Romeo Bandinelli
email: romeo.bandinelli@unifi.it

Elisa D'avolio
email: elisa.davolio@unifi.it

Monica Rossi
email: monica.rossi@polimi.it

Sergio Terzi
email: sergio.terzi@unibg.it

Rinaldo Rinaldi
email: rinaldo.rinaldi@unifi.it

Assessing the role of knowledge management in the new product development process: an empirical study

Keywords: Knowledge Management (KM), New Product Development (NPD), New Product Development performance, New Product Development strategies

The actual competitive context is stressing the importance of knowledge management (KM) enhancing organizational performance through the creation, sharing and reuse of knowledge.

The purpose of the present study is to empirically explore the role of KM in the enhancement of new product development (NPD) process within a set of companies with at least a R&D department located in Italy. The authors formulated a conceptual research model, including the relation between KM, NPD performance and strategies, that has been validated through statistical analyses. The outcomes confirm the positive relation between KM maturity and NPD performance in the Electrical sector. Moreover, the identification of Critical Success Factors (CSFs), is influenced by KM techniques adopted, revealing the impact of KM on NPD strategies. Finally the achievement of good NPD performances, such as the ease to fill out reports and projects, appears related to appropriate NPD strategies.

Introduction

Nowadays knowledge management (KM) is becoming a strategic resource to be challenging in the present competitive context. The increasing amount of data and information to manage in different industries has led to the fact that leveraging knowledge in an organization can sustain its long term-term competitive advantage [START_REF] Bhatt | Knowledge management in organizations: examining the interaction between technologies, techniques, and people[END_REF].

Moreover, enterprises have to innovate their products in order to survive in this complex market, choosing appropriate new product development (NPD) strategies and measuring the performances. Reducing the time to develop new products and decreasing time-to-market constitute strategic business goals.

Knowledge management competencies are fundamental to innovation, enabling it to survive competitively and to grow [START_REF] Pitt | Knowledge management for new product Development[END_REF]. The potential benefits of systematic knowledge diffusion and recombination are now acknowledged to render NPD more effective and efficient.

The purpose of the present study is to empirically explore the role of KM in the enhancement of NPD process within a set of companies having at least a R&D department located in Italy.

The paper is structured as follows: in the first section a literature review describing the general features of KM and its relationship to NPD is presented. Then, the authors have focused on the methodology adopted, designing a research model and formulating several hypotheses that have been tested through an empirical research, based on questionnaires distributed to different companies. In the following section, the authors have performed an item analysis to validate the internal consistency of the items in the research model and then other statistical analyses to verify the initial hypotheses and the positive correlation through the items. Finally, a discussion of the main results and conclusive remarks have been proposed.

Literature review

This section provides a review of the existing literature, focusing on the knowledge management frameworks realized by several authors and the impact of KM on NPD process.

The main keywords have been inserted in the following databases: Google Scholar, Sciencedirect, Emeraldinsight, Ingenta connect, Scopus and IEEE. The paper found at this stage, have been read and their references highlighted, to make sure any relevant works not detected through original database search were included, and a decision about their inclusion was then made.

The definition of the term "knowledge" has represented, for many authors, the first step to introduce the theme of KM. In particular it has been distinguished from the terms "information" and "data". Data are considered as raw facts, information is regarded as an organized set of data, and knowledge is perceived as meaningful information [START_REF] Bhatt | Knowledge management in organizations: examining the interaction between technologies, techniques, and people[END_REF]. According to [START_REF] Alavi | Knowledge Management Systems: issues, challenges and benefits[END_REF], knowledge is personalized or subjective information related to facts, procedures, concepts, interpretations, ideas, observations and judgments. But, from an organizational viewpoint, it is also an organized combination of data, assimilated with a set of rules, procedures, and operations learnt through experience and practice [START_REF] Bhatt | Knowledge management in organizations: examining the interaction between technologies, techniques, and people[END_REF]. Knowledge can be either tacit or explicit [START_REF] Hahn | A Framework of Knowledge Management Systems: issues and challenges for theory and practice[END_REF]: tacit knowledge refers to the knowledge that has a personal quality that makes it hard to articulate or communicate; in contrast, explicit knowledge refers to the codifiable component that can be disembodied and transmitted.

The purpose of knowledge management is to enhance organizational performance by explicitly designing and implementing tools, processes, systems, structures and cultures to improve the creation, sharing, and use of all types of knowledge that are critical for decision making [START_REF] Long | Diagnosing Cultural Barriers to Knowledge Management[END_REF].

Companies are beginning to implement information systems designed specifically to facilitate the codification, collection, integration, and dissemination of organizational knowledge, that are referred to as Knowledge Management Systems (KMS) [START_REF] Mcdermott | Why Information Technology inspired but cannot deliver Knowledge Management[END_REF].

The relationship between KM and New Product Development

This first step of literature review has shown a lack of frameworks assessing the important role of KM in the NPD process, but a description of the relationship between the two processes exists.

Liu et al. [START_REF] Liu | An empirical study on the correlation between the knowledge management method and new product development strategy on product performance in Taiwan's industries[END_REF] conduct a research on the correlation between the knowledge management method and new product development strategy performance. The intention to engage KM in new product development is to decrease the uncertainty in the course of new product development thought that knowledge management integration is dependent on a wider and trans-functional integration capability.

Therefore, the effectiveness of the knowledge management method plays an important role in new product development strategy. In fact the authors have found that the stronger the knowledge management method, the higher the new product development performance.

According to Pitt and MacVaugh [START_REF] Pitt | Knowledge management for new product Development[END_REF], for organizations whose long-run competitive advantage and economic success is based on knowledge-intensive activities, effective NPD processes are crucial. The methods and practices of knowledge management significantly affect how the organization generates, stores, accesses, recombines and mobilises what it knows about NPD .

A knowledge strategy for NPD should accommodate and integrate human processes with technical processes in the complex socio-technical system that is NPD.

The challenge is to underpin the processes that are necessary for NPD by enabling information and knowledge flows that aid knowledge creation and recombination, via enhanced communication, both formal and informal. This holistic conception of knowledge management, if implemented effectively, can have a positive impact on the effectiveness of NPD processes.

A KMS supporting the NPD process has to capture informal, internal knowledge including externalization of tacit knowledge. To transfer tacit knowledge from individuals to a repository, Ramesh and Tiwana [START_REF] Tiwana | Supporting Collaborative Process Knowledge Management in New Product Development Teams[END_REF] suggest support for some form of community-based electronic discussion. A key feature of a KM support is its ability to capture and retrieve uncodified or tacit knowledge. Therefore, intelligent support for the capture, use, and maintenance of process knowledge appears to be essential for the NPD success.

Methodology Purpose and assumptions

The goal of the research is to adopt an empirical approach to analyse the influence of KM on the NPD process managed by a generic company.

The first section has represented an important background to introduce the issue of KM enhancing the NPD process. The set of reviewed papers has not been exhaustive for the purpose of the present study: the relationship between KM and NPD is basically discussed through dissertations and the existing frameworks are related to a specific concern. In order to fulfil this gap, the authors formulated a conceptual research model (Fig. 1), inspired to the one developed by Liu et al. [START_REF] Liu | An empirical study on the correlation between the knowledge management method and new product development strategy on product performance in Taiwan's industries[END_REF], in which three main hypotheses, guiding the following results, have been highlighted:

H1: The relationship between KM and NPD performance. H1-1: a stronger KM approach has a positive correlation with NPD performance. H1-2: the opportunity to manage formal, informal, internal and external knowledge and to update it frequently has a positive correlation with NPD performance. H1-3: the use of advanced KM techniques and tools has a positive correlation with NPD performance. H1-4: the use of proper KM software has a positive correlation with NPD performance.

H2: the relationship between KM and NPD strategy. H2-1: a stronger KM approach has a positive correlation with NPD strategy. H2-2: the opportunity to manage formal, informal, internal and external knowledge and to update it frequently has a positive correlation with NPD strategy. H2-3: the use of advanced KM techniques and tools has a positive correlation with NPD strategy. H2-4: the use of proper KM software has a positive correlation with NPD strategy.

H3: the relationship between NPD strategy and NPD performance. H3-1: the use of performance measurement sets will affect NPD performance. H3-2: the decision to improve the NPD process will affect NPD performance. H3-3: the importance of different CSFs will affect NPD performance.

Data collection and analysis

The empirical study has been carried out through the administration of a questionnaire, properly structured to test the hypotheses listed above. The questionnaire has been distributed to 103 companies with at least a R&D department located in Italy and it gave the authors the opportunity to investigate the three domains represented in Fig. 1.

The domain of KM refers to the way KM is managed within the processes: knowledge capturing, packaging, distribution and reusing. The domain contains four measurement items:

1. the KM approach, which refers to the planned methods to manage knowledge in the NPD process; 2. the type of knowledge managed, that may be formal/informal and internal/external, and its update frequency; 3. techniques and tools, i.e. the way to capture, store, share and reuse design knowledge in the company (lessons learned documents, questionnaire/checklist, poster and visual management, Intranet, Wiki, PDM/PLM Systems); 4. software implemented to support KM in the NPD process.

The domain of NPD performance includes three measurement items:

1. the ease to fill out reports and documentation, that refers to a minimum time wasted by the designers in this process; 2. the ease to reuse knowledge of previous projects, that refers to a minimum time wasted in knowledge reusing; 3. reduction of manual work thanks to the software used; Finally, the NPD strategies domain contains three measurement items:

1. Performance measurement, that describes if and how performance are measured within NPD process; 2. NPD improvement decisions, that refers to the eventuality of a strategic plan enhancing the NPD process; 3. Main CSFs, that describes the importance of CSFs (Return On Investment, costs related to life cycle costs, Time To Market, range of different products, level of customization, level of innovation, brand image and environmental sustainability) in NPD decisions;

The sample of companies that have answered to the questions includes different industries: industrial machinery and equipment, defence, automotive, industrial engineering, household electrical appliance, electric equipment, software and telecommunications, electronic equipment, fashion, chemicals and materials manufacturing. The subjects interviewed had full understanding of KM and NPD: in fact, in order to enhance the questionnaire validity, it has been administered to R&D and Information and Communication Technology (ICT) mangers and new product development directors. From January 2012 to April 2013a total of 103 complete questionnaires have been collected.

A structured questionnaire with closed questions has allowed the author to use quantitative and comparable scales, especially required in the verification of results. The outcomes presented in the following section have been provided by the statistic software Minitab 17. A first Item analysis has been used to assess how reliably multiple items in the survey measure the same construct. Then, in order to verify the hypotheses, t-test, multiple regression and other method for verification analysis have been performed.

Findings

The research model designed in Fig. 1 has been validated through an Item analysis: it is a measure of internal consistency used to test the same skill or characteristic. The Cronbach coefficients for the three domains were calculated: each domain included several items, corresponding to questions within the survey. If Cronbach's alpha is low, then the items may not reliably measure a single construct. Typically, a value of 0.7 or higher is considered good.

The values in Table 1 are greater than the common benchmark of 0.7 and suggest that the items are measuring the same construct. Ease to fill out reports and documentation

0,7096

Ease to reuse knowledge of previous projects Reduction of manual work thanks to the software used NPD strategies Performance measurement 0,7592

NPD improvement decisions

CSFs

This study has shown that the requirements for reliability have been satisfied so the items can be arranged as done in the research model.

Moreover, the relation between the three domains has been verified. A t-Test for KM, NPD performance and NPD strategies has been conducted, followed by a multiple regression analysis.

The t-Test results

A t-Test has been performed in order to prove the validity of H1-1-H1-4, H2-1-H2-4 and H3-1-H3-3. Samples are normally distributed and independent, so the t-Test can be used. If the p-value is below a specified level of significance of 0.05, the test's null hypothesis can be rejected and the differences between means are statistically significant.

All KM aspects, analysed in the detail, have a significant effect on the NPD performance because p-value is lower than the α-level. Therefore, the H1-1-H1-4 were proven valid.

The t-Test for KM and NPD strategies has shown greater problems of significance. In fact, not many KM aspects have shown significance on the NPD strategy according to low p-values. It seems that the use of several advanced KM techniques and software is related to the adopted NPD strategy.

Therefore, the strategies fixed by the companies interviewed, focused on the need to measure performances, to improve NPD and to identify CSFs, are not influenced by the type of knowledge managed. Only hypotheses H2-3-H2-4 were proven valid.

Finally, almost all new product development strategy aspects have shown significance on new product development performance. Only the relation between the use of performance measurement, as an NPD strategy, and the NPD performances does not show significance, due to its higher p-value. In particular, the decision to improve the NPD process and the identification of CSFs affect the performances achieved within the NPD. Therefore, hypotheses H3-2-H3-3 were proven valid.

The multiple regression analysis

Multiple regression is an example of complex multivariate statistics that analyses the effects of two or more independent variables on the dependent variable. Parameters as R 2 , adjusted R 2 , and S (Standard Error of the regression) are measures of how well the model fits the data. R 2 is a statistic used to evaluate the fit of the model, i.e. the percentage of variation in the response that is explained by the model. R 2 adjusted is a modified R that has been adjusted for the number of terms in the model. S is measured in the units of the response variable and represents the standard distance that data values fall from the regression line. The better the equation predicts the response, the lower S is and the larger the R 2 , the better the model fits the data.

In the present study, the multiple-regression analysis has provided some insights and challenges. In fact the outputs of Minitab about correlation and regression have shown that the model doesn't fit the data. Therefore the original data have been filtered basing on the sector the companies belong to, in order to understand if the organizational aspects are able to improve the analysis.

Four main sectors have been identified: the first includes Mechanics and Industrials, the second includes Electrical and household products, the third concerns Technology and telecommunications, and the fourth is devoted to Chemicals, food, textile and apparel.

The regression has been applied to analyse the relation between KM, NPD performance and NPD strategies, as previously stated in the research model. The relation between KM and NPD performance has been confirmed only by the companies belonging to the second sector.

The regression analysis based on the particular response "reduction of manual work thanks to the software used", has shown high R 2 values and a S lower than 2,5 revealing that the model is precise enough (Table 2). Nevertheless, just between the KM approach and the management of formal and external knowledge there is a strongly significant and positive relationship (underlined in grey), confirmed by a p-value lower than 0,05 (α level). The other variables appear to be not significantly related or negatively related.

The results shows that the adoption of software reducing manual work has a positive correlation with the maturity of KM, explained basically in the KM approach. Moving to the second hypothesis, the aim has been to find a positive relation between KM and NPD strategies. The sectors one, three and four have shown that the model based on the response "CSFs" fits the data. The management of formal and internal knowledge and several KM techniques and tools are positively related to a particular NPD strategy, that is the identification of CSFs. The other sectors analysed have confirmed the same relation, excluding additional linkages between other KM methods or other NPD strategies. Therefore the type of knowledge managed has a positive correlation with the strategy of identifying CSFs.

Finally, the third hypothesis based on the relationship between NPD strategy and NPD performance has been confirmed in the second and third sectors. Two models, based on two main responses, concerning the ease to fill out reports and documentation and the ease to reuse knowledge of previous projects, fit the data. Hence, the Technology and telecommunications sector, that identifies the range of different products as its main CSF, reveals also the interest to improve NPD performances.

Within the second sector, the response "ease to reuse knowledge of previous projects" has a significant and positive relationship with the NPD-oriented strategy and with the identification of CSFs related to innovation and sustainability.

Discussion and Conclusion

In the present study the role of KM in the improvement of NPD process has been analysed combining a literature review and an empirical research. The first methodology has allowed a comprehension of knowledge management, identifying its main processes and its relation with the new product development.

Literature review has triggered the formulation of three hypotheses, based on the relationships between KM, NPD performance and NPD strategies, as suggested by Liu et al. [START_REF] Liu | An empirical study on the correlation between the knowledge management method and new product development strategy on product performance in Taiwan's industries[END_REF]. A research model has been designed, containing several items gathered from literature.

A survey has been proposed to enterprises with at least a R&D department located in Italy belonging to a wide range of industries, containing questions specifically defined to validate the research model. The set of data collected has been elaborated by a statistical software providing lots of interesting outcomes.

First of all, the internal consistency of the items in the research model has been confirmed through the item analysis. Then, the mentioned hypotheses have been tested through a t-Test and a multiple regression analysis. The latter has aimed to identify a positive relation between independent and dependent variables present in the model. The organizational aspects have been taken into account because the regression analysis has been conducted on the different sectors the companies belong to. Nevertheless, not all the hypotheses have been validated.

In the Electrical sector, a stronger KM approach and the opportunity to frequently manage formal knowledge has a positive correlation with the reduction of manual work in the NPD process. On the other hand, companies providing tools such as Product Data Management and Product Lifecycle Management (PDM/PLM) do not necessarily better perform in the NPD process.

In Mechanics, Technology, Chemical, Food and Fashion industries the regression analysis has shown that the frequent management of formal and internal knowledge and the KM tools, such as intranet and checklists, are positively related to the definition of CSFs, among the possible NPD strategies.

In the Electrical and also in the Technology and telecommunication industries, the definition of particular CSFs, including the range of different products, the level of innovation and the sustainability aspects, and the NPD improvement decisions are positively correlated to the achievement of NPD performances, such as the ease to fill out reports and to reuse knowledge.

Therefore, better NPD performances may be ensured through a mature KM approach and also through the definition of appropriate NPD strategies.

The paper provides valuable insights from the academic viewpoint, because of the use of different statistical analyses to validate or reject the initial hypotheses and also thanks to the overview of literature about KM. But the present study also underlines the impacts on enterprises belonging to different industries.

Especially in mechanical and technology sectors, enterprises need to acknowledge the importance of KM and its impact on performances and long-term strategies related to the NPD process in order to be challenging in the competitive context.

The present research may be improved through the collection of more questionnaires in order to enhance the consistency of the analysis. This way, probably the hypotheses that have been rejected could be accepted, according to a different behaviour of the variables.

Moreover, the research may be extended to other industries or focus on a particular one to assess the importance of KM in the NPD process. Other issues, such as the impact of KM on increasing innovation and sustainability should be examined.

Fig. 1

 1 Fig. 1 The research model

Table 1The Item analysis for the research model.

 1The

	Domain	Items	Cronbach α
	KM	KM approach	0,7302
		Type of knowledge managed
		Technique and tools
		Software
	NPD performance

Table 2 The multiple-regression analysis for the KM method on the NPD performance "re- duction of manual work thanks to the software used" (Sector 2)

 2

	Model one	Term	Coefficient Standard	T-Value P-Value
				Error
		Constant	6,78	1,94	3,49	0,004
	KM approach	KM approach	0,478	0,170	2,81	0,016
	Type of knowledge	Formal and internal knowledge	0,182	0,262	0,70	0,500
	managed	managed
		Formal and external knowledge	0,381	0,182	2,09	0,058
		managed
	Techniques and tools Lessons learned documents	-0,083	0,140	-0,59	0,565
		Questionnaire/checklist	0,179	0,150	1,19	0,258
		Poster and visual management 0,027	0,130	0,21	0,841
		Intranet	0,084	0,122	0,69	0,503
		Wiki	0,134	0,231	0,58	0,573
		PDM/PLM Systems	0,134	0,109	1,23	0,243
	Software:	Knowledge Based Engineering	-0,573	0,163	-3,52	0,004
		(KBE) and Design Automation
	R 2	72,47%
	Adjusted R 2	42,65%
	S	1,47232


Kazuo Hiekata

Hiroyuki Yamato

Jingyu Sun

Hiroya Matsubara
email: amatsubara@is.k.u-tokyo.ac.jp

Naoji Toki

Study on improving accuracy for edge measurement using 3D laser scanner

Keywords: Laser Scanner, Point Cloud Data, Edge Measurement, 3D Target

A high accuracy edge measurement method of edges of components using point cloud data by 3D laser scanner is proposed in this paper. The proposed method consists of two parts: method for shape measurement of edges by using points of side faces of the components and method for length measurement of edges by attaching the 3D targets. At the experiments with a surface plate and a curved shell plate, the results give suggestions for having possibility of applying this proposed method for actual shipbuilding components.

Introduction

At different manufacturing sites, accurate measurement of large components is of vital importance.

For example, at each manufacturing stage of shipbuilding process, the shapes and sizes of the ship's components are measured and the accuracy is evaluated to reduce the cost of the rework in subsequent manufacturing stages including welding process specifically. Different 3D measurement system is employed for accuracy evaluation of components. Kim et al. suggested the 3D virtual simulation method of shipbuilding blocks using 3D measurement data by total station [START_REF] Kim | A Virtual Erection Simulation System for a Steel Structure Based on 3-D Measurement Data[END_REF]. According to Shibahara et al., a method of 3D welding deformation measurement based on stereo imaging technique was developed [START_REF] Shibahara | Development of 3-dimansional Welding Deformation Measurement Based on Stereo Imaging Technique[END_REF].

Because most of the ship's components are very large, it is efficient to conduct measurement by using 3D laser scanner which can measures the whole surface of components at one time as one set of point cloud data. An edge measurement method for shipbuilding blocks was developed [START_REF] Hiekata | Development of Three Dimensional Measured Data Management System in Shipbuilding Manufacturing Process[END_REF]. In this existing method, the points of edges are directly extracted from point cloud data based on the distance from a floor panel of the shipbuilding block. However, it is difficult to obtain the accurate shape and the length of edges from the measured point cloud due to intervals existing even in high density point cloud data of the large component.

In this study, a high accuracy edge measurement method of components using 3D laser scanner is proposed. Concretely, the method for edge's shape measurement using the point cloud of the component's side face and the method for edge's length measurement using the feature points of the 3D targets are explained.

2

Proposed Method

Overview

Fig. 1 illustrates the overview of the proposed method. The proposed method consists of two parts: method for shape measurement of edges by using points of side faces of the components and method for length measurement of edges by attaching the 3D targets.

In the method for shape measurement of edges, first of all, the component is measured by 3D laser scanner and the point cloud data including the surface part and side faces of the component are obtained. The continuous surface part is extracted using region growing method. To shorten the calculating time, the point cloud data of the surface part is thinned and the points of the thinned points cloud data's boundaries are detected. Using the detected boundary points, the points of the side faces are recognized and plane fitting is carried out locally to-ward the recognized points. Finally, the points on the edges are calculated according to the surface part and the fitted side face planes.

In the method for length measurement of edges, at first the 3D targets, whose upper side is regular octahedron and the lower side is cube, are attached on the corners of the component and the component is measured by 3D laser scanner. Firstly, planar domains are detected from the obtained point cloud data and the position and direction information of the targets are recognized by the proper combination of the detected planes. Then, the vertices representing the edge's endpoints on the base of the targets are extracted. If the shape of the edge is a straight line, the length of the edge is calculated by calculating the distance be-tween the vertices. On the other hand, if the shape is curve, the shape is acquired by the shape measurement method of edges mentioned above and the length is calculated by fitting a cubic spline curve toward the calculated edge points.

Fig. 1. Overview of the proposed method

Edge's Shape Measurement Method

First, the component is measured by 3D laser scanner and point cloud data including the surface part and the side faces of the component are obtained. An arbitrary point on the surface of the component is selected from the measured data and only the points of surface part are extracted by region growing method [START_REF] Forsyth | Computer Vision[END_REF]. The point cloud except for the extracted surface part including the component's side face is detected at following process.

In order to reduce computational complexity in the following process, the point cloud of the surface part is thinned. The concrete procedure of thinning is described following.

1. Principal component of the surface part is analyzed and it is divided into two equal parts by a plane which is vertical to the first principal component vector. 2. The operation 1 is repeated for the other divided parts until the number of points from each separated part becomes less than a fixed threshold. 3. A set of each part's gravity center is considered as thinned points of the surface part.

Next, the points on the boundary are detected from the thinned point cloud of the surface using edge detecting method from Kalogerakis [START_REF] Kalogerakis | Extracting Lines of Curvature from Noisy Point Clouds[END_REF].

Using the points on boundary (E), the shape of edges is acquired. Fig. 2 illustrates how points of edges are fitted. The procedure of edge fitting is described as following.

The neighborhood of E (

) is searched by k-NN BBF method [START_REF] Beis | Shape Indexing Using Approximate Nearest-neighbour Search in High-dimensional Spaces[END_REF] from the point cloud of the surface part. 2. gravity center of (G) is calculated and an approximate plane is fitted to . 3. The projection point of E to plane ( ) is calculated. The line segment is extended toward and a point on the line ( ) is obtained. The neighborhood of ( ) is searched from the points except the surface in the same way as step 1.

4. An approximate plane is fitted to and the intersection of the line and plane is calculated. It is one of the points describing the edge.

The above procedure is conducted for each point on the boundary and the shape of the edge is obtained.

Fig. 3. Attachment of the 3D targets

Here the measured point cloud data is divided into continuous planar domains using region growing method [START_REF] Forsyth | Computer Vision[END_REF]. A seed point for region growing is chosen randomly from the point cloud and all of the planes are recognized one by one.

The position and posture of the targets are recognized by doing proper combination of these recognized planes. Combination of four plane surfaces of octahedron part and one of cube part is found automatically and a target's position can be found uniquely. From the known size of 3D targets, the relative coordinates of the vertices on the base of targets is calculated.

If the shape of the edge is a straight line, the length of the edge is measured by calculating the distance between the selected two vertices of targets representing the corner of the component as shown in the left image of Fig. 4. Otherwise, if the shape of the edge is a curved line, the shape is obtained by the edge's shape measurement method introduced in section 2.2. A cubic spline curve is formed from two selected vertices of targets and the acquired points which describe the shape of the edge. An example of formed cubic spline curve is shown as a white line in the right image of Fig. 4. The edge's length is calculated from the obtained curve.

Fig. 4. Calculation of edge length 3 Experiment

Two experiments were performed to validate these proposed methods. The objective of the experiment with the measurement of a surface plate is to confirm the accuracy of measurement by the proposed method. The experiment using the curved shell plate measurement is performed to validate this proposed method with an actual sample of shipbuilding components.

In this study, the experiments are carried out by a FARO Focus3D laser scanner. Its specifications are shown in Table 1. As an experiment in a laboratory environment, a surface plate was measured by 3D laser scanner. The surface plate is illustrated in the left image of Fig. 5. The plate and a laser scanner were located as shown in the right side of Fig. 5. First, the shape of edges was measured by the method proposed in section 2.2. In this measurement, a steel plate was erected along the edge 3 to measure the edges 1, 2 and 3 at the same time as shown in the right of Fig. 5. Fig. 6 illustrates the measured edge shape. The top image of Fig. 6 shows the whole edge shape and the bottom does an enlarged view of the framed part in the top. This shows that the points describing edges by the proposed method are obtained on the intersection of the surface and side face exactly. The error of distance between the edges 1 and 3 was -1.68mm compared with the measured value with a tape measure.

Second, a length of an edge was measured by the method proposed in section 2.3. Two 3D targets A and B were attached on corners of the surface plate as shown in the left side of Fig. 5. The length of the edge 1 was calculated ten times. The result is shown in Table 2. The average of error is -0.15mm compared with the measured value by tape measure and standard deviation is 0.56mm. Two-sigma range is included from -1.27mm to 0.97mm. The accuracy of the error of length measured by the proposed method is less than 2mm.

Edge Measurement of the Curved Shell Plate

A curved shell plate of ship as shown in the left of Fig. 7 was measured by the proposed method. Its size by tape measure is illustrated in the right picture of Fig. 7.

In this case study, the bottom edge of the plate in Fig. 7 was measured. The result of measurement of edge shape by the method of section 2.2 is shown in Fig. 8. It is confirmed that the obtained white points describing the shape of the edge are in the correct position visually. The length of the same edge was calculated by the method of section 2.3. The error compared with measured value by tape measure was 0.89mm.

Discussion

It is necessary to manufacture shipbuilding components with less than 5mm error and detection of the error is required by measurement. In the experiment, the shape and length of edges were measured with less than 2mm error by the proposed method. Improvement in accuracy of edge measurement is achieved by using the points of the component's side face in measurement of shape and the feature points of the 3D target in measurement of length. It is considered that the error of measurement by the proposed method is small enough to evaluate accuracy of manufactured shipbuilding components such as curved shell plate.

In this study, measurement was performed only in a part of edges. There is a possibility of measuring all the shape and length of edges at one time by applying the proposed method to point cloud data obtained by registration of some data scanned from different directions.

Conclusion

A high accuracy edge measurement method of components using 3D laser scanner is proposed. Specifically, the method for edge's shape measurement by fitting planes to surface part and side face part of the component locally, and the method for edge's length measurement by using extracted vertices of the 3D targets attached on the component are developed. At the experiment with a surface plate, the shape and length were measured by the proposed method and the results indicated that the error was less than 2mm. At the measurement of a curved shell plate, the results give suggestions for having possibility of applying this proposed method for actual shipbuilding components.

Fig. 2 .

 2 Fig. 2. Procedure of edge fitting

Fig. 5 .

 5 Fig. 5. Measured surface plate and arrangement of objects

Fig. 6 . 2 .

 62 Fig. 6. Result of measurement of edge shape in an experiment with a surface plate

Fig. 7 .Fig. 8 .

 78 Fig. 7. Measured curved shell plate and its size

Table 1 .

 1 Specifications of FARO Focus3D[START_REF]FARO Focus3D Features, Benefits & Technical Specifications[END_REF]

	Vertical field of view (vertical/horizontal)	305° / 360°
	Range	0.6 -120m
	Measurement speed	122,000 -976,000 points/sec
	Ranging error	±2mm@25m
	Wavelength	905nm
	Beam diamiter at exit	3.0mm

Yokohama, Japan, 7 th -9 th July 2014

Acknowledgments. UNICUS Co., Ltd. gives us technical supports for employing point cloud data processing platform "Pupulpit". The authors would like to thank that.


Jorma Papinniemi
email: jorma.papinniemi@lut.fi

Johannes Fritz

Lea Hannola

Andrea Denger

Hannele Lampela

Lifecycle-based requirements of product-service system in customer-centric manufacturing

Keywords: Requirements information management, lifecycle-based requirements, sustainability, traceability, reusability, product-service system (PSS), product lifecycle management (PLM), customer-centric manufacturing

Managing through-life information of products and services has become an important competitive means in customer-centric industries. The need for managing new types of product-service requirements for sustainability, traceability and performance have widened the traditional perspective of PLM information to integrate new issues, e.g. service information. In this study we examine how the diverse and through-life requirements information could better be integrated in product and business processes of customer-centric manufacturing. The study is based on a literature review and two case interviews. The objective is to elicit requirements information for Product-Service System (PSS). The study introduces the concepts of product-service system, and outlines through-life requirements in customer-centric business. PSS is a new concept for customer-centric business to improve the performance of sustainability, traceability, reusability and repeatability.

Introduction

In today's global and competitive market, companies are facing many challenges especially in shortening the time to market, changing regulations, price competition, increasing product complexity and diversity, support and maintenance of products, sustainability and environmental concerns and end of life issues. As response to these challenges, manufacturing companies are striving for accelerating innovation and developing lean supply and product processes through lifecycle.

One related challenge is the effective and efficient management of scattered information on product requirements through lifecycle. Product related requirements and available information is a key factor for business success and competitive advantage. However, requirements are always on the move. Customers often change their mind; market drivers change; authorities keep adding new constraints related to environ-mental or safety concerns; and sometimes the project encounters difficulties that require a revision of the initial targets.

Effective use of through-life information is an important means to response faster to changes in customer needs and product related requirements in customer-centric manufacturing environment [START_REF] Gomez | Through-Life Integration Using PLM[END_REF]. Product lifecycle requirements also include servicebased requirements throughout the lifecycle forming the concept of a product-service system (PSS).

In this study, we focus especially in the less studied topic of requirements information, related to the upcoming demand of integrative products and services in the context of customer-centric strategies. We utilize the concept of through-life requirements to describe the integration aspects throughout lifecycle, e.g. sustainability, traceability, reusability and repeatability.

The objective of this paper is to examine:

(1) What are the trends and drivers for managing information of new lifecyclebased requirements in customer-centric industry?

(2) What kind of through-life requirements are to be considered for productservices in project-based business (ETO) and mass-customizing business (ATO)?

This study is based on a literature review on product lifecycle requirements, and the characteristics of customer-centric manufacturing business. The study introduces trends and drivers for managing information [START_REF] Terzi | Product Lifecycle Management, from its history to its new role[END_REF][START_REF] Bras | Sustainability and product life cycle management -issues and challenges[END_REF], concepts of product-service system [START_REF] Gomez | Through-Life Integration Using PLM[END_REF][START_REF] Baines | State-of-the-art in product-service systems[END_REF][START_REF] Goedkoop | Product service systems, ecological and economic basis[END_REF][START_REF] Tukker | Eight types of product-service system: Eight ways to sustainability? Experiences from SusProNet[END_REF] and outlines through-life requirements in customer-centric business. PSS is a new concept for business in improving the performance of sustainability, traceability, reusability and repeatability.

A comparative study on requirements information for the PSS was based on interviews in two B2B case companies. The companies represented two different kinds of customer-centric industries: low-volume project-oriented manufacturing mostly based on engineer-to-order (ETO) strategy, and high-volume mass-customization, typically based on assemble-to-order (ATO) strategy [START_REF] Forza | Product Information Management for Mass Customization[END_REF][START_REF] Haug | From engineer-to-order to mass customization[END_REF][START_REF] Steger-Jensen | Issues of mass customisation and supporting IT-solutions[END_REF].

2

Literature review of related work

In the following sections, the theoretical background of the paper is introduced from three viewpoints. First, some of the trends and developments leading to new product information requirements and customer-centric PLM are discussed, second a comparison of the typical features of customer-centric manufacturing strategies (ETO and ATO) is presented, and third, the characteristics of requirements management are clarified for this study.

PLM in customer-centric manufacturing

Product knowledge management throughout the lifecycle of the product and the related services has attracted increasing consideration in research and (mainly manufacturing) company practices in recent years. There is a variety of related concepts and definitions in the literature on product lifecycle management, depending on the focus of the authors. Novel customer needs and the interest of the end customer towards sustainability and lifecycle aspects of both consumer products and business to business investment products has led to increasingly complex information requirements covering the whole value network [START_REF] Seifi | Information requirements for sustainable consumption[END_REF]. However, current practices for productrelated information management in manufacturing and service organizations are still mainly focused on developing internal processes and information flows, and are not always adaptable for a larger network of organizations during the lifecycle of the product.

Also from a political view, the tightening environmental legislation in the EU and US sets new requirements for sustainable manufacturing and end-of-life considerations of all kinds of goods produced (e.g. machinery, medicine, chemicals, food, and other sectors) and the responsibility of the manufacturer is emphasized [START_REF] Messina | Environmental Regulations Impose New Product Lifecycle Information Requirements[END_REF]. As a response to these pressures, the concepts and practices of sustainable PSS [START_REF] Roy | Sustainable product-service systems[END_REF], closed-loop manufacturing/production/supply chains [START_REF] Savaskan | Closed-Loop Supply Chain Models with Product Remanufacturing[END_REF] and closed-loop PLM [START_REF] Jacopo | A new closed-loop PLM Standard for mass products[END_REF][START_REF] Kiritsis | How closed-loop PLM improves Knowledge Management over the complete product lifecycle and enables the factory of the future[END_REF] have been developed in recent years. These approaches to manufacturing and product information management are based on the ecological ideal of a closed-loop system, which does not exchange any matter outside its boundaries. Similarly, in manufacturing context, the aim of securing the continuous material and information flows between product lifecycle stages and closing the manufacturing loop by end-oflife operations such as recycling or remanufacturing are considered as integral parts of the manufacturing process.

The need for managing new types of product and service requirements for sustainability, traceability and performance have widened the traditional perspective of PLM information to integrate sustainability issues with other PLM information. The systemic nature of the sustainability concept also poses new challenges for the PLM tools and operating processes:

"Despite advances, integration of environmental issues and tools in existing PLM operating procedures is still lacking and a significant challenge.…. The 'ultimate' sustainable product life cycle design and management system would contain a variety of product and process design, modelling and analyses modules." [START_REF] Bras | Sustainability and product life cycle management -issues and challenges[END_REF] At the same time, companies are increasingly focusing on defining their operations from the customer point of view, and rethinking their strategies in terms of customercentricity. The concept of customer-centric PLM describes the aim to gather and utilize customer information (e.g. feedback) efficiently to streamline operations and information flows between different lifecycle stages and to create value to customers along the lifecycle of the product [START_REF] Schulte | Customer centric PLM: integrating customers' feedback into product data and lifecycle processes[END_REF].

Customer-centric strategies and business models

The degree of customer orientation is determined by the customer coupling point and the amount of customer-oriented information [START_REF] Forza | Product Information Management for Mass Customization[END_REF], [START_REF] Steger-Jensen | Issues of mass customisation and supporting IT-solutions[END_REF], see Fig. 1. The more and earlier the customer is involved in the business process (design-fabrication-assemblydistribution), the more customer contact and information is needed.

In pure customization most intensive customer orientation is achieved by engineerto-order (ETO) strategy and products. ETO production is suitable for unique products that have similar features, and the production is based on receiving a customer order and developing a technical specification accordingly. Customer orders are often organized as projects [START_REF] Yang | Key practices, manufacturing capability and attainment of manufacturing goals: The perspective of project/engineer-to-order manufacturing[END_REF][START_REF] Tonchia | Industrial Project Management -Planning, Design, and Construction[END_REF]. Another important customer-centric strategy in this study is the customized assembly (ATO) or customized configuration (CTO). In this case, customer requirements directly influence the assembly activities, not the design and manufacturing process. Products are made with a set of standard components and modules, but the assembly of this set is customized to satisfy specific customer needs. [START_REF] Forza | Product Information Management for Mass Customization[END_REF] Managing strategies in manufacturing companies are often too product oriented and pay relatively little attention to services as part of the product offering. Due to the growing servitization of products, manufacturers need to better understand the nature of services and their impact on the design and operation functions [START_REF] Zhang | Engineering network configuration: transition from products to services[END_REF]. Differences in characteristics of ETO and ATO have been analyzed by Haug et al. [START_REF] Haug | From engineer-to-order to mass customization[END_REF], especially from the point of transitions in strategy choices and business models. General characteristics of these customer-centric strategies are adapted in Table 1.

Characteristics of requirements management

Requirements management is a critical part of a development process for all products, not only for software [START_REF] Turk | Requirements management[END_REF], where the requirement management practices are traditionally well known. According to Grady [START_REF] Grady | System Requirements Analysis[END_REF], perhaps the greatest mistakes in system development performance in industry appears in requirements management. Requirements practices include requirements elicitation, analysis, prioritization, documentation, verification and management. These practices are often discussed together under the umbrella 'requirements management' (RQM). Halbleib [START_REF] Halbleib | Requirements management[END_REF] states that it is important to treat RQM as a process and not a single event, since requirements change and their status must be tracked throughout the project.

Requirements management support is needed in engineering design [START_REF] Baxter | A framework to integrate design knowledge reuse and requirements management in engineering design[END_REF]. Literature covers several practices, techniques and methods to support the critical process of requirements management. In addition, there are a large number of proposals for managing requirements, and several commercial software tools are available for requirements management [START_REF] Baxter | A framework to integrate design knowledge reuse and requirements management in engineering design[END_REF]. Requirement management methods and tools enable procedures to document requirements and check their progress through the product development project [START_REF] Baxter | A framework to integrate design knowledge reuse and requirements management in engineering design[END_REF].

According to Jiao and Chen [START_REF] Jiao | Customer Requirements Management in Product Development: A review of Research Issues[END_REF], understanding and fulfilling customer's requirements individually has recognized as a compelling challenge for companies across industries. Poor understanding and analysis of customer requirements during the elicitation and analysis of requirement information have negative implications on design and manufacturing of a product in terms of quality, lead time, and cost [START_REF] Jiao | Customer Requirements Management in Product Development: A review of Research Issues[END_REF]. One of the biggest challenges in requirements management is that the requirements change; customers or other stakeholders require new requirements or want to remove the original ones at any phase of development lifecycle. Other challenges related to requirements management are e.g. inadequate processes, shortage of resources, poor communication (such as a communication gap between customers and developers), complexity of requirements and lack of support from executive management [START_REF] Hannola | Challenging front-end-of-innovation in information systems[END_REF].

Concepts of product-service systems

Product-service systems (PSS) represent "a marketable set of products and services capable of jointly fulfilling a user's need. The product/service ratio in this set can vary, either in terms of function fulfilment or economic value." [START_REF] Goedkoop | Product service systems, ecological and economic basis[END_REF] PSS are seen as an excellent mean to enhance the competitive ability "and to foster sustainability at the same time" [START_REF] Tukker | Eight types of product-service system: Eight ways to sustainability? Experiences from SusProNet[END_REF]. This implies a dualistic approach on attracting the customer's attention. The feature-driven product perspective is supported by the more solution-oriented service perspective [START_REF] Vargo | Evolving to a new dominant logic for marketing[END_REF]. Mont summarizes five characteristics for PSS as followed: [START_REF] Mont | Clarifying the concept of product-service system[END_REF] • A PSS may consist of products, services, or various combinations of them. Products substituted by services are largely an ideal category without many practical or consistent examples, because any service, even nonmaterial per se, requires material or energy inputs.

• Services, at the point of sale, comprise personal assistance in shops, financial schemes provided to customers, explanations about product use. • Different concepts of product use consist of two categories: use oriented, where product utility is extracted by the user, and result oriented, where product utility is extracted by the utility provider for the user. • Maintenance services include servicing of products with the goal of prolonging product life cycle, comprising maintenance and upgrading. • Revalorization services include offers that aim at closing the product material cycle by taking products back, secondary utilization of usable parts in new products and recycling of materials if reuse is not feasible. Therefore, Tukker [START_REF] Tukker | Eight types of product-service system: Eight ways to sustainability? Experiences from SusProNet[END_REF] characterizes eight types of PSS in his work -a range from product-related services to a functional result for the consumer.

According to Meier, Roy & Seliger [START_REF] Meier | Industrial Product-Service Systems-IPS²[END_REF], in the domains of mechanical engineering and plant design, "product-related services are usually considered as an add-on to the actual product. Industrial PSS deal with dynamic interdependencies of products and services in production. Research areas cover new concepts and methods which enable the machine producers to design the potential services in an optimal way, already during the development of the machine. This paradigm shift from the separated consideration of products and services to a new product understanding consisting of integrated products and services creates innovation potential to increase the sustainable competitiveness of mechanical engineering and plant design. The latter allows business models which do not focus on the machine sales but on the use for the customer e.g. in form of continuously available machines. The business model determines the complexity of delivery processes. Characteristics of industrial PSS allow covering all market demands." [START_REF] Meier | Industrial Product-Service Systems-IPS²[END_REF]

Research process

Two case studies were employed to illustrate and compare the key characteristics and differences of product-service requirements of through-life information by two different strategy choices. Initial description of the case companies is introduced in Table 2. The primary data was collected by making semi-structured interviews in the case companies. In total, 10 persons were interviewed. Interviewees were chosen from the key functions, representing at least by one person related to both case companies.

Table 2. Description of case company characteristics

Case company characteristics

The topic areas of the interviews were as follows: Business model/strategy transition; Structuring of product and product related information; Lifecycle related requirements; Role/accountability of services and lifecycle business; Practices of product-service information sharing in company networks; Principles of modularization of products/services/processes/information; Expanded product data (3D+) in designmanufacturing -service; Applying ECM processes in product/service specifications. The elicitation of the requirements information for PSS was based on combining literature results, earlier research in the manufacturing industry, authors' particular experience and insights, and the company-specific results discovered in the case companies.

Derived requirements for product-service system

As a result of the interviews, requirements on product, service and product-service in ATO and ETO strategy are identified and classified. Derived requirements are presented in Table 3. The capabilities suitable for each requirements class (product, service, PSS) were selected. The derived requirements were analyzed for ETO and ATO strategies in terms of customer coupling-point, structuring base of productservice and related information, principles of product modularization, sustainability of PSS and managing through-life PLM processes and networks.

Several differences in ATO and ETO strategies were found in the study. For instance, in ATO strategy offered requirements are frozen in the early stage of development which enables better utilization of module-based PSS for customer needs. Respectively, in ETO strategy the challenge is to manage simultaneously frequent requirements changes and through-life requirements related to e.g. sustainability, reusability, repeatability and traceability. The key observation found in this study is that the timing of the customer-coupling point affects strongly on the possibilities of modularity and utilization of through-life requirements.

Conclusions

This paper combines the knowledge on requirements information management and PLM by examining the research questions through literature and two practical case examples. The questions were answered by a literature overview identifying the trends in the business environment of manufacturing companies and following changes in new product information requirements, mainly focusing on sustainability, traceability and performance. Changes in strategy towards more customer orientation, legislation changes towards environmental sustainability, and closed-loop manufacturing /PLM have had a profound effect on information needs and product information management practices in manufacturing companies. Eliciting product-service lifecycle requirements in customer-centric business models is based on the literature and empirical findings, confirming the practical relevance of the findings. A comparison between ETO (project-based) and ATO (masscustomized) operating strategies and their influence on the product-service system requirements was presented based on literature and empirical findings from two case companies.

The results of the paper can be utilized in companies planning and developing their product-service offering, and related requirements and processes both on business strategy level and technical information management level. The elicitation of product service requirements acts as basis for comprehensive data structure planning. Although the empirical results of this study are limited on two case studies, they are selected to highlight typical features of different customer-centric manufacturing strategies and thus provide a possibility for adapting the results wider in similar companies.

Further research is needed to develop a framework for data structures in companies following ETO and ATO strategies, as well as for testing the applicability of the findings in other contexts.

Fig. 1 :

 1 Fig. 1: Scope of customization along customer coupling point (adapted Forza et al. [7], p.10)

Table 1 .

 1 General characteristics of customer-centric strategies (adapted Haug et al.[START_REF] Haug | From engineer-to-order to mass customization[END_REF]).

	Customer-centric	High-volume mass-	Low-volume project-
	strategy	customizing business	based business strategy
		strategy (ATO/CTO)	(ETO)
	Product variety	Increase variety	Limit variety
	Customer view	Create valuable variety	Create adequate variety
	Manufacturing cost Slight increase	Decrease
	Business purpose	Increase sales	Optimize processes
	Configurator chal-	User interfaces	Knowledge base
	lenge

Table 3 .

 3 Through-life requirements for product-service systems

	Capabilities for PSS	ATO Strategy	ETO Strategy
	Product requirements
	Customer coupling-point	Product offering as-designed	As-ideated functionalities,
			performance proposition
	Structuring base of product	eBOM & mBOM structures,	Design for supply
	and related information	Requirements & Features	/installation processBOM
	Principles of product	Ready-designed modules to	Through-life knowledge
	modularization	be ATO as product variants	base of ETO as `reference
			project-products`
	Managing through-life PLM	Reusability of product infor-	Repeatability of processes
	Processes	mation: designs / structures	related to `project-products`
		/ changes
	Service requirements
	Customer coupling-point	Service order as-maintained	Service contract as-
			maintained
	Structuring base of service	dBOM & sBOM of individual	ProcessBOM of service &
	and related information	product delivered	operation
	Principles of service	Serviceability of component /	Serviceability for usability
	modularization	module /system /product	of machinery and produc-
			tion
	Role of services and	Accountability for concurrent	Accountability for lifecycle
	lifecycle business in company	product & service develop-	support of product & service
		ment
	Product-service requirements
	Customer coupling-point	Combined product-service	Combined lifecycle support
		offering as-ordered	of product-service system
	Structuring base of product-	Global BOM integrated by	Global processBOM of
	service and related information	concurrent product & service	`extended project product`
		eBOM,mBOM,dBOM,sBOM
	Sustainability of PSS	Closed-loop concurrent de-	Closed-loop concurrent
		sign of module based product,	design of through-life `pro-
		components and related ser-	ject product` and lifecycle
		vices	services
	Managing through-life PLM	Through-life traceability of	Through-life traceability of
	processes and networks	product & service structures	extended 'project-product'
			with service processes

Acknowledgments. Research conducted for this paper was coordinated by FIMECC, Finnish Metals and Engineering Competence Cluster, and funded by the Finnish Technology Agency, Tekes and the participating companies. The Austrian authors would like to acknowledge the financial support of "COMET K2-Research Centres for Excellent Technologies Programme" of the Austrian Federal Ministry for Transport, Innovation and Technology (BMVIT), the Austrian Federal Ministry of Economy, Family and Youth (BMWFJ), the Austrian Research Promotion Agency (FFG), the Province of Styria and the Styrian Business Promotion Agency (SFG).


Rainer Stark

Thomas Damerau
email: thomas.damerau@ipk.fraunhofer.de

Haygazun Hayka

Sebastian Neumeyer

Robert Woll

Intelligent information technologies to enable next generation PLM

Keywords: PLM, Next Generation, Engineering Operating System, Intelligence 1

The steadily growing complexity of products, interfacing processes, value creation networks and IT environments drive today's PLM solutions to their limits. How does this effect engineers? Over 1,400 engineers from the German industry provided feedback in 2011 -with alarming but expected results. Almost two thirds of the respondents can only spend 20% or less time on average for core tasks such as development, design or validation. The study confirmed a lack of time for creative engineering activities caused by a massive coordination and communication overhead. Could engineers and designers be relieved from routine and administrative tasks in product lifecycle management by means of current "intelligent" technologies? In constant dialogue with industry and PLM experts, the Fraunhofer IPK and TUB have investigated the demand for intelligence in product lifecycle management. This paper reflects on the current situation of PLM and introduces a conceptual framework (Engineering Operating System) for next generation PLM. Subsequently, an Engineering Automation Capabilities (EAC) stair step model is proposed and selected research results for "intelligence" in PLM are presented.

Fig. 1. Challenges and drivers for future PLM

To cope with these challenges from the IT perspective, PLM thus has to ensure that massive amounts of information can be stored, searched and used efficiently. Furthermore, PLM must ensure that information from different domains and lifecycle phases is logically and dynamically linked according to variable business intelligences.

However, current PLM solutions have three essential shortcomings addressed in this paper. Firstly, the underlying data models (describing how information from different engineering disciplines can be linked together) and the actual linking of information are left to the companies implementing a PLM solution into their IT environment. Secondly, PDM/PLM vendors have still not found or proposed a simple way for integrating third-party task-specific solutions besides the ones they offer themselves. And thirdly, almost any kind of "intelligence" in data management today is expected to be provided by the users (engineers, planners, managers). The first stated shortcoming of current PLM solutions calls for standardized, industry-wide accepted high-level PLM data models and for specific solutions that support the modelling of links between data from different domains and the analysis of interlinked data. The second shortcoming may not be obvious at first sight. It must be understood that task-specific solutions and the PDM backbone that they build upon are two types of IT solutions; such implementation oriented separation is necessary to allow for companies to choose the best breed of task-specific solutions and also allow them to develop their individual solutions if needed. In the following, these standalone task-specific solutions will be called PLM solutions, as opposed to PDM/PLM solutions which represent a tightly coupled, combined set of PLM solutions and a PDM backbone that are all offered by the same IT vendor. A future PLM environment has to allow for any PLM solutions to simply be plugged into an already existing PDM backbone. The third shortcoming of today's PLM solutions is characterized by the fact that no sort of intelligence is provided that could relieve engineers from some of the rather dull data-management-centric activities. This leads to an innovation backlog that was confirmed by a survey that 1,400 engineers from the German industry provided feedback to in 2012/2013 [START_REF] Müller | Kollaborative Produktentwicklung und digitale Werkzeuge: Defizite heute -Potenziale morgen[END_REF][START_REF] Müller | Study on collaborative product development and digital engineering tools[END_REF]. Almost two thirds of the respondents could only spend 20% or less time on average for core tasks such as development, design or validation. The study confirmed a lack of time for creative engineering activities caused by a massive coordination and communication overhead. Due to these shortcomings this paper aims to introduce and review concepts and technologies that will lead to a more intelligent support of engineering activities and an automation of data management related activities in future PLM solutions.

Research approach. The development of intelligence in PLM solutions demands extensive research activities; a holistic consideration of industrial needs, methodical requirements, technical feasibility and other aspects is necessary for this. To cope with this challenge a research approach was defined in order to answer the research question: How should information technologies be used to best relieve engineers from routine tasks in PLM? The approach contains the following steps:

 proposal and validation of a holistic conceptual PLM framework,  proposal of a stair step model for "intelligent" PLM solutions, and  review of existing "intelligent" PLM building blocks and classification according to the stair step model for gap identification.

Conceptual framework for PLM solutions

This chapter introduces two models; first a conceptual framework for PLM, designated as Engineering Operating System (EOS) [START_REF] Thomas | Illuminating the path: the research and development agenda for visual analytics[END_REF], proposed by Fraunhofer IPK in 2013, and second, a maturity stair step model, designated as Engineering Automation Capabilities (EAC) model, for PLM solutions. The EOS was drafted with the intention to ensure product creation capabilities in the context of a holistic framework. It is based on four different levels that need to be taken into consideration when PLM processes, solutions or strategies are conceived. The core message of the EOS is that these four different levels must be understood as an integrated system in order to unfold the complete potential of PLM. These four levels are characterized as follows (compare Fig. 2):

 Process and Organization: At this level it has to be ensured that roles, responsibilities, and processes exist that define which value creation activities are to be executed by whom and hence need to be supported by PLM solutions. Furthermore, the information needs of business processes and management must be understood in order to support an effective and efficient information ex-change between engineering activities and management activities.  Engineering Activities: Engineering Activities generally comprise all activities during the lifecycle that contribute to the generation, verification and validation of a virtual model of a product and to accompany a physical product from its production down to its end of life. While the process and organization layer focusses on the high-level processes, dependencies and responsibilities and thus on the questions of when things are done, in which order and by whom, the engineering activities focus on "how" exactly things are done in detail. From a PLM perspective it must be ensured that engineers have the required access to product-related information when they perform engineering or service activities. In addition it must be ensured that engineers can define and store new information in a way that allows a later reuse of that information (where needed in the lifecycle).  Data and Information Models: On this layer PLM must thrive to understand the way in which different engineering stakeholders exchange information and data, how they interact with it from their different perspectives and how data is converted and filtered. PLM must ensure that information from different do-mains can be interlinked, that limited views on information can be established according to different application contexts and access policies, and that the lifecycle of data is managed.  IT-Implementation: In order for all product-related information to be properly managed PLM must ensure that a suitable technological backbone for data management is in place (e.g. servers, networks, service platforms). This comprises the availability of reliable and safe data storages, the use of flexible, efficient and ideally open and standardized data formats, the provision of powerful and user-friendly information retrieval interfaces and the realization of efficient IT system integration mechanisms.

Yet accomplished innovations in PLM solutions were mainly driven from an operational perspective and therefore mostly focused on the top layer "Process and Organization". Examples are the inclusion of project-, workflow management or applications for the conversion of digital models. The engineering activity level is almost not addressed at all. PLM solutions barely support engineers in their key activities such as technical assessment, alternative solution identification and creation, technical system artefact traceability, decision making but mainly address administrative tasks and digital model management.

Fig. 2. Solution Layers of the conceptual Engineering Operation System (EOS) model

At the data and information models layer, many PLM solutions, however, still employ proprietary data formats that hinder a fluent information exchange across organizations, and at the "IT-Implementation" layer they usually do not provide open programming interfaces that would allow them to be easily coupled with other PLM solutions. The authors suggest that the use of intelligent information technologies may improve the degree of integration across PLM solutions, have a positive impact on the holistic integration of the two top layers and lead to both, an increased engineering effectiveness and efficiency, and a relief of engineers form routine tasks.

To gain clarity and for the classification of existing and targeted next generation PLM solutions, a stair step maturity model is proposed. The Engineering Automation Capabilities (EAC) model describes the expected and required increments of engineering automation capabilities towards intelligent PLM solutions in four steps (see Fig. 3 and data aggregation for the configuration of a digital mockup). This is the first step towards artificial intelligence (AI) in PLM. Semi automation already uses inherent business intelligences to trigger new engineering tasks or at least information related analysis, synthesis or assessments.  Automated Engineering: The last step is enabling the automation of routine engineering activities and data management centric activities. Data and information is linked both, vertical through all solution levels of the EOS and the involved domains (mechanic, electronic and informatics), and horizontal along the product lifecycle. Therefore, the anticipation of engineering activities can be enhanced. Furthermore consequences of decisions can be estimated and aligned with business and engineering objectives automatically.

The step "automated engineering" in the stair step model can be seen as the ultimate goal. The next section elaborates present existing research results in context of the EOS and the associated stair step model. Research solutions for intelligence in PLM

In this section intelligent PLM-solutions are presented that have been developed at Fraunhofer IPK and the Chair of Industrial Information Technology at the Berlin University of Technology.

As described in the previous chapter, past PLM initiatives focused on the layer of "Process and Organization" of EOS. Also the layer of "Data and Information Models" is covered by PLM Solutions, even if the integration of heterogeneous data into PLM still has to be performed by an engineer or by customization and configuration effort in PLM-Solutions. Due to new methods like Systems Engineering (SE), but also due to the increased amount of data and information in the product lifecycle management processes, intelligent PLM-solutions also have to address intelligent solutions for the EOS layer of "Engineering Activities" as well as increased assistance and automation in "Data and Information Modeling" layer. Such solutions and their application in PLM are now presented and allocated in the introduced EAC stair step model.

Visual Analytics for PLM. According to Thomas & Cook, visual analytics is an approach to understand past and current situations, as well as the events that lead to these circumstances. Likewise, visual analytics support the visualization of multiple data layers, the abstraction and integration of data from multiple data sources into a common representation [START_REF] Thomas | Illuminating the path: the research and development agenda for visual analytics[END_REF]. This especially suits the needs in heterogeneous PLM IT environments, as today's PDM-Systems, which are used as backbone for PLM, including heterogeneous data as well as multiple levels of data layers. But such an amount of data and information can barely be controlled by the engineer.

In Figure 4, an approach developed at the Chair Industrial Information Technology at the Berlin University of Technology is shown, where a potential delay in the development process of a product is analyzed with the support of visual analytics. To analyze or assess such a risk, the data stored in the PDM-System, e. g. the number of design iterations (revisions or engineering changes) done in a past project, is analyzed. By dynamic filtering of the number of iterations (threshold) of an object (see Fig. 4 a,b,c), the engineer receives direct visual feedback of similar challenges within the current project (P1) as well as the past project (P2). By applying a second indicator on the selected data in the PDM System, delay in design, it is possible to identify all those objects with the same level of criticality in the design process (see Figure 4 d).

The relevant object, a single part or an assembly, can be further investigated with the retrieved detail information (Figure 4e) and referenced or highlighted for the next risk review.

Other applications of visual analytics are e.g. to understand the chronology of change requests using a 3D product model or the visualization of impacts of changed parameters of a system model on a linked geometrical model. Visual Analytics provides intelligent support to information retrieval and thus fits into the 2nd step "Assisted Automation" of the EAC.

Fig. 4. Example for an indicator based comparison of two projects using Visual Analytics

Traceability in Systems Engineering. Today, the intelligent engineering work in Systems Engineering (SE) covers the task of comprehension of the different domain models as well as the identification of the relevant parameters, requirements and interdependencies between do-main models. To handle this complexity, today's SE approaches propose the creation of tracelinks.

In order to improve the efficiency of tracelink modeling, the "Model Tracer" application has been developed within the research project ISYPROM 1 . This modeler allows the creation and analysis of links between isolated data artifacts (Figure 5) to ensure consistency and to identify dependencies between data artifacts as well as to estimate the impact of engineering changes. Also other application scenarios like the Fault Tree Analysis, Failure Mode and Effects analysis, Pareto Analysis and tracelink based progress monitoring can be supported using the "ModelTracer" [START_REF] Beier | Supporting Product Development through Cross-Discipline Dependency-Modeling: Novel Approaches for Traceability-Usage[END_REF].

With a consequent tracelink approach, information on the interdependencies between domain models and other artifacts is made explicit thus allowing for enhanced data analysis. The analysis of such interdependencies would otherwise have to be performed manually by the engineer. Given the amount of different models this is a tedious activity with a high risk of overlooking implicit interdependencies. The "Model Tracer" thus also improves the reliability and efficiency of analyzing model interdependencies. Hence, it mainly addresses the "Data and Information Models" layer of the EOS. Providing intelligent support at data level it also fits step 2 of the EAC.

Fig. 5. Graphical presentation tracelinks that have been create with the "ModelTracer"

Ontologies for semantic data integration. An important prerequisite before the ModelTracer can be used, is the definition of a data metamodel that defines which types of tracelinks can be defined between which elements from different data models. In the research field of data integration, a data metamodel that specifies possible relations between heterogeneous data formats is called a "mediated schema". In the EU-funded research project iProd 2 Fraunhofer IPK has investigated the use of ontologies as mediated schemas. The goal of these schemas was not only to allow for tracelink modelling but also for providing a technological foundation that allows for intelligent algorithms (so-called "reasoners") to process existing engineering data. These reasoners shall discover logical links between heterogeneous product data automatically in order to reduce the time for manual tracelinking even further than the ModelTracer allows.

In the iProd project Fraunhofer IPK has developed ontologies specifying a highlevel engineering metamodel for interlinking different engineering artefacts that are generated and used along the Product Development Process (PDP). These ontologies reuse many concepts known from SysML and STEP 233 and they are designed to be easily adaptable for different engineering domains (such as the automotive, aerospace or home appliances domains) [START_REF] Woll | Semantic integration of product data models for the verification of product requirements, in: Concurrent engineering approaches for sustainable product development in a multi-disciplinary environment[END_REF]. When adapted to the specific vocabulary of one company, an additional ontology-based data integration framework allows for specifying interfaces for different PLM solutions. Through these interfaces, existing engineering data can be linked and subsequently be processed by reasoning algorithms. Using reasoning, not only tracelink modelling can be sped up but also other logical rules can be applied to engineering data. For example, consistency rules between different behavior simulation models could be defined that allow for automatic model checking through a reasoner. Hence, by mapping non-ontological data to an ontology as a mediated schema, additional semantics can be "added" to existing engineering data without modifying it.

Decision making support for sustainable product design. For dedicated tasks in engineering, e.g. for sustainable product design, the interdependencies between design decisions during development and sustainability effects of corresponding value creation are so complex, an engineer can't estimate them without support [START_REF] Pförtner | Concept for decision-making support in sustainable product design based on semantic ontology[END_REF]. Currently, at the Chair of Industrial Information Technology at the Berlin University of Technology, an assistance system for semi-automated decision assistance in sustainable product design is under development in the research project CRC1026 3 .

For this approach, several concepts for decision support have been investigated. One approach is to use ontologies for interlinking data from different phases in the product lifecycle. The sustainability information handled today in many different IT-Systems and databases are combined with knowledge artefacts based on sustainability related methods (Figure 6). With the help of several data bases, different methods as well as aggregated, sustainability-related knowledge, an assessment of different design alternatives is possible.

Another approach is the usage of a knowledge base, where the core functionality of the support system is the classification of the various properties and characteristics of a product with respect to its sustainability. For this, the environmental and social impacts over the entire lifecycle are considered and information is gathered about the sustainability context areas, e. g. the manufacturing processes and equipment, and about re-use. The system evaluates both designer's intention and the stakeholder's needs and requirements by evaluation criteria. [START_REF] Lindow | Decision-Making Support for Sustainable Product Development[END_REF].

Such semi-automated solutions enable the engineer to gather all stored knowledge and make the right decisions by pre-defined rules. Nevertheless, even if the 2nd and 3rd step of EAC can be achieved through interlinking the layers "engineering activity" and "data and information models" of the EOS, additional re-search is necessary in order to achieve the 4th step in the EAC model, the "Auto-mated Engineering" as the final evolution in assistance capabilities. Fig. 6. Approach for integrating sustainability information into an engineer's working environment [START_REF] Beier | Supporting Product Development through Cross-Discipline Dependency-Modeling: Novel Approaches for Traceability-Usage[END_REF] 4

Conceptual framework for PLM solutions

In this paper it has been argued that engineers spend too much time on nonengineering-centric activities. The presented EOS suggests that PLM must always consider four different layers and ensure that these are harmonized. Furthermore, it postulates that PLM must assist engineering activities by providing the necessary information to engineers and that data management centric activities should be automated as much as possible. The EAC stair step model defines the different degrees to which PLM may employ intelligent approaches in order to reach these goals.

Fraunhofer IPK and TUB are introducing technologies and approaches for more intelligent PLM solutions. While a set of promising, existing research results are presented, a coherent vision for, and realization of, future PLM, that addresses all layers of the EOS, is still under development. The essence of that vision is to make better use of artificial intelligence approaches and to build a more integrated PLM IT environment. This future vision can only be reached if research, IT vendors, and industry end users work hand in hand. Towards this, further research steps will be taken by Fraunhofer IPK and TUB:

 Application of a scenario technique [START_REF] Gausemeier | Zukunftsorientierte Unternehmensgestaltung: Strategien, Geschäftsprozesse und IT-Systeme für die Produktion von morgen[END_REF] to identify major challenges for intelligent data management from the industry perspective and definition of use-cases for the application of intelligent management.  Implementation of a PLM based simulation environment to quantify and qualify the impact and synergies of presented and further approaches in the context of defined use-cases.

This paper is a contribution to the research field of next generation PLM, in awareness of the need for intensified interdisciplinary scientific discourse. Growing PLM user demands for more intelligence in PLM can be best satisfied by the collaborative research with PLM vendors and PLM users as well.

Fig. 3 .

 3 Fig. 3. Evolution steps for intelligent PLM solutions in the context of engineering automation capabilities

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 3

  .)  User-centered Automation: This step represents today's state of the art in industry. PLM solutions consist of stand-alone PLM applications. Although PLM vendors offer partly integrated solutions e.g. integrated requirements management with linking functionalities to the product structure, these are not widely diffused into industrial practice. Hence, extensive customizing and configuration efforts are made. The engineer is the central source for the intelligent automation of data management centric activities in PLM.  Assisted Automation: This second level offers developments towards intelligent approaches for data and information management. The engineer is still the single source of intelligence, but is assisted by tools during data search, link-age, usage and other data management centric activities.  Semi-automated Engineering: Semi-automated engineering means that PLM solutions are able to anticipate engineering activities (e.g. action alternatives

Research project ISYPROM. URL: http://www.isyprom.de/

Research project iProd URL: http: //www.iprod-project.eu

Collaborative Research Center 1026 Sustainable Manufacturing -Shaping Global Value creation URL: http://www.sustainable-manufacturing.net/en_GB/