Optimizing R&D Resource Allocation for Enterprise Innovation Team's in Ethiopia
DOI:
https://doi.org/10.54097/2b11cs32Keywords:
R&D, Resource Allocation, Game Theory, Ethiopian EnterprisesAbstract
This paper examines the optimal strategies for distributing limited R&D resources, considering the impact of governmental incentives and intra-enterprise competition teams in Ethiopia. We investigate that Success probabilities are increasing likelihood of innovation success with enhanced resource and effort inputs, but with diminishing returns. Equilibrium analysis and optimization problems, solved via MATLAB simulations, illustrate how resource allocation can significantly enhance innovation efficiency. We find while focusing resources on a single team may maximize outputs under specific conditions, distributing resources across multiple teams can promote beneficial competition and exploit diverse capabilities, thus optimizing the overall innovation within enterprises. This paper contributes valuable insights to strategic management in Ethiopia by outlining mathematical methods through which Ethiopian enterprises can maximize their innovation potential in the midst of resource constraints and competitive dynamics.
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[1] Mulugeta, T., Impacts of R&D expenditures on firms’ innovation and financial performance: A panel data evidence from Ethiopian firms. 2021.
[2] Daksa, M.D., et al., Enterprise innovation in developing countries: an evidence from Ethiopia. Journal of Innovation and Entrepreneurship, 2018. 7: p. 1-19.
[3] Tesfaye, G., et al., A LINEAR PROGRAMMING METHOD TO ENHANCE RESOURCE UTILIZATION CASE OF ETHIOPIAN APPAREL SECTOR. International Journal for Quality Research, 2016. 10(2).
[4] Deng, M., J. Chen, and J. Ding, Improving success probability of innovation through multi-agent collaboration: a differential game model. Technology Analysis & Strategic Management, 2023: p. 1-16.
[5] Ranadheer, A. and L.R. Parvathy. An Innovation Success Prediction Model of Android Application Using Logistic Regression Over MLC in Combination with PCA. in 2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF). 2023. IEEE.
[6] Jové Llopis, E. and A. Segarra Blasco, Innovation success: What is the role of innovation strategies? 2015.
[7] Rajaiya, H., Innovation success and capital structure. Journal of Corporate Finance, 2023. 79: p. 102345.
[8] Feng, K., et al., An innovative estimation of failure probability function based on conditional probability of parameter interval and augmented failure probability. Mechanical Systems and Signal Processing, 2019. 123: p. 606-625.
[9] Barney, J., Firm resources and sustained competitive advantage. Journal of management, 1991. 17(1): p. 99-120.
[10] Cooper, R., S. Edgett, and E. Kleinschmidt, Portfolio management for new product development: results of an industry practices study. r&D Management, 2001. 31(4): p. 361-380.
[11] Porter, M.E., Competitive advantage: Creating and sustaining superior performance. 2008: simon and schuster.
[12] Demiray, O., E.D. Güneş, and L. Örmeci, Modeling and Optimizing Resource Allocation Decisions through Multi-model Markov Decision Processes with Capacity Constraints. arXiv preprint arXiv:2011.04357, 2020.
[13] Friedkin, N.E., et al., Mathematical structures in group decision-making on resource allocation distributions. Scientific reports, 2019. 9(1): p. 1377.
[14] Bastian, N.D., et al., Resource allocation decision making in the military health system. IIE Transactions on Healthcare Systems Engineering, 2014. 4(2): p. 80-87.
[15] Burlov, V. and M. Grachev. Mathematical Model of Management Decision Making That Takes Into Account the Technical and Human Factors. in ICST. 2020.
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