Economic Viability of Decision Support Systems for Emergency Risk Reduction in Chemical Industries
DOI:
https://doi.org/10.61978/catalyx.v2i4.1343Keywords:
decision support system, cost-benefit analysis, chemical safety, ROI, emergency management, industrial risk, economic evaluationAbstract
This study assesses the economic feasibility of implementing a Decision Support System (DSS) in chemical process industries for emergency management and risk reduction. Recognizing the financial stakes involved in industrial incidents, the research evaluates the costs and returns associated with DSS adoption through a structured cost-benefit analysis. The methodology incorporates capital expenditure (CAPEX), operating expenditure (OPEX), avoided incident losses, and insurance premium reductions. Using data modeling and sensitivity analysis, the study calculates net financial benefits and payback periods under various scenarios, ranging from conservative to optimistic projections. Key findings reveal that the DSS investment of USD 450,000, with an annual OPEX of USD 85,000, yields annual economic benefits of USD 290,000 through incident cost avoidance and insurance savings. This translates to a net benefit of USD 205,000 annually and a payback period of approximately 2.2 years. Even under conservative assumptions, the system demonstrates economic viability, confirming its potential to deliver substantial returns beyond its safety functions. The conclusion affirms that DSS integration not only enhances operational safety but also provides compelling financial justification. It encourages broader adoption in high-risk industrial sectors and advocates for future research that integrates intangible benefits and long-term impacts into economic evaluations.
References
Adenuga, A. H., Jack, C., Olagunju, K. O., & Ashfield, A. (2020). Economic Viability of Adoption of Automated Oestrus Detection Technologies on Dairy Farms: A Review. Animals, 10(7), 1241. https://doi.org/10.3390/ani10071241 DOI: https://doi.org/10.3390/ani10071241
Archetti, C., Montanelli, A., Finazzi, D., Caimi, L., & Garrafa, E. (2017). Clinical Laboratory Automation: A Case Study. Journal of Public Health Research, 6(1). https://doi.org/10.4081/jphr.2017.881 DOI: https://doi.org/10.4081/jphr.2017.881
Bach, M. P., Topalović, A., Krstić, Ž., & Ivec, A. (2023). Predictive Maintenance in Industry 4.0 for the SMEs: A Decision Support System Case Study Using Open-Source Software. Designs, 7(4), 98. https://doi.org/10.3390/designs7040098 DOI: https://doi.org/10.3390/designs7040098
Baruffaldi, G., Accorsi, R., & Manzini, R. (2019). Warehouse management system customization and information availability in 3pl companies: A decision-support tool. Industrial Management and Data Systems, 119(2), 251–273. https://doi.org/10.1108/IMDS-01-2018-0033 DOI: https://doi.org/10.1108/IMDS-01-2018-0033
Blouin, A. S. (2023). Innovations in Nursing Workforce Management. Jona the Journal of Nursing Administration, 53(11), 601–606. https://doi.org/10.1097/nna.0000000000001352 DOI: https://doi.org/10.1097/NNA.0000000000001352
Cánovas-Segura, B., Morales, A., Juarez, J. M., & Campos, M. (2023). Meaningful time-related aspects of alerts in Clinical Decision Support Systems. A unified framework. Journal of Biomedical Informatics, 143. https://doi.org/10.1016/j.jbi.2023.104397 DOI: https://doi.org/10.1016/j.jbi.2023.104397
Çınar, Z. M., Nuhu, A. A., Zeeshan, Q., Korhan, O., Asmael, M., & Safaei, B. (2020). Machine Learning in Predictive Maintenance Towards Sustainable Smart Manufacturing in Industry 4.0. Sustainability, 12(19), 8211. https://doi.org/10.3390/su12198211 DOI: https://doi.org/10.3390/su12198211
Cui, Y., & Wu, B. (2016). Chemical Tank Leakage Risk Assessment Based on Set Pair Analysis and LabVIEW. The Open Fuels & Energy Science Journal, 9(1), 102–113. https://doi.org/10.2174/1876973x01609010102 DOI: https://doi.org/10.2174/1876973X01609010102
Cui, M., Li, W., & Kamoche, K. (2025). Building Trust in Decision-Support Artificial Intelligence: A Boundary Spanning Perspective. Information Systems Journal. https://doi.org/10.1111/isj.70015 DOI: https://doi.org/10.1111/isj.70015
Fasoro, O. A., & Ajewole, O. I. (2019). Investment Analysis of Small Scale Private Forest Plantation Development in Ogun State, Nigeria. Asian Journal of Advances in Agricultural Research, 1–8. https://doi.org/10.9734/ajaar/2019/v10i130020 DOI: https://doi.org/10.9734/ajaar/2019/v10i130020
Guimapi, R. A., Mohamed, S. A., Biber‐Freudenberger, L., Mwangi, W., Ekesi, S., Borgemeister, C., & Tonnang, H. E. Z. (2020). Decision Support System for Fitting and Mapping Nonlinear Functions With Application to Insect Pest Management in the Biological Control Context. Algorithms, 13(4), 104. https://doi.org/10.3390/a13040104 DOI: https://doi.org/10.3390/a13040104
Gurung, Y. B., Zhao, J., Kc, B. K., Wu, X., Suwal, B. R., & Whittington, D. (2017). The Costs of Delay in Infrastructure Investments: A Comparison of 2001 and 2014 Household Water Supply Coping Costs in the Kathmandu Valley, Nepal. Water Resources Research, 53(8), 7078–7102. https://doi.org/10.1002/2016wr019529 DOI: https://doi.org/10.1002/2016WR019529
Kim, K., Lee, S., Kim, T. H., & Lee, S. G. (2022). Economic Evaluation of Total Laboratory Automation in the Clinical Laboratory of a Tertiary Care Hospital. Annals of Laboratory Medicine, 42(1), 89–95. https://doi.org/10.3343/alm.2022.42.1.89 DOI: https://doi.org/10.3343/alm.2022.42.1.89
Krasuski, A., Zimny, M., Hostikka, S., & Makowski, R. (2022). Risk-Aware Decision Making in the Safety Investments—Application of Stochastic Simulations and Judgment Value Method. Fire Safety Journal, 127, 103491. https://doi.org/10.1016/j.firesaf.2021.103491 DOI: https://doi.org/10.1016/j.firesaf.2021.103491
Lin, X., Negenborn, R. R., & Lodewijks, G. (2018). Predictive Quality-Aware Control for Scheduling of Potato Starch Production. Computers and Electronics in Agriculture, 150, 266–278. https://doi.org/10.1016/j.compag.2018.04.020 DOI: https://doi.org/10.1016/j.compag.2018.04.020
Linnerooth‐Bayer, J., Surminski, S., Bouwer, L. M., Noy, I., & Mechler, R. (2018). Insurance as a Response to Loss and Damage? 483–512. https://doi.org/10.1007/978-3-319-72026-5_21 DOI: https://doi.org/10.1007/978-3-319-72026-5_21
Maluleka, M. L., & Chummun, B. Z. (2024). The Function of Competitive Intelligence in South African Insurance Post-Covid-19 Outbreak. Sa Journal of Information Management, 26(1). https://doi.org/10.4102/sajim.v26i1.1738 DOI: https://doi.org/10.4102/SAJIM.v26i1.1738
Mendoza, R. L. (2016). Which Moral Hazard? Health Care Reform Under the Affordable Care Act of 2010. Journal of Health Organization and Management, 30(4), 510–529. https://doi.org/10.1108/jhom-03-2015-0054 DOI: https://doi.org/10.1108/JHOM-03-2015-0054
Mittas, N., & Mitrοpoulos, A. C. (2022). A Data-Driven Framework for Probabilistic Estimates in Oil and Gas Project Cost Management: A Benchmark Experiment on Natural Gas Pipeline Projects. Computation, 10(5), 75. https://doi.org/10.3390/computation10050075 DOI: https://doi.org/10.3390/computation10050075
Nwulu, E. O., Adikwu, F. E., Odujobi, O., Onyekwe, F. O., Ozobu, C. O., & Daraojimba, A. I. (2024). Financial Modeling for EHS Investments: Advancing the Cost-Benefit Analysis of Industrial Hygiene Programs in Preventing Occupational Diseases. International Journal of Multidisciplinary Research and Growth Evaluation, 5(1), 1438–1450. https://doi.org/10.54660/.ijmrge.2024.5.1.1438-1450 DOI: https://doi.org/10.54660/.IJMRGE.2024.5.1.1438-1450
Rahman, M., Ming, T. H., Baigh, T. A., & Sarker, M. (2021). Adoption of Artificial Intelligence in Banking Services: An Empirical Analysis. International Journal of Emerging Markets, 18(10), 4270–4300. https://doi.org/10.1108/ijoem-06-2020-0724 DOI: https://doi.org/10.1108/IJOEM-06-2020-0724
Sano, K., & Koshiba, Y. (2024). Decision‐making Regarding Safety Investments: A Case Study of Fluidized‐bed Reactors. Process Safety Progress, 43(3), 550–560. https://doi.org/10.1002/prs.12631 DOI: https://doi.org/10.1002/prs.12631
Sarkar, B. (2016). Supply Chain Coordination With Variable Backorder, Inspections, and Discount Policy for Fixed Lifetime Products. Mathematical Problems in Engineering, 2016, 1–14. https://doi.org/10.1155/2016/6318737 DOI: https://doi.org/10.1155/2016/6318737
Shafiee, M. (2024). Extending the Lifetime of Offshore Wind Turbines: Challenges and Opportunities. Energies, 17(16), 4191. https://doi.org/10.3390/en17164191 DOI: https://doi.org/10.3390/en17164191
Shafiee, M., & Animah, I. (2017). Life Extension Decision Making of Safety Critical Systems: An Overview. Journal of Loss Prevention in the Process Industries, 47, 174–188. https://doi.org/10.1016/j.jlp.2017.03.008 DOI: https://doi.org/10.1016/j.jlp.2017.03.008
Symeonaki, E., Arvanitis, K. G., & Piromalis, D. (2020). A Context-Aware Middleware Cloud Approach for Integrating Precision Farming Facilities Into the IoT Toward Agriculture 4.0. Applied Sciences, 10(3), 813. https://doi.org/10.3390/app10030813. DOI: https://doi.org/10.3390/app10030813
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