to different views" [START_REF] Schuh | Process oriented framework to support PLM implementation[END_REF] in order to allow the different production environments to make different demands on the structure" [START_REF] Maull | The role of the bill-of-materials as a CAD/CAPM interface and the key importance of engineering change control[END_REF]. For example, the construction schedule and cost estimate remain as two separate autonomous files in typical approaches to BIM [START_REF] Hardin | BIM and Construction Management: Proven Tools, Methods, and Workflows[END_REF].

Fig. 3. Example list of model element hierarchies in Revit

The approach taken by Trimble's Vico software does provide the ability for the building information model to become somewhat more integrated relative to the notion of a Product Structure in manufacturing. It achieves this by removing the need to link disparate schedules generated by what are typically separate and independent 3D, 4D and 5D models [START_REF] Hardin | BIM and Construction Management: Proven Tools, Methods, and Workflows[END_REF]. The integrated Vico model thus enables the definition of how long it will take to install the amount of materials being derived from the model. This approach reflects a manufacturing mindset as the unified schedule becomes a product of what and how much will be built and represents this in a particular order that has construction logic. Indeed, BIM use encompasses many processes (3D, 4D and 5D) that call for different views of the product structure. Moreover, in the framework of a specific BIM use (e.g. 3D coordination, cost estimation, phase planning, etc.) many sub-processes corresponding to different views are necessary throughout the project lifecycle. For example, based on the study conducted by Monteiro and Martins [START_REF] Monteiro | A survey on modeling guidelines for quantity takeofforiented BIM-based design[END_REF], Figure 4 proposes the different views necessary for the various processes of Cost Estimation throughout the project lifecycle. The purposes of such views are also indicated. In current BIM approach, it is possible to make correspondences between some of the representations used (Model Element Table, Quantity Take-Off, Design Brief, Contract Program, etc.) and the BOM and the Product Structure in discrete manufacturing. However, and unlike what is seen in PLM, they are all disconnected and aren't linked with associated product data and customized product families. Then, the main gap in expliciting a Product Structure in the BIM tools concerns its integration within the model. That is to provide links and representations of this data for different BIM dimensions (3D, 4D and 5D) and BIM uses purposes. Moreover the model validation (even at the handover stages) is rarely undertaken and the quality of data beyond geometry is largely poor (even naming and layer conventions are often very poor throughout design stages) as the quality of data is not audited systematically and progressively. The discipline-based modelling process (architectural, structural, mechanical, electrical, hydraulic, fire, and site models) requires frequent federation processes that go beyond the typical design coordination reviews and extend into model quality audits. Future works will deepen the comparison between how PLM and BIM tools allow product structure manipulation. The aim is to provide a factual and precise comparative approach which could eventually, according to the findings, lead to a cross pollination from the PLM approach to the BIM approach. Moreover it will be important to study how the IFC information model can efficiently be replaced at the heart of BIM practices in order to play the critical structuring backbone role.

Fig. 1 .

 1 Fig. 1. Example of a multi-view product structure (Source [10])

Fig. 4 .

 4 Fig. 4. Possible evolution of the product structure views for cost estimation BIM use

http://www.hingepoint.com/products/bom-revit-app

http://sysque.com/sysque-modules/sysque-bom


B Damayanthi Jesudas

B Gurumoorthy

Establishing semantic equivalences in aircraft ontology to enable semantic interoperability

Keywords: Product Lifecycle Management (PLM), Semantic interoperability, Aircraft ontology, A320 aircraft, POS tagging

In the use of ontologies to address semantic interoperability problems in PLM, either a lexicon or a list of synonyms of the terms in the domain are used to establish semantic mapping. This paper proposes a methodology to establish semantic equivalence of concepts with those in a given ontology based on some textual information about the new concept. This approach involves the classification of the information input into a structure form using natural language processing tools. Aircraft ontology available in literature, is taken as reference domain ontology for this purpose. Text input about the concept is parsed and the parts of speech (POS) tags are analyzed to obtain a structured representation. The structure is then compared to that in the domain ontology to decide if the input concept is part of the reference ontology and if so the concept it is equivalent to is identified. Results from an implementation of this procedure are shown and further work discussed.

Introduction

Product Lifecycle Management (PLM) "is a systematic, controlled method for managing and developing industrially manufactured products and related information. The core of PLM (product lifecycle management) is the creation, preservation and storage of data relating to the company's products and activities" [START_REF] Saaksvuori | Product Lifecycle Management[END_REF]. "A major requirement for efficient PLM is the traceability of the product which is the acquirement of information along the product's lifecycle about the product" [START_REF] Matsokis | An Ontology-Based Approach for Product Lifecycle Management[END_REF]. The information and knowledge resources used during the product development process emerge from different application domains like design, manufacturing, support, production, services, so on and these are developed by experts from the respective domains. Multiple source vendors, contract manufacturers, distribution, and sales partners also add importance to the product by using existing information [START_REF] Dutta | An Introduction to Product Lifecycle Management (PLM)[END_REF]. "Especially knowledge that is produced from different expert domains is not comprehensible for everybody, though the access to knowledge from these expert domains by others from outside the expert domain is inevitable" [START_REF] Felic | Process-Oriented Semantic Knowledge Management in Product Lifecycle Management[END_REF]. One common problem in acquiring knowledge is the use of terms that are particular to the group or the expert responsible for the knowledge. This lack of shared vocabulary across users is referred to as the semantic interoperability problem [START_REF] Jesudas | Natural Language Processing (NLP) Driven Ontology Modeling to Address Semantic Interoperability in Information Retrieval[END_REF].

Semantic interoperability refers to "the ability of different organizations to understand the exchanged data in a similar way" [START_REF] Sharma | Towards XML Interoperability[END_REF]. Semantic interoperability problem arises due to semantic heterogeneity, which occurs whenever two contexts do not share the same interpretation of information [START_REF] Mao | Ontology Mapping: Towards Semantic Interoperability in Distributed and Heterogeneous Environments[END_REF]. The causes of semantic heterogeneity [START_REF] Freitas | Coping with data variety in the big data era: The semantic computing approach[END_REF] are, 1. Decentralized content generation. Geographically and functionally distributed teams are involved in the development process [START_REF] Boeing | [END_REF][START_REF]Boeing homepage[END_REF], and the teams use different ways of representing product data. 2. Multiple perspectives (conceptualizations) of the reality. Each individual has different interpretations as they have different backgrounds and expertise. For example, 'afterburner' is also termed as 'reheater' in an aircraft and 'reheater' is also termed as 'superheater' in Energy/Power plants. [START_REF] Dutta | An Introduction to Product Lifecycle Management (PLM)[END_REF]. Ambiguity, inconsistency and vagueness. This happens because of the usage of different terminologies. For example, the part combustion chamber may appear in documents as blast chamber, combustion chamber, firing chamber or combustor [START_REF] Gupta | Shape-Function-Relationship (SFR) Framework for Semantic Interoperability of Product Model[END_REF]. The above mentioned are the main causes of semantic heterogeneity which leads to semantic interoperability problems. Thus, it is evident that different information resources/applications may use different terminologies for the same entity. Even when applications use the same terminology, they often associate different semantics with the terms or represent different parts/systems [START_REF] Gupta | Shape-Function-Relationship (SFR) Framework for Semantic Interoperability of Product Model[END_REF]. Also, "semantics associated with a term in different applications are different" [START_REF] Ciocoiu | Ontologies for Integrating Engineering Applications[END_REF]. Thus, it is difficult to achieve semantic interoperability by merely sharing terminologies, as semantic interoperability requires explicit definition of specifying the semantics of the terminologies. This lack of explicit definition of the semantics hinders the information retrieval process.

Ontologies are used to address semantic interoperability as these provide better understanding and processing of information in the form of concepts and relations associated between them. These are used for knowledge representation, as they specify the knowledge in a well-defined and unambiguous manner. The useful features of ontologies are, to share common understanding of the information among different application systems and to reuse the existing domain knowledge. "Ontologies support interoperability by enabling the specification of semanticspreserving mappings between the terminologies of different applications. "If 'term' in Application A and 'term' in Application B have logically equivalent definitions, then the mapping of these two terms preserves their semantics" [START_REF] Ciocoiu | Ontologies for Integrating Engineering Applications[END_REF].

This paper addresses the issue of mapping concepts to a given domain ontology. The result of mapping could be that the concept exists in the given ontology in which case semantic equivalence (important for addressing semantic interoperability) is established. The other two options are that the concept belongs to the ontology but is not present in it. In this case the ontology gets automatically populated. The last case is that the concept does not belong to the domain of the ontology. Traditionally the first case of establishing semantic equivalence has been achieved with keyword search with the aid of a lexicon or through the use of synonyms. These approaches do not solve the problem as lexicon may not be complete to provide meanings to all terminologies. For example, terminologies like 'combustion turbine' an equivalent of 'gas turbine', 'combustor' equivalent of 'combustion chamber' etc., are not found in WordNet. Therefore, it is proposed to give some text as information about the concept and from this unstructured data, structured information is obtained that can then be compared with the domain ontology. This aids to analyse, compare and process the information easily. Here, mapping is between the user's input and the domain ontology. For this purpose, example aircraft ontology [START_REF]Aircraft-Ontology[END_REF] is taken as reference ontology. The text given by the user as input to query the ontology is parsed using Stanford dependency parser [START_REF]Stanford dependency parser[END_REF] for assigning parts of speech tags to each word in the text as noun, verb, adjective, etc. This is analysed to construct a structured representation. Then semantic equivalence is established between user's input and ontology and semantic information from text is extracted.

2

State of the art

Ontologies are being used for a long time to support semantic interoperability as these define the domain knowledge explicitly. Ciocoiu et al [START_REF] Ciocoiu | Ontologies for Integrating Engineering Applications[END_REF] gave an overview of the use of ontologies to facilitate interoperability among manufacturing process applications. It is indicated that interoperability is affected by the context in which it is viewed, different representations of the same information, using different concepts and terminology for the same terms in different contexts to mean different things. In the manufacturing scenario, the problem is that each of the teams will be using different software applications to represent and reason about the products and processes from their particular perspectives.

Stoimenov et al [START_REF] Stoimenov | Semantic Interoperability Using Multiple Ontologies[END_REF] presented architecture for semantic heterogeneity of geographic information based on ontologies. A semantic mediator called GeoNis is provided as a solution, which is a framework for interoperability of GIS applications.

Bittner et al [START_REF] Bittner | Ontology and Semantic Interoperability[END_REF] explained the use of ontologies in facilitating interoperability between software systems used in computer aided design, architecture engineering and construction, and geographic information processing. Orgun et al [START_REF] Orgun | Approaches for Semantic Interoperability between Domain Ontologies[END_REF] discussed the various approaches to providing interoperability to multiple heterogeneous ontologies, which is still primarily a semi-automated process.

Zhan et al [START_REF] Zhan | Ontology-Based Semantic Description Model for Discovery and Retrieval of Geo-Spatial Information[END_REF] proposed an ontology-based method for discovery and retrieval of geographic information to overcome shortage of approaches based on keywords. A top level ontology is used to find relevant concepts. Besides, implementation for this should be developed. Felic et al [START_REF] Felic | Process-Oriented Semantic Knowledge Management in Product Lifecycle Management[END_REF] presented an approach for process-oriented semantic management that allows management and sharing of knowledge across the product lifecycle. Knowledge links are introduced between knowledge base extensions for knowledge to be understandable between experts, which are a way to achieve semantic interoperability. It is from the perspective of process chain rather than product model. Creating and establishing knowledge links for collaborators of different domains is yet to be worked on.

From the above literature it is evident that a lot of study has been done on the use of ontologies in addressing semantic interoperability. Also, proposals are made in using ontologies as semantic mediators to address semantic interoperability, implementation of which however has not been reported. Ontologies are also employed in document retrieval using semantic similarity measures. In most cases, a top level/local ontology is used for aiding the semantics. Semantic interoperability between ontologies is addresses through ontology mappings. It is proposed that two data types are semantically equivalent if they have the same defining attributes and operations and the same set of constraints on those attributes and operations [START_REF] Orgun | Approaches for Semantic Interoperability between Domain Ontologies[END_REF]. Unlike the approaches discussed above, this work classifies a concept with respect to a given ontology without using similarity measures.

Methodology

In the use of ontologies to address semantic interoperability the difficuty is, the use of a lexicon corresponding to the domain or a list of synoyms for each concept in the ontology, that assist in mapping for similar meaning terms. Both approaches, development of lexicon specific to a domain or identification of synonyms for each concept are very cumbersome and completeness of either the lexicon or the list of synonyms can never be guaranteed. An alternate approach is proposed in this paper, which uses NLP techniques for classifying a concept with respect to a given ontology.

It is proposed to take some textual information about the concept as input. The user input text is parsed and the parts of speech (POS) tags are analyzed to obtain a structured representation that is then compared with the domain ontology. There are three outcomes of the classification. They are:

1. The concept is similar to a concept in the ontology. In this case semantically equivalence between the two is recorded. 2. The structured form in which the input concept is captured is similar to the structure for concepts in the ontology but the concept is not similar to any concept in the ontology. In this case the concept is added as a new concept to the ontology. 3. The structured form in which the input concept is captured is different from the structure for concepts in the ontology. The methodology developed is shown in figure 1. The input text is parsed for (POS) Parts-Of-Speech tagging using available natural language processing tools.

From the POS tagged texts, determiners, prepositions, conjunctions, verb forms etc., are filtered. As the ontology has parts and parameters, verb forms are also filtered. POS tags that falls under different types of nouns, adjectives that describe the nouns and cardinal numbers are considered. The words in the text that are tagged are then analyzed to identify a structured representation for the concept described by the text. Firstly, the extracted information is searched for similar concepts in the ontology. As some terms appear in more than one concept structure, all terms that appear in a particular concept structure are considered for establishing equivalence. That is, aircraft has engine, fuselage etc., and engine is connected to fin, fuselage and wing. So, fuselage appears both in aircraft attributes as well in engine attributes with a different relation. Primarily in this work all terms appearing in a single concept structure are considered. And the above stated existence of terms in multiple concept structures is another issue which is to be addressed. The first condition is, if the concept is present in the ontology then the attributes corresponding to the concept are checked and if they are same, equivalence is established. The second condition is, if concept is not present in the ontology, then also the attributes are checked and if the attributes are similar for a particular concept, then equivalence is established between them

The flow chart for algorithm for mapping the obtained structure of information and ontology is shown in figure 2.

Preliminary results and discussion

The proposed methodology has been implemented. The domain ontology used is, an aircraft ontology available in the public domain [START_REF]Aircraft-Ontology[END_REF]. This is built using NeOn Methodology, with four first level basic classes, "Aircraft", "AircraftSubComponent", "AircraftAspect", "AircraftParameter". Figure 3 shows part of the aircraft ontology. The procedure for obtaining a structure for the input concept and comparison with the domain ontology has been implemented in C# on a Windows platform. Input text that describe concepts is taken from information available on the A320 aircraft online [19,20,21]. Some inputs are given generally with A320 data to test the system. First, the input text was chosen such that they contained terminologies related to aircraft domain like 'aircraft', 'engine', 'fin', 'thrust', 'cabin'. These were used as seed words which aid in obtaining the structure. Type of inputs with examples for the three subcases of the first case, semantically equivalent information is provided.

Fig. 4. Extracted representation for a given input

Next, input text that did not contain any keywords from the reference ontology was considered. For example, in the text 'CFM56 has a bypass ratio 6:1, generating 150 KN thrust', the term 'engine' is not stated for 'CFM56'. The POS tagged for this text is, 'CFM56/NNP has/VBZ a/DT bypass/NN ratio/NN 6:1/CD ,/, generating/VBG 150/CD kN/NN thrust/NN'./. The extracted representation for this is shown in figure 5. Here based on comparison with the domain ontology, the CFM56 is labeled as an instance of concept Engine. Figure 8 shows the other case, wherein the top level concept doesn't match but the entire attributes match. This information is considered similar and is indicated to the user. In this case, it is evident that user has given similar word in different form and the similarity in the values of the attributes help in resolving this problem. As there are numerical values describing aircraft parameters, mapping is based on their equivalence. Also, the extracted information contains terms whereas the information in the ontology contains 'relation' plus the term like 'has' engine. While mapping the relations are replaced with only terminologies. Also, the text given as input should be grammatically correct, as it is parsed for parts of speech tagging with a standard parser. Grammatical errors may cause inappropriate tagging by the parser because of the misplacement of parts of speech which leads to inappropriate mapping. Therefore, it is advisable to give well-formed text as input, which can be interpreted correctly.

Conclusions

The use of ontologies in supporting semantic interoperability has problems owing to the problem of different semantics for terminologies. Even, the use of a lexicon or a list of terms doesn't assist in mapping as they don't provide meanings to all terminologies. An alternate approach is proposed in this paper for classifying a concept with respect to a given ontology. It is proposed to take some textual information about the concept as input. The user's input is parsed using Stanford online dependency parser and the parts of speech (POS) tagged text is analysed for extracting information similar to that of the reference ontology. Available aircraft ontology is taken as reference ontology. Text on 'Airbus A320' aircraft is used as input. Three issues that occur while mapping the extracted information from the input text to the reference ontology are identified. They are semantically equivalent information, information that doesn't exist in the ontology and information that doesn't belong to the ontology. In this work, the first case has been addressed. The second and third cases are to be addressed and work is ongoing for the same.

Fig. 1 .

 1 Fig. 1. Methodology developed for addressing semantic interoperability for a given domain ontology.

Fig. 2 .

 2 Fig. 2. Flow chart for the algorithm for mapping

Fig. 3 .

 3 Fig. 3. Example aircraft ontology [13]. For an example text, 'A320 aircraft is powered by CFM56 engine with a single-aisle fuselage', the POS tagged text is 'A320/NNP aircraft/NN is/VBZ powered/VBN by/IN CFM56/CD engine/NN with/IN a/DT single-aisle/JJ fuselage/NN'. /. From the tagged text, noun (NN), proper noun (NNP), cardinal number (CD), adjective (JJ) are considered and verb forms (VBZ, VBN), preposition (IN), determiner (DT) are filtered, which ends up in useful information. From the above tagged text, the content that is of interest is (A320/NNP aircraft/NN), (CFM56/CD engine/NN) and (singleaisle/JJ fuselage/NN). It is evident that the terminologies 'aircraft', 'engine' and 'fuselage' help in information extraction from input text. The aircraft, noun (NN) is identified by the proper noun (NNP), A320. The 'engine' by cardinal number (CD) 'CFM56' and 'fuselage' by the adjective (JJ) 'single-aisle'. The information is extracted using C# and the extracted representation for this is shown in figure 4.

Fig. 5 .Figure 6

 56 Fig. 5. Extracted representation for a given input

Fig. 6 .Fig. 7 .

 67 Fig. 6. Part of domain ontology with A320 aircraft data

Fig. 8 .

 8 Fig. 8. Establishing semantic equivalence for second case of user input and domain ontology


Shuichi Fukuda
email: shufukuda@gmail.com

Diverse Scope Coordination in Design Management

Keywords: Design Management, Diverse Members, Scope Coordination, Lazy Evaluation, Satisficing

This is a position paper to share our experience how Lazy Evaluation approach in AI is useful for team decision making with members with diverse knowledge and experience from many different fields. For example, Industrie 4.0 is a German proposal of a new framework to team up SMEs in order to cope with the frequent and extensive changes today. Until recently, we could fight with 11 best players, but today we need to fight with best 11. If the situations do not change then best players in each position can form a good team and win. But just as soccer demonstrates, such fixed formation does not work anymore. All players have to changes their roles flexibly and adaptively in response to the change. But unlike soccer where a goal is very clear, engineering teams need to determine a common goal first of all. But the greatest difficulty is members have different knowledge and experience, especially so when members come from different fields. Thus, how we can coordinate their diverse scopes and come up with an identical scope which satisfy all members and which leads to fruitful collaboration is a big challenge. This paper describes how Lazy Evaluation is effective for negotiation among members with diverse knowledge and experience and for coming up with an identical goal toward fruitful collaboration.

Design Scope Coordination

Although there are many papers on design management, there are very few on design scope coordination. For example, in project management, work scope and product scope are taken up. Work scope is about how to run the project and product scope is what product to produce. Although there are detailed discussion about how these scopes should be executed, how the scope should be framed is not well discussed.

But most important issue is how we frame our scope. This is essentially the same problem as has been discussed in AI research field as the Frame Problem. "The Frame Problem is the challenge of representing the effects of action in logic without having to represent explicitly a large number of intuitively obvious non-effects in AI, but it can be regarded as a more general problem of How do we account for apparent ability to make decisions on the basis only of what is relevant to an ongoing situation without having explicitly to consider all that is not relevant?" [1].

Design scope coordination is a typical example of the Frame Problem. With the progress of engineering and with the quickly spreading diversification and personalization, engineering problems cannot be solved without involvement of many experts from many different fields. Thus, what becomes important, but is not taken up for discussion is how we fill up their differences and reach a final goal acceptable to all members involved.

In project management, PMBOK [START_REF]A Guide to the Project Management Body of Knowledge (PMBOK Guide), Fourth Edition[END_REF] plays an important role, but it does not describe these diverse "common senses". And when they discuss parameters such as functional, design, etc., the scope is already fixed. The most important issue how we can frame our strategic scope is not discussed enough.

Pre-stage of Project Management becomes, however, increasingly important especially in PLM, because PLM requires very broad knowledge and experience across many different fields as it requires not only knowledge about design, manufacture, but also operation, i.e., it covers a very broad context.

Increasing Importance of Decision Making

This issue boils down to the problem of how we make decisions. Most of the research on project management and related works are dealing with the problems after a decision is made. But decision making in cases which relate to a wide variety of fields and broad contexts must be accepted by all members concerned who come from diverse fields. Of course, all members do not share the same knowledge and experience. They have their own views and even the same knowledge is used differently from field to field. So scope coordination for decision making is increasing its importance. Then, how can we overcome such difficulties and make appropriate decisions in an age of increasing diversification and personalization?

Lazy Evaluation

If we look back and consider how we make decisions, we soon realize that we do not make decisions all at once. The more difficult the problem is, the more time or stages we take to come up with the final decision. In other words, the larger problem space is, the more time or stages we need to fix our scope. In fact, when the problem space is very large, we cannot make decisions rationally due to such problems of combinatorial explosion, computational complexity, etc., and we have to look for satisficing as Herbert Simon pointed out [START_REF] Simon | Rational Choice and the Structure of the Environment[END_REF]. Interestingly enough, another economist John Keynes pointed out economic agents make decisions rationally for short term expectations, but when it comes to long term, they rely on their confidence [START_REF] Keynes | The General Theory of Employment, Interest and Money[END_REF]. Thus, how we can let members feel satisfied and how they can agree upon the final goal with confidence is most crucial in team decision making with members from diverse fields.

The difficulty of decision making in a problem relating to diverse fields is scopes are different from member to member so that they are not identical. Of course, members do their best to increase the overlapping common area, but no matter how much efforts they may pay to increase it, there still remains areas which are not covered by all members (Fig. 1). The most important thing is how we can find a common area, or intersection if we use set theory term, among all members involved with satisfaction and with confidence.

If this common area or intersection can be found easily, then there is no problem. There might be slight differences, for example, regarding the definition of parameters, etc., among diverse members, but such small discrepancies can be easily solved if detailed discussion is made. Although it may take time, it is essentially not different from one time decision making.

Such one time decision making can be done when the problem is not so much complicated or large. Essentially rational approaches can be applied and the problem will be solved.

But when the problem relates to a wide variety of fields, such rational approaches cannot be applied and we have to introduce multi-stage decision making and repeat the process of convincing members that the line of reasoning is adequate. If the problem is small and simple, members can understand the line of reasoning easily and although it may take time, we can reach the common goal.

What makes multi-stage decision making difficult which relates to a wide variety of fields is there are issues which some of the members cannot understand at some stages, because the lines of reasoning vary from field to field. These members have to wait until the reasoning comes to the stage when they understand how the problem is going to be solved. When the reasoning comes to the stage they can understand, they can express their views and opinions and ask for revision, etc., based on their own knowledge and experience.

Then, how can we deal with this problem of multi-stage decision making in a situation where the problem is related to many diverse fields?

Lazy evaluation is the answer to this problem. We postpone our decision until later stage when we can make a decision. When the discussion comes to the stage you understand, then you can express your opinion and demand, if necessary, to change the conclusion which is obtained through the previous stages or give advice to Fig. 1. Intersection improve the conclusion, based on your own field. And in the worst case, when you cannot understand the whole process, the constraints must be relaxed and repeat such trial and error approach from the first until every member understands the line of reasoning and can agree upon the final decision. This way, the final conclusion is reached by unanimous agreement of all members (Fig. 2).

Fig.2. Illustrative Explanation of Lazy Evaluation Used for Team Decision Making with Diverse Members with Diverse Backgrounds

In other words, the common area or interaction varies with how the discussion goes on. In rational approaches, the common area or interaction does not change. But in the case of multi-stage decision making with diverse members, we have to note that the common area or the common goal varies from case to case. If some members are taking the initiative, the goal would reflect their knowledge and experience more.

Thus multi-stage decision making in a team with diverse members from diverse backgrounds is nothing other than negotiation. We have to remember negotiation comes first before collaboration. In other words, when the problem cannot be solved rationally, we have to resort to negotiation among members from diverse fields. But how we can negotiate to arrive at the common goal by coordinating the scope is not well discussed.

Thus, this final solution may not be the optimum in the strict sense, but it is a satisficing solution. Herbert Simon pointed out that if the number of variables is too many, we cannot find the optimum mathematically due to such a problem of computational complexity, etc. so that we have to satisfy ourselves with the notion of satisficing. The term Satisficing is a combination of satisfy and suffice and it means satisfy enough [START_REF] Simon | Rational Choice and the Structure of the Environment[END_REF].

To understand Simon's idea better, let us consider global optimization. Although the optimum can be obtained rationally if it is local optimization, we cannot obtain such a mathematically accurate optimum, when it comes to global optimization. Take Simulated Annealing for example. We repeat searching many times to reach an optimum, but it is nothing other than to regard the highest (or the lowest) one as the optimum, because we have repeated so many searches. There is no mathematical guarantee that it is the highest. We only believe or would like to believe it is the highest because we tried so many times. Thus, global optimization just makes us emotionally satisfied. It is nothing other than satisficing.

Lazy Evaluation is a method originally proposed in programming language theory. The primary reason why it was invented was to reduce the running time and to increase performance, because it avoids repeated evaluation. But the point which this paper emphasizes is very much different. This approach works very well for negotiation for decision making for a team with members of many different backgrounds Lazy evaluation permits very flexible and adaptable line of reasoning. Thus, it provides us with a tool to reach emotionally satisfied conclusions. Again, this approach is useful to convince every member of a team with different backgrounds that the line of reasoning is adequate and their knowledge and experience are duly reflected in the final conclusion so that they accept it with enough satisfaction. But we have to remember that nobody sees or understands the whole picture. So this approach is completely different from an approach to construct a meta-level knowledge and experience representation for decision making.

System to Produce Welding Procedure Specification

This chapter describes our past experience of producing a legal document called Welding Procedure Specification. We formed a consortium with about 12 industry members with the leadership of Welding Research Institute, Osaka University. The members include Nippon Steel, Mitsubishi Heavy Industry, Kawasaki Heavy Industry, Kobe Steel, Toyo Engineering, Shimizu Corporation, IHI, Shin-Meiwa, etc. Their field are heavy industries, shipbuilding, airplanes, automobiles, etc. As is well known, welding requires a wide variety of knowledge and experience such as material, electrical, mechanical, etc. and its application areas are also diverse such as civil, architectural, transportations, etc. But to carry out welding, there are legal rules which require us to specify how welding will be done.

These rules about welding is different from country to country. American Welding Society introduce such concepts as welding of box-type components and set down legal rules to apply to welding of boxes. Such boxes are used for containers, and used as components in ships, truck, etc. So such legal rules based on structural modules can be applied in a straightforward manner.

But in Japan, welding rules are set based upon the concept of weldline. Thus, we have to work out rules for each case because weldments vary widely from structure to structure and from product to product. Japanese rules pay attention only to welding, and they do not take into consideration such constraints as structures, etc. So industries have to form Welding Procedure Specification (WPS) to demonstrate that the welding they are going to perform follows the legal rules of Japanese welding rules and in addition that their way is reasonable enough from other respects.

This process of producing WPS, however, used to be a world of the rule of thumb. When industries worked in their own fields and their products and services were not so much diversified as they are today, the rule of thumb worked well.

But when shipping business was very bad in Japan, their welding department attempted to secure employment of their welders by receiving orders from other industries such as construction, etc. But they could not. This is because they relied too much on their rule of thumb and this rule does not work in other industries. Such failures prompted them to introduce a more rational and versatile approach.

To respond to their request, we developed an AI-based system which adopts Lazy Evaluation and Constraint Management [START_REF] Fukuda | Development of an Expert System for Weld Design Support[END_REF], [START_REF]This system was awarded[END_REF], [START_REF]The development of this system led to Lifetime Achievement Award[END_REF].

The system itself is not special in the sense of programming. But we paid out chief attention to how members will feel satisfied with the flow of reasoning. We constructed a knowledge and experience representation, by noting that members with adjacent area can communicate easily. By putting together such member to member pieces together, we come up with the final representation as shown in Fig. 3. Then, we adopted Lazy Evaluation technique to utilize this representation.

Fig. 3. Knowledge and Experience Representation for Welding Procedure Specification

This system met industry needs and it was adopted by many major Japanese industries. Their fields include Shipbuilding, Building, Construction, Machinery, etc., although each industry customized it to meet their needs. But the basic structure is the same. Their system introduced Lazy Evaluation. They are being used today, although they are updated of course.

This demonstrates how a system was needed and desired by industries. But at that time, industries were working in their own fields and there were few problems which required such a wide variety of knowledge and experience as in welding, so they could manage to solve the difficulty by accumulating experience.

But with quickly increasing diversification, the walls between industries are falling quickly apart and industries have to collaborate across many different areas. Thus, the need for more adaptable and flexible decision making tools are increasing rapidly.

Although the time we developed the system and the time Japanese industries started to use our system in welding was long time ago, before such diversification as we see today started, such needs were present in such a multi-expertise field as welding because it relates to many different fields so that collaborative decision making is called for.

The technique or programming of Lazy Evaluation can be easily found in AI literatures. What this paper emphasizes is that it can be used very effective for another application, i.e., negotiation before collaboration, although it was originally developed for programming efficiency.

The need for such a system for supporting decision making among members from widely different fields is quickly increasing, such as in Industrie 4.0 [START_REF]Securing the Future of German Manufacturing Industry Recommendations fr Implementing the Strategic Initiative INDUSTRIE 4.0[END_REF]. Although the system we developed was only for welding, the basic framework certainly works for other applications. As the number of variables are rapidly increasing and the backgrounds of participating members are quickly diversifying, such an approach to reach a strategic goal acceptable by all members is increasing its importance.

Summary

Increasing diversification and personalization call for collaboration among many members from many different fields. And the more diversified the requirements become, the more difficult it becomes to reach a common strategic goal acceptable to all members, because knowledge and experience are different from field to field and sometimes the same knowledge is used in a different way. Therefore, how we can coordinate their different scopes and come up with an identical scope is a great challenge and its solution is strongly called for. In other words, negotiation is needed for satisficing and convincing collaboration.

This paper points out by sharing our experience in welding that Lazy Evaluation, which is one of the AI techniques for computer processing efficiency, can be used in a very different way. It is very effective for negotiation to come up with an identical scope and to determine a common goal which is satisficing and convincing to all members.

As Industrie 4.0 indicates, our world is moving more toward best 11 from 11 best, as American football coach Knuth Rockne pointed out. It is no more the time if we assemble 11 best players, we can win. We have to consider how we can utilize the presently available resources to the maximum. Rockne demonstrated his belief by bringing University of Notre Dame from the bottom to the top. This can also be observed in soccer. Situations change so frequently and widely in today's soccer so that it becomes increasingly important how we can form a strategy flexibly and adaptively to the changes.

Another important point is as everything is speeding up these days, we do not have enough time to develop new technology to cope with the changing situations. Thus, what we need now is "Do what you can, with what you have, where you are" as Theodore Roosevelt said. We have to utilize the current resources to the maximum in order to achieve the goal. We have to form a well working team, but with the available members.

Lazy Evaluation can be used, in spite of its misleading name, to arrive at a common goal shared by all member quickly and to satisfice them all. Although it was originally a technique to facilitate processing speed on a computer, but what this paper emphasizes is that it can be used for facilitating negotiation before collaboration.