- Email: [email protected]
Development of Intelligent Decision Support System based on Unified Concept for Distributed Manufacturing Planning
7th IFAC Conference on Manufacturing Modelling, Management, and Control International Federation of Automatic Control June 19-21, 2013. Saint Petersburg, Russia
978-3-902823-35-9/2013 © IFAC
454
10.3182/20130619-3-RU-3018.00351
2013 IFAC MIM June 19-21, 2013. Saint Petersburg, Russia
455
2013 IFAC MIM June 19-21, 2013. Saint Petersburg, Russia
knowledge as well as the resources specifications used to process the product at a node in consideration. The Query Generator takes each node one by one and generates relevant query. The query engine takes each query generated against each node and infers potential environmental impact of the node in consideration. In case the sought information is found in the knowledgebase, the query engine delivers information to Evaluation and Decision Making Module. If no result is found, that would infer that the case is completely new and therefore the node has to be sent to the LCA simulation tool for environmental impact assessment. The model synthesizer takes information concerning the selected node and generates appropriate query. This query scans the knowledgebase and generates the possible potential environmental impact as well as the relevant indicators and nodal input to environmental indictor output model. This model is actually input-output simulation model which can be discovered or inferred from the application specific knowledgebase. The planner can visualize the model and can reject or accept the model. Upon approval, the input-output model information is sent to the simulation tool for simulation of potential environment impact. The calculated results are delivered to both the Evaluation and Decision Making Module and the Knowledgebase. The system works in connection with the existing legacy software tools for planning through web-service over the web. This allows planners to share as well as store information over the web about simulated cases and make the inference capabilities of the tool powerful in assisting the choice of optimal production schemes based on environmental impact.
the whole case base is evaluated. Based on the evaluation, the individual cases are entered into the case base of the CBR system. The task of the ontology-based case-based reasoning (CBR) for automotive ramp-ups is intended for storing practical knowledge of the employees. It is particularly useful to be reused in case a knowledge holder leaves the company. Hence, it is used pragmatically to solve problems that would occur in future automotive ramp-ups. Employees can use experience from previous ramp-ups to help solve current problems and therefore make the automotive ramp-up process more efficient (Puppe et al. 2003). Case-based reasoning, as a technique of artificial intelligence, compares a new problem (case) with problem cases from previous ramp-ups already stored in the system. When an inquiry is made, a new case is compared with the cases already stored in the case base (Aamodt and Plaza 1994). The programmed mathematical algorithm calculates the similarity of the cases stored in the case base with the new case. The experience and problemsolving knowledge contained in the most similar cases can be adapted or used as an aid for the new case in current automotive ramp-ups and projects. By conducting repetitions, the user ascertains whether the solution was successful or unsatisfactory for the new case. However, the new case is saved in the case base regardless of the result of this revision. This ensures that unsuccessful solutions are not attempted repeatedly. Thus the case base continues to grow through this automatic learning, following a circular process (Cocea 2011). With the help of this methodology, the practical knowledge of the employees is stored systematically and used pragmatically in future automotive projects.
The implicit knowledge of the planners is collected through formal means by providing a web-based Delphi survey conducted among the experts of the particular domain. Delphi method has been used which is a well establish method and acknowledged in the field of information science (Okoli and Pawlowski 2004, Cegielski 2001, Hayne and Pollard 2000,Holsapple and Joshi 2002, Lai and Chung 2002, Mulligan 2002, Nambisan et al. 1999, Schmidt et al. 2001). The advantages of Delphi surveys are that the experts can describe and supplement their cases over several rounds and can post comments to the cases of the other experts anonymously. This ensures that each case is viewed and reviewed several times. Lastly, there is an opportunity to evaluate the cases and make statements regarding their quality. It is also possible to conduct the Delphi survey via web-based application as a location independent solution. The survey is supported by a monitoring team. The team is responsible for identifying experts and for organizing the survey. During the survey, they are supporting the experts, summarizing interim results and may obtain information for the next round. In addition, they carry out preparatory and follow-up steps of Delphi survey. A generic process of a Delphi survey has been described in (Okoli and Pawlowski 2004, Weimer and Seuring 2008, Skulmoski et al. 2007); the survey for this research activity will be structured in the same manner. During the Delphi survey, the illustrated process will be conducted in each round. Initially, the questionnaire is developed. After each round, the monitoring team conducts evaluation and prepares for next rounds. After four rounds, the cases developed are stored in the case base. Subsequently,
5. IMPLEMENTATION AND VALIDATION Two individual software solutions were implemented based on the unified concept and validated based on two different pilot cases. The first case (Case I) represents potential environmental impact of manufacturing of customized hood assembly in decentralized manufacturing environment. The other case (Case II) relates to ramp-up management in screwing of automotive components. The Case I is shown in Fig. 1. at the left side of the partition line XY. The web client application of Evaluation and Decision Making Module (EDMM) loads the production scheme file (xml file format) from the web server. The Scheme Parser dissolves the production scheme into several production nodes. The information is sent nodewise to the Query Generator which later on creates query relevant to the selected node in a SPARQL format. A shark fish search based algorithms are implemented to enable search in the knowledgebase based on several ontologies. The results are generated by the Query Engine and stored back to the knowledgebase as well as sent to EDMM in an xml format over the web. The knowledgebase is modelled using ontologies. A semantic web reasoner is developed based on Jena Engine and enriched with semantic web-rules concerning pragmatic cases of the potential environmental impact of the possible materials, resources and process technologies to consolidate inference capabilities of knowledgebase. Fig.2. illustrates the ontology modelled for this purpose with a specific case of manufacturing customized hood assembly in a decentralized manufacturing network. 456
2013 IFAC MIM June 19-21, 2013. Saint Petersburg, Russia
Delphi surveys with its individual rounds efficiently. In addition, the CBR system and the similarity algorithm are also integrated. Since these three methods are to be linked to each other, a common platform is created for their intercommunications. The illustration (see Fig. 4. ) shows the IT structure of the prototype.
Fig. 2. Ontology for customized manufacturing process environment
hood
assembly
For planners to search knowledge through the intelligent assistance system, the client applications allows users connected over web to customize their search more concrete instead of searching it using natural language query where precision of the searched information is a not ensured to a great extent and often fetch high number of results. Therefore, a graphical user interface has been developed that guides a planner for customizing query for more concrete search of information. This graphical user interface can customize the specifications of product, process technology and resource to support easy generation of SPARQL based query which is easy to navigate information through the RDF structure. Similarity algorithms are implemented along with heuristic searching rules to navigate the information inside the knowledgebase fast. The searched information is then compiled in an xml file format and finally uploaded on server. The software module connects the several modules including databases, the ontologies and the graphical user interface to the web server using freely available Apache Tomcat Server.
Fig. 3. Graphical user interface for customizing user queries The prototype offers an opportunity to implement ontologies modeled with the standardized software like Protégé into the system. In addition, the cases from the database can be linked through the web server. The web server functions as an interface for the direct exchange of information with the database. A freely available Apache Tomcat server is used for this purpose; the application for the Delphi survey is installed on it. The module of the Delphi survey implemented in the software prototype is constructed flexibly. This means that the layout or the user interface is made adjustable to different requirements (company divisions). It is also important that the different ontologies from the respectively relevant areas of ramp-up management can be integrated. The structure of the individual survey rounds is adjustable to the corresponding ontologies. Through this way, the usability of the software across divisions has been guaranteed. The programmed CBR application is also implemented. The database system used is PostgrSQL, which is also freely available. It can be used to create user profiles and passwords, for example, which allow the assignment of the cases to the respective experts and govern the individual rights of use. The expert knowledge gathered by the Delphi surveys and the CBR System is further saved in the database. The database recognizes whether a case entered within the Delphi survey is completely concluded or must still be improved by the experts in further survey rounds. Also, the
For the second case (Case II), the software prototype has been developed that provides planners and experts to conduct several rounds of Delphi surveys in production ramp-up of screwing processes. The automatic generation of questionnaires for Delphi surveys is supported using ontologies. The cases are generated after completion of Delphi surveys. This ontology relates to ramp-up of screwing processes in automotive sector and serves as a starting point for gathering expert knowledge. On one hand, it is used to represent the topics and terms from ramp-up management area, for the experts in a hierarchical and connected manner. On the other hand, the ontology structures the gathered expert knowledge so that it can then be used by the CBR System. The ramp-up management of screwing processes is validated based on the concept with software programming of three selected, refined, and linked methods (Ontology, Delphi method, and CBR) in practice, This prototype is able to model and represent the ontology as well as to conduct 457
2013 IFAC MIM June 19-21, 2013. Saint Petersburg, Russia
manufacturing network. Web-based software solution comprised of customized user query interface, ontology based knowledge base and Scheme parser is implemented and validated for the pilot case. The software takes input through two means i.e. direct from users through customized user queries for searching the information from the knowledgebase and deliver results relevant to the query. This query is generated after the user has customized his input information. In case of data file as input to the intelligent assistance system, the information contained in the data file is first parsed through Scheme Parser and Query is generated in a sequential manner against the parsed information. The query engine is used to search information from ontologies. The information is delivered to the decision module for processing further in decision making process. The second pilot cases relates to the extraction, storage and automatic learning from personal implicit knowledge through formal means. The knowledge is discovered through web-based multi-round Delphi surveys and later stored in ontology part of Case Base Reasoning module. The software is tested for ramp-up of screwing process.
detailed information from the CBR survey for determining similar cases from the past is saved in the database. This data is used again finally as an input for further development of the case base at the later stage. After having been shown the results of similar cases from past ramp-ups via the web server, the user can ascertain from them new solution approaches for confronted ramp-up problem. The newly gained knowledge during the solution should be input back to the CBR case base so that it is available to other users for global access. For this reason, the database makes available the data that was previously entered into the system while searching, in order to be able to save new cases in the system built upon it. Work station
Web server
Database
Ontology Thing
Screw connections
Retrieval Relation screw Relation part Production equipment «
Screw
Screw surface
Screw material
Parts
Screw parts handling
Parts surface
Production equipment
In future, the software will be extended to implement and use ontologies for inferring generic input-output environment simulation model of material, process and resource based on the nature of manufacturing process. This model will be directly used inside the LCA simulation tool. The generated results will be used to enrich the knowledgebase for better inference capabilities. The validation scenario will be expanded to assist decision making for manufacturing of other products such as customized orthotics in a decentralized manufacturing network. For enhancing the software module dedicated for screwing process ramp-up, the same approach will be used for rolling out further topic areas in the ramp-up management. For this purpose ontologies that structure the knowledge domains of the respective areas must first be created with the relevant experts. It will be followed by Delphi surveys, so that the case base can be filled with the valuable empirical knowledge of ramp-up management experts.
Loose part Screw surface « « « « « « « «
«
Parts Parts Screwing Screwing Periphery material handling tool program
Fig. 4. Structure of the prototype for screw process ramp-up management With the IT structure shown, it is possible to access the programmed applications from any workstation globally through the internet. For example, experts residing on different continents may participate in Delphi surveys. It is also possible to use the CBR system from anywhere in the world. A global knowledge management system is thus created making it possible for the globally available knowhow in the area of ramp-up management to be identified (Delphi survey), stored in an intelligent structure (ontology), and utilized pragmatically by all users (CBR system). This global usage of the system is of particular importance. By this means a comprehensive knowledge base for the ontologysupported CBR System can be built and used to master the challenges for a knowledge management system for vehicle ramp-ups described at the beginning.
For both modules of the software representing the corresponding pilot cases, the unified software will be tested by employees and further refinement will be made based on the feedback from the end users. 7. ACKNOWLEDGEMENTS The work reported is this paper is partially supported by the European Commission Project e-Custom FP7-2010-NMPICT-FoF 260067. REFERENCES Aamodt, A., Plaza, E. (1994). Case-Based Reasoning: Foundational Issues, Methodological Var-iations, and System Approaches. Artificial Intelligence, 7/1, pp. 3952. Beissel, S. (2011). Ontology-based Cased-based Reasoning. Betriebswirtschaftlicher Verlag Gabler. 1. edition. Wiesbaden. ISBN-10: 3834930644. ISBN-13: 9783834930644.
6. CONCLUSIONS AND OUTLOOK A unified concept for integrating the two software solutions, for extracting and reusing the implicit knowledge from the planners as well as the planning related softwares have been developed and validated through pilot cases. The first pilot case relates to the environmental impact assessment of customized hood manufacturing in the decentralized 458
2013 IFAC MIM June 19-21, 2013. Saint Petersburg, Russia
Skulmoski, G. J., Hartmann, F. T., Krahn, J. (2007) .The Delphi Method for Graduate Research. Journal of Information Technology Education, Volume 6, pp. 1-21. Weimer, G., Seuring, S. (2008). Information needs in the outsourcing lifecycle. In Industrial Management & Data Systems, Vol. 108 No. 1, pp. 107-121.
Cegielski, C.G. (2001) A Model of the Factors that Affect the Integration of Emerging Information Technology Into Corporate Strategy. Unpublished Doctoral Dissertation, University of Mississippi. Cocea, M. (2011). User Modelling and Adaption in Exploratory Learning. Doctoral Dissertation, University of London. F. Puppe, H. Stoyan und R. Studer (2003). Knowledge Engineering. In Görz, G., Rollinger, C.-R., Schneeberger, J. (eds.) Handbuch der Künstlichen Intelligenz, 4rth edition, pp. 599±641. Oldenbourg, München. Hayne, S., Pollard, C. (2000) A comparative analysis of critical issues facing Canadian information systems personnel: a national and global perspective. Information & Management, 38 (2), pp. 73±86. Hefke, M. (2009). Technology-based Introduction of Knowledge Management. Südwestdeutscher Verlag für Hochschulschriften. Saarbrücken. ISBN-10: 3838101472, ISBN-13: 978-3838101477. Heisig, P., Orth, R. (2005). Knowledge Management Frameworks in research and practice. European Resarch Center for Knowledge and Innovation. 1. edition. TU Berlin. ISBN-10: 3000172440. ISBN-13: 9783000172441. Holsapple, P., Joshi, K. (2002) Knowledge manipulation activities: results of a Delphi study. In-formation & Management, 39 (6), pp. 477±490. Kuhn, A., Wiendahl, H.-P., Eversheim, W., Schuh, G. (2002)³fast ramp-up". Verlag Praxiswissen, Dortmund. Lai, V., Chung, W. (2002) Managing international data communications. Information & Man-agement, 45 (3), pp. 89±93. Minhas, S., Juzek, C., Berger, U. (2012)a. Ontology based intelligent assistance system to support manufacturing activities in a distributed manufacturing environment. In Proceedings of 45th CIRP Conference on Manufacturing Systems (CIRP CMS), Athens, Greece. Minhas, S., Juzek, C., Berger, U. (2012)b. Elicitation of requirements for a knowledge management concept in decentralized production planning. In International Conference on Mechanical, Industrial and Manufacturing Technology (WASET2012), Berlin, Germany. Mulligan, P. (2002) Specification of a capability-based IT classification framework. Information & Management, 39 (8), pp. 647±658. Nambisan, S., Agarwal, R., Tanniru, M.: Organizational mechanisms for enhancing user innovation in information technology. MIS Quarterly, 23 (3), pp. 365± 395. Okoli, C., Pawlowski, S. D. (2004). The Delphi method as a research tool: an example, design considerations and applications. Information & Management, 42, pp. 15-29. Brancheau, J.C., Janz, B.D., Wetherbe, J.C. (1996). Key issues in information systems manage-ment, 1994±95 SIM Delphi results. MIS Quarterly, 20 (2), pp. 225±242. Schmidt, R.C., Lyytinen, K., Keil, M., Cule, P. (2001). Identifying software project risks: an in-ternational Delphi study. Journal of Management Information Systems, 17 (4), pp. 5±36.
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978-3-902823-35-9/2013 © IFAC
454
10.3182/20130619-3-RU-3018.00351
2013 IFAC MIM June 19-21, 2013. Saint Petersburg, Russia
455
2013 IFAC MIM June 19-21, 2013. Saint Petersburg, Russia
knowledge as well as the resources specifications used to process the product at a node in consideration. The Query Generator takes each node one by one and generates relevant query. The query engine takes each query generated against each node and infers potential environmental impact of the node in consideration. In case the sought information is found in the knowledgebase, the query engine delivers information to Evaluation and Decision Making Module. If no result is found, that would infer that the case is completely new and therefore the node has to be sent to the LCA simulation tool for environmental impact assessment. The model synthesizer takes information concerning the selected node and generates appropriate query. This query scans the knowledgebase and generates the possible potential environmental impact as well as the relevant indicators and nodal input to environmental indictor output model. This model is actually input-output simulation model which can be discovered or inferred from the application specific knowledgebase. The planner can visualize the model and can reject or accept the model. Upon approval, the input-output model information is sent to the simulation tool for simulation of potential environment impact. The calculated results are delivered to both the Evaluation and Decision Making Module and the Knowledgebase. The system works in connection with the existing legacy software tools for planning through web-service over the web. This allows planners to share as well as store information over the web about simulated cases and make the inference capabilities of the tool powerful in assisting the choice of optimal production schemes based on environmental impact.
the whole case base is evaluated. Based on the evaluation, the individual cases are entered into the case base of the CBR system. The task of the ontology-based case-based reasoning (CBR) for automotive ramp-ups is intended for storing practical knowledge of the employees. It is particularly useful to be reused in case a knowledge holder leaves the company. Hence, it is used pragmatically to solve problems that would occur in future automotive ramp-ups. Employees can use experience from previous ramp-ups to help solve current problems and therefore make the automotive ramp-up process more efficient (Puppe et al. 2003). Case-based reasoning, as a technique of artificial intelligence, compares a new problem (case) with problem cases from previous ramp-ups already stored in the system. When an inquiry is made, a new case is compared with the cases already stored in the case base (Aamodt and Plaza 1994). The programmed mathematical algorithm calculates the similarity of the cases stored in the case base with the new case. The experience and problemsolving knowledge contained in the most similar cases can be adapted or used as an aid for the new case in current automotive ramp-ups and projects. By conducting repetitions, the user ascertains whether the solution was successful or unsatisfactory for the new case. However, the new case is saved in the case base regardless of the result of this revision. This ensures that unsuccessful solutions are not attempted repeatedly. Thus the case base continues to grow through this automatic learning, following a circular process (Cocea 2011). With the help of this methodology, the practical knowledge of the employees is stored systematically and used pragmatically in future automotive projects.
The implicit knowledge of the planners is collected through formal means by providing a web-based Delphi survey conducted among the experts of the particular domain. Delphi method has been used which is a well establish method and acknowledged in the field of information science (Okoli and Pawlowski 2004, Cegielski 2001, Hayne and Pollard 2000,Holsapple and Joshi 2002, Lai and Chung 2002, Mulligan 2002, Nambisan et al. 1999, Schmidt et al. 2001). The advantages of Delphi surveys are that the experts can describe and supplement their cases over several rounds and can post comments to the cases of the other experts anonymously. This ensures that each case is viewed and reviewed several times. Lastly, there is an opportunity to evaluate the cases and make statements regarding their quality. It is also possible to conduct the Delphi survey via web-based application as a location independent solution. The survey is supported by a monitoring team. The team is responsible for identifying experts and for organizing the survey. During the survey, they are supporting the experts, summarizing interim results and may obtain information for the next round. In addition, they carry out preparatory and follow-up steps of Delphi survey. A generic process of a Delphi survey has been described in (Okoli and Pawlowski 2004, Weimer and Seuring 2008, Skulmoski et al. 2007); the survey for this research activity will be structured in the same manner. During the Delphi survey, the illustrated process will be conducted in each round. Initially, the questionnaire is developed. After each round, the monitoring team conducts evaluation and prepares for next rounds. After four rounds, the cases developed are stored in the case base. Subsequently,
5. IMPLEMENTATION AND VALIDATION Two individual software solutions were implemented based on the unified concept and validated based on two different pilot cases. The first case (Case I) represents potential environmental impact of manufacturing of customized hood assembly in decentralized manufacturing environment. The other case (Case II) relates to ramp-up management in screwing of automotive components. The Case I is shown in Fig. 1. at the left side of the partition line XY. The web client application of Evaluation and Decision Making Module (EDMM) loads the production scheme file (xml file format) from the web server. The Scheme Parser dissolves the production scheme into several production nodes. The information is sent nodewise to the Query Generator which later on creates query relevant to the selected node in a SPARQL format. A shark fish search based algorithms are implemented to enable search in the knowledgebase based on several ontologies. The results are generated by the Query Engine and stored back to the knowledgebase as well as sent to EDMM in an xml format over the web. The knowledgebase is modelled using ontologies. A semantic web reasoner is developed based on Jena Engine and enriched with semantic web-rules concerning pragmatic cases of the potential environmental impact of the possible materials, resources and process technologies to consolidate inference capabilities of knowledgebase. Fig.2. illustrates the ontology modelled for this purpose with a specific case of manufacturing customized hood assembly in a decentralized manufacturing network. 456
2013 IFAC MIM June 19-21, 2013. Saint Petersburg, Russia
Delphi surveys with its individual rounds efficiently. In addition, the CBR system and the similarity algorithm are also integrated. Since these three methods are to be linked to each other, a common platform is created for their intercommunications. The illustration (see Fig. 4. ) shows the IT structure of the prototype.
Fig. 2. Ontology for customized manufacturing process environment
hood
assembly
For planners to search knowledge through the intelligent assistance system, the client applications allows users connected over web to customize their search more concrete instead of searching it using natural language query where precision of the searched information is a not ensured to a great extent and often fetch high number of results. Therefore, a graphical user interface has been developed that guides a planner for customizing query for more concrete search of information. This graphical user interface can customize the specifications of product, process technology and resource to support easy generation of SPARQL based query which is easy to navigate information through the RDF structure. Similarity algorithms are implemented along with heuristic searching rules to navigate the information inside the knowledgebase fast. The searched information is then compiled in an xml file format and finally uploaded on server. The software module connects the several modules including databases, the ontologies and the graphical user interface to the web server using freely available Apache Tomcat Server.
Fig. 3. Graphical user interface for customizing user queries The prototype offers an opportunity to implement ontologies modeled with the standardized software like Protégé into the system. In addition, the cases from the database can be linked through the web server. The web server functions as an interface for the direct exchange of information with the database. A freely available Apache Tomcat server is used for this purpose; the application for the Delphi survey is installed on it. The module of the Delphi survey implemented in the software prototype is constructed flexibly. This means that the layout or the user interface is made adjustable to different requirements (company divisions). It is also important that the different ontologies from the respectively relevant areas of ramp-up management can be integrated. The structure of the individual survey rounds is adjustable to the corresponding ontologies. Through this way, the usability of the software across divisions has been guaranteed. The programmed CBR application is also implemented. The database system used is PostgrSQL, which is also freely available. It can be used to create user profiles and passwords, for example, which allow the assignment of the cases to the respective experts and govern the individual rights of use. The expert knowledge gathered by the Delphi surveys and the CBR System is further saved in the database. The database recognizes whether a case entered within the Delphi survey is completely concluded or must still be improved by the experts in further survey rounds. Also, the
For the second case (Case II), the software prototype has been developed that provides planners and experts to conduct several rounds of Delphi surveys in production ramp-up of screwing processes. The automatic generation of questionnaires for Delphi surveys is supported using ontologies. The cases are generated after completion of Delphi surveys. This ontology relates to ramp-up of screwing processes in automotive sector and serves as a starting point for gathering expert knowledge. On one hand, it is used to represent the topics and terms from ramp-up management area, for the experts in a hierarchical and connected manner. On the other hand, the ontology structures the gathered expert knowledge so that it can then be used by the CBR System. The ramp-up management of screwing processes is validated based on the concept with software programming of three selected, refined, and linked methods (Ontology, Delphi method, and CBR) in practice, This prototype is able to model and represent the ontology as well as to conduct 457
2013 IFAC MIM June 19-21, 2013. Saint Petersburg, Russia
manufacturing network. Web-based software solution comprised of customized user query interface, ontology based knowledge base and Scheme parser is implemented and validated for the pilot case. The software takes input through two means i.e. direct from users through customized user queries for searching the information from the knowledgebase and deliver results relevant to the query. This query is generated after the user has customized his input information. In case of data file as input to the intelligent assistance system, the information contained in the data file is first parsed through Scheme Parser and Query is generated in a sequential manner against the parsed information. The query engine is used to search information from ontologies. The information is delivered to the decision module for processing further in decision making process. The second pilot cases relates to the extraction, storage and automatic learning from personal implicit knowledge through formal means. The knowledge is discovered through web-based multi-round Delphi surveys and later stored in ontology part of Case Base Reasoning module. The software is tested for ramp-up of screwing process.
detailed information from the CBR survey for determining similar cases from the past is saved in the database. This data is used again finally as an input for further development of the case base at the later stage. After having been shown the results of similar cases from past ramp-ups via the web server, the user can ascertain from them new solution approaches for confronted ramp-up problem. The newly gained knowledge during the solution should be input back to the CBR case base so that it is available to other users for global access. For this reason, the database makes available the data that was previously entered into the system while searching, in order to be able to save new cases in the system built upon it. Work station
Web server
Database
Ontology Thing
Screw connections
Retrieval Relation screw Relation part Production equipment «
Screw
Screw surface
Screw material
Parts
Screw parts handling
Parts surface
Production equipment
In future, the software will be extended to implement and use ontologies for inferring generic input-output environment simulation model of material, process and resource based on the nature of manufacturing process. This model will be directly used inside the LCA simulation tool. The generated results will be used to enrich the knowledgebase for better inference capabilities. The validation scenario will be expanded to assist decision making for manufacturing of other products such as customized orthotics in a decentralized manufacturing network. For enhancing the software module dedicated for screwing process ramp-up, the same approach will be used for rolling out further topic areas in the ramp-up management. For this purpose ontologies that structure the knowledge domains of the respective areas must first be created with the relevant experts. It will be followed by Delphi surveys, so that the case base can be filled with the valuable empirical knowledge of ramp-up management experts.
Loose part Screw surface « « « « « « « «
«
Parts Parts Screwing Screwing Periphery material handling tool program
Fig. 4. Structure of the prototype for screw process ramp-up management With the IT structure shown, it is possible to access the programmed applications from any workstation globally through the internet. For example, experts residing on different continents may participate in Delphi surveys. It is also possible to use the CBR system from anywhere in the world. A global knowledge management system is thus created making it possible for the globally available knowhow in the area of ramp-up management to be identified (Delphi survey), stored in an intelligent structure (ontology), and utilized pragmatically by all users (CBR system). This global usage of the system is of particular importance. By this means a comprehensive knowledge base for the ontologysupported CBR System can be built and used to master the challenges for a knowledge management system for vehicle ramp-ups described at the beginning.
For both modules of the software representing the corresponding pilot cases, the unified software will be tested by employees and further refinement will be made based on the feedback from the end users. 7. ACKNOWLEDGEMENTS The work reported is this paper is partially supported by the European Commission Project e-Custom FP7-2010-NMPICT-FoF 260067. REFERENCES Aamodt, A., Plaza, E. (1994). Case-Based Reasoning: Foundational Issues, Methodological Var-iations, and System Approaches. Artificial Intelligence, 7/1, pp. 3952. Beissel, S. (2011). Ontology-based Cased-based Reasoning. Betriebswirtschaftlicher Verlag Gabler. 1. edition. Wiesbaden. ISBN-10: 3834930644. ISBN-13: 9783834930644.
6. CONCLUSIONS AND OUTLOOK A unified concept for integrating the two software solutions, for extracting and reusing the implicit knowledge from the planners as well as the planning related softwares have been developed and validated through pilot cases. The first pilot case relates to the environmental impact assessment of customized hood manufacturing in the decentralized 458
2013 IFAC MIM June 19-21, 2013. Saint Petersburg, Russia
Skulmoski, G. J., Hartmann, F. T., Krahn, J. (2007) .The Delphi Method for Graduate Research. Journal of Information Technology Education, Volume 6, pp. 1-21. Weimer, G., Seuring, S. (2008). Information needs in the outsourcing lifecycle. In Industrial Management & Data Systems, Vol. 108 No. 1, pp. 107-121.
Cegielski, C.G. (2001) A Model of the Factors that Affect the Integration of Emerging Information Technology Into Corporate Strategy. Unpublished Doctoral Dissertation, University of Mississippi. Cocea, M. (2011). User Modelling and Adaption in Exploratory Learning. Doctoral Dissertation, University of London. F. Puppe, H. Stoyan und R. Studer (2003). Knowledge Engineering. In Görz, G., Rollinger, C.-R., Schneeberger, J. (eds.) Handbuch der Künstlichen Intelligenz, 4rth edition, pp. 599±641. Oldenbourg, München. Hayne, S., Pollard, C. (2000) A comparative analysis of critical issues facing Canadian information systems personnel: a national and global perspective. Information & Management, 38 (2), pp. 73±86. Hefke, M. (2009). Technology-based Introduction of Knowledge Management. Südwestdeutscher Verlag für Hochschulschriften. Saarbrücken. ISBN-10: 3838101472, ISBN-13: 978-3838101477. Heisig, P., Orth, R. (2005). Knowledge Management Frameworks in research and practice. European Resarch Center for Knowledge and Innovation. 1. edition. TU Berlin. ISBN-10: 3000172440. ISBN-13: 9783000172441. Holsapple, P., Joshi, K. (2002) Knowledge manipulation activities: results of a Delphi study. In-formation & Management, 39 (6), pp. 477±490. Kuhn, A., Wiendahl, H.-P., Eversheim, W., Schuh, G. (2002)³fast ramp-up". Verlag Praxiswissen, Dortmund. Lai, V., Chung, W. (2002) Managing international data communications. Information & Man-agement, 45 (3), pp. 89±93. Minhas, S., Juzek, C., Berger, U. (2012)a. Ontology based intelligent assistance system to support manufacturing activities in a distributed manufacturing environment. In Proceedings of 45th CIRP Conference on Manufacturing Systems (CIRP CMS), Athens, Greece. Minhas, S., Juzek, C., Berger, U. (2012)b. Elicitation of requirements for a knowledge management concept in decentralized production planning. In International Conference on Mechanical, Industrial and Manufacturing Technology (WASET2012), Berlin, Germany. Mulligan, P. (2002) Specification of a capability-based IT classification framework. Information & Management, 39 (8), pp. 647±658. Nambisan, S., Agarwal, R., Tanniru, M.: Organizational mechanisms for enhancing user innovation in information technology. MIS Quarterly, 23 (3), pp. 365± 395. Okoli, C., Pawlowski, S. D. (2004). The Delphi method as a research tool: an example, design considerations and applications. Information & Management, 42, pp. 15-29. Brancheau, J.C., Janz, B.D., Wetherbe, J.C. (1996). Key issues in information systems manage-ment, 1994±95 SIM Delphi results. MIS Quarterly, 20 (2), pp. 225±242. Schmidt, R.C., Lyytinen, K., Keil, M., Cule, P. (2001). Identifying software project risks: an in-ternational Delphi study. Journal of Management Information Systems, 17 (4), pp. 5±36.
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