George Pintzos

Loukas Rentzos

Konstantinos Efthymiou

Nikolaos Papakostas

George Chryssolouris

A Knowledge based collaborative platform for the design and deployment of manufacturing systems

Keywords: Collaboration platform, Production design, Ontology

The PLM systems do not provide any link to actual performance indicators, related to cost, time, and quality parameters. This paper describes a collaboration platform, based on a semantic core that expresses knowledge about Products, Processes and Resources. The platform is internet based, connecting engineers in the extended enterprise with shopfloor personnel. The concept is based both on a series of distinct components and elements comprising the knowledge base, and on a set of software agents. The latter are interfaced with a set of Computer Aided (CAx) systems and provide decision support capabilities for the design of a production line.

Introduction

Most industrial sectors strive to find methods to reduce costs, time-to-market as well as to improve quality and expand market opportunities [START_REF] Chryssolouris | Manufacturing Systems: Theory and Practice[END_REF]. Production line design and development can be regarded as one of the most challenging decisionmaking processes, since it is affected by numerous design factors and criteria. Furthermore, it involves collaborative work carried out by several engineers such as process, industrial, and manufacturing equipment engineers [START_REF] Yoshimura | A rapid analysis method for production line design[END_REF]. Thus, different models, methods, techniques and technologies have been researched and developed to aid engineers in managing the complexity of production line design and development. Khan and Day in 2002, introduced a knowledge-based conceptual design methodology for single, multi-and mixed-product assembly lines; this methodology, based upon a selection of a suitable assembly systems can decide on suitable cycle times, parallel work station requirements, and parallel line imple-mentation achieving an economical number of work stations [START_REF] Khan | A Knowledge Based Design Methodology for manufacturing assembly lines[END_REF]. Several system analysis methods were developed for assisting engineers with the optimization of their design, namely in [START_REF] Donohue | Optimal design of stochastic production lines: a dynamic programming approach[END_REF][START_REF] Han | Performance analysis and optimization of cyclic production lines[END_REF] where dynamic programming algorithms and Taylor series expansions were used in order to determine values such as the number of machines for each station and analyse the average steady-state throughput of cyclic production lines, respectively. Two noticeable examples of integrated simulation models, in production design processes, comprise a method of the simulation tools being used as modules for JIT manufacturing design [START_REF] Martínez | Modular simulation tool for modelling JIT manufacturing[END_REF] and an integration framework of analytic and simulation tools for the assembly line design [START_REF] Hsieh | Hybrid analytic and simulation models for assembly line design and production planning[END_REF]. A later approach has discussed the implementation of a web-based workflow management system for integrated production engineering, where the workflow of a production engineering project could be configured within a BPEL Engine layer [START_REF] Alexopoulos | A Web-Services oriented workflow management system for integrated production engineering[END_REF].

These simulation tools can demonstrate the behaviour of production lines, before their implementation, and contribute to a reduction in the development leadtime. However, they are subject to the availability of detailed engineering information, before useful simulation models for analysis can be developed. Consequently, these tools are usually utilized during the final stages of the design process in order for the configurations of design solutions, already planned in detail, to be verified [START_REF] Yoshimura | A rapid analysis method for production line design[END_REF], whereas analytical methods are applied during the conceptual design stage, when design solution outlines are constructed [START_REF] Daaboul | Design for mass customization: Product variety vs. process variety[END_REF]. Furthermore, these design methodologies do not consider the collaborative aspect of the design and the development processes, meaning that similarly to the product development process, the production line design process is a very knowledge-intensive and collaborative task that involves different stakeholders and knowledge bearers [START_REF] Wang | Assembly process planning and its future in collaborative manufacturing: a review[END_REF]. While knowledge-intensive collaboration has emerged as a promising discipline for dealing with the modelling and decision-making processes, in distributed product design systems, the same cannot be said for the design of manufacturing systems and production lines [START_REF] Zha | Knowledge-intensive collaborative design modeling and support. Part I: Review, distributed models and framework[END_REF]. However, with the advent of the Internet, software frameworks will continue being developed for improving the ability to represent, capture and reuse design knowledge [START_REF] Chandrasegaran | The evolution, challenges, and future of knowledge representation in product design systems[END_REF].

Platform architecture

The proposed platform is expanded into four main domains; the Execution, the Workflow, the Interface and the Knowledge domain (Figure 1). The Knowledge and Workflow domains include the so-called "Knowledge base", which is a semantic description of the acquired knowledge and workflow structure of production design processes, comprising production data structures as well as information about the production parameters. Further details concerning the Knowledge domain will be given in the next section.

The Execution domain includes software agents and applications that directly assist the production design participants in performing various tasks, with reference to project execution and project management. The latter is supported by separate agents (Workflow agents), which inform engineers of their task (e.g. regarding the location of necessary input data files) on the basis of a project's latest progress and keep track of it (through the Workflow domain). The execution of tasks is supported by specific agents that, depending on the expected results of a task, provide links to either web-services working as tools for specific steps (e.g. for line balancing, cycle time calculation) or translation services, regarding the possible incompatibility of file formats (e.g. translation of proprietary file formats to open standard formats, such as PLCopen or STEP). The Engineering data represent a cloud service in the form of an Infrastructure as a Service (IaaS) that includes a common database. It is connected to the knowledge repository and the physical data repositories placed in each stakeholder's facilities. When data have to be shared, the Workflow agents use the Project management information model, in the Knowledge base, for the calculation of the access rights that either provide the relevant link to the file or not. Finally, the interface domain includes all the necessary interfaces among the SW agents, the web-based services and the production design participants that work in the various projects.

Knowledge structure in the extended engineering

The structure of the information and consequently the formation of knowledge, as regards production design and deployment, was realised with the use of an ontology modelling and it was based on two main requirements:

 The model that will be used should have a very definite set of concepts including the relations between the concepts.  The concepts should be as generic as possible in order to satisfy possible differences within the organisations in extended engineering.

These requirements are somewhat contradictory, since the greater the depiction of concepts, the lower the generic character of the model. Therefore, the modelling was performed on the basis of cases created by industry partners that served as Original Equipment Manufacturers (OEM), production system Integrators and parts/ equipment suppliers.

Product-Process-Resource model and relevant files

The Product, Process and Resource views are common terms in PLM systems and represent the different views that engineers have on CAx files during the production design execution. In the proposed platform, the first sets of concepts of the Product-Process-Resource (PPR) include the modelling of the actual digital, production planning related data. The main concepts here are Product, Process and Resource. The modelling of these concepts derives from the higher level structures of relevant files, used by commercial CAx products and STEP Part 21, at the extent of their being considered consistent with each other. The selection of the CAx tools was based on questionnaires, delivered both to OEMs and SMEs, as well as a production system integrator. The lower levels of the structures were not used since there were inconsistencies between the different CAx products' file structures. In cases that information from the lower levels of a file's structure needs to be extracted, e.g. for the identification of process times from a CAM file, this can be done through the parsing of files or the direct input by the user of the relevant attribute values through a Graphical User Interface. The product data have their own structure that links them to the part data with a simple object relation. The processes can also be depicted as a CAx file, while the resource files are also described as product data through a simple "has" relation. The relevant data objects are not only CAx files. Therefore, a special concept named DesignDataObject, was defined as shown in the figure below. The specific components of this concept represent documents, spreadsheets and files, relevant to one or more tasks of the design process and the creation of various documentations. The specific concept also includes XML files that can be manufacturing related, such as machine controller programs (e.g. PLCopen XML format) or they can express process sequences or even support business processes such as tendering (selection of suppliers).

Workflow and Human resources

This section describes the main concepts that are related to project workflow management and human resources. The term human resources here is used to describing the relevant actors participating in the design of a production system. The main concept, regarding project workflow management, is the Project Element, which has as subclasses the project, the phases and tasks of a certain project. The project individuals coming from the Project subclass serve as a reference that lasts throughout the design and deployment phases. The data attributes can be filled in through a web-application that works by means of collecting all the initial specifications for the design project (e.g. by an OEM to the Integrator). Each project element is linked to another through relations, namely the hasTask or hasPhase. When a new project concept is created, the individuals of the Phase concept are already set: Planning Process/ Layout, Planning of tools and equipment, Line balancing, Specification of tools & equipment, Purchasing of equipment, Realisation/Ramp up. The tasks of each phase can also be copied from the previous versions on the basis of similarity measurements, regarding the initial specifications of the project. Each Project element is linked to one or more Design data object(s) (described in the previous section) through relations such as hasInput and hasOutput. Therefore, the workflow also represents a data flow in the sense of certain files, required as input or output from each phase.

As regards human resources, the main concept connecting people to tasks is that of the Role (Figure 5). Each person is inserted into the platform as an Actor, a concept which includes information with reference to a person's experience and background. At each time instance of the project, the information about a person's involvement in the project is described. Furthermore, the most relevant people that can fit in a role can be identified by simple queries that can be performed by SW agents. In the following section, the technological implementation that allows the mapping and the data exchange between the central ontology and the local data bases of the project's stakeholders is presented.

Knowledge base mapping

Although the models presented fulfil the requirements of the end users, the elements that represent the actual files, used by engineers, should be somehow linked to the locations of the actual data. This section describes the way that the ontology elements' structure described in section 3.1 can be mapped to the local database structures of the various stakeholders, participating in a collaborative project. The knowledge base mapping refers to the linking of the corresponding elements and the data exchange between the central ontology and the local data bases of the project stakeholders. The implementation is made through a middle step (Figure 6, Connector), in the Jena framework (Java). Via Jena, all the relevant ontology objects can be linked to the corresponding files in local data repositories. The persistence layer is the main link between the different objects, while the service layer is where different web-services and agents can interact with the ontology model and in the course of it with the relevant data attributes, files etc. Since the ontology describes a number of projects (complete and on-going), the relevant concepts are not used as they are, but are automatically recreated each time that a new project is set up. These concepts are included in separate OWL files, connected to the main ontology through uniform resource identifiers (URIs). This helps boost the editing, reading and reasoning functionalities of the model. The re-instantiated concepts include all the PPR models as well as the relevant tasks, roles etc.

Case study

In order for the utilization of the proposed platform to be demonstrated, a case study is presented below. The actors belong to a system integrator that has been commissioned by an OEM to design a new line.

One of the platform's main functionalities is the use of web-based applications that can interact with the ontology. In this case study, the application considers the initial rough line balancing from an assembly line that has to be performed at the initial steps of the line balancing phase. The application can be provided as a link to the relevant actor that has the corresponding role. The application reads the ontology of the project and calculates the cycle time using data from the project's instance (individual). More specifically, the application automatically uses capacity, working days, shifts and working time per shift values (see Figure 4) and provides the value of the cycle time. Then, the user is requested to input the number of processes, including their expected times as well as any alternatives that derive from the use of different equipment. This can be done through a common web-based interface, where the information from the engineer is sent to the application through XML (Figure 7). Finally, the application forwards the results, which the user can either accept or try using different data, or even inform the relevant actors that the setup has to change (change request). In this case, the platform agents can inform the other participants of the change requests, by reasoning through the hasInput and hasOutput semantic relationships for the identification of the tasks that provide the input to this task, the relevant Roles and subsequently, the Actors. Since, the initial rough process times have been inserted into the line's description, another actor can use the semantic repository available in order to identify appropriate equipment for the rest of the processes (e.g. electric screwdrivers). The platform uses a semantic Rule that groups from previous projects processes similar to the ones that the user has defined based on their name (e.g. transmission tightening), the type (e.g. manual) along with their corresponding duration. After the grouping, a query that provides the resources belonging to the similar processes is executed. The engineer can now select the equipment that can be reused in the new project and automatically, the project's data including the line's cost are updated in the repository.

Conclusions

In this paper, there is a presentation of a collaborative platform that uses an ontology model for the description of different engineering files, business processes and human resources. The ontology serves as a communication layer between the different organisations, within extended engineering and can be handled by different SW agents as well as by engineering related web-based applications. The ontology is used not only as a representation of the engineering processes but also as a description and a mapping between engineering and business processes, which can be regarded as an extension of the current PLM approaches to production design. It is anticipated that this approach will enable the boosting of collaborative processes, including project management and data exchange between different stakeholders. Furthermore, the ontology can be used for the storage of knowledge with reference to certain business and engineering processes that can be identified by SW agents in new projects. Possible challenges that should be addressed in the future are the access rights management, concerning files shared by different companies as well as the support for the exchange of open standards by commercial CAx tools. Moreover, ontology related technologies need to be further evolved as regards their integration with various database technologies. Incorporating human-centred interface technologies, such as Augmented and Virtual Reality into the platform and expanding the business processes could be two very interesting paths to be explored in a future study.

Fig. 1 :

 1 Fig. 1: Specific domains of the proposed platform.

Fig. 2 :

 2 Fig. 2: Product-Process-Resource model.

Fig. 3 :

 3 Fig. 3: Design data objects' structure.

Fig. 4 :

 4 Fig. 4: Project elements' concept.

Fig. 5 :

 5 Fig. 5: Human resource model.

Fig. 6 :

 6 Fig. 6: Knowledge base mapping.

Fig. 7 :

 7 Fig. 7: Rough line balancing using the platform components.

Corresponding author, e-mail: xrisol@lms.mech.upatras.gr

Acknowledgments

This study was partially supported by the IP project Know4Car/ FoF-ICT-2011.7.4 -284602, funded by the European Commission in the 7th Framework Programme.


Daniel Fitzpatrick
email: daniel.fitzpatrick@etsmtl.net

François Coallier

Sylvie Ratté

A reference architecture for an enterprise knowledge infrastructure

Keywords: Knowledge management, multi-domain ontology, data integration, product lifecycle management, enterprise architecture

With the emergence of social media, data available for market analytics has grown significantly, especially in the context of product lifecycle value analysis. Existing architecture frameworks do not support knowledge management, required to process massive amount of market data, or 'big data'. In order to perform product lifecycle value analysis, product managers need to access, in a seamless manner, data from several domains from systems within the organization and from external sources such as social media, government and industry sites, to name a few. The structured and integrated data must then be transformed into information, or contextualized data, and ultimately into actionable information or knowledge. To achieve this objective, this paper proposes an innovative approach, the Reference Architecture of an Enterprise Knowledge Infrastructure (RA-EKI) that provides a holistic approach to manage the complete knowledge lifecycle.

Introduction

Product Lifecycle Management (PLM) reveals itself as a crucial business-enabling paradigm. It supports the business effort to ensure efficiency and consistency in the entire product lifecycle. Interoperability between PLM, Customer Relationship Management (CRM), Enterprise Resource Planning (ERP) and Manufacturing Execution Systems (MES) is also required to provide better process efficiency and customer satisfaction. These information systems need to be integrated in order to satisfy data quality requirements [START_REF] Khedher | Quality improvement of product data exchanged between engineering and production through the integration of dedicated information systems[END_REF].

Within and between each of these systems, knowledge has become a strategic asset to the enterprise. It increases the innovative capability and core resource utilization, such as people and assembly lines [START_REF] Marchetta | A reference framework following a proactive approach for Product Lifecycle Management[END_REF]. It also favors knowledge reuse, helps improving workers efficiency and safety. Many obstacles still hinder the generalized adoption of cross-enterprise knowledge management in the PLM paradigm. Current PLM business process definition, product information model and information treatment approaches lack the required agnostic and holistic quality to support industry independent solution patterns [START_REF] Fitzpatrick | A holistic approach for the architecture and design of an ontology-based data integration capability in product master data management[END_REF]. The difficulty in extracting information stored in several types of formats such as image, video and audio constitutes an important hurdle to the enterprises' progress. Such a heterogeneous information environment conceals crucial knowledge. Locating and retrieving such knowledge imposes costly processes and specialized human resources [START_REF] Mazumdar | A Knowledge Dashboard for Manufacturing Industries[END_REF].

These many constraints may explain the great level of difficulty currently experienced by the organizations to adapt their PLM processes, and others, so that they can be more agile, flexible and adaptive to the rapidly changing market conditions [START_REF] Han | Process-centered knowledge model and enterprise ontology for the development of knowledge management system[END_REF][START_REF] Raza | INFORMATION MODELING AND KNOWLEDGE MANAGEMENT APPROACH TO RECONFIGURING MANUFACTURING ENTERPRISES[END_REF].

This research project intends to provide a Reference Architecture for an Enterprise Knowledge Infrastructure (RA-EKI), inspired form The Open Group Architecture Framework's TOGAF Reference Architecture -Information Integration Infrastructure (RA-III) to address these shortcomings. RA-EKI proposes a set of generic applications and a multi-domain ontology at its center. A description of the inductive research method used to elicit ontology patterns in this project can be found in [START_REF] Fitzpatrick | A holistic approach for the architecture and design of an ontology-based data integration capability in product master data management[END_REF]. RA-EKI's fundamental purpose is to deal with the issue of being data rich and knowledge poor, to be inundated with massive quantity of data but with limited capability to convert it into valuable knowledge.

The next section provides an insight on related projects that propose various ontology-based architecture approaches. Section 3 provides an overview of RA-EKI and a more detailed perspective on RA-EKI's generic applications. Section 4 covers a use case on the use of RA-EKI in support of product value analysis and section 5 concludes the paper.

Related work

As presented in [START_REF] Khedher | Quality improvement of product data exchanged between engineering and production through the integration of dedicated information systems[END_REF], data quality constitutes one of the purposes of knowledge management models. Since enterprise knowledge is often unearthed from corporate data, data quality plays an important role in the presented models. Certain models extend their coverage of PLM and reach out to other process-centric paradigms such as Manufacturing Execution Systems (MES) [START_REF] Khedher | Quality improvement of product data exchanged between engineering and production through the integration of dedicated information systems[END_REF][START_REF] Terkaj | Virtual Factory Data Model[END_REF], Enterprise Resource Planning (ERP) [START_REF] Khedher | Quality improvement of product data exchanged between engineering and production through the integration of dedicated information systems[END_REF] and Customer Relationship Management (CRM) [START_REF] Lu | Ontology Alignment for Networked Enterprises Information Systems Interoperability in Supply Chain Environment[END_REF], forming a synergy that would increase even more the potential of sustainable growth in revenue, profitability and market share.

Proposed methods use various semantic exchange mechanisms such as mediation web services [START_REF] Khedher | Quality improvement of product data exchanged between engineering and production through the integration of dedicated information systems[END_REF][START_REF] Yusuf | Extensive Overview of an Ontology-based Architecture for Accessing Multi-format Information for Disaster Management[END_REF], Service-Oriented Architecture (SOA) semantic services [START_REF] Raza | INFORMATION MODELING AND KNOWLEDGE MANAGEMENT APPROACH TO RECONFIGURING MANUFACTURING ENTERPRISES[END_REF][START_REF] Yusuf | Extensive Overview of an Ontology-based Architecture for Accessing Multi-format Information for Disaster Management[END_REF], intelligent agents [START_REF] Marchetta | A reference framework following a proactive approach for Product Lifecycle Management[END_REF][START_REF] Wang | Service-oriented architecture for ontologies supporting multi-agent system negotiations in virtual enterprise[END_REF], Extraction, Transformation and Load (ETL) [START_REF] Mazumdar | A Knowledge Dashboard for Manufacturing Industries[END_REF][START_REF] Thames | A Semantic Association Hardware AcCeleration System for Integrated Product Data Management[END_REF] and Enterprise Application Integration (EAI) message broadcasting [START_REF] Han | Process-centered knowledge model and enterprise ontology for the development of knowledge management system[END_REF] to resolve the syntactic and semantic heterogeneity between the systems.

The reference models, such as SCOR [START_REF] Lu | Ontology Alignment for Networked Enterprises Information Systems Interoperability in Supply Chain Environment[END_REF] and VFDM [START_REF] Terkaj | Virtual Factory Data Model[END_REF] put forward knowledge management infrastructure functions that handle the transformation of raw data to refined knowledge. Table 1 synthesizes these knowledge management functions as described in the cited literature.

Table 1. Knowledge management functions Functions Description Data Quality A mediation approach to resolve syntactic and semantic heterogeneities between business processes [START_REF] Khedher | Quality improvement of product data exchanged between engineering and production through the integration of dedicated information systems[END_REF].

Knowledge extraction

Applications can extract information from unstructured and semi-structured data, Information is grouped and ultimately forms unit of knowledge. The applica-tions annotate text files using W3C standards for XML, adding rich meta-information [START_REF] Marchetta | A reference framework following a proactive approach for Product Lifecycle Management[END_REF][START_REF] Raza | INFORMATION MODELING AND KNOWLEDGE MANAGEMENT APPROACH TO RECONFIGURING MANUFACTURING ENTERPRISES[END_REF][START_REF] Yusuf | Extensive Overview of an Ontology-based Architecture for Accessing Multi-format Information for Disaster Management[END_REF].

Knowledge structuring

Knowledge is structured after extraction from data into knowledge representation formats such as RDF triples [START_REF] Mazumdar | A Knowledge Dashboard for Manufacturing Industries[END_REF][START_REF] Raza | INFORMATION MODELING AND KNOWLEDGE MANAGEMENT APPROACH TO RECONFIGURING MANUFACTURING ENTERPRISES[END_REF].

Knowledge storage

Archiving enterprise knowledge in a way to facilitate retrieval. Relational database and XML document technologies are used [START_REF] Marchetta | A reference framework following a proactive approach for Product Lifecycle Management[END_REF][START_REF] Liu | Research of Ontology-based coal Enterprise Knowledge Management Model system[END_REF].

Knowledge access

The treatment of queries to access stored knowledge. This can be accomplished through query processors or with the use of knowledge dashboards [START_REF] Mazumdar | A Knowledge Dashboard for Manufacturing Industries[END_REF][START_REF] Raza | INFORMATION MODELING AND KNOWLEDGE MANAGEMENT APPROACH TO RECONFIGURING MANUFACTURING ENTERPRISES[END_REF][START_REF] Liu | Research of Ontology-based coal Enterprise Knowledge Management Model system[END_REF].

Intelligent agents

Applications dedicated to specific tasks such as supporting transactions between business entities [START_REF] Marchetta | A reference framework following a proactive approach for Product Lifecycle Management[END_REF][START_REF] Wang | Service-oriented architecture for ontologies supporting multi-agent system negotiations in virtual enterprise[END_REF].

Process definition

An ontology-based design-time application that assists the enterprise to create or modify business processes with the use of generic process template and process modeling such as BPMN [START_REF] Han | Process-centered knowledge model and enterprise ontology for the development of knowledge management system[END_REF].

Data integration

Applications that map data from heterogeneous sources to a global schema and allowing to be restructured as contextualized data, or information, to be circulated and processed for knowledge extraction [START_REF] Fitzpatrick | A holistic approach for the architecture and design of an ontology-based data integration capability in product master data management[END_REF][START_REF] Raza | INFORMATION MODELING AND KNOWLEDGE MANAGEMENT APPROACH TO RECONFIGURING MANUFACTURING ENTERPRISES[END_REF][START_REF] Calvanese | Conceptual modeling for data integration[END_REF]. Natural Language Processing (NLP) An problem solving method that uses stored knowledge to convert unstructured data in a form of text to structured data and information [START_REF] Raza | INFORMATION MODELING AND KNOWLEDGE MANAGEMENT APPROACH TO RECONFIGURING MANUFACTURING ENTERPRISES[END_REF][START_REF] Feldman | The text mining handbook: Advanced approaches in analyzing unstructured data[END_REF].

The knowledge management models utilize various ontology approaches to provide a formal vocabulary to their semantic applications. Most models use widely known ontologies such as STEP, CPM, Onto-PDM and TOVE [START_REF] Khedher | Quality improvement of product data exchanged between engineering and production through the integration of dedicated information systems[END_REF][START_REF] Marchetta | A reference framework following a proactive approach for Product Lifecycle Management[END_REF][START_REF] Terkaj | Virtual Factory Data Model[END_REF][START_REF] Lu | Ontology Alignment for Networked Enterprises Information Systems Interoperability in Supply Chain Environment[END_REF]. With these ontologies and others built from within the projects, the models cover many concepts considered unrelated to PLM such as customer data. The pervasiveness of data subjects as explained in [START_REF] Terzi | Product lifecycle management -from its history to its new role[END_REF] highlights the changing nature of not only PLM but all of the other process-centric paradigms as well. The cited papers enunciated a significant set of concepts, such as: customer demographic data, orders, invoices, complaints, transactions, contracts, material orders, market information, new legislations affecting regulatory compliance, etc.

3

Description of RA-EKI

Overview

RA-EKI, as illustrated in figure 1, comprises a set of processes, a collection of orthogonally linked ontologies and databases to process unstructured, semi-structured and structured data. Further processing converts data into information and ultimately into knowledge. A more detail description of RA-EKI ontology structure can be found in [START_REF] Fitzpatrick | A holistic approach for the architecture and design of an ontology-based data integration capability in product master data management[END_REF]. Furthermore, since the inductive phase of the research is on-going, the final findings of the ontology structure including the multi-domain ontology will be the subject of future publications. Data can be extracted from within the organization, such as from structured databases, documents, emails, etc., and from external sources such as social networks, customer and government sites, etc. RA-EKI is described in greater depth in the next section. RA-EKI's applications may be implemented as agents or in other forms. Furthermore, although this paper covers RA-EKI with a warehouse style core database, this reference architecture may be implemented without a persistent central database, relying solely on mediated services or a hybrid configuration using both approaches.

3.2

Description of RA-EKI`s applications

Transformation of unstructured data and semi-structured to structured data

The data transformation applications, as an assembly line, progressively convert unorganized, massive amount of symbols into structured data, as illustrated in figure 2.

There are three data structures used for storage: the unstructured data repository, the semi-structured database and the multimedia database core. The unstructured data repository contains raw text XML files pre-processed and summarily annotated by the crawler application. The semi-structured database contains refined XML text files processed by the NLP part 1 application with syntactic transformation. The core database, contains structured data and multimedia material. Along with the ontology structure, the core database is RA-EKI's central data structure. This database is struc-tured semantically in line with the multi-domain ontology. Assertions are stored in RDF triples and incorporated in an object relational database such as the core database for performance reasons [START_REF] Khouri | DWOBS: Data Warehouse Design from Ontology-Based Sources[END_REF].

The crawler application, using a decay concept and genetic programming as proposed by [START_REF] Bazarganigilani | FOCUSED WEB CRAWLING USING DECAY CONCEPT AND GENETIC PROGRAMMING[END_REF], and following search goals stored in the core database, navigates through the web and detects internet material of interest. The crawler extracts HTML pages, per example, and pre-processes them by removing unnecessary items such promotional hyperlinks. The crawler annotates the raw XML file with metainformation such as author, location and time, thus providing a useful context for downstream applications [START_REF] Bazarganigilani | FOCUSED WEB CRAWLING USING DECAY CONCEPT AND GENETIC PROGRAMMING[END_REF].

The NLP part 1 application performs syntactic, morphological and lexical analysis on the raw data and provides a sentence structure to the text. Nouns, verbs and other sentence items are tagged and meta-information is added. The NLP part 2 application finalizes the text mining process by semantically converting the semi-structured text and by extracting concepts found in the ontology structure and by storing these concepts in the core database. [START_REF] Feldman | The text mining handbook: Advanced approaches in analyzing unstructured data[END_REF][START_REF] Wimalasuriya | Ontology-based information extraction: An introduction and a survey of current approaches[END_REF].

WWW Crawler

Source profiling, data integration and identity matching applications

The Source profiling application, as illustrated in figure 3, analyzes a new source database by generating volumetric and other statistics about databases. This information is then stored in the core database to be used especially for designing data integration mappings. It also provides an assessment on data quality issues.

The ontology-driven data integration applications convert in run time data from a heterogeneous source to a global schema that follows the same conceptualization as the multi-domain ontology and the core database. The three approaches used are EAI message broadcasting [START_REF] Han | Process-centered knowledge model and enterprise ontology for the development of knowledge management system[END_REF], SOA [START_REF] Raza | INFORMATION MODELING AND KNOWLEDGE MANAGEMENT APPROACH TO RECONFIGURING MANUFACTURING ENTERPRISES[END_REF][START_REF] Yusuf | Extensive Overview of an Ontology-based Architecture for Accessing Multi-format Information for Disaster Management[END_REF][START_REF] Wang | Service-oriented architecture for ontologies supporting multi-agent system negotiations in virtual enterprise[END_REF] and ETL [START_REF] Thames | A Semantic Association Hardware AcCeleration System for Integrated Product Data Management[END_REF][START_REF] Maynard | Natural language technology for information integration in business intelligence[END_REF]. Although the underlying technologies are different, the fundamental mechanism remains the same. Mapping rules are also added to translate data from a heterogeneous source databases to the target global schema, represented in the ontology structure and core database. The mapping rules ensure that the source data are translated into the syntactic and semantic structures of RA-EKI's ontologies and core database. The same process is reversed when data is sent back to the source system either using SOA services or EAI message broadcasting. Current research issues can also be found notably in [START_REF] Calvanese | Conceptual modeling for data integration[END_REF] about data integration in the context of knowledge management. Finally, the identity matching application is used in the attempt to associate objects, people and organizations per example, originating from different source databases but being potentially the same individual.

Source database

Source profiling

Multimedia Data Core

Data integration Identity matching Fig. 3. Source profiling, data integration and identity matching applications

Transformation of structured data into information

As highlighted by [START_REF] Mazumdar | A Knowledge Dashboard for Manufacturing Industries[END_REF] and illustrated in figure 4, one of the more efficient and popular mean to distribute information consists in pulling decontextualized data from RA-EKI's core database and then re-contextualizing the data in the form of a userfriendly dashboard. The dashboard publisher is ontology based in RA-EKI. The datamart generator uses corporate objectives, critical success factors, key performance indicators and other performance monitoring requirements that are stored in the ontology structure and the core database. A requirements ontology-driven datamart generator may reduce the time for delivering a datamart [START_REF] Ta'a | Goal-ontology approach for modeling and designing ETL processes[END_REF].

Transformation of information into knowledge

The data mining application, ironically, extracts knowledge not data. Through predictive and descriptive modeling, this design-time ontology-based application leverages the data scientist's skills and expertise to produce knowledge that can be encapsulated in a XML variant, a Predictive Model Markup Language (PMML). PMML is a standard adopted by most database technology manufacturers to facilitate the development and deployment of algorithms developed using data mining techniques. Research is currently conducted to perform ontology-driven knowledge extraction from PMML files [START_REF] Sottara | Enhancing a production rule engine with predictive models using PMML[END_REF]. RA-EKI knowledge extraction application stores extracted knowledge into the core database in transit to the downstream ontology building application. Various ontology-building methods are proposed in [START_REF] Mazumdar | A Knowledge Dashboard for Manufacturing Industries[END_REF][START_REF] Raza | INFORMATION MODELING AND KNOWLEDGE MANAGEMENT APPROACH TO RECONFIGURING MANUFACTURING ENTERPRISES[END_REF][START_REF] Khouri | DWOBS: Data Warehouse Design from Ontology-Based Sources[END_REF]. See figure 5 for the transformation of information into knowledge.

Complex event processing

The Complex Event Processing (CEP) application, represented in figure 6, scans RA-EKI's databases on a continual basis. As these databases can be populated in right time, some data, such as those related to events, can be summarily analyzed by the CEP application. The ontology-driven CEP application attempts to detect any event of significance for the enterprise, guided by a set of prioritized subjects stored in the core database. This application may broadcast an alert through an EAI message, SOA service or using email services. The authors in [START_REF] Schaaf | Semantic Complex Event Processing[END_REF] also describe an event ontology, an application and technology architectures dedicated to a CEP application. In RA-EKI, the CEP application shares with the other applications the same ontology structure and core database. The CEP application also stores the event in the core database.

Other applications

The other applications, although playing equally important roles, will be covered in a subsequent publication. The data quality application contributes significantly to the overall capacity of RA-EKI to meet its challenges. The Archiving application effectively stores historical versions of data, information and knowledge that can be used by other application. Challenging research issues associated with multimedia mining are being addressed notably by [START_REF] Perner | Learning an ontology for visual tasks[END_REF].

Uses case in product lifecycle value analysis

The following use case, outlined in table 2, illustrates the capacity of the RA-EKI model to provide assistance in performing product lifecycle value analysis by enhancing the process to transform massive amount of data into useful knowledge. [START_REF] Terzi | Product lifecycle management -from its history to its new role[END_REF] The use case formulates a competency question, describes the workflow used by ontologydriven components of the RA-EKI described, maps the concepts specific to the case against abstract concepts of RA-EKI's multi-domain ontology and core database, and identifies the sought benefits. As indicated in [START_REF] Chandrasegaran | The evolution, challenges, and future of knowledge representation in product design systems[END_REF], product design, that include product lifecycle value analysis, constitute one of the crucial stages in the entire product lifecycle. It often lacks procedural rigor and can progress through trial and error because of the complexity at hand. Furthermore, the massive amount of data affects product development, forcing the designers to spend a significant amount of time manipulating data in order to harvest crucial knowledge that may influence the product's commercial success. environmental value of the new product currently under development?" RA-EKI workflow summary:

• The crawler detects government, social media and competitor sites, notably, in all countries or regions considered for the market. The crawler will pre-process acquired text in removing unnecessary items, annotate the text and generating annotation tokens to be stored in the core database; • The NLP function further annotates unstructured text and extracts structured data from internal documents, competitor web sites, social media texts with relevant material for sentiment analysis, government regulatory documents, past similar product recall events, etc.; • Imaging and voice-recognition functions also annotate pictures (similar products...) and voice recording from customer service centers' logs [START_REF] Perner | Learning an ontology for visual tasks[END_REF]; • The data mining support function allows the data analysts and researchers to develop targeting models to detect correlations and potential causalities that may influence the future product's market success; • The datamart generator, based on an goal oriented approach from [START_REF] Ta'a | Goal-ontology approach for modeling and designing ETL processes[END_REF], can produced in a semi-supervised mode, datamarts to provide a rich set of information to the product designers and to the product management governance stakeholders.

• The knowledge extraction and integration functions pulls new semantic material from the core database and converts it into subsumed concepts, in a semi-supervised mode, in the ontology structure. [ Benefits sought:

1. Provide the product designers and managers a 360-degree perspective on a new product to maximize its value and competitive position on the market. 2. Mitigate the risks by leveraging on lessons learned.

Conclusion

A significant number of publications have addressed the challenge of developing integrative ontologies to assist in the various stages of the product lifecycle manage-ment, and notably, in product lifecycle values analysis. This paper proposes a reference architecture, the RA-EKI, as the foundation of a comprehensive knowledge management that would allow a full cycle unstructured data to knowledge and knowhow. The final results of this current project will constitute the foundation for future research involving the development of an implementation of the RA-EKI in the form of a prototype in laboratory settings and ultimately in a trial process to be performed the industry.

Fig. 1 .

 1 Fig. 1. Reference Architecture of an enterprise knowledge infrastructure (RA-EKI)

Fig. 2 .

 2 Fig. 2. Transformation of unstructured and semi-structured data into structured data

Fig. 4 .

 4 Fig. 4. Transformation of structured data into information

Fig. 5 .

 5 Fig. 5. Transformation of information into knowledge

Fig. 6 .

 6 Fig. 6. Complex Event Processing (CEP)

Table 2 .

 2 Use case in product lifecycle value analysis Competency"What are the factors that may influence the financial, customer and question:


Nancy Doumit
email: nancy.doumit@polymtl.ca

Greg Huet
email: gregory.huet@etsmtl.ca

Clément Fortin
email: clement.fortin@polymtl.ca

The role of enterprise social media in the development of aerospace industry best practices

This paper presents a new approach for the development of best practices in the aerospace industry through social media. A survey and semistructured interviews used on a smaller sample suggest that integrating social media to formal organizational structures around a community of practice could indeed be employed to facilitate knowledge and experience sharing between established experts and participants. Finally, this paper also explores the extension of this framework to complement more formal and structured information processes typically managed by PLM solutions to better support the development of best practices via communities of practice. In this case, a complementary link between best practices under development in a community of practice and the design solution being developed in the Digital Mock-Up is presented.

Introduction

Best practices as clarified by [START_REF] Szulanski | Exploring internal stickiness: Impediments to the transfer of best practice within the firm[END_REF] are a -replication of an internal practice that is performed in a superior way to internal alternated practices and known alternatives outside the company. The practice refers to the organization routine use of knowledge and often has tacit component embedded‖. The widespread terminology of -best‖ practice however can easily lead to confusion; it is not so much a universal practice, but rather a localized one related to a specific context. Methodologies and tools in engineering are typically based on a set of theoretical foundations but many are then adapted in their practical formulation in order to meet the specific needs of their implementation context. A common illustration of this would be the use of the House of Quality methodology in different companies. Although the general framework does not vary, each firm will adapt the tool to suit the specific objectives of its implementation. These -in house‖ adaptations or variants are typically documented in the form of best practices [START_REF] Cristiano | Customer-driven product development through quallity function deployment in the U[END_REF]. In order to generate and validate this type of corporate knowledge, companies rely on experienced users to rationalize, document, and disseminate best practices using a network of colleagues and following a standard verification and validation process. Although best practices are quite obviously a core item of corporate knowledge, their production is time consuming and their use across projects and departments can be erratic. This research targets improved approaches to best practice development in aerospace, more specifically the study explores how enterprise social media could be used to improve best practice sharing within a community of practice. The data is based on an industrial case study from which qualitative aspects have been analyzed. The observations and conclusions reported in the paper are grounded on information gathered from interviews and process maps. Before presenting the results of the case study, the current role and perceived value of social media in the enterprise is reviewed.

Social media in the enterprise

In the context of the enterprise and from a systems perspective, social media poses interesting challenges. Indeed, enterprise social networking activities through internet based social software suggest a number of different scenarios that in some cases involve radical transformations with respect to the borders of the enterprise system and its environment. To some extent, many of these -new‖ collaboration scenarios have always existed but through informal and often secretive mechanisms; the support offered by social software and the internet has quite simply extended communication modes and collaborative knowledge sources that could not have been imagined even a decade ago.

Should social media be bounded by the borders of the enterprise? Wouldn't this limit the collaborative knowledge potential that social networking brings to the table? Are some departments or teams at risk of jeopardizing their decisional influence with respect to operational processes?

As these question suggest, most of the concerns related to the deployment of social software in the enterprise are related to information security and quality; the communication and collaboration potential of this new medium characterized by its informal and spontaneous attributes is counterbalanced by a risk of Intellectual Property leakage. Moreover, policies in place imply that all information shared, whether it is be through formal or informal communication processes, is subjected to governmental aviation authority regulations (EASA, FAA, etc.). From a judicial perspective, social media are considered an extension of electronically stored information -ESI- [START_REF] Carlisle | Legal issues for business use of social media[END_REF]) and the same tracking rules apply to any information/document shared on this kind of platform. Businesses are therefore obliged to develop guidelines, policies and procedures to safeguard from legal action. In this paper, the integration of social computing in the enterprise is viewed in a context which avoids these organizational dilemmas. The role of enterprise social media in the development of aerospace industry best practices is studied in a specific environment where the networking activities take place within the boundaries of a firm.

Use of social media to support Communities of Practice

Over the last two decades, a new recognized group structure -Communities of Practice -has emerged to complement the existing formal work breakdown structures that divide a company, e.g. departments, functions or Integrated Product Teams. According to [START_REF] Wenger | Communities of practice: The organizational frontier[END_REF] Communities of Practice (CoP) can be defined as -groups of people informally bound together by shared expertise and passion for a joint enterprise‖. A CoP is therefore characterized by a domain, a group of people (the community), and shared practices. The domain is typically an expertise that is practiced by a number of individuals engaged in sharing the knowledge and experiences through joint activities and discussions. In an enterprise context, CoPs are consolidated through five means: mutual engagement, interpersonal relationships, joint enterprise, identity acquisition and shared repertory [START_REF] Wenger | Communities of practice: Learning, meaning and identity[END_REF].

With the various collaborative features proposed by social software, technology now plays an important role in the establishment and vitality of CoPs. They enable the virtual exchange of information between collocated and distributed communities, also known as Virtual Communities of Practice, i.e. -CoPs supported by information and communication technologies‖ [START_REF] Annabi | Guidelines to align communities of practice with business objectives: An application of social media[END_REF].

Social media rely on a number of collective participation tools such as blogs (individual authorship) or wikis (group authorship) to capture and distribute information. [START_REF] Mcafee | Enterprise 2.0: The dawn of emergent collaboration[END_REF] proposes an interesting classification of the types of activities that can be supported by social software in the context of the enterprise under the acronym -SLATES‖ (Search, Links, Authoring, Tags, Extensions and Signals).

Improving best practices through social media

The field study on best practices carried out by the authors encompasses two specific investigations: a study of the company's current processes and semi structured interviews. Results from these two investigations are reported in this paper. It is important to note that the notions of -best practices‖ in this study are essentially related to the following types of contents:

 Design aspects that needed to be taken into consideration during the preliminary design phase. ( e.g.: Maintenance and accessibility criteria)  The types of analysis required. (e.g.: Pressure drop analysis)  Design evaluation criteria and preferred tool to meet those criteria. (e.g.:

Nominal clearance between systems)  Procedures specific to the department under study. (e.g.: Parts release)

Process based Analysis

In this section, the descriptive study of a best practice development process aims at providing the reader with an illustrated reference of the actual approach taken by the company without the use of a social media. The data used to build the best practice development process presented in Fig. 1 was based on existing documentation and a work session with various experts in the department to validate the map. The process highlights a long validation process of the best practice's check list. It is very sequential and activities are therefore executed through a linear flow path up to the validation gate. It is important to note that information is not shared among the different roles for feedback unless the designer has collected a minimum required set of proof of compliance. This is a rather inefficient approach as external parties have generally more objectivity to identify missing information. Furthermore the close supervision of an expert would help point to similarities in previous designs. The information is only shared late in the process hence exposing the best practice development to greater rework.

Interview analysis

The preliminary process analysis was used to build a semi structured interview focusing on current best practice development process in the company. An initial list of participants was suggested by the company. Most engineers who were interviewed also recommended colleagues to interview or referred to another department or discipline. In this study the authors only reported interviews that were conducted in one specific department. Semi structured interviews were conducted with ten engineers with different degrees of seniority working in the same department but sometimes on different projects. The results presented in this paper have been summarized according to four research questions.

Question 1: When do you refer to best practices to validate your design activities?

The experienced engineers suggested that they had a working knowledge of the best practices and therefore hardly ever consult the documents. New employees (2 years of experience or less) are not always aware of the existence of a best practice for a given design task and for them finding any kind of useful information and guidance is already a challenge in itself. This case highlighted a basic yet important requirement for best practice distribution: when a design task is initiated, the users must have a reminder of the corresponding company best practices.

Question 2: What happens when a given design task ignores an existing best practice?

If the design does not follow the best practice's guideline, five typical cases were raised by the participants and have been summarized in table 3. A forum under a question/answer format could ease the clarification of a specific requirement or interpretation of an analysis. Since several companies have already their internal wikis, the majority of interviewees agreed on adding a wiki platform to share definitions and know how; -currently, only one person can work on a document at any one time. The only way around this is to use shared worksheets on public drives‖ more specifically some asked for -wiki pages‖. People expressed also their need to have a list of people outside the team with their expertise to know who they must contact.

The scope of the interview was wider than just best practice development and aimed at measuring the interest of using social media around a community of practice to facilitate knowledge and experience sharing between established employees. Fourteen engineers answered the questions.

Of all the respondents, only one had heard about an existing community of practice in another department. A short review on CoPs was therefore programmed as a training activity for the department. For the purpose of this study, only the results concerning the features of social software are presented. Figure 2 summarizes the results for each category of feature. This analysis confirmed what had been said during the interviews. Indeed, users need to edit documents at the same time while having a unified search (36 %). Engineers first want to solve their current barriers in information sharing; once a single platform (7%) answers their needs in term of co-authoring (36%) and tagging (14%) they might need extra features to enhance the collaboration process.

Future state: developing best practices using social media

In this prescribed scenario, engineers can openly join any engineering community or can be requested to join one for the purpose of a specific project. The community exists as long as the design is not in production or there is interest to keep it (indicating the existence of a key knowledge domain).

As a first interaction, designers would make use of the -discussion‖ type features such as a question/answer (Q/A) forum to get insight about the job to do. As shown in Figure 3, the essential contribution of the design focused virtual community of practice is to facilitate the problem definition phase, i.e. when designers make hypotheses and look for constraints related to the perceived tasks (typically found in company best practices). Thanks to the virtual CoP, knowledge is gradually and collaboratively explored with the inputs from at least the following key participants: moderator, best practice specialist, subject expert, security/legal representative.

Based on the investigations reported in the previous sections of this paper, the authors prescribed a preliminary best practice development process integrating social media. A generic version of this process is illustrated in figure 3.

The potential role of PLM and future state

With the advent of Social PLM [START_REF] Brown | Social Business Collaboration and the Product Lifecycle: Combining the Collaborative Power of Social Media with PLM[END_REF], the aforementioned benefits of social software could be integrated to a more secure information platform used by a number of engineering designers. Hence, information about a product and the Figure 3 :Prescribed best practice development related know how could be exchanged in a continuous workflow and once formalized would then be pushed to a single secure location in the PLM. Distributed information on social media would follow a maturity curve up to a certain level of confidence where the information is formalized and attached to the controlled product structure. Social tools could be extended to support some formal data as the maturity of the data evolves.

Since the focus of ongoing research is on the best practice definition and refinement, a correlation with the above proposition can be made with the current study: knowledge about a specific component could be shared among CoPs based on the best practices as knowledge references, then formalized and linked to the product structure as it reaches a certain maturity level.

For the aerospace case study, this framework can be achieved using two different approaches: either two separate platforms (PLM and Social Media) or imbedded social functionalities within the PLM software. In the case where both platforms a separated, the social networking advantages are made available to the entire company and focused on knowledge sharing at large; indeed, PLM remains more of a specialist tool and as such does not propose the same user friendliness nor does it really extend to the entire enterprise [START_REF] Huet | Theoretical foundations supporting the implementation of complementary information structures across the life of a product[END_REF]. On the other hand, imbedded social features propose a unified platform to engineers. Both approaches need nonetheless to be pursued as social media is now well documented to offer the unique advantage of converting collaborative tacit knowledge into explicit information [START_REF] Panahi | H P Social Media and Tacit Knowledge Sharing: Developing a Conceptual Model[END_REF].

Even though using social media to support virtual CoPs has been successfully demonstrated [START_REF] Michaelides | Online communities of practice for innovation and knowledge transfer: A case study in the UK[END_REF]), there are few reported studies on the use of virtual CoPs to share engineering design knowledge. Further work needs to be done to ensure the validity of the proposition and as such this paper should be regarded as a work in progress. The research is now set to focus on establishing the detailed requirements for the customization of enterprise social software in the company with the constraints of complementing the use of a large PLM platform.

Figure 1

 1 Figure 1 Best practice development process in an aerospace company

Figure 2 .

 2 Figure 2. Social media features relevant to engineering day to day work

Table 3 .

 3 Discrepancy scenarios between a design tasks and its corresponding best practice What would be your suggestions in terms of tools to improve best practice dissemination?-Message boards with very specific topics. Currently we write memos to summarize the knowledge but they are not generally categorized. Organizing them into a coherent structure would certainly help in finding relevant data.‖."Efficient search engine and better indexing, the ability to write and properly control non-technical data is what we need".

	Discrepancy	Typical mitigation
	Case 1: The process or	The designer must detail a -proof of similarity‖ with previous
	methodology used is similar to	designs. The proof is verified by best practice specialists
	the best practice	before approval


Masoud Niknam
email: masoud.niknam@kit.edu

Jivka Ovtcharova

Towards Higher Configuration Management Maturity

Keywords: Product Lifecycle Management, Configuration Management, Maturity model, Continuous Improvement, Self-assessment

Configuration Management (CM) as a discipline assuring the consistency between product information with the reality all along the product lifecycle has recently been more appreciated by most industries. Although the extensive benefits of this discipline such as direct impact on increasing return on investment, lowering lifecycle costs and leadtime, are realized by most organizations, there is no specific maturity model in the field for evaluating different aspects of organizations' CM activities. Therefore, a Configuration Management Maturity Assessment framework is developed in this paper using state-of-the-art, standards and other maturity models. In order to evaluate the overall CM maturity of various industry sectors using the developed maturity model, the maturity appraisal material in the frame of a web-survey has been sent to a wide range of CM related employees in different industrial sectors. The extensive results coming out of this analysis has shown the overall competency level of various industries as well as potentials for improvement.

Introduction

Configuration management (CM) is a managerial discipline that aims at providing consistency and accuracy of product knowledge throughout its lifecycle and for the same purpose it is being used in different extents in most of the organizations. The primary objective of CM is to ensure that in all the phases of the product lifecycle, changes to product components such as requirements, design and "as-made" information for both software and hardware aspects are assessed and approved before being implemented and recorded and traced after implementation [START_REF] Rouse | Handbook of Systems Engineering and Management[END_REF]. In a shorter form, CM ensures that products and facilities, including all the systems, equipment and components, are accurately described all the time [START_REF]Application of Configuration Management in Nuclear Power Plants[END_REF]. It is noted by many researchers that implementing effective configuration management processes not only improves the safety in organizations, but also has direct positive impacts on return on investment, product lifecycle costs, on-time deliveries and product quality [START_REF] Rouse | Handbook of Systems Engineering and Management[END_REF][START_REF] Dvir | In search of project classification : a non-universal approach to project success factors[END_REF][START_REF]Configuration management guidelines[END_REF]. Although the benefits of effective CM discipline in place are prominent to all professional organizations, there have not been many works to elucidate a clear roadmap to evaluate their maturity in the field, prioritize their improvement activities and implement missing elements step-by-step. Therefore, it is highly beneficial for organizations to have a framework by which they can evaluate their CM activities in order to uncover their gaps and focus on future improvements in a more efficient manner. This lack escalates in higher levels when organizations intend to compare their know-hows in the field with best practices in their industrial sectors. The rest of this paper is organized in the following order. Next section gives a brief description about the nature of maturity models. Section 3 illustrates the key features of CM in standards and maturity models. Accordingly, in Section 4 the concept of CM maturity model is presented. Section 5 describes the research methodology followed for the overall industry assessment. Section 6 provides the results of the survey followed in section 7 by conclusion and overview of future work.

Maturity Models

Due to the high importance of continuous improvement in organizations for obtaining more competitive advantages, there are always needs for supportive tools to assess the "as-is" situation, prioritize improvement measures and control the progress of such improvements. Maturity models are the essential tools to address these issues [START_REF] Bruin | Understanding the main phases of developing a maturity assessment model[END_REF][START_REF] Becker | Developing Maturity Models for IT Management -A Procedure Model and Its Application[END_REF]. A maturity model consists of a sequence of maturity levels ranging from the very basic level to the completely mature level for each important criterion within the discipline being measured. [START_REF] Röglinger | Maturity models in business process management[END_REF][START_REF] Saco | Maturity Models: Inject New Life[END_REF] The main elements of maturity models according to [START_REF] Mettler | Towards a Classification of Maturity Models in Information Systems, Management of the Interconnected World[END_REF], are (1) a number of maturity levels, (2) a descriptor for each level, (3) the characteristics of organizations in each maturity level, (4) a number of important dimensions of the discipline being assessed, (5) important activities under each dimension and (6) the description of the way each activity might be performed in each maturity level. So far Configuration Management has mostly been considered as part of other subject areas and therefore has not been covered comprehensively and with sufficient level of details in maturity models [START_REF] Hass | Configuration Management Principles and Practice[END_REF]. Thus, the aim of this paper is to extract the primary dimensions of Configuration Management and develop a CM Maturity Model with sufficient level of detail. For this purpose, the maturity model design framework proposed by De Bruin et al. [START_REF] Bruin | Understanding the main phases of developing a maturity assessment model[END_REF] is chosen for developing the Configuration Management Maturity Model (Figure 1). Based on these principles, first the most important dimensions of CM and the critical activities under each dimension shall be extracted and formalized by using state-of-the-art analysis and comprehensive review of current maturity models and standards in the field. The collected information then was verified by using expert in the field feedback through a case study at CERN [START_REF] Niknam | Configuration Management Maturity in Scientific Facilities[END_REF]. Subsequently, suitable maturity levels were developed based on the Idea of maturity and organizational alignment [START_REF] Batenburg | PLM roadmap: stepwise PLM implementation based on the concepts of maturity and alignment[END_REF]. Finally the appraisal material in the frame of a web survey was developed and used.

Figure 1. Development phases of a maturity model [5]

Nantes, France, 6 th -10 th July 2013

CM in maturity models and standards

Configuration Management has so far been mostly considered as part of other disciplines. Therefore, its coverage in other maturity models has to a high extent been limited to application of change management processes. The available Maturity Models in the field of business processes are mostly focusing on the organizational development in general and although some of them include requirements for CM, the requirements vary from model to model depending on the processes they are focused on. [START_REF] Hass | Configuration Management Principles and Practice[END_REF] CMMI (Capability Maturity Model Integrated) [START_REF]CMMI for Development, Version 1.3[END_REF], developed by Software Engineering Institute (SEI) at Carnegie Mellon University, as one of the most comprehensive maturity models, covers and evaluates organizations Configuration Management processes as one of the sixteen core functions in an organization. Here, Configuration Management is a support process that shall be followed along with some other processes for the organization to reach the second level of maturity out of five. As an example of a necessity for complementary requirements from other aspect than sole processes to this maturity model, Gupta & RAO [START_REF] Gupta | Best Practices to Achieve CMMI Level 2 Configuration Management Process Area through VSS tool[END_REF] acknowledge the role of IT tools for reaching higher maturity levels in Configuration Management practices and try to find matches between the CM process areas described in the CMMI standard and IT tools for supporting those process areas. In its proposed self assessment framework, IAEA (International Atomic Energy Agency) [START_REF]Application of Configuration Management in Nuclear Power Plants[END_REF], more deeply focuses on CM discipline and defines main criteria for assessing the Configuration Management discipline in safety-critical environments based on experience and best practices gathered from different nuclear power plants. These criteria include program management, design requirements, information and change control, assessment and training. BOOTSTRAP, a European assessment process was developed in the 1990s for assessing the capability of the European Software industry [START_REF] Kuvaja | Bootstrap -A European Assessment Methodology[END_REF]. Bootstrap served as a basis for SPICE (now ISO 15504 [16]) as an overall framework for developing maturity assessment models and was later extended to include guidelines from the ISO 9000 [START_REF] Pereira | A Maturity Model for Implementing ITIL V3 in Practice[END_REF]. Similar to SEI Software assessment model [START_REF] Humphrey | Characterizing the Software Process: A Maturity Framework[END_REF] it utilizes both Capability and Maturity levels. Configuration Management, as a support process, is a necessity for process areas to reach capability level 2. For this purpose, a CM strategy shall be developed and all process and project items shall be identified and baselined. Modifications to those items shall be controlled, recorded and reported. Finally storage, handling and delivery of the item shall be recorded. The Project Management Maturity Model [START_REF] Crawford | Project Management Maturity Model[END_REF] developed by the US Project Management Institute (PMI) based on the Project Management Body of Knowledge (PMBOK® Guide [START_REF]A Guide to Project Management Body of Knowledge[END_REF]). Here, CM is part of the Project Integration Management knowledge area which mainly focuses on the integration of the different project deliverables and documents and thus it is being assessed as an individual function. In order for reaching higher levels of maturity, organizations shall practice change monitoring and control to the scope, schedule and cost and also communicate the information about changes to all stakeholders. [START_REF] Crawford | Project Management Maturity Model[END_REF] In the American standard EIA-731.1 or Systems Engineering Capability Model, CM involves Identification, Change control, Status accounting and auditing of the product and its elements. The CM definitions here are taken from the National Consensus Standard for Configuration management, EIA-STD-649-A. [21] ISO/IEC 12207 [START_REF]Systems and Software Engineering -Software Life cycle Processes, Software and Systems Engineering Standards Committee[END_REF] emphasizes on the importance of defining a CM strategy and policy which shall include the authorities for decision making and change control as well as methodology and process storage to be used for the CM system. In this standard, the organizations are suggested to ensure the changes to baselines are properly identified, evaluated, approved, incorporated and verified. For further information this standard refers to more specific CM standard, ISO 10007. ISO 10007:2003 standard [START_REF]Quality Management Systems -Guidelines for Configuration Management[END_REF] is developed to give a better understanding of the CM subject to organizations and promote the use of CM as well as assist the organizations in applying this discipline. According to this standard, the CM process is comprised of the main five stages of planning, identification, change control, status accounting and audit. This standard provides a more detailed description of what is expected in a Configuration Management Plan. This shows the importance of having a CM strategy and policy together with a clear set of defined roadmaps and methodologies, as well as clearly defined responsibilities and authorities to be used in each process stage. US military standard EIA-649-B [START_REF]Configuration Management Standard 25[END_REF] which replaces the old MIL-STD-973 covers Configuration Management principles and practices more comprehensively than the others. The importance of using a clear set of terminology for Configuration Management is acknowledged and followed in this standard. However, the main functions of CM are similar to the main five functions introduced in ISO 10007. EIA-649-B proposes that implementing policies, assigning functional responsibilities, CM-related training, considering CM-tools and their necessary functionalities, establishing KPIs for CM, assuring supplier's involvement in CM activities and integrating organization wide CM processes shall be included for CM planning & Management. Currently, the U.S. Department of Defence (DoD) is in the process of releasing a new standard for Configuration Management (MIL-STD-3046 [25]). The draft version issued for feedback collection purposes shows more or less the same level of comprehensiveness as EIA-649-B with more focus on standardization of processes. This purpose is achieved by providing standard and simplified process steps and forms for CM functions.

Configuration Management Maturity Model

According to the various functions and categories discussed in the previous section, the authors propose the following five primary dimensions of CM discipline and the sub-dimensions in each area for maturity assessment purposes in organizations (for further details also see [START_REF] Niknam | Configuration Management Maturity in Scientific Facilities[END_REF]).(Figure 3)

Figure 3. Configuration Management primary dimensions and sub-dimensions

As one of the support disciplines in systems engineering and product lifecycle management, the extent up to which it permeate into the organization's structure and activities could represent the level of organization's maturity in this discipline. Therefore, the concept of Maturity and Organizational Alignment utilized by [START_REF] Batenburg | PLM roadmap: stepwise PLM implementation based on the concepts of maturity and alignment[END_REF] is used for categorizing different maturity levels of organizations with respect to their CM activities. Figure 4 represents the authors' proposition for CM four maturity levels.

Figure 4. Configuration Management Maturity Levels

Considering the CM maturity dimensions and sub-categories discussed in previous section, the detailed description for organizations fulfilling the requirements for each maturity level is brought below. [START_REF] Niknam | Configuration Management Maturity in Scientific Facilities[END_REF] Initial -In this level, there is no specific level of Configuration Management dimensions and sub-dimensions in place and people just rely on their own grasp of every situation. Managed -At this level, the generic need for a formal CM discipline is still missing; however some specific methods are in place in some projects or groups.

Standard -At this level, due to the understood need for CM discipline, clear methods are in place as standard way of doing things in most/all projects and groups. However, such methods are not being reviewed and updated based on the benchmark results. Optimizing -The Configuration Management activities and processes are regularly updated. The organization has processes for collecting feedback to improve its CMrelated activities continuously.

Research Methodology

In order for the appraisal material to be distributed rapidly and easily to all target cases and at the same time collect as detail information as possible, online survey methodology was selected [START_REF] Czaja | Designing Surveys: A guide to divisions and procedures[END_REF]. Through a partnership with CM experts from a leading consultant company in CM area, the survey went through 11 iterations for the content and 20 iterations for the visuals. For raising the quality and quantity of the answers, the survey was made available in both English and German languages. Altogether there were 53 questions in the survey which were mostly multiple-choice questions with the choices matching the maturity areas described in section 4. This was chosen to give a logical measure for the respondents for rating the maturity of their organization in the respective subject. After completion, the survey was tested by three CM professionals who were not involved in the process. The content was approved to be sufficiently covering various CM aspects and a few editorial comments were made and implemented. Afterwards, the survey link was distributed to about 150 professionals and CM-related employees from different industrial sectors. Simultaneously the link was posted on well-known online CM communities to collect data from international experts and practitioners. The survey material could be sent for further use upon contacting the authors.

Results

Altogether 61 complete answers were recorded of which 4 were excluded due to having lower response time than average. The analysis result of the remaining responses is clustered in the following sections.

Demographic and General

Important characteristics such as organization sizes and nature, respondents' involvement extent in CM activities, distribution of industrial sectors and level of CM application in various lifecycle phases among participants are shown in Figure 5.

Criteria-based maturity results

The overall maturity of each dimension is calculated based on a simple weighted average formula shown below.

Where M is the maturity in the dimension e.g. Strategy & Performance, i is the maturity level from 1 corresponding to the level initial to 4 corresponding to the level optimizing. xi is the percentage of answers in the i th choice and j is the number of questions in the dimension. For the whole Strategy & Performance dimension overall maturity observed from all responses is 2.25 which is just a bit more than managed level. This shows the need for a more advanced focus on measuring strategic performance in organizations. In the Process dimension, the outcome maturity from the detailed questions in CM Process showed a maturity level of 2.29. This level still leaves much room for improvement of the CM processes to higher levels of Standard and Optimizing. In the Information Technology dimension, the interesting results show that only 33 percent of the organizations use single IT systems while 16 percent don't even use an information system support for CM and about 51 percent use different CM IT systems for different projects or groups of the organization. Some organizations have introduced more than 6 different tools in this respect. More specifically for [START_REF] Rouse | Handbook of Systems Engineering and Management[END_REF] Engineering Change Management process, the support of IT systems is in a low level of managed only and considering the impact of this function needs immediate care and focus. With the overall Maturity of 2.15 in IT dimension, obviously more standardization among different groups and project teams as well as more consistency among the tools being chosen for the whole lifecycle could lead to much better results. In Organization and Value-stream dimension, more than 60% of the respondents believe their choice of CM organization structure to a high extent does not correspond to the organization complexity and needs. As it was expected before, the maturity of organizations with respect to consideration of subcontractors' CM tasks during contract negotiations is very low (1.8) where more than 70 percent of the companies do not have an standard and consistent procedure for this matter. The overall maturity in this dimension is 2.17 with the strength of organizations being the involvement of key stakeholders in major changes. The last dimension Knowledge & Support shows an overall maturity of 1.9 where the use of internal and external benchmarks has a maturity of as low as 1.67 which could show the lack of continuous improvement in this discipline's practices. Another interesting aspect could be the low maturity (1.81) for communication of CM benefits by management which could show the lack of support, promotion and motivation. The overall maturity of target organizations in CM discipline could be observed in Figure 7a.

Industry-Specific Maturity

In this section the maturity results of specific industries which had the most respondents are discussed and illustrated in Figure 7. In the Aerospace industry with the majority of respondents, the overall maturity level is 2.61 which compared to 1.65 in Transportation with lowest maturity is rather high. The results show that in most industries especially transportation, focusing on Information Technology, Knowledge & Support and Strategy modules could result in higher efficiency. In this paper the key influential factors for the success of Configuration Management activities have been extracted and categorized. Configuration Management maturity model was developed and was used to identify the level of maturity and gaps in various industrial organizations. The results demonstrated a rather high potential for improvement in different aspects associated with this discipline. Considering the direct and indirect effect of CM discipline on product leadtime, cost, quality, safety, etc, filling the gap could be of high importance and benefit for all organizations. Nature of the CM maturity model, developed in this paper, is so far descriptive in the sense that it is limited to evaluation of CM maturity level in the target organizations and identification of gaps in different areas. However, in order for the organizations to have an ideal model, next step would be to develop the model further for having prescriptive abilities and leading the organizations for filling the identified gaps in an optimized way. Also using weights for various sub-dimensions according to their importance could obviously lead to more practical results but the importance of each subject could vary in different industries. The choice of web survey was made for its suitability to industry-wide studies and its simplicity in simultaneous data collection from different organizations and in analysis of the quantitative data. However, the level of detail that could be covered in this method is as deep as the level that understanding of the questions would not be difficult. In order to have a specific case study for one organization, a more comprehensive appraisal material shall be developed for other methods such as focus group meetings and semi-structured interviews.

Figure 5 .

 5 Figure 5. Various characteristics of the survey participants and their organizations

Figure 7 .

 7 Figure 7. CM maturity in a) all industries b) Aerospace c) Automotive d) Transportation e) Military & Government f) IT

Acknowledgments. The authors would like to appreciate FP7 European Union Marie Curie program for providing the funding in the framework of PURESAFE project. The authors also would like to thank P3 Group for their support in data collection.