Devising a technology for managing outsourcing IT-projects with the application of fuzzy logic

Authors

DOI:

https://doi.org/10.15587/1729-4061.2021.224529

Keywords:

IT outsourcing, project management, fuzzy logic, inference mechanism, semantic network, expert system

Abstract

An outsourcing IT project management model has been developed. The proposed model features taking into account the specifics of project management processes at outsourcing IT companies in terms of the uncertainty of the external and internal environment of their operation. The model is based on the stage-gate project management framework with fuzzy logic tools. The proposed modification of the fuzzy inference mechanism makes it possible to refuse to save the intermediate results which reduce the load on the database and create the possibility of using semantic networks. The technology of expert consultations was demonstrated by the example of decision-making regarding the assessment of the current status of the IT projects accepted by the outsourcing company for development.

Dynamic nature and cyclical management of the portfolio of IT projects involves constant monitoring of the results of implementation with an appropriate regular portfolio reforming. The model was developed to improve the efficiency of the software development sub-process and minimize the negative consequences of financial dependence on the customer.

The application software developed on the basis of the model of management of outsourcing IT projects and modification of the fuzzy inference mechanism has found practical application and was implemented in the computational practice of HYS Enterprise B.V. outsourcing IT company. Testing of the program shell has shown positive results in the course of solving the tasks peculiar to concrete stages of IT project management.

The proposed structure and composition of the fuzzy knowledgebase of the expert shell are quite typical in terms of IT outsourcing problems. It is expedient to use the developed model at outsourcing IT companies in the process of project portfolio management

Author Biographies

Zoia Sokolovska, Odessa National Polytechnic University

Doctor of Economic Sciences, Professor, Head of Department

Department of Economic Cybernetics and Information Technologies 

Oleksii Dudnyk, Odessa National Polytechnic University

Postgraduate Student

Department of Economic Cybernetics and Information Technologies 

References

  1. Rosenau, M. D., Githens, G. D. (2005). Successful Project Management: A Step-by-Step Approach with Practical Examples. John Wiley & Sons, 384.
  2. Polak, J., Wojcik, P. (2015). Knowledge management in it outsourcing/offshoring projects. PM World Journal, 4 (8). Available at: https://pmworldlibrary.net/wp-content/uploads/2015/08/pmwj37-Aug2015-Polak-Wojcik-knowledge-management-IT-oursourcing-second-edition.pdf
  3. Keynes, J. M. (1937). The General Theory of Employment. The Quarterly Journal of Economics, 51 (2), 209. doi: https://doi.org/10.2307/1882087
  4. Atkinson, R., Crawford, L., Ward, S. (2006). Fundamental uncertainties in projects and the scope of project management. International Journal of Project Management, 24 (8), 687–698. doi: https://doi.org/10.1016/j.ijproman.2006.09.011
  5. Kumar, C., Doja, M. N. (2018). A Novel Framework for Portfolio Selection Model Using Modified ANFIS and Fuzzy Sets. Computers, 7 (4), 57. doi: https://doi.org/10.3390/computers7040057
  6. Hassanzadeh, F., Collan, M., Modarres, M. (2012). A Practical Approach to R&D Portfolio Selection Using the Fuzzy Pay-Off Method. IEEE Transactions on Fuzzy Systems, 20 (4), 615–622. doi: https://doi.org/10.1109/tfuzz.2011.2180380
  7. Pai, G. A. V. (2017). Fuzzy Decision Theory Based Metaheuristic Portfolio Optimization and Active Rebalancing Using Interval Type-2 Fuzzy Sets. IEEE Transactions on Fuzzy Systems, 25 (2), 377–391. doi: https://doi.org/10.1109/tfuzz.2016.2633972
  8. Nguyen, T. T., Gordon-Brown, L., Khosravi, A., Creighton, D., Nahavandi, S. (2015). Fuzzy Portfolio Allocation Models Through a New Risk Measure and Fuzzy Sharpe Ratio. IEEE Transactions on Fuzzy Systems, 23 (3), 656–676. doi: https://doi.org/10.1109/tfuzz.2014.2321614
  9. Wang, S., Wang, B., Watada, J. (2017). Adaptive Budget-Portfolio Investment Optimization Under Risk Tolerance Ambiguity. IEEE Transactions on Fuzzy Systems, 25 (2), 363–376. doi: https://doi.org/10.1109/tfuzz.2016.2582906
  10. Habibi, F., Taghipour Birgani, O., Koppelaar, H., Radenović, S. (2018). Using fuzzy logic to improve the project time and cost estimation based on Project Evaluation and Review Technique (PERT). Journal of Project Management, 3, 183–196. doi: https://doi.org/10.5267/j.jpm.2018.4.002
  11. Acar Yildirim, H., Akcay, C. (2019). Time-cost optimization model proposal for construction projects with genetic algorithm and fuzzy logic approach. Revista de La Construcción, 18 (3), 554–567. doi: https://doi.org/10.7764/rdlc.18.3.554
  12. Adeola, O. S., Ganiyu, A. R. (2020). A Fuzzy System for Evaluating Human Resources in Project Management. International Journal of Technology Diffusion, 11 (1), 66–95. doi: https://doi.org/10.4018/ijtd.2020010105
  13. Hughes, R. T. (1996). Expert judgement as an estimating method. Information and Software Technology, 38 (2), 67–75. doi: https://doi.org/10.1016/0950-5849(95)01045-9
  14. Miyazaki, Y., Terakado, M., Ozaki, K., Nozaki, H. (1994). Robust regression for developing software estimation models. Journal of Systems and Software, 27 (1), 3–16. doi: https://doi.org/10.1016/0164-1212(94)90110-4
  15. Gray, A. R., MacDonell, S. G. (1999). Fuzzy logic for software metric models throughout the development life-cycle. 18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397). doi: https://doi.org/10.1109/nafips.1999.781694
  16. Stage-gate model. Available at: https://www.stage-gate.com/stage-gate-model
  17. McConnell, S. (2006). Software Estimation: Demystifying the Black Art. Microsoft Press, 308.
  18. Sharma, S., Sarkar, D., Gupta, D. (2012). Agile processes and methodologies: A conceptual study. International Journal on Computer Science and Engineering, 4 (5), 892–898. Available at: https://www.yashada.org/yash/egovcii/static_pgs/TC/IJCSE12-04-05-186.pdf
  19. Cooper, R., Kielgast, S., Vedsmand, T. (2016). Integrating Agile with Stage-Gate® – How New Agile-Scrum Methods Lead to Faster and Better Innovation. Available at: https://innovationmanagement.se/2016/08/09/integrating-agile-with-stage-gate
  20. Shapiro, S. C., Rapaport, W. J. (1992). The SNePS family. Computers & Mathematics with Applications, 23 (2-5), 243–275. doi: https://doi.org/10.1016/0898-1221(92)90143-6
  21. Schlegel, D. R., Shapiro, S. C. (2014). Concurrent Reasoning with Inference Graphs. Graph Structures for Knowledge Representation and Reasoning, 138–164. doi: https://doi.org/10.1007/978-3-319-04534-4_10
  22. Choi, J., Shapiro, S. C. (1992). Efficient implementation of non-standard connectives and quantifiers in deductive reasoning systems. Proceedings of the Twenty-Fifth Hawaii International Conference on System Sciences. doi: https://doi.org/10.1109/hicss.1992.183186
  23. Fuzzy Logic Toolbox. MathWorks. Available at: https://uk.mathworks.com/products/fuzzy-logic.html
  24. Neely, A., Gregory, M., Platts, K. (1995). Performance measurement system design: A literature review and research agenda. International Journal of Operations & Production Management, 15 (4), 80–116. doi: https://doi.org/10.1108/01443579510083622
  25. Shenhar, A. J., Levy, O., Dvir, D. (1997). Mapping the dimensions of project success. Project management journal, 28 (2), 5–13. Available at: http://www.reinventingprojectmanagement.com/material/other/7.%20Mapping%20dimensions%20of%20projects%20success%20PMJ%201997.pdf
  26. Baccarini, D. (1999). The Logical Framework Method for Defining Project Success. Project Management Journal, 30 (4), 25–32. doi: https://doi.org/10.1177/875697289903000405
  27. Papke-Shields, K. E., Beise, C., Quan, J. (2010). Do project managers practice what they preach, and does it matter to project success? International Journal of Project Management, 28 (7), 650–662. doi: https://doi.org/10.1016/j.ijproman.2009.11.002
  28. Rad, P. F., Cioffi, D. F. (2000). Work and resource breakdown structures for formalized bottom-up estimating. The George Washington University, Washington, D.C. Available at: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.583.8756&rep=rep1&type=pdf
  29. Might, R. J., Fischer, W. A. (1985). The role of structural factors in determining project management success. IEEE Transactions on Engineering Management, EM-32 (2), 71–77. doi: https://doi.org/10.1109/tem.1985.6447584
  30. Freeman, M., Beale, P. (1992). Measuring project success. Project Management Journal, 23 (1), 8–17.
  31. Orchard, R. A. (1998). FuzzyCLIPS Version 6.04A User’s Guide. Integrated Reasoning. Available at: https://quentin.pradet.me/blog/media/FuzzyCLIPS/fzdocs.pdf

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Published

2021-04-30

How to Cite

Sokolovska, Z., & Dudnyk, O. (2021). Devising a technology for managing outsourcing IT-projects with the application of fuzzy logic. Eastern-European Journal of Enterprise Technologies, 2(3 (110), 52–65. https://doi.org/10.15587/1729-4061.2021.224529

Issue

Section

Control processes