Exploration of Dynamic Model-Based Decision Support Systems in Various Sectors: Systematic Literature Review and Visual Analysis
DOI:
https://doi.org/10.55927/ijabm.v4i1.33Keywords:
Decision Support System, Dynamic Model, Systematic Literature Review, Visual Analysis, Decision MakingAbstract
Dynamic model-based decision support systems can be used to assist strategic decision making in various sectors in facing complex and uncertain environmental challenges. The main problem in the development and application of dynamic model-based decision support systems is the lack of comprehensive mapping of research trends, challenges, and opportunities. This study aims to explore the application of dynamic model-based decision support systems to identify trends, as well as theoretical and practical contributions in various sectors. This study uses a systematic literature review method. Data collection techniques involve a visual filtering and analysis process to understand the pattern of decision support system application. The results of the study indicate that dynamic model-based decision support systems, such as simulation, system dynamics, and real option theory, are effective in supporting strategic decision making in the industrial, public policy, and management sectors. The integration of artificial intelligence (AI) technology and data analytics also strengthens the flexibility and accuracy of the system
References
Abou Jaoude, G., Mumm, O., & Carlow, V. M. (2022). An Overview of Scenario Approaches: A Guide for Urban Design and Planning. Journal of Planning Literature, 37(3), 467-487. https://doi.org/10.1177/08854122221083546
Alamanos, A., Rolston, A., & Papaioannou, G. (2021). Development of a Decision Support System for Sustainable Environmental Management and Stakeholder Engagement. Hydrology, 8(1), 40. https://doi.org/10.3390/hydrology8010040
Antonio, F., Atayde, J., Yamzon, M., & Sy, C. (2022). An optimization model for the design of supply chains considering disruptions from pandemic uncertainty and infection trends. Cleaner Engineering and Technology, 11, 100577. https://doi.org/10.1016/j.clet.2022.100577
Badakhshan, E., Mustafee, N., & Bahadori, R. (2024). Application of simulation and machine learning in supply chain management: A synthesis of the literature using the Sim-ML literature classification framework. Computers & Industrial Engineering, 198, 110649. https://doi.org/10.1016/j.cie.2024.110649
Bayu, F., Berhan, E., & Ebinger, F. (2022). A System Dynamics Model for Dynamic Capability Driven Sustainability Management. Journal of Open Innovation: Technology, Market, and Complexity, 8(1), 56. https://doi.org/10.3390/joitmc8010056
Betto, F., Sardi, A., Garengo, P., & Sorano, E. (2022). The Evolution of Balanced Scorecard in Healthcare: A Systematic Review of Its Design, Implementation, Use, and Review. International journal of environmental research and public health, 19(16), 10291. https://doi.org/10.3390/ijerph191610291
Biloslavo, R., Edgar, D., Aydin, E., & Bulut, C. (2024). Artificial intelligence (AI) and strategic planning process within VUCA environments: a research agenda and guidelines. Management Decision. https://doi.org/10.1108/MD-10-2023-1944
Bozdoğan, A., Görkemli Aykut, L., & Demirel, N. (2023). An agent-based modeling framework for the design of a dynamic closed-loop supply chain network. Complex Intell Syst, 9, 247–265. https://doi.org/10.1007/s40747-022-00780-z
Brewis, C., Dibb, S., & Meadows, M. (2023). Leveraging big data for strategic marketing: A dynamic capabilities model for incumbent firms. Technological Forecasting and Social Change, 190, 122402. https://doi.org/10.1016/j.techfore.2023.122402
Brüggemann, S., Chan, T., Wardi, G., Mandel, J., Fontanesi, J., & Bitmead, R.R. (2021). Decision support tool for hospital resource allocation during the COVID-19 pandemic. Informatics in Medicine Unlocked, 24, 100618. https://doi.org/10.1016/j.imu.2021.100618
Canco, I., Kruja, D., & Iancu, T. (2021). AHP, a Reliable Method for Quality Decision Making: A Case Study in Business. Sustainability, 13(24), 13932. https://doi.org/10.3390/su132413932
Castilla-Rodríguez, I., Expósito-Izquierdo, C., Melián-Batista, B., Aguilar, R.M., & Moreno-Vega, J.M. (2020). Simulation-optimization for the management of the transshipment operations at maritime container terminals. Expert Systems with Applications, 139, 112852. https://doi.org/10.1016/j.eswa.2019.112852
Chatterjee, S., Chaudhuri, R., Gupta, S., Sivarajah, U., & Bag, S. (2023). Assessing the impact of big data analytics on decision-making processes, forecasting, and performance of a firm. Technological Forecasting and Social Change, 196, 122824. https://doi.org/10.1016/j.techfore.2023.122824
Chen, W., Men, Y., Fuster, N., Osorio, C., & Juan, A.A. (2024). Artificial Intelligence in Logistics Optimization with Sustainable Criteria: A Review. Sustainability, 16(21), 9145. https://doi.org/10.3390/su16219145
Cordova-Pozo, K., & Rouwette, E. A.J.A. (2023). Types of scenario planning and their effectiveness: A review of reviews. Futures, 149, 103153. https://doi.org/10.1016/j.futures.2023.103153
Dasović, B., Galić, M., & Klanšek, U. (2020). A Survey on Integration of Optimization and Project Management Tools for Sustainable Construction Scheduling. Sustainability, 12(8), 3405. https://doi.org/10.3390/su12083405
de la Torre, R., Corlu, C.G., Faulin, J., Onggo, B.S., & Juan, A.A. (2021). Simulation, Optimization, and Machine Learning in Sustainable Transportation Systems: Models and Applications. Sustainability, 13(3), 1551. https://doi.org/10.3390/su13031551
Donelli, C.C., Fanelli, S., Zangrandi, A., & Elefanti, M. (2022). Disruptive crisis management: lessons from managing a hospital during the COVID-19 pandemic. Management Decision, 60(13), 66-91. https://doi.org/10.1108/MD-02-2021-0279
Elkady, S., Hernantes, J., & Labaka, L. (2024). Decision-making for community resilience: A review of decision support systems and their applications. Heliyon, 10(12), e33116. https://doi.org/10.1016/j.heliyon.2024.e33116
Fattahi, M., Keyvanshokooh, E., Kannan, D., & Govindan, K. (2023). Resource planning strategies for healthcare systems during a pandemic. European Journal of Operational Research, 304(1), 192-206. https://doi.org/10.1016/j.ejor.2022.01.023
Filippou, K., Aifantis, G., Papakostas, G. A., & Tsekouras, G. E. (2023). Structure Learning and Hyperparameter Optimization Using an Automated Machine Learning (AutoML) Pipeline. Information, 14(4), 232. https://doi.org/10.3390/info14040232
Gandrita, D. M. (2023). Improving Strategic Planning: The Crucial Role of Enhancing Relationships between Management Levels. Administrative Sciences, 13(10), 211. https://doi.org/10.3390/admsci13100211
Garay-Rondero, C.L., Martinez-Flores, J.L., Smith, N.R., Caballero Morales, S.O., & Aldrette-Malacara, A. (2020). Digital supply chain model in Industry 4.0. Journal of Manufacturing Technology Management, 31(5), 887-933. https://doi.org/10.1108/JMTM-08-2018-0280
Gasset, D., Paillalef, F., Payacán, S., Gatica, G., Herrera-Vidal, G., Linfati, R., & Coronado-Hernández, J. R. (2024). Route Optimization for Open Vehicle Routing Problem (OVRP): A Mathematical and Solution Approach. Applied Sciences, 14(16), 6931. https://doi.org/10.3390/app14166931
Glette-Iversen, I., Flage, R., & Aven, T. (2023). Extending and improving current frameworks for risk management and decision-making: A new approach for incorporating dynamic aspects of risk and uncertainty. Safety Science, 168, 106317. https://doi.org/10.1016/j.ssci.2023.106317
Grander, G., da Silva, L.F., & Santibañez Gonzalez, E.D.R. (2021). Big data as a value generator in decision support systems: a literature review. Revista de Gestão, 28(3), 205-222. https://doi.org/10.1108/REGE-03-2020-0014
Guo, K., & Zhang, L. (2022). Multi-objective optimization for improved project management: Current status and future directions. Automation in Construction, 139, 104256. https://doi.org/10.1016/j.autcon.2022.104256
Haddaway, N. R., Page, M. J., Pritchard, C. C., & McGuinness, L. A. (2022). PRISMA2020: An R package and Shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis Campbell Systematic Reviews, 18, e1230. https://doi.org/10.1002/cl2.1230
Illahi, U., & Mir, M.S. (2021). Maintaining efficient logistics and supply chain management operations during and after coronavirus (COVID-19) pandemic: learning from the past experiences. Environ Dev Sustain, 23, 11157–11178. https://doi.org/10.1007/s10668-020-01115-z
Jonsdottir, A.T., Johannsdottir, L., & Davidsdottir, B. (2024). Systematic literature review on system dynamic modeling of sustainable business model strategies. Cleaner Environmental Systems, 13, 100200. https://doi.org/10.1016/j.cesys.2024.100200
Jung, D., Tran Tuan, V., Quoc Tran, D., Park, M., & Park, S. (2020). Conceptual Framework of an Intelligent Decision Support System for Smart City Disaster Management. Applied Sciences, 10(2), 666. https://doi.org/10.3390/app10020666
Karthikeyan, A., Garg, A., Vinod, P. K., & Priyakumar, U. D. (2021). Machine Learning Based Clinical Decision Support System for Early COVID-19 Mortality Prediction. Frontiers in public health, 9, 626697. https://doi.org/10.3389/fpubh.2021.626697
Keyhanpour, M.J., Jahromi, S.H.M., & Ebrahimi, H. (2021). System dynamics model of sustainable water resources management using the Nexus Water-Food-Energy approach. Ain Shams Engineering Journal, 12(2), 1267-1281. https://doi.org/10.1016/j.asej.2020.07.029
Khawka, Z. M. H., Abd Rahman, A., Sidek, S. B., Ahmed, S. A. B., Al-Hadeethi, R. H. F., & Al-Dabbagh, T. (2024). Effect of lean supply chain on competitive advantage: a systematic literature review. Cogent Business & Management, 11(1). https://doi.org/10.1080/23311975.2024.2370445
Kim, J., & Chung, C. (2023). Surrogate-based optimization approach for capacitated hub location problem with uncertainty. Cogent Engineering, 10(1). https://doi.org/10.1080/23311916.2023.2185948
Kim, S., Choi, Y., & Kim, S. (2023). Simulation Modeling in Supply Chain Management Research of Ethanol: A Review. Energies, 16(21), 7429. https://doi.org/10.3390/en16217429
Korder, B., Maheut, J., & Konle, M. (2024). Simulation Methods and Digital Strategies for Supply Chains Facing Disruptions: Insights from a Systematic Literature Review. Sustainability, 16(14), 5957. https://doi.org/10.3390/su16145957
Kovari, A. (2024). AI for Decision Support: Balancing Accuracy, Transparency, and Trust Across Sectors. Information, 15(11), 725. https://doi.org/10.3390/info15110725
Kurpiela, S., & Teuteberg, F. (2024). Linking business analytics affordances to corporate strategic planning and decision making outcomes. Inf Syst E-Bus Manage, 22, 33–60. https://doi.org/10.1007/s10257-023-00661-z
Laghari, F., Ahmed, F., & López García, M. L. N. (2023). Cash flow management and its effect on firm performance: Empirical evidence on non-financial firms of China. PloS one, 18(6), e0287135. https://doi.org/10.1371/journal.pone.0287135
Li, W., Waris, I., & Bhutto, M.Y. (2024). Understanding the nexus among big data analytics capabilities, green dynamic capabilities, supply chain agility and green competitive advantage: the moderating effect of supply chain innovativeness. Journal of Manufacturing Technology Management, 35(1), 119-140. https://doi.org/10.1108/JMTM-07-2023-0263
Li, Z., Gu, W., & Meng, Q. (2023). The impact of COVID‐19 on logistics and coping strategies: A literature review. Regional Science Policy & Practice, 15(8), 1768-1795. https://doi.org/10.1111/rsp3.12665
McKinsey & Company. (2024). How can data analytics be leveraged to enhance organizational performance and decisionmaking?. Psico-smart, URL https://psico-smart.com/en/blogs/blog-how-can-data-analytics-be-leveraged-to-enhance-organizational-performance-and-decisionmaking-154541
Moharil, A., Vanschoren, J., Singh, P. et al. (2024). Towards efficient AutoML: a pipeline synthesis approach leveraging pre-trained transformers for multimodal data. Mach Learn, 113, 7011–7053. https://doi.org/10.1007/s10994-024-06568-1
Naeem, K., Zghibi, A., Elomri, A., Mazzoni, A., & Triki, C. (2023). A Literature Review on System Dynamics Modeling for Sustainable Management of Water Supply and Demand. Sustainability, 15(8), 6826. https://doi.org/10.3390/su15086826
Ordu, M., Demir, E., Tofallis, C., & Gunal, M.M. (2023). A comprehensive and integrated hospital decision support system for efficient and effective healthcare services delivery using discrete event simulation. Healthcare Analytics, 4, 100248. https://doi.org/10.1016/j.health.2023.100248
Pekarcikova, M., Trebuna, P., Kliment, M., Dic, M. (2021). Solution of Bottlenecks in the Logistics Flow by Applying the Kanban Module in the Tecnomatix Plant Simulation Software. Sustainability, 13(14), 7989. https://doi.org/10.3390/su13147989
Portela, R., Vicente, J.R., Roiloa, S.R., & Cabral, J.A. (2020). A dynamic model-based framework to test the effectiveness of biocontrol targeting a new plant invader– the case of Alternanthera philoxeroides in the Iberian Peninsula. Journal of Environmental Management, 264, 110349. https://doi.org/10.1016/j.jenvman.2020.110349
Quesado, P., Marques, S., Silva, R., & Ribeiro, A. (2022). The Balanced Scorecard as a Strategic Management Tool in the Textile Sector. Administrative Sciences, 12(1), 38. https://doi.org/10.3390/admsci12010038
Rodgers, W., Murray, J.M., Stefanidis, A., Degbey, W.Y., & Tarba, S.Y. (2023). An artificial intelligence algorithmic approach to ethical decision-making in human resource management processes. Human Resource Management Review, 33(1), 100925. https://doi.org/10.1016/j.hrmr.2022.100925
Roldán Bravo, M.I., Maqueira-Marin, J.M., & Moyano-Fuentes, J. (2023). Supply chain 4.0 ambidexterity and lean supply chain management: interrelationships and effect on the focal firm’s operational performance. Supply Chain Management, 28(7), 112-128. https://doi.org/10.1108/SCM-05-2023-0274
Schmitt, M. (2023). Automated machine learning: AI-driven decision making in business analytics. Intelligent Systems with Applications, 18, 200188. https://doi.org/10.1016/j.iswa.2023.200188
Sequeira, M., Hilletofth, P., & Adlemo, A. (2021). AHP-based support tools for initial screening of manufacturing reshoring decisions. Journal of Global Operations and Strategic Sourcing, 14(3). 502-527. https://doi.org/10.1108/JGOSS-07-2020-0037
Silva, B.N., Khan, M., & Han, K. (2020). Futuristic Sustainable Energy Management in Smart Environments: A Review of Peak Load Shaving and Demand Response Strategies, Challenges, and Opportunities. Sustainability, 12(14), 5561. https://doi.org/10.3390/su12145561
Soori, M., Jough, F.K.G., Dastres, R., & Arezoo, B. (2024). AI-Based Decision Support Systems in Industry 4.0, A Review. Journal of Economy and Technology. https://doi.org/10.1016/j.ject.2024.08.005
Stofkova, J., Krejnus, M., Stofkova, K. R., Malega, P., & Binasova, V. (2022). Use of the Analytic Hierarchy Process and Selected Methods in the Managerial Decision-Making Process in the Context of Sustainable Development. Sustainability, 14(18), 11546. https://doi.org/10.3390/su141811546
Strelkovskii, N., Komendantova, N., Sizov, S., & Rovenskaya, E. (2020). Building plausible futures: Scenario-based strategic planning of industrial development of Kyrgyzstan. Futures, 124, 102646. https://doi.org/10.1016/j.futures.2020.102646
Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020). An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ digital medicine, 3, 17. https://doi.org/10.1038/s41746-020-0221-y
Tang, J., Dai, Z., Jiang, W., Wu, X., Zhuravkov, M.A., Xue, Z., & Wang, J. (2024). A Comprehensive Review of Theories, Methods, and Techniques for Bottleneck Identification and Management in Manufacturing Systems. Applied Sciences, 14(17), 7712. https://doi.org/10.3390/app14177712
Terning, G., Brun, E. C., & El-Thalji, I. (2022). The Patient Flow Effect of Pandemic Policies: A Hybrid Simulation Study in a Norwegian Emergency Department. Healthcare (Basel, Switzerland), 11(1), 1. https://doi.org/10.3390/healthcare11010001
Thangam, D., Muniraju, H., Ramesh, R., Narasimhaiah, R., Khan, N.M.A., Booshan, S., Booshan, B., Manickam, T., & Ganesh, R.S. (2024). Impact of Data Centers on Power Consumption, Climate Change, and Sustainability. IGI Global, 24. https://doi.org/10.4018/979-8-3693-1552-1.ch004
Tripathi, V., Chattopadhyaya, S., Mukhopadhyay, A. K., Sharma, S., Li, C., Singh, S., Saleem, W., Salah, B., & Mohamed, A. (2022). Recent Progression Developments on Process Optimization Approach for Inherent Issues in Production Shop Floor Management for Industry 4.0. Processes, 10(8), 1587. https://doi.org/10.3390/pr10081587
Utku, D. H. (2023). The Evaluation and Improvement of the Production Processes of an Automotive Industry Company via Simulation and Optimization. Sustainability, 15(3), 2331. https://doi.org/10.3390/su15032331
Wang, C., Fan, H., & Qiang, X. (2023). A Review of Uncertainty-Based Multidisciplinary Design Optimization Methods Based on Intelligent Strategies. Symmetry, 15(10), 1875. https://doi.org/10.3390/sym15101875
Xu, Y., Abdelaziz, F.B., Sahnoun, M., & Louis, A. (2024). Optimized cooperative vehicle routing in multi-depot collection systems for circular economy: Model development and case study. Journal of Cleaner Production, 144417. https://doi.org/10.1016/j.jclepro.2024.144417
Yawson, R. M., & Paros, A. K. B. (2023). Systems Perspective of the Use of the Balanced Scorecard for Organization Development and Change. Sage Open, 13(4). https://doi.org/10.1177/21582440231218064
Yinusa, A., & Faezipour, M. (2023). Optimizing Healthcare Delivery: A Model for Staffing, Patient Assignment, and Resource Allocation. Applied System Innovation, 6(5), 78. https://doi.org/10.3390/asi6050078
Zakeri, S., Konstantas, D., Sorooshian, S., & Chatterjee, P. (2024). A novel ML-MCDM-based decision support system for evaluating autonomous vehicle integration scenarios in Geneva’s public transportation. Artif Intell Rev, 57, 310. https://doi.org/10.1007/s10462-024-10917-w
Zanella, F., & Vaz, C.B. (2023). Sustainable Short-Term Production Planning Optimization. SN COMPUT. SCI. 4, 824. https://doi.org/10.1007/s42979-023-02261-7
Zanker, M., Bureš, V., & Tučník, P. (2021). Environment, Business, and Health Care Prevail: A Comprehensive, Systematic Review of System Dynamics Application Domains. Systems, 9(2), 28. https://doi.org/10.3390/systems9020028
Zhang, X., & Yin, J. (2023). Assessment of investment decisions in bulk shipping through fuzzy real options analysis. Marit Econ Logist, 25, 122–139. https://doi.org/10.1057/s41278-021-00201-x
