OKMD Model

OKMD Model

OKMD model
THE OKMD MODEL 

OKMD (Ontology-based Knowledge Management process for DMAIC) model is an integrated conceptual model that combines activities of DMAIC process, knowledge management and ontology engineering. The ultimate goal of OKMD model is to construct a knowledge base to accumulate and share knowledge created by DMAIC improvement process. Knowledge available should be represented by existing Ontologies. Thereby, knowledge resource of DMAIC improvement process will be preserved and reused sustainably. 

Activities of DMAIC process are planned and deployed step by step according to the recommended Six Sigma guideline (Kifor & Baral, 2013), Gate Review sessions are arranged by the project participants recommended in (ISO13053-1, 2011) and tools and techniques proposed in (ISO13053-2, 2011). These activities provide opportunities to generate new knowledge that is then accumulated and reused in the during DMAIC process execution. The activities of knowledge management  involving Knowledge Creation/Acquisition, Knowledge Structure & Storage, Knowledge Protection, and Knowledge Application (Gold, Albert, & Arvind, 2001) are executed continuously within each of five DMAIC steps consisting of Define, Measure, Analysis, Improve, and Control. DMAIC knowledge is structured and stored by using Ontology engineering into sub-knowledge bases. Ontologies are built by using supported tools in IT area such as Ontology editor tools, programming languages, and natural language processing techniques. Moreover, activities of knowledge accumulation and reuse are fulfilled by knowledge workers (Mikael, Ioana, & Sachin, 2003) through Knowledge Portal where various information sources such as reports, plans, and text-based documents are collected. It provides activities of knowledge management with the functionalities: Content presentation, user account, chat room, forums, news module, and online quiz, questionnaire with multiple choices, file uploader, file downloader, search/query generator, and Knowledge Reasoner/inference engine.

In OKMD model, a knowledge management process typically consists of four stages videlicet K-Creation/Acquisition, K-Structure & Storage, K-Protection, and   K-Application. The typical activities executed by working teams who participate a Six Sigma project, experts, specialists of Information technology, and web users. 

The activities of K-Creation/Acquisition stage (arrow path 1) is to obtain new knowledge (Gold, Albert, & Arvind, 2001). The stage should be started at the Gate review section of every DMAIC step where members of project team such as Black Belt, Green Belt, domain experts discuss and review problem-solving solutions or improvement plans basing on reports and documents created. The support of Knowledge Portal allows them to submit or upload their reports, documents, writings, and relevant files to Knowledge Portal. Data collected at the Gate review section is used to extract potentially valuable knowledge (King, 2009) for reusing in the next stages. Domain experts can be interviewed in case tacit knowledge need to be supplemented. K-Structure & Storage stage (arrow path 2) aims at cumulating new knowledge into sub-knowledge bases based on Ontology Engineering. Data collected on Knowledge Portal is extracted to explicit knowledge under the support of Ontologies available, Ontology building tools (such as Protégé), natural language processing techniques, and programing languages. Concepts of new knowledge as well as their relationship are defined and updated by the support of IT specialists into each of DMAIC stages correspondently.

K-Protection stage (arrow path 3) plays an important role in protecting crucial knowledge before it would be distributed to the next stages. This stage is proposed in order to prevent illegal or inappropriate behaviors of web users who are querying knowledge available on Knowledge Portal. It provides security mechanism for web users and supporting tools for maintaining and validating knowledge. The knowledge access and activities of web users are monitored and controlled through their accounts. Experts are required to validate knowledge available, and their opinions are helpful to contribute to making created valuable knowledge and eliminating outmoded knowledge. Ontologies are updated by expert’s opinion and IT specialists. K-Application stage (arrow path 4) is necessary to share and reuse created knowledge. Activities consist of querying knowledge, and applying valuable knowledge. Knowledge found can support improvement activities of the next steps of DMAIC. A knowledge request of a web user is sent to the query generator where a command of knowledge query is generated. Intelligent results of the query are then responded by Knowledge Reasoner after appropriate knowledge is reasoned from a knowledge base.     

Copyright statement 

Copyright ©2015 IETEC-BRCEBE’15, Thanh-Dat NGUYEN, Claudiu Vasile KIFOR: The authors assign to IETEC-BRCEBE’15 a non-exclusive license to use this document for personal use and in courses of instruction provided that the article is used in full and this copyright statement is reproduced.  The authors also grant a non-exclusive license to IETEC-BRCEBE’15 to publish this document in full on the World Wide Web (prime sites and mirrors) on CD-ROM and in printed form within the IETEC-BRCEBE’15 conference proceedings. Any other usage is prohibited without the express permission of the authors.

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REFERENCES:

  • Gold, A. H., Albert, H. S., & Arvind, M. (2001). Knowledge Management: An Organizational Capability Perspective. Journal of Management Information Systems, 185-214.
  • ISO13053-1. (2011). ISO guideline on Quantitative methods in process improvement- Six Sigma-Part 1: DMAIC methodology, . Reference number: ISO 13053-1:2011 (E), , pp. 24.
  • ISO13053-2. (2011). ISO guideline on Quantitative methods in process improvement - Six Sigma. Part 2: Tools and Techniques.
  • Kifor, C. V., & Baral, L. M. (2013). An Integrated Dmaic-Knowledge Management Conceptual Model for Six Sigma Quality Management. International Conference on Manufacturing Science and Education- Mse, 12-15.
  • King, W. R. (2009). Knowledge Management and Organizational Learning. Annals of Information Systems 4, 3-13.
  • Mikael, L., Ioana, R., & Sachin, S. S. (2003). Technology support for knowledge management. Advances in Learning Software Organizations (pp. 94-103). Berlin Heidelberg: Springer.
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