Mitigating scope variability for PMs in MNC in research projects doing research for engineering NPDs using a decision support system and model
List of Authors
  • Lew K.L. , Sundram D.R.

Keyword
  • Scope Variability, Engineering Management, Decision Support System (DSS), Decision Support Model (DSM), Prioritization Indicator (PI)

Abstract
  • This paper proposes a Decision Support Model (DSM) to be used by Project Managers (PM)s in the research of new products within multinational companies. PMs in engineering research projects shall be able to select the right option to mitigate scope variability during critical junctures like project continuity. On-the-shelf DSMs are leveraged to overcome their shortcomings with more effective and relevant solutions. The proposed DSM is a consolidation of on-the-shelf DSMs and methodologies such as Balanced Scorecard, Quality Function Deployment, Analytic Hierarchy Process, Strength, Weakness, Opportunities and Threats, and Agile methodology, leading to a Decision Support System (DSS). The application of the DSM into a DSS is done using Progressive Web Applications. The input was collected through interviews with PMs who have significant expertise in managing research projects. Performance indicators were then developed to be reviewed by industry experts to verify their applicability. Based on the feedback obtained, it is shown that the DSM and its application as DSS is effective as the surveyed PMs state that it is useful in certain situation. The developed model contributes to the engineering research industry as it mitigates common challenges that PMs face, by efficiently providing a solution-seeking guideline.

Reference
  • 1. Abbassi, M., Ashrafi, M., & Sharifi Tashnizi, E. (2014). Selecting balanced portfolios of R&D projects with interdependencies: A cross-entropy based methodology. Technovation, 34(1), 54–63. https://doi.org/10.1016/j.technovation.2013.09.001

    2. Adrion, W. R., Branstad, M. A., & Cherniavsky, J. C. (1982). Validation, Verification, and Testing of Computer Software. ACM Computing Surveys, 14(2), 159–192. https://doi.org/10.1145/356876.356879

    3. Alenljung, B., & Persson, A. (2008). Portraying the practice of decision-making in requirements engineering: A case of large scale bespoke development. Requirements Engineering, 13(4), 257–279. https://doi.org/10.1007/s00766-008-0068-2

    4. Amann, S., Proksch, S., Nadi, S., & Mezini, M. (2016). A Study of Visual Studio Usage in Practice. 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER), 124–134. https://doi.org/10.1109/SANER.2016.39

    5. Barjtya, S., Sharma, A., & Rani, U. (2017). A Study of Software Development Life Cycle Process Models. International Journal Of Engineering And Computer Science, 6(7), 2319–7242. https://doi.org/10.2139/ssrn.2988291

    6. Castellion, G., & Markham, S. K. (2013). Perspective: New product failure rates: Influence of Argumentum ad populum and self-interest. In Journal of Product Innovation Management. https://doi.org/10.1111/j.1540-5885.2012.01009.x

    7. Chan, L. K., & Wu, M. L. (2002). Quality function deployment: A literature review. European Journal of Operational Research, 143(3), 463–497. https://doi.org/10.1016/S0377-2217(02)00178-9

    8. Dabbish, L., Stuart, C., Tsay, J., & Herbsleb, J. (2012). Social coding in GitHub: Transparency and collaboration in an open software repository. Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW, 1277–1286. https://doi.org/10.1145/2145204.2145396

    9. Desyatirikova, E. N., Belousov, V. E., Fedosova, S. P., & Ievleva, A. A. (2017). DSS design for risk management of projects. 2017 International Conference “Quality Management,Transport and Information Security, Information Technologies” (IT&QM&IS), 492–495. https://doi.org/10.1109/ITMQIS.2017.8085869

    10. Díez, E., & McIntosh, B. S. (2009). A review of the factors which influence the use and usefulness of information systems. Environmental Modelling and Software, 24(5), 588–602. https://doi.org/10.1016/j.envsoft.2008.10.009

    11. Du, J., Leten, B., & Vanhaverbeke, W. (2014). Managing open innovation projects with science-based and market-based partners. Research Policy, 43(5), 828–840. https://doi.org/10.1016/j.respol.2013.12.008

    12. Dwivedi, R., Chakraborty, S., Sinha, A. K., Singh, S., & Richa. (2018). Development of a performance measurement tool for an agricultural enterprise using BSC and QFD models. IOP Conference Series: Materials Science and Engineering. https://doi.org/10.1088/1757-899X/377/1/012214

    13. Expenditure on research and development. (2019). National Survey of Research and Development in Malaysia.

    14. Fraczek, K., & Plechawska-Wojcik, M. (2017). Comparative Analysis of Relational and Non-relational Databases in the Context of Performance in Web Applications. In Communications in Computer and Information Science (Vol. 716, Issue April, pp. 153–164). https://doi.org/10.1007/978-3-319-58274-0_13

    15. Froese, T. M. (2010). The impact of emerging information technology on project management for construction. Automation in Construction, 19(5), 531–538. https://doi.org/10.1016/j.autcon.2009.11.004

    16. Gerlach, S., & Brem, A. (2017). Idea management revisited: A review of the literature and guide for implementation. International Journal of Innovation Studies, 1(2), 144–161. https://doi.org/10.1016/j.ijis.2017.10.004

    17. Grimm, V., Johnston, A. S. A., Thulke, H.-H., Forbes, V. E., & Thorbek, P. (2020). Three questions to ask before using model outputs for decision support. Nature Communications, 11(1), 4959. https://doi.org/10.1038/s41467-020-17785-2

    18. Gunasekaran, A., Irani, Z., Choy, K. L., Filippi, L., & Papadopoulos, T. (2015). Performance measures and metrics in outsourcing decisions: A review for research and applications. In International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2014.12.021

    19. Gürel, E. (2017). SWOT Analysis: A Theoretical Review. Journal of International Social Research, 10(51), 994–1006. https://doi.org/10.17719/jisr.2017.1832

    20. Gutierrez, D. M., Scavarda, L. F., Fiorencio, L., & Martins, R. A. (2015). Evolution of the performance measurement system in the Logistics Department of a broadcasting company: An action research. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2014.08.012

    21. Hajikhani, A. (2013). Developing a Mix Method of SWOT , BSC & QFD toward Strategic Planning. Interdisciplinary Journal of Contemporary Research in Business, 5(1), 476–489.

    22. Havins, S. R. (2020). Decision Support Systems for Managing Innovation Through Project Selection in Public Sector R&D Environments. IEEE Engineering Management Review, 6(c), 1–1. https://doi.org/10.1109/EMR.2020.3007748

    23. Herbert, A. S. G. B. D. (2009). Decision Making and Problem Solving. Interfaces, September 2015, 59–59. https://doi.org/10.5005/jp/books/10444_10

    24. Highsmith, J., & Cockburn, A. (2001). Agile software development: the business of innovation. Computer, 34(9), 120–127. https://doi.org/10.1109/2.947100

    25. Hill, S., Martin, R., & Harris, M. (2000). Decentralization, Integration and the Post-Bureaucratic Organization: The Case of R&d. Journal of Management Studies. https://doi.org/10.1111/1467-6486.00194

    26. Iyigun, M. G. (1993). A decision support system for R&D project selection and resource allocation under uncertainty - The 1993 student paper award winner. Project Management Journal, 24(4), 5–13.

    27. Kalliamvakou, E., Gousios, G., Blincoe, K., Singer, L., German, D. M., & Damian, D. (2014). The promises and perils of mining GitHub. Proceedings of the 11th Working Conference on Mining Software Repositories - MSR 2014, 92–101. https://doi.org/10.1145/2597073.2597074

    28. Kaluža, M., & Vukelić, B. (2018). Comparison of front-end frameworks for web applications development. Zbornik Veleučilišta u Rijeci, 6(1), 261–282. https://doi.org/10.31784/zvr.6.1.19

    29. Conceptual Foundations of the Balanced Scorecard, 3 Handbooks of Management Accounting Research 1253 (2009). https://doi.org/10.1016/S1751-3243(07)03003-9

    30. Khurana, N., Singh Chhillar, R., & Chhillar, U. (2016). A Novel Technique for Generation and Optimization of Test Cases Using Use Case, Sequence, Activity Diagram and Genetic Algorithm. Journal of Software, 11(3), 242–250. https://doi.org/10.17706/jsw.11.3.242-250

    31. Kolodneer, J. L. (1991). Improving human decision making through case-based decision aiding. AI Magazine, 12(2), 52–68. https://doi.org/10.1609

    32. Kucukaltan, B., Irani, Z., & Aktas, E. (2016). A decision support model for identification and prioritization of key performance indicators in the logistics industry. Computers in Human Behavior, 65, 346–358. https://doi.org/10.1016/j.chb.2016.08.045

    33. Kuhlmann, S. (2018). Introduction to discussion paper on ‘Three Frames for Innovation Policy: R&D, Systems of Innovation and Transformative Change.’ Research Policy, 47(9), 1553. https://doi.org/10.1016/j.respol.2018.08.010

    34. Lee, S. F., & Sai On Ko, A. (2000). Building balanced scorecard with SWOT analysis, and implementing “Sun Tzu’s The Art of Business Management Strategies” on QFD methodology. Managerial Auditing Journal, 15(1/2), 68–76. https://doi.org/10.1108/02686900010304669

    35. Louw, R. E. (2002). Decision Support Systems.

    36. Lu, S., & Tzeng, G. (2002). A Decision Support System for Construction Project Risk Assessment. The Second International Conference on Electronic Business.

    37. Luo, J., & Yu, R. (2015). Follow the heart or the head? The interactive influence model of emotion and cognition. In Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2015.00573

    38. Ma, W. J. (2019). Bayesian Decision Models: A Primer. Neuron, 104(1), 164–175. https://doi.org/10.1016/j.neuron.2019.09.037

    39. Macal, C. M., & North, M. J. (2005). Tutorial on agent-based modeling and simulation. Proceedings - Winter Simulation Conference, 2005, 2–15. https://doi.org/10.1109/WSC.2005.1574234

    40. Mahalakshmi, M., & Sundararajan, D. M. (2008). Traditional SDLC Vs Scrum Methodology-A Comparative Study. International Journal of Emerging Technology and Advanced Engineering, 3(6), 192–196. www.ijetae.com

    41. Masroor, N., & Asim, M. (2019). SMEs in the Contemporary Era of Global Competition. Procedia Computer Science, 158, 632–641. https://doi.org/10.1016/j.procs.2019.09.097

    42. Ofori-kuragu, J. K., Baiden, B. K., & Badu, E. (2016). Key Performance Indicators for Project Success in Ghanaian Contractors. International Journal of Construction Engineering and Management, 5(1), 1–10. https://doi.org/10.5923/j.ijcem.20160501.01

    43. Ong, H. T. (2017). The Applications of Decision Support System ( DSS ) among the Top Corporations in Metro Manila and its Perceived Advantages and Disadvantages. Review of Integrative and Economics Research, 6(4), 243–254.

    44. Poteralska, B. (2017). Decision Support System in the Area of Generating Innovative Research Projects of the Future. Procedia Engineering, 182, 587–593. https://doi.org/10.1016/j.proeng.2017.03.160

    45. Power, D. J. (2020). A Brief History of Decision Support Systems. Intelligent Systems Reference Library, 157, 57–70. https://doi.org/10.1007/978-3-030-14354-1_2

    46. Revilla, M. A., Saris, W. E., & Krosnick, J. A. (2014). Choosing the Number of Categories in Agree-Disagree Scales. Sociological Methods and Research. https://doi.org/10.1177/0049124113509605

    47. Saaty, T. L. (2006). The Analytic Network Process. In Decision Making with the Analytic Network Process (Vol. 1, Issue 1, pp. 1–26). Springer US. https://doi.org/10.1007/0-387-33987-6_1

    48. Saaty, T. L., & Cho, Y. (2001). The decision by the US congress on China’s trade status: a multicriteria analysis. Socio-Economic Planning Sciences, 35(4), 243–252. https://doi.org/10.1016/S0038-0121(01)00016-7

    49. Sandström, J., & Toivanen, J. (2002). The problem of managing product development engineers: Can the balanced scorecard be an answer? International Journal of Production Economics, 78(1), 79–90. https://doi.org/10.1016/S0925-5273(01)00199-2

    50. Schifreen, R. (n.d.). How to create Web sites and applications with HTML, CSS, Javascript, PHP and MySQL. Retrieved July 8, 2021, from www.the-web-book.com/hosts.html

    51. Shaw, S., Grant, D. B., & Mangan, J. (2010). Developing environmental supply chain performance measures. In Benchmarking: An International Journal. https://doi.org/10.1108/14635771011049326

    52. Stewart, T. J. (1991). A Multi-criteria Decision Support System for R&D Project Selection. Journal of the Operational Research Society, 42(1), 17–26. https://doi.org/10.1057/jors.1991.3

    53. Tandel, S. J. A. (2018). Impact of progressive web apps on web app development. International Journal of Innovative Research in Science, Engineering and Technology, 7(9), 9439–9444. https://doi.org/10.15680/IJIRSET.2018.0709021

    54. Tzeng, G. H., Chiang, C. H., & Li, C. W. (2007). Evaluating intertwined effects in e-learning programs: A novel hybrid MCDM model based on factor analysis and DEMATEL. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2006.02.004

    55. Vasilescu, B., Filkov, V., & Serebrenik, A. (2013). StackOverflow and GitHub: Associations between Software Development and Crowdsourced Knowledge. 2013 International Conference on Social Computing, 188–195. https://doi.org/10.1109/SocialCom.2013.35

    56. Walsh, T. (2019). Shared decision-making in action. In Finding What Matters Most to Patients (pp. 47–60). Productivity Press. https://doi.org/10.4324/9780429440861-6

    57. Wang, J., Lin, W., & Huang, Y. H. (2010). A performance-oriented risk management framework for innovative R&D projects. Technovation, 30(11–12), 601–611. https://doi.org/10.1016/j.technovation.2010.07.003

    58. Yang, C. L., Chuang, S. P., & Huang, R. H. (2009). Manufacturing evaluation system based on AHP/ANP approach for wafer fabricating industry. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2009.03.023