مطالعات مدیریت کسب و کار هوشمند

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری رشته مدیریت صنعتی، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات، تهران، ایران

2 استاد، دانشکده مدیریت و اقتصاد، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات تهران، ایران نویسنده مسئول: toloei@srbiau.ac.ir

3 استاد، دانشکده مدیریت و اقتصاد، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات تهران، ایران

چکیده

فرآیندهای دانش‌بنیان جزء جدایی ناپذیر فرآیندهای کسب و کار شرکتهای فعال در حوزه فناوری اطلاعات هستند. در این صنعت، فرآیندهای دانش بنیان که مبتنی بر دانش نیروی ماهر اجرا می‌شوند و در زنجیره ارزش شرکتهای فعال این حوزه نقشی اساسی ایفا می‌کنند. مهمترین عنصر در فرآیندهای دانش بنیان، تصمیم‌گیری‌های صورت گرفته در این فرآیندها است. از این رو مساله شناسایی قواعد و مدلهای تصمیم‌ فرآیندهای دانش‌بنیان دارای اهمیت به‌سزایی است. در این مقاله یکی از مهمترین فرآیندهای موجود در صنعت نرم افزار (فرآیند شناسایی و تصمیم گیری در خصوص ایده های مطرح شده) مورد بررسی قرار می‌گیرد و با استفاده از لاگ‌های تصمیم گیری موجود در یکی از بزرگترین شرکتهای نرم افزاری کشور، به تحلیل این فرآیند با استفاده از نظریه مجموعه‌های ژولیده پرداخته می‌شود. بر اساس این نظریه و با بهره گیری از الگوریتم کاست سریع، روشی گام به گام برای تحلیل داده‌ها و شناسایی قواعد تصمیم‌گیری ارایه می‌شود. این الگوریتم در گام نخست خصیصه های حیاتی مورد استفاده در نقطه تصمیم‌ را شناسایی نموده و سپس مدل تصمیم‌گیری را به صورت قواعد اگر-آنگاه ارایه می‌کند. نتایج ارزیابی نشان می‌دهد که در نتیجه بهره‌گیری از این مدل حجم مورد نیاز برای مراجعه مستقیم به تصمیم‌گیرندگان کاهش قابل توجهی خواهد داشت و فرآیند تصمیم‌گیری و در نتیجه مدت زمان کل فرآیند کاهش قابل ملاحظه‌ای خواهد یافت.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Decision mining in information technology processes - a case study of the new idea discovery process

نویسندگان [English]

  • Mehri Chehrehpak 1
  • Abbas Tolouei Ashlaghi 2
  • Kamran Mohammadkhani 3

1 Ph.D Candidate, Management and Economy Faculty, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Professor, Management and Economy Faculty, Science and Research Branch, Islamic Azad University, Tehran, Iran Corresponding Author: toloei@srbiau.ac.ir

3 Professor, Management and Economy Faculty, Science and Research Branch, Islamic Azad University, Tehran, Iran

چکیده [English]

Effective knowledge-based processes are essential for companies operating in the information technology industry. These processes rely on the expertise of skilled workers and play a crucial role in the value chain of such organizations. Decision-making is a critical element of knowledge-based processes, highlighting the need to identify decision rules and models accurately. In this paper, we examine the process of identifying and deciding on proposed ideas in the software industry, analyzing decision logs from a leading software company. The Rough sets theory and fast Reduction algorithm are employed to provide a step-by-step approach to data analysis and decision mining. The algorithm identifies vital features used in decision-making and presents the decision model as if-then rules, utilizing existing equivalence rules between data. The results demonstrate that this model can significantly reduce the direct involvement of decision-makers and the duration of the decision-making process.

کلیدواژه‌ها [English]

  • Process Mining
  • Decision Mining
  • Rough Set Theory
  • Knowledge-intensive Process
  • Information Technology
  1. حسینی، س.، ی، مصلح، ع.، حسینی، م. (1397). تحلیل فرآیندهای الکترونیکی با استفاده از تکنیک فرآیندکاوی (موردمطالعه: فرآیند ترفیع پایه اعضای هیئت‌علمی دانشگاه خلیج‌فارس. در چشم‌انداز مدیریت صنعتی، 29، 113-135. https://jimp.sbu.ac.ir/article_87182_d31fbaf5808a3a9425099e1729d3bbe5.pdf
  2. اقدسی، م.، ذگردی، س.، اسکندری، ح.، ح.، ملیحی، س.، ا. (1390). مدل شناسایی مؤثرترین قواعد کسب‌وکار تعبیه‌شده در سیستم‌های اطلاعاتی برای دستیابی به انتظارات استراتژیک با استفاده از تئوری مجموعه‌های ژولیده مطالعه موردی: فرآیند اعطای تسهیلات در بانک. در مدیریت فناوری اطلاعات، 3 (8)، 19-42. https://jitm.ut.ac.ir/article_24000_00aca189f675e0db0f29ad5c3b724795.pdf

Refrences

  1. Bazhenova, E., & Weske, M. (2016). Deriving decision models from process models by enhanced decision mining. In Business Process Management Workshops: BPM 2015, 13th International Workshops, Innsbruck, Austria, August 31–September 3, 2015, Revised Papers 14(pp. 444-457). Springer International Publishing.
    https://doi.org/10.1007/978-3-319-42887-1_36
  2. Becker, G. S. (2009). Human capital: A theoretical and empirical analysis, with special reference to education. University of Chicago press. https://books.google.com/books?hl=en&lr=&id=9t69iICmrZ0C&oi=fnd&pg=PR9&ots=Wzxvo-PBlW&sig=6X8INpQKQPtgXXkYFOLKsjDIVTU#v=onepage&q&f=false
  3. Bolisani, E., & Scarso, E. (1999). Information technology management: a knowledge-based perspective. Technovation19(4), 209-217. https://doi.org/10.1016/S0166-4972(98)00109-6
  1. De Leoni, M., & van der Aalst, W. M. (2013, March). Data-aware process mining: discovering decisions in processes using alignments. In Proceedings of the 28th annual ACM symposium on applied computing(pp. 1454-1461).https://doi.org/10.1145/2480362.2480633
  2. Etzkowitz, H., & Leydesdorff, L. (2000). The dynamics of innovation: from National Systems and “Mode 2” to a Triple Helix of university–industry–government relations. Research policy29(2), 109-123. https://doi.org/10.1016/S0048-7333(99)00055-4
  3. Fagerberg, J. (2006). Innovation: A Guide to the Literature. In: Fagerberg, J., Mowery, D.C., & Nelson, R.R. (Eds.), The Oxford Handbook of Innovation. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199286805.003.0001
  4. Fauzi, R., & Andreswari, R. (2022). Business process analysis of programmer job role in software development using process mining. Procedia Computer Science197, 701-708.
    https://doi.org/10.1016/j.procs.2021.12.191
  5. Floyd, S. W., & Wooldridge, B. (1992). Middle management involvement in strategy and its association with strategic type: A research note. Strategic management journal13(S1), 153-167.  https://doi.org/10.1002/smj.4250131012
  6. Gordon, I. Porter, ME (1990), The Competitive Advantage of Nations, Macmillan. https://www.researchgate.net/profile/Ian-Gordon-4/publication/359064880_London_World_City_political_and_organisational_constraints_on_territorial_competition/links/625723d7709c5c2adb786a0f/London-World-City-political-and-organisational-constraints-on-territorial-competition.pdf
  7. Grützner, T., Schnider, C., Zollinger, D., Seyfang, B. C., & Künzle, N. (2016). Reducing time to market by innovative development and production strategies. Chemical Engineering & Technology39(10), 1835-1844. https://doi.org/10.1002/ceat.201600113
  8. Gupta, B., Rawat, A., Jain, A., Arora, A., & Dhami, N. (2017). Analysis of various decision tree algorithms for classification in data mining. International Journal of Computer Applications163(8), 15-19. https://d1wqtxts1xzle7.cloudfront.net/69970061/ijca2017913660-libre.pdf?1632131227=&response-content-disposition=inline%3B+filename%3DAnalysis_of_Various_Decision_Tree_Algori.pdf&Expires=1710499984&Signature=Ki42u7ag1ahHvmaIAGwqODNtGZgFxmIOi0LEOi7jTDpt-n8oZrQwn7LngAPObsJOtAsOwd5LaM4~B1g3k0kiEdS0iXN-6Gn8Z2Z2Cg6ZlDU83-iuB7l537tgCzREBlqHcqWhf76NqLc70mirV~nNk93T2bI-7IfxfqNqQoIln-VO1HgR4-byjyGrpJ-1rldefhz9BU04OLci0BcpJFzWMRTGt6ExLcibqcMIgxZCW4cnVFwiUDKZYM34cJb2QuPqGoWJLyEpeyJg787-gwLBqQ-YVw5OV9hSP3Gh2lfttFTP3v62fUWueU2NqPd2HShyzuzJvH3FuRREEtH05ikllg__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA
  9. Hofstede, G. (2001). Culture’s Consequences: Comparing Values, Behaviors, Institutions, and Organizations Across Nations. https://doi.org/10.1016/S0005-7967(02)00184-5
  10. Huang, H., & Li, F. (2021). Innovation climate, knowledge management, and innovative work behavior in small software companies. Social Behavior and Personality: an international journal, 49(4), 1-17.
    https://doi.org/10.2224/sbp.9780
  11. Jensen R. and Shen Q. (2001) "A Rough Set Aided System for Sorting WWW Bookmarks", Proceedings of the First Asia-Pacific Conference on Web Intelligence: Research and Development, Springer-Verlag, London, UK https://doi.org/10.1007/3-540-45490-X_10
  12. Koc, T. (2007). Organizational determinants of innovation capacity in software companies. Computers & industrial engineering, 53(3), 373-385. https://doi.org/10.1016/j.cie.2007.05.003
  13. Larsen, I. B. (2022). Fostering an entrepreneurial mindset: A typology for aligning instructional strategies with three dominant entrepreneurial mindset conceptualizations. Industry and Higher Education36(3), 236-251. https://doi.org/10.1177/09504222211038212
  14. Liu, Y., Soroka, A., Han, L., Jian, J., & Tang, M. (2020). Cloud-based big data analytics for customer insight-driven design innovation in SMEs. International Journal of Information Management51, 102034. https://doi.org/10.1016/j.ijinfomgt.2019.11.002
  15. Luo, J. (2022). Data-driven innovation: What is it?. IEEE Transactions on Engineering Management70(2), 784-790. https://doi.org/‌10.1109/TEM.2022.3145231
  16. Marjanovic, O., Skaf-Molli, H., Molli, P., & Godart, C. (2007, November). Collaborative practice-oriented business processes Creating a new case for business process management and CSCW synergy. In 2007 International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2007)(pp. 448-455). IEEE. doi: 10.1109/COLCOM.2007.4553874.
  17. Marxt, C., & Brunner, C. (2013). Analyzing and improving the national innovation system of highly developed countries—The case of Switzerland. Technological Forecasting and Social Change80(6), 1035-1049. https://doi.org/10.1016/j.techfore.2012.07.008
  18. Mohemad, R., Hamdan, A. R., Othman, Z. A., & Noor, N. M. M. (2010). Decision support systems (DSS) in construction tendering processes. International Journal of Computer Science Issues, 7, 35–45.
    https://doi.org/10.48550/arXiv.1004.3260
  19. Nwosu, N. T., Babatunde, S. O., & Ijomah, T. (2024). Enhancing customer experience and market penetration through advanced data analytics in the health industry. World Journal of Advanced Research and Reviews22(3), 1157-1170. https://doi.org/10.30574/‌wjarr.2024.22.3.1810
  20. Paternoster, N., Giardino, C., Unterkalmsteiner, M., Gorschek, T., & Abrahamsson, P. (2014). Software development in startup companies: A systematic mapping study. Information and Software Technology, 56(10), 1200-1218. https://doi.org/10.1016/j.infsof.2014.04.014
  21. Pawlak, Z. (1991) "Rough Sets: Theoretical Aspects of Reasoning about Data", Kluwer Academic Publishing, Dordrecht. https://doi.org/10.1007/978-94-011-3534-4
  22. Pawlak, Z., Polkowski, L., & Skowron, A. (2001). Rough set theory. KI15(3), 38-39.
    https://doi.org/10.1002/9780470050118.ecse466
  23. Poppe, E., Pika, A., Wynn, M. T., Eden, R., Andrews, R., & ter Hofstede, A. H. (2021). Extracting Best-Practice Using Mixed-Methods: Insights and Recommendations from a Case Study in Insurance Claims Processing. Business & Information Systems Engineering, 1-15. https://doi.org/10.1007/s12599-021-00698-9
  24. Porter, M.E. (1998). Competitive Strategy: Techniques for Analyzing Industries and Competitors. Free Press. https://s3.us-east-1.amazonaws.com/storage.thanksforthehelp.com/qfile/porter-michael-e-1980-extract-competitive-strategy-vyr2a2bw.pdf
  25. Portolani, P., Savoia, D., Ballarino, A., & Matteucci, M. (2023, May). A Novel Decision Mining Method Considering Multiple Model Paths. In International Conference on Business Process Modeling, Development and Support(pp. 79-87). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-34241-7_6
  26. Löhr, B., Brennig, K., Bartelheimer, C., Beverungen, D., & Müller, O. (2022, September). Process mining of knowledge-intensive processes: an action design research study in manufacturing. In International Conference on Business Process Management(pp. 251-267). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-16103-2_18
  27. Reichert, M., & Weber, B. (2012). Enabling flexibility in process-aware information systems: challenges, methods, technologies. Springer Science & Business Media. https://doi.org/10.1007/978-3-642-30409-5
  28. Rozinat, A., & van der Aalst, W. M. (2006, September). Decision mining in ProM. In International Conference on Business Process Management(pp. 420-425). Springer, Berlin, Heidelberg. https://doi.org/10.1007/11841760_33
  29. Schmidt, D. M., Braun, F., Schenkl, S. A., & Mörtl, M. (2016). Interview study: How can Product-Service Systems increase customer acceptance of innovations?. CIRP Journal of Manufacturing Science and Technology15, 82-93. https://doi.org/10.1016/j.cirpj.2016.04.002
  30. Shapiro, C. (2000). Navigating the patent thicket: Cross licenses, patent pools, and standard setting. Innovation policy and the economy1, 119-150. https://doi.org/10.1086/ipe.1.25056143
  31. Som, T., Shreevastava, S., Tiwari, A. K., & Singh, S. (2020). Fuzzy Rough Set Theory‐Based Feature Selection: A Review. Mathematical Methods in Interdisciplinary Sciences, 145-166. https://doi.org/10.1002/9781119585640.ch9
  32. Srivastava, S. (2021). Process mining techniques for detecting fraud in banks: A study. Turkish Journal of Computer and Mathematics Education (TURCOMAT)12(12), 3358-3375. https://doi.org/‌10.17762/‌turcomat.v12i12.8058
  33. Swiniarski, R. W. and A. Skowron (2003) "Rough Set Methods in Feature Selection and Recognition", Pattern Recognition Letters, vol. 24, pp. 833-849. https://doi.org/10.1016/S0167-8655(02)00196-4
  34. Urrea-Contreras, S. J., Flores-Rios, B. L., Astorga-Vargas, M. A., & Ibarra-Esquer, J. E. (2021, August). Process Mining Perspectives in Software Engineering: A Systematic Literature Review. In 2021 Mexican International Conference on Computer Science (ENC)(pp. 1-8). IEEE. https://doi.org/10.1109/ENC53357.2021.9534824.
  35. Valle, A. M., Santos, E. A., & Loures, E. R. (2017). Applying process mining techniques in software process appraisals. Information and software technology, 87, 19-31. https://doi.org/10.1016/j.infsof.2017.01.004
  36. Weske, M. (2019). Business Process Management: Concepts, Languages, Architectures. Springer.
  37. Yin, D., Dong, L., Cheng, H., Liu, X., Chang, K. W., Wei, F., & Gao, J. (2022). A survey of knowledge-intensive nlp with pre-trained language models. arXiv preprint arXiv:2202.08772.
    https://doi.org/‌10.48550/arXiv.2202.08772
  38. Zhu, J., He, P., Fu, Q., Zhang, H., Lyu, M. R., & Zhang, D. (2015, May). Learning to log: Helping developers make informed logging decisions. In 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering(Vol. 1, pp. 415-425). IEEE.
    https://doi.org/10.1109/ICSE.2015.60.
  39. Ziarko W. (1993) Variable Precision Rough Set Model. in Journal of Computer and System Sciences, 46, 44-54. https://doi.org/10.1016/0022-0000(93)90048-2
  40. References [in Persian]
1.    Aghdasi, M., Zegordi, S., Eskandari, H, Malihi, S. E. (2011) A Model to Identify the Most Effective Business Rule in Information Systems using Rough Set Theory: Study on Loan Business Process. In Journal of Information Technology Management, 3 (8), 19-42. https://jitm.ut.ac.ir/article_24000_00aca189f675e0db0f29ad5c3b724795.pdf [in Persian]
  1. Hoseini, S., Y., Mosleh, A., Hoseini, M. (2018). Electronic processes analyzing using process mining techniques (Case study: The basic promotion process of faculty members at Persian Gulf University.) in Industrial Management Perspective, 29, 113-135 https://jimp.sbu.ac.ir/article_87182_d31fbaf5808a3a9425099e1729d3bbe5.pdf [in Persian]