Modeling of the information system for agribusiness management entities
DOI:
https://doi.org/10.51599/are.2023.09.02.03Keywords:
management, information support, agribusiness, model, system.Abstract
Purpose. The purpose of the research is to formulate recommendations for modeling the information support system of agribusiness management entities, which is characterized by correctness, adequacy and high quality of input, processing and monitoring of data necessary for agribusiness management entities to make and implement rational management decisions. To achieve this goal, the possibilities of applying methodological and applied tools are analyzed in order to avoid data distortions in the system of information support of agribusiness management entities and to identify the needs of operational adjustment of business decisions. An economic justification of the optimal direction of resource flows by types of economic activity of agribusiness was carried out. The sequence of performing managerial and analytical operations during the processing of information characterizing the choice of the best of alternatives regarding the planning of resource flows and flows of finished products is specified. The components of the model of the information support system of agribusiness management entities are identified.
Methodology / approach. The method of interference-resistant coding of management data was applied to avoid data distortions in the information support system of agribusiness management subjects. The control chart method was used to identify the need for prompt adjustment of business decisions. Binary relations and Saati’s methodology are applied for the economic justification of the optimal direction of resource flows by types of economic activity and the processing of information characterizing the choice of the best of the alternatives regarding the planning of resource flows and flows of finished products. The method of abstraction and the process-structural scientific-methodical approach were used to isolate the components of the model of the information support system of the entities of agribusiness management.
Results. It is argued that combination of Hemming’s method of interference-resistant coding of management data with the method of control charts makes it possible to avoid data distortions in the system of information support of agribusiness management entities and allows timely identification of the needs for prompt adjustment of business decisions. The method of identifying vectors of optimal direction of resource flows by agribusiness according to the types of their economic activity is substantiated. It is advisable to use this method also for processing information that characterizes the choice of the best of the alternatives regarding the planning of resource flows and flows of finished products. The structure of the model of the system of information support of agribusiness management subjects and the nature of the connections between the components of the model are specified.
Originality / scientific novelty. Recommendations for modeling the system of information support of agribusiness management subjects have been developed, which are based on the application of a process-structural methodical approach and a set of methodical tools. This makes it possible to avoid data distortions in the system of information support of agribusiness management entities and allows timely identification of the needs for operational adjustment of business decisions.
Practical value / implications. The practical value of applying the developed recommendations for modeling the information support system is to obtain additional opportunities for subjects of agribusiness management. These include: optimization of management processes, in particular to identify vectors of direction of resource flows by types of their economic activity; implementation of a reasoned choice of the best of the alternatives regarding the planning of resource flows and flows of finished products.
References
Wojtynia N., van Dijk J., Derks M., Groot Koerkamp P. W. G., Hekkert M. P. A new green revolution or agribusiness as usual? Uncovering alignment issues and potential transition complications in agri-food system transitions. Agronomy for Sustainable Development. 2021. Vol. 41. 77. https://doi.org/10.1007/s13593-021-00734-8.
Kizilaslan N. Agricultural information systems: a national case study. Library Review. 2006. Vol. 55. No. 8. Pp. 497–507. https://doi.org/10.1108/00242530610689347.
de Oliveira T. H. M., Painho M., Santos V., Sian O., Barriguinha А. Development of an agricultural management information system based on open-source solutions. Procedia Technology. 2014. Vol. 16. Pp. 342–354. https://doi.org/10.1016/j.protcy.2014.10.100.
Ureña-Espaillat H. J., Briones-Peñalver A. J., Bernal-Conesa J. A., Cordoba-Pachon J. R. Knowledge and innovation management in agribusiness: a study in the Dominican Republic. Business Strategy and the Environment. 2022. Vol. 32. Is. 4. Рр. 2008–2021. https://doi.org/10.1002/bse.3233.
Vajjhala N. R. Introduction to agricultural information systems. Opportunities and strategic use of agribusiness information systems; eds. F. Che, K. Strang, N. Vajjhala. IGI Global, 2021. Pp. 1–12. https://doi.org/10.4018/978-1-7998-4849-3.ch001.
Konovalyuk І., Knyaz S., Kucher L., Pavlenko O. et al. Developing a monitoring system of agricultural enterprises’ propension to bankruptcy. Scientific Papers: Series «Management, Economic Engineering in Agriculture and Rural Development». 2022. Vol. 22. Is. 1. Pp. 341–350. URL: https://managementjournal.usamv.ro/index.php/scientific-papers/93-vol-22-issue-1/2781-developing-a-monitoring-system-of-agricultural-enterprises-propension-to-bankruptcy.
Milian Gómez J., Delgado Triana Y. Sustainable management of environmental risks in agricultural production: ensuring the right to food. Global Jurist. 2022. Vol. 22. No. 3. Pp. 517–535. https://doi.org/10.1515/gj-2021-0086.
Burliai A., Burliai O., Revutska A., Smolii L., Klymenko L. Organizational and economic risks of greening of agriculture. Agricultural and Resource Economics. 2021. Vol. 7. Is. 1. Рр. 96–114. https://doi.org/10.51599 / are.2021.07.01.06.
Kucher L., Knіaz S., Pavlenko O., Holovina O. et al. Development of entrepreneurial initiatives in agricultural business: a methodological approach. European Journal of Sustainable Development. 2021. Vol. 10. No. 2. Рp. 321–335. https://doi.org/10.14207/ejsd.2021.v10n2p321.
Barreiro Hurle J., Bogonos M., Himics M., Hristov J. et al. Modelling environmental and climate ambition in the agricultural sector with the CAPRI model. Publications Office of the European Union, Luxembourg, 2021. Рp. 12–20. https://doi.org/10.2760/98160, JRC121368.
Kniaz S., Kosovska V., Shayda O., Novosad Z. et al. Method of selection of indicators in the context of information and analytical support of evaluation of development of foreign economic activity of enterprises. 11th International Conference on Advanced Computer Information Technologies, ACIT 2021. (Deggendorf, 15–17 September, 2021). Рp. 389–392. URL: https://acit.wunu.edu.ua.
Aslam A., Ahmad N., Saba T., Almazyad A. S. et al. Decision support system for risk assessment and management strategies in distributed software development. IEEE Access. 2017. Vol. 5. Рр. 20349–20373. https://doi.org/10.1109/access.2017.2757605.
Partiti E. The place of voluntary standards in managing social and environmental risks in global value chains. European Journal of Risk Regulation. 2022.Vol. 13. No. 1. Pp. 114–137. https://doi.org/10.1017/err.2021.34.
Guo Y., Wang J. Spatiotemporal changes of chemical fertilizer application and its environmental risks in China from 2000 to 2019. International Journal of Environmental Research and Public Health. 2021. Vol. 18. Is. 22. 11911. https://doi.org/10.3390/ijerph182211911.
Taherdoost H. A review on risk management in information systems: risk policy, control and fraud detection. Electronics. 2021. Vol. 10. Is. 24. 3065. https://doi.org/10.3390/electronics10243065.
Ghazieh L., Chebana N. The effectiveness of risk management system and firm performance in the European context. Journal of Economics, Finance and Administrative Science. 2021. Vol. 26. No. 52. Рр. 182–196. https://doi.org/10.1108/jefas-07-2019-0118.
Amraoui S., Elmaallam M., Bensaid H., Kriouile A. Information systems risk management: litterature review. Computer and Information Science. 2019. Vol. 12. No. 3. https://doi.org/10.5539/cis.v12n3p1.
Gerardo V., Fajar A. N. Academic IS Risk Management using OCTAVE Allegro in educational institution. Journal of Information Systems and Informatics. 2022. Vol. 4. No. 3. Рр. 687–708. https://doi.org/10.51519/journalisi.v4i3.319.
Sumets A., Tyrkalo Y., Popovych N., Poliakova J., Krupin V. Modeling of the environmental risk management system of agroholdings considering the sustainable development values. Agricultural and Resource Economics. 2022. Vol. 8. No. 4. Рр. 244–265. https://doi.org/10.51599/are.2022.08.04.11.
Semenova N. Management control systems in response to social and environmental risk in large Nordic companies. International Journal of Corporate Social Responsibility. 2021. Vol. 6. 13. https://doi.org/10.1186/s40991-021-00067-5.
Kniaz S., Brych V., Marhasova V., Tyrkalo Y. et al. Modeling of the information system of environmental risk management of an enterprise. 2022 12th International Conference on Advanced Computer Information Technologies (ACIT), Ruzomberok, Slovakia, 2022. Рp. 215–218. https://doi.org/10.1109/ACIT54803.2022.9912743.
Wang J., Liu S., Song Y., Wang J., Wu H. Environmental risk assessment of subway station construction to achieve sustainability using the intuitionistic fuzzy analytic hierarchy process and set pair analysis. Discrete Dynamics in Nature and Society. 2021. Vol. 2021. 5541493. https://doi.org/10.1155/2021/5541493.
Andryani R., Negara E. S., Triadi D. Social media analytics: data utilization of social media for research. Journal of Information Systems and Informatics. 2019. Vol. 1. No. 2. Pp. 193–205. https://doi.org/10.33557/journalisi.v1i2.23.
Nanda P., Kumar V. Information processing and data analytics for decision making: a journey from traditional to modern approaches. Information Resources Management Journal. 2022. Vol. 35. Is. 2. Pp. 1–14. http://doi.org/10.4018/IRMJ.291693.
Islam M. Data analysis: types, process, methods, techniques and tools. International Journal on Data Science and Technology. 2020. Vol. 6. Is. 1. Pp. 10–15. http://doi.org/10.11648/j.ijdst.20200601.12.
Sharma B. Processing of data and analysis. Biostatistics and Epidemiology International Journal. 2018. Vol. 1. Is. 1. Рр. 3–5. http://doi.org/10.30881/beij.00003.
Hariri R. H., Fredericks E. M., Bowers K. M. Uncertainty in big data analytics: survey, opportunities, and challenges. Journal of Big Data. 2019. Vol. 6. 44. http://doi.org/10.1186/s40537-019-0206-3.
Ismail A., Truong H.-L., Kastner W. Manufacturing process data analysis pipelines: a requirements analysis and survey. Journal of Big Data. 2019. Vol. 6. 1. http://doi.org/10.1186/s40537-018-0162-3.
Makkonen H. Information processing perspective on organisational innovation adoption process. Technology Analysis & Strategic Management. 2021. Vol. 33. Is. 6. Рр. 612–624. http://doi.org/10.1080/09537325.2020.1832218.
Faizullah M. Role of information technology in transaction processing system. Information Technology Journal. 2003. Vol. 2. Рр. 128–134. URL: https://scialert.net/abstract/?doi=itj.2003.128.134.
Afolayan A. O., De La Harpe A. C. The role of evaluation in SMMEs’ strategic decision-making on new technology adoption. Technology Analysis & Strategic Management. 2020. Vol. 32. Is. 6. Рр. 697–710. http://doi.org/10.1080/09537325.2019.1702637.
Liu Y. C., Huang Y.-A. Information systems outsourcing success- a perspective of information processing theory. 2020 14th International Conference on Ubiquitous Information Management and Communication (IMCOM). Taichung, Taiwan, 2020. Рр. 1–3. http://doi.org/10.1109/IMCOM48794.2020.9001801.
Poliukhovych N., Raicheva L., Ivanov A. Mathematical modeling of risk assessment of enterprise management. Baltic Journal of Economic Studies. 2022. Vol. 8. Is. 3. Рр. 166–173. https://doi.org/10.30525/2256-0742/2022-8-3-166-173.
Wang Z., Rangaiah G. P. Application and analysis of methods for selecting an optimal solution from the pareto-optimal front obtained by multiobjective optimization. Industrial & Engineering Chemistry Research. 2017. Vol. 56. Is. 2. Рр. 560–574. https://doi.org/10.1021/acs.iecr.6b03453.
Wang N., Zhao W.-jie, Wu N., Wu D. Multi-objective optimization: a method for selecting the optimal solution from Pareto non-inferior solutions. Expert Systems with Applications. 2017. Vol. 74. Рр. 96–104. https://doi.org/10.1016/j.eswa.2017.01.004.
Rurik W., Mazumdar A. Hamming codes as error-reducing codes. 2016 IEEE Information Theory Workshop (ITW). Cambridge, 2016. Рp. 404–408. https://doi.org/10.1109/ITW.2016.7606865.
Li L., Chang C.-C., Bai J., Le H.-D., Chen C.-C., Meen T.-H. Hamming code strategy for medical image sharing. Applied System Innovation. 2020. Vol. 3. Is. 8. Рр. 1–17. https://doi.org/10.3390/asi3010008.
Prasad S., Pal A. K. Hamming code and logistic-map based pixel-level active forgery detection scheme using fragile watermarking. Multimedia Tools and Applications. 2020. Vol. 79. Рр. 20897–20928. https://doi.org/10.1007/s11042-020-08715-x.
Sokil O., Podolchak N., Kniaz S., Sokil Y., Kucher L. Sustainable development prediction of start-ups in Ukraine. Journal of Environmental Management and Tourism. 2023. Vol. 13. No. 7. Рp. 1901–1910. https://doi.org/10.14505/jemt.v13.7(63).10.
Журавчак Л. М. Дискретна математика для програмістів: навч. посібн. Львів: Видавництво Львівської політехніки, 2019. 420 с.
Кігель В. Р. Методи і моделі підтримки прийняття рішень у ринковій економіці: моногр. Київ: ЦУЛ, 2003. 202 с.
Лапач С. Н., Чубенко А. В., Бабич П. Н. Статистика в науке и бизнесе. Киев: МОРИОН, 2002. 640 с.
References
Wojtynia, N., van Dijk, J., Derks, M., Groot Koerkamp, P. W. G., & Hekkert, M. P. (2021). A new green revolution or agribusiness as usual? Uncovering alignment issues and potential transition complications in agri-food system transitions. Agronomy for Sustainable Development, 41, 77. https://doi.org/10.1007/s13593-021-00734-8.
Kizilaslan, N. (2006). Agricultural information systems: a national case study. Library Review, 55(8), 497–507. https://doi.org/10.1108/00242530610689347.
de Oliveira, T. H. M., Painho, M., Santos, V., Sian, O., & Barriguinha, А. (2014). Development of an agricultural management information system based on open-source solutions. Procedia Technology, 16, 342–354. https://doi.org/10.1016/j.protcy.2014.10.100.
Ureña-Espaillat, H. J., Briones-Peñalver, A. J., Bernal-Conesa, J. A., & Cordoba-Pachon, J. R. (2022). Knowledge and innovation management inagribusiness: a study in the Dominican Republic. Business Strategy and the Environment, 32(4), 2008–2021. https://doi.org/10.1002/bse.3233.
Vajjhala, N. R. (2021). Introduction to agricultural information systems. In F. Che, K. Strang, N. Vajjhala (Eds.), Opportunities and strategic use of agribusiness information systems (pp. 1–12). IGI Global. https://doi.org/10.4018/978-1-7998-4849-3.ch001.
Konovalyuk, І., Knyaz, S., Kucher, L., Pavlenko, O., Shauda, O., Kosovska, V., & Moskvyak, Ya. (2022). Developing a monitoring system of agricultural enterprises’ propension to bankruptcy. Scientific Papers: Series «Management, Economic Engineering in Agriculture and Rural Development», 22(1), 341–350. Available at: https://managementjournal.usamv.ro/index.php/scientific-papers/93-vol-22-issue-1/2781-developing-a-monitoring-system-of-agricultural-enterprises-propension-to-bankruptcy.
Milian Gómez, J., & Delgado Triana, Y. (2022). Sustainable management of environmental risks in agricultural production: ensuring the right to food. Global Jurist, 22(3), 517–535. https://doi.org/10.1515/gj-2021-0086.
Burliai, A., Burliai, O., Revutska, A., Smolii, L., & Klymenko, L. (2021). Organizational and economic risks of greening of agriculture. Agricultural and Resource Economics, 7(1), 96–114. https://doi.org/10.51599 / are.2021.07.01.06.
Kucher, L., Knіaz, S., Pavlenko, O., Holovina, O., Shayda, O., Franiv, I., & Dzvonyk, V. (2021). Development of entrepreneurial initiatives in agricultural business: a methodological approach. European Journal of Sustainable Development, 10(2), 321–335. https://doi.org/10.14207/ejsd.2021.v10n2p321.
Barreiro Hurle, J., Bogonos, M., Himics, M., Hristov, J., Perez Dominguez, I., Sahoo, A., … & Elleby, C. (2021). Modelling environmental and climate ambition in the agricultural sector with the CAPRI model. Publications Office of the European Union, Luxembourg. Available at: https://doi.org/10.2760/98160, JRC121368.
Kniaz S., Kosovska V., Shayda O., Novosad Z., Yaremko, L., & Fedyuk, V. (2021). Method of selection of indicators in the context of information and analytical support of evaluation of development of foreign economic activity of enterprises. 11th International Conference on Advanced Computer Information Technologies, ACIT 2021 (Deggendorf, 15–17 September, 2021). Available at: https://acit.wunu.edu.ua.
Aslam, A., Ahmad, N., Saba, T., Almazyad, A. S., Rehman, A., Anjum, A., & Khan, A. (2017). Decision support system for risk assessment and management strategies in distributed software development. IEEE Access, 5, 20349–20373. https://doi.org/10.1109/access.2017.2757605.
Partiti, E. (2021). The place of voluntary standards in managing social and environmental risks in global value chains. European Journal of Risk Regulation, 13(1), 114–137. https://doi.org/10.1017/err.2021.34.
Guo, Y., & Wang, J. (2021). Spatiotemporal changes of chemical fertilizer application and its environmental risks in China from 2000 to 2019. International Journal of Environmental Research and Public Health, 18(22), 11911. https://doi.org/10.3390/ijerph182211911.
Taherdoost, H. (2021). A review on risk management in information systems: risk policy, control and fraud detection. Electronics, 10(24), 3065. https://doi.org/10.3390/electronics10243065.
Ghazieh, L., & Chebana, N. (2021). The effectiveness of risk management system and firm performance in the European context. Journal of Economics, Finance and Administrative Science, 26(52), 182–196. https://doi.org/10.1108/jefas-07-2019-0118.
Amraoui, S., Elmaallam, M., Bensaid, H., & Kriouile, A. (2019). Information systems risk management: litterature review. Computer and Information Science, 12(3). https://doi.org/10.5539/cis.v12n3p1.
Gerardo, V., & Fajar, A. N. (2022). Academic IS Risk Management using OCTAVE Allegro in Educational Institution. Journal of Information Systems and Informatics, 4(3), 687–708. https://doi:10.51519/journalisi.v4i3.319.
Sumets, A., Tyrkalo, Y., Popovych, N., Poliakova, J., & Krupin, V. (2022). Modeling of the environmental risk management system of agroholdings considering the sustainable development values. Agricultural and Resource Economics, 8(4), 244–265. https://doi.org/10.51599/are.2022.08.04.11.
Semenova, N. (2021). Management control systems in response to social and environmental risk in large Nordic companies. International Journal of Corporate Social Responsibility, 6, 13. https://doi.org/10.1186/s40991-021-00067-5.
Kniaz, S., Brych, V., Marhasova, V., Tyrkalo, Y., Skrynkovskyy, R., & Sumets, A. (2022). Modeling of the information system of environmental risk management of an enterprise. 2022 12th International Conference on Advanced Computer Information Technologies (ACIT), Ruzomberok, Slovakia. https://doi.org/10.1109/ACIT54803.2022.9912743.
Wang, J., Liu, S., Song, Y., Wang, J., & Wu, H. (2021). Environmental risk assessment of subway station construction to achieve sustainability using the intuitionistic fuzzy analytic hierarchy process and set pair analysis. Discrete Dynamics in Nature and Society, 2021, 5541493. https://doi.org/10.1155/2021/5541493.
Andryani, R., Negara, E. S., & Triadi, D. (2019). Social media analytics: data utilization of social media for research. Journal of Information Systems and Informatics, 1(2), 193–205. https://doi.org/10.33557/journalisi.v1i2.23.
Nanda, P., & Kumar, V. (2022). Information processing and data analytics for decision making: a journey from traditional to modern approaches. Information Resources Management Journal, 35(2), 1–14. http://doi.org/10.4018/IRMJ.291693.
Islam, M. (2020). Data analysis: types, process, methods, techniques and tools. International Journal on Data Science and Technology, 6(1), 10–15. http://doi.org/10.11648/j.ijdst.20200601.12.
Sharma, B. (2018). Processing of data and analysis. Biostatistics and Epidemiology International Journal, 1(1), 3–5. https://doi.org/10.30881/beij.00003.
Hariri, R. H., Fredericks, E. M., & Bowers K. M. (2019). Uncertainty in big data analytics: survey, opportunities, and challenges. Journal of Big Data, 6, 44. http://doi.org/10.1186/s40537-019-0206-3.
Ismail, A., Truong, H.-L., & Kastner, W. (2019). Manufacturing process data analysis pipelines: a requirements analysis and survey. Journal of Big Data, 6, 1. https://doi.org/10.1186/s40537-018-0162-3.
Makkonen, H. (2021). Information processing perspective on organisational innovation adoption process. Technology Analysis & Strategic Management, 33(6), 612–624. https://doi.org/10.1080/09537325.2020.1832218.
Faizullah, M. (2003). Role of information technology in transaction processing system. Information Technology Journal, 2, 128–134. Available at: https://scialert.net/abstract/?doi=itj.2003.128.134.
Afolayan, A. O., & De La Harpe, A. C. (2020). The role of evaluation in SMMEs’ strategic decision-making on new technology adoption. Technology Analysis & Strategic Management, 32(6), 697–710. https://doi.org/10.1080/09537325.2019.1702637.
Liu, Y. C., & Huang, Y.-A. (2020). Information systems outsourcing success- a perspective of information processing theory. 2020 14th International Conference on Ubiquitous Information Management and Communication (IMCOM). Taichung, Taiwan. https://doi.org/10.1109/IMCOM48794.2020.9001801.
Poliukhovych, N., Raicheva, L., & Ivanov, A. (2022). Mathematical modeling of risk assessment of enterprise management. Baltic Journal of Economic Studies, 8(3), 166–173. https://doi.org/10.30525/2256-0742/2022-8-3-166-173.
Wang, Z., & Rangaiah, G. P. (2017). Application and analysis of methods for selecting an optimal solution from the pareto-optimal front obtained by multiobjective optimization. Industrial & Engineering Chemistry Research, 56(2), 560–574. https://doi.org/10.1021/acs.iecr.6b03453.
Wang, N., Zhao, W.-jie, Wu, N., & Wu, D. (2017). Multi-objective optimization: a method for selecting the optimal solution from Pareto non-inferior solutions. Expert Systems with Applications, 74, 96–104. https://doi.org/10.1016/j.eswa.2017.01.004.
Rurik, W., & Mazumdar, A. (2016). Hamming codes as error-reducing codes. 2016 IEEE Information Theory Workshop, Cambridge, UK. https://doi.org/10.1109/ITW.2016.7606865.
Li, L., Chang, C.-C., Bai, J., Le, H.-D., Chen, C.-C., & Meen, T.-H. (2020). Hamming code strategy for medical image sharing. Applied System Innovation, 3(8), 1–17. https://doi.org/10.3390/asi3010008.
Prasad, S., & Pal, A. K. (2020). Hamming code and logistic-map based pixel-level active forgery detection scheme using fragile watermarking. Multimedia Tools and Applications, 79, 20897–20928. https://doi.org/10.1007/s11042-020-08715-x.
Sokil, O., Podolchak, N., Kniaz, S., Sokil, Y., & Kucher, L. (2023). Sustainable development prediction of start-ups in Ukraine. Journal of Environmental Management and Tourism, 13(7), 1901–1910. https://doi.org/10.14505/jemt.v13.7(63).10.
Zhuravchak, L. M. (2019). Dyskretna matematyka dlia prohramistiv [Discrete mathematics for programmers]. Publishing House of Lviv Polytechnic, Lviv.
Kigel, V. R. (2003). Metody i modeli pidtrymky pryiniattia rishen u rynkovii ekonomitsi [Methods and models of decision support in the market economy]. TsUL, Kyiv.
Lapach, S. N., Chubenko, A. V., & Babych, P. N. (2002). Statystyka v nauke y byznese [Statistics in science and business]. MORION, Kyiv.
