DEVELOPING THE ADAPTIVE KNOWLEDGE MANAGEMENT IN CONTEXT OF ENGINEERING COMPANY PROJECT ACTIVITIES
Keywords:adaptive knowledge acquisition, competence level, adaptation potential, Cobb and Douglas’s model, Harrington's desirability function
The concept of Adaptive Knowledge Management is an approach essentially increasing the effectiveness of project participants in their processes that are unpredictable or initialized in advance by unknown events. The research deals with developing the strategy of proactive formation of project team competencies within an industrial enterprise. The most important component of this strategy is the formation of “knowledge coordinators” who are responsible for knowledge management and the creation of training and knowledge sharing regulations. On the basis of this strategy the method of determining the adaptation potential of team members is developed, which is based on the assessment of project task implementation of different complexity, taking into account the identifiers of continuous knowledge growth, which allows predicting the possibilities of increasing the competence level and determining the sustainability of project team members’ knowledge. This method can be used to form a project team considering the accumulated experience and knowledge of its participants.
A. E. Akgün, “Team wisdom in software development projects and its impact on project performance,” International Journal of Information Management, 50, pp. 228–243, 2019. https://doi.org/10.1016/j.ijinfomgt.2019.05.019
T. Bjorvatn, A. Wald, “Project complexity and team-level absorptive capacity as drivers of project management performance,” International Journal of Project Management, vol. 36, issue 6, pp. 876–888, 2018. https://doi.org/10.1016/j.ijproman.2018.5.003
D. Monticolo, J. Badin, S. Gomes, E. Bonjour, “A meta-model for knowledge configuration management to support collaborative engineering,” Computers in Industry, vol. 66, pp. 11–20, 2018. https://doi.org/10.1016/j.compind.2014.08.001
M. Ritou, F. Belkadi, Z. Yahouni, C. Da Cunha, F. Laroche, B. Furet, “Knowledge-based multi-level aggregation for decision aid in the machining industry,” CIRP Annals, vol. 68, issue 1, pp. 475 – 478, 2019. https://doi.org/10.1016/j.cirp.2019.03.009
W. Jun, W. Wei, D. Liting, J. Li, “Method for analyzing the knowledge collaboration effect of R&D project teams based on Bloom’s taxonomy,” Computers & Industrial Engineering, 103, pp. 158–167, 2017. https://doi.org/10.1016/j.cie.2016.11.010
J. C.R., Tseng, H.-Ch. Chu, G.-J. Hwang, Ch.-Ch. Tsai, “Development of an adaptive learning system with two sources of personalization information,” Computers & Education, 51, pp. 776–786, 2008. https://doi.org/10.1016/j.compedu.2007.08.002
P. Tomei, C.M. Verrelli, “Advances on adaptive learning control: The case of non–minimum phase linear systems,” Systems & Control Letters, vol. 115, pp. 55–62, 2018. https://doi.org/10.1016/j.sysconle.2018.03.006
B. Chandra, Sh. K. Rajesh, “Deep learning with adaptive learning rate using Laplacian score,” Expert Systems with Applications, vol. 63, pp. 1–7, 2016. https://doi.org/10.1016/j.eswa.2016.05.022
V. Gogunskii, D. Lukianov, O. Vlasenko, “Identification of knowledge cores on a competence graph of project managers,” Eastern-European Journal of Enterprise Technologies, no. 1(10(55)), pp. 26-28, 2012. http://dx.doi.org/10.15587/1729-4061.2012.3487.
B.H. Reich, A. Gemino, C. Sauer, “How knowledge management impacts performance in projects: An emperical study,” International Journal of Project Management, 32, pp. 590–602, 2014. https://doi.org/10.1016/j.ijproman.2013.09.004
A. Kuwertz, J. Beyerer, “Extending adaptive world modeling by identifying and handling insufficient knowledge models,” Journal of Applied Logic, vol. 19, part 2, pp. 102-127, 2016. https://doi.org/10.1016/j.jal.2016.05.005
Y. He, “A framework for IT-based knowledge management,” Proceedings of the 2009 First IEEE International Conference on Information Science and Engineering, Nanjing, 2009, pp. 2794-2797. https://doi.org/10.1109/ICISE.2009.35.
S. Yu, L. Liu and M. Fu, “The application research on knowledge management of project manager,” Proceedings of the 2009 IEEE International Conference on Information Management, Innovation Management and Industrial Engineering, Xi'an, 2009, pp. 340-343, https://doi.org/10.1109/ICIII.2009.391.
A Guide to the Project Management Body of Knowledge, 6th edition, PMI, 2017.
S. Wan, D. Li, J. Gao, R. Roy, Yi. Tong, “Process and knowledge management in a collaborative maintenance planning system for high value machine tools,” Computers in Industry, vol. 84, pp. 14-24, 2017. https://doi.org/10.1016/j.compind.2016.11.002.
K. Venkitachalam, H. Willmott, “Strategic knowledge management – Insights and pitfalls,” International Journal of Information Management, vol. 37, issue 4, pp. 313-316, 2017. https://doi.org/10.1016/j.ijinfomgt.2017.02.002.
E.V. Kolesnikova, “Assessment of the competence of personnel furnace project of computer simulator,” Eastern European Journal of Advanced Technologies, no. 5/1 (65), pp. 45 – 48, 2013.
S. Lupuleac, Z.-L. Lupuleac, and C. Rusu, “Problems of assessing team roles balance – team design,” Procedia Economics and Finance, vol. 3, pp. 935–940, October 2012. doi: 10.1016/s2212-5671(12)00253-5.
M. Caniëls, F. Chiocchio, and N. Van Loon, “Collaboration in project teams: The role of mastery and performance climates,” International Journal of Project Management, nol. 37, pp. 1 – 13, 2019. https://doi.org/10.1016/j.ijproman.2018.09.006.
P. Różewski, and O. Zaikin, “Integrated mathematical model of competence-based learning/teaching process,” Bulletin of the Polish Academy of Sciences. Technical Sciences, vol. 63, no 1, pp. 245-258, 2015.
O. Dunets, C. Wolff, A. Sachenko, G. Hladiy, I. Dobrotvor, “Multi-agent system of IT project planning”, Proceedings of the 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), 2017, pp. 548–552. https://doi.org/10.1109/IDAACS.2017.8095141.
How to Cite
LicenseInternational Journal of Computing is an open access journal. Authors who publish with this journal agree to the following terms:
• Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
• Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
• Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.