Improving efficiency and effectiveness of budget, labor, and inventory allocation decision making through decision support system
DOI:
https://doi.org/10.35335/cit.Vol15.2024.712.pp270-276Keywords:
Budget Allocation, Decision Making, Operational Efficiency, Resource Management, TOPSIS MethodAbstract
This research aims to improve the efficiency and effectiveness of budget, labor, and inventory allocation decision making at PT Telkom through the application of the TOPSIS method. Using a decision matrix that includes five alternatives and three criteria, this analysis ranks each alternative based on proximity to the positive ideal solution and distance to the negative ideal solution. The results show that Alternative D is the best choice, signifying superiority in the combination of measured values. These recommendations provide strategic guidance for PT Telkom in optimizing resource management, but keep in mind that the results are relative and need periodic evaluation to maintain relevance in the context of dynamic changes. This research contributes to the decision-making and resource management literature by applying systematic methods to complex business situations.
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References
Z. Wu and M. Pagell, “Balancing priorities: Decision-making in sustainable supply chain management,” J. Oper. Manag., vol. 29, no. 6, pp. 577–590, 2011.
W. Setyowati, R. Widayanti, and D. Supriyanti, “Implementation of e-business information system in indonesia: Prospects and challenges,” Int. J. Cyber IT Serv. Manag., vol. 1, no. 2, pp. 180–188, 2021.
Y. Niu, L. Ying, J. Yang, M. Bao, and C. B. Sivaparthipan, “Organizational business intelligence and decision making using big data analytics,” Inf. Process. Manag., vol. 58, no. 6, p. 102725, 2021.
A. Ferraris, A. Mazzoleni, A. Devalle, and J. Couturier, “Big data analytics capabilities and knowledge management: impact on firm performance,” Manag. Decis., vol. 57, no. 8, pp. 1923–1936, 2019.
L. A. Orobia, J. Nakibuuka, J. Bananuka, and R. Akisimire, “Inventory management, managerial competence and financial performance of small businesses,” J. Account. Emerg. Econ., vol. 10, no. 3, pp. 379–398, 2020.
A. F. Folajinmi and A. O. Peter, “Financial management practices and performance of small and medium scale poultry industry in Ogun State, Nigeria,” J. Financ. Account., vol. 8, no. 2, p. 90, 2020.
K. Moons, G. Waeyenbergh, and L. Pintelon, “Measuring the logistics performance of internal hospital supply chains–a literature study,” Omega, vol. 82, pp. 205–217, 2019.
N. Kunnathuvalappil Hariharan, “Rethinking budgeting process in times of uncertainty,” 2020.
G. Y. Ostaev et al., “Integrated budgeting at agricultural enterprises: functionality and management decision making,” Amaz. Investig., vol. 8, no. 22, pp. 593–601, 2019.
B. Singh Patel, C. Samuel, and G. Sutar, “Designing of an agility control system: a case of an Indian manufacturing organization,” J. Model. Manag., vol. 15, no. 4, pp. 1591–1612, 2020.
M. Ghasemaghaei, “Does data analytics use improve firm decision making quality? The role of knowledge sharing and data analytics competency,” Decis. Support Syst., vol. 120, pp. 14–24, 2019.
Y. Duan, J. S. Edwards, and Y. K. Dwivedi, “Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda,” Int. J. Inf. Manage., vol. 48, pp. 63–71, 2019.
A. S. Aydiner, E. Tatoglu, E. Bayraktar, and S. Zaim, “Information system capabilities and firm performance: Opening the black box through decision-making performance and business-process performance,” Int. J. Inf. Manage., vol. 47, pp. 168–182, 2019.
B. S. Onggo, C. G. Corlu, A. A. Juan, T. Monks, and R. de la Torre, “Combining symbiotic simulation systems with enterprise data storage systems for real-time decision-making,” Enterp. Inf. Syst., vol. 15, no. 2, pp. 230–247, 2021.
M. W. L. Moreira, J. J. P. C. Rodrigues, V. Korotaev, J. Al-Muhtadi, and N. Kumar, “A comprehensive review on smart decision support systems for health care,” IEEE Syst. J., vol. 13, no. 3, pp. 3536–3545, 2019.
M. Andronie, G. L?z?roiu, M. Iatagan, C. U??, R. ?tef?nescu, and M. Coco?atu, “Artificial intelligence-based decision-making algorithms, internet of things sensing networks, and deep learning-assisted smart process management in cyber-physical production systems,” Electron., vol. 10, no. 20, 2021, doi: 10.3390/electronics10202497.
F. Ahmed, Y. J. Qin, and L. Martínez, “Sustainable change management through employee readiness: Decision support system adoption in technology-intensive British e-businesses,” Sustainability, vol. 11, no. 11, p. 2998, 2019.
A. K. Fuller, D. J. Decker, M. V Schiavone, and A. B. Forstchen, “Ratcheting up rigor in wildlife management decision making,” Wildl. Soc. Bull., vol. 44, no. 1, pp. 29–41, 2020.
O. Benfeldt, J. S. Persson, and S. Madsen, “Data governance as a collective action problem,” Inf. Syst. Front., vol. 22, pp. 299–313, 2020.
H. Alami, M.-P. Gagnon, and J.-P. Fortin, “Some multidimensional unintended consequences of telehealth utilization: a multi-project evaluation synthesis,” Int. J. Heal. policy Manag., vol. 8, no. 6, p. 337, 2019.
R. T. Sutton, D. Pincock, D. C. Baumgart, D. C. Sadowski, R. N. Fedorak, and K. I. Kroeker, “An overview of clinical decision support systems: benefits, risks, and strategies for success,” NPJ Digit. Med., vol. 3, no. 1, p. 17, 2020.
M. A. Musen, B. Middleton, and R. A. Greenes, “Clinical decision-support systems,” in Biomedical informatics: computer applications in health care and biomedicine, Springer, 2021, pp. 795–840.
P. B. Keenan and P. Jankowski, “Spatial decision support systems: Three decades on,” Decis. Support Syst., vol. 116, pp. 64–76, 2019.
H. S. Lallie et al., “Cyber security in the age of COVID-19: A timeline and analysis of cyber-crime and cyber-attacks during the pandemic,” Comput. Secur., vol. 105, p. 102248, 2021.
P. J. Taylor, T. Dargahi, A. Dehghantanha, R. M. Parizi, and K.-K. R. Choo, “A systematic literature review of blockchain cyber security,” Digit. Commun. Networks, vol. 6, no. 2, pp. 147–156, 2020.
P. Grover and S. Prasad, “A Review on Block chain and Data Mining Based Data Security Methods,” in 2021 2nd International Conference on Big Data Analytics and Practices (IBDAP), 2021, pp. 112–118.
Y. ?ahin and ?. DOGRU, “An Enterprise Data Privacy Governance Model: Security-Centric Multi-Model Data Anonymization,” Int. J. Eng. Res. Dev., vol. 15, no. 2, pp. 574–583, 2023.
B. Uzun, M. Taiwo, A. Syidanova, and D. Uzun Ozsahin, “The technique for order of preference by similarity to ideal solution (TOPSIS),” Appl. Multi-Criteria Decis. Anal. Environ. Civ. Eng., pp. 25–30, 2021.
L. G. Ramón?Canul et al., “Technique for order of preference by similarity to ideal solution (TOPSIS) method for the generation of external preference mapping using rapid sensometric techniques,” J. Sci. Food Agric., vol. 101, no. 8, pp. 3298–3307, 2021.
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