Risk based control of the negative effect of discontinued automated processes – a case from the agricultural domain

dc.contributor.authorPodaras, Athanasios
dc.contributor.otherEkonomická fakultacs
dc.date.accessioned2017-12-20
dc.date.available2017-12-20
dc.date.issued2017-12-20
dc.description.abstractThe current paper delineates a modern algorithmic procedure for estimating the risk and calculating a realistic duration of interrupted critical computerized business activities, in order to mitigate or prevent their corresponding negative consequences. The contribution is formulated via merging risk management and business continuity concepts. The formulation of an integrated business continuity management policy includes the proactive determination of approximate recovery timeframes for critical business functions. Practically, this estimation is based on recovery tests which are executed under ideal conditions, and unexpected factors which may emerge during a real process interruption and significantly delay its recovery are ignored. Agriculture is a domain where the incorporation of an integrated business continuity management system is a crucial issue. The interruption of agricultural computerized activities can be triggered by and can result to various undesirable environmental phenomena. Thus, especially for agriculture, the consideration of unexpected factors when executing recovery tests is highly demanded. The currently presented algorithm accepts as initial input the estimated recovery time which is based on recovery exercises executed under ideal conditions. Then, a precise number of potential unpredictable hazards (factors) are taken into consideration and the risk magnitude of each threat is semi-quantitatively estimated. The total risk magnitude is utilized to estimate the time deviation from the initially defined recovery time. After the risk analysis process is terminated, a new recovery timeframe is proposed. The time deviation from the initially defined recovery time is calculated in its absolute value. The algorithm is finally validated by applying the calculated extended timeframe to the system availability formula which measures the achieved system availability levels for any information system. The validation of the approach is demonstrated via a practical case study from the agricultural domain, namely the greenhouse irrigation scheduling system interruption scenario.en
dc.formattext
dc.format.extent11 stran
dc.identifier.doi10.15240/tul/001/2017-4-017
dc.identifier.eissn2336-5597
dc.identifier.issn1212-3605
dc.identifier.urihttps://dspace.tul.cz/handle/15240/21391
dc.language.isoen
dc.publisherTechnical university of Liberec, Czech Republicen
dc.publisherTechnická Univerzita v Libercics
dc.publisher.abbreviationTUL
dc.relation.isbasedonAhrary, A., & Ludena, D. A. R. (2015). A Cloud-Based Vegetable Production and Distribution System. In R. Neves-Silva et al. (Eds.), Intelligent Decision Tecnologies (KES-IDT 2015). Smart Innovation, Systems and Technologies 39 (pp. 11-20). Switzerland: Springer International Publishing.
dc.relation.isbasedonAukidy, M. A., Verlicchi, P., & Voulvoulis, N. (2014). A Framework for the Assessment of the Environmental Risk Posed by Pharmaceuticals Originating from Hospital Effluents. Science of the Total Environment, 493, 54-64. doi:10.1016/j.scitotenv.2014.05.128.
dc.relation.isbasedonBarron, F. H., & Barrett, B. E. (1996): Decision Quality Using Ranked Attribute Weights. Management Science, 42(11), 1515-1523. doi:10.1287/mnsc.42.11.1515.
dc.relation.isbasedonBorghesi, A., & Gaudenzi, B. (Eds.). (2013). Risk Management, Perspectives in Business Culture. Italia: Springer – Verlag.
dc.relation.isbasedonBreuer, C., Haasis, H. D., Siestrup, G. (2015): Operational Risk Response for Business Continuity in Logistics Agglomerations. In J. Dethloff, H. D. Haasis, H. Kopfer, H. Kotzab, & J. Schönberger (Eds.), Logistics Management. Lecture Notes in Logistics (pp. 107-120). Switzerland: Springer International Publishing.
dc.relation.isbasedonBurns, W. J., & Slovic, P. (2012). Risk Perception and Behaviors: anticipating and responding to crises Risk Analysis, 32(4), 579-582. doi:10.1111/j.1539-6924.2012.01791.x.
dc.relation.isbasedonChartzoulakis, K., & Bertaki, M. (2015). Sustainable Water Management in Agriculture under Climate Change. Agriculture and Agricultural Science Procedia, 4, 88-98. doi:10.1016/j.aaspro.2015.03.011.
dc.relation.isbasedonContreras, J. I., Alonso, F., Cánovas, G., & Baeza, R. (2017). Irrigation management of greenhouse zucchini with different soilmatric potential level. Agronomic and environmental effects. Agricultural Water Management, 183, 26-34. doi:10.1016/j.agwat.2016.09.025.
dc.relation.isbasedonDanielson, M., Ekenberg, L., & Sygel, K. (2015). Robust Psychiatric Decision Support Using Surrogate Numbers. Communications in Computer and Information Science, 532, 575-585, doi:10.1007/978-3-319-22689-7_44.
dc.relation.isbasedonFaertes, D. (2015). Reliability of Supply Chains and Business Continuity Management. Procedia Computer Science, 55, 1400-1409. doi:10.1016/j.procs.2015.07.130.
dc.relation.isbasedonFAO. (2009). Semi-quantitative risk characterization. Risk Characterization of Microbiological Hazards in food. Retrieved April 20, 2017, from http://www.fao.org/docrep/012/i1134e/i1134e04.pdf.
dc.relation.isbasedonGarcía, I. E. M., Sánchez, A. S., & Barbati, S. (2016). Reliability and Preventive Maintenance. In W. Ostachowicz et al. (Eds.), MARE-WINT. New Materials and Reliability in Offshore Wind Turbine Technology (pp. 235-272). Switzerland: Springer International Publishing AG.
dc.relation.isbasedonHájek, P., & Urbancová, H. (2013). Using of Business Continuity Standards in Agriculture, Industry and ICT. Agris Online Papers in Economics and Informatics, 5(4), 55-67.
dc.relation.isbasedonHerbane, B. (2010). The Evolution of Business Continuity Management: A Historical Review of Practices and Drivers. Business History, 52(6), 978-1002. doi:10.1080/00076791.2010.511185.
dc.relation.isbasedonISO. (2012). ISO 22301-2012: Societal Security – Business Continuity Management Systems – Requirements. Retrieved March 25, 2017, from https://www.iso.org/obp/ui/#iso:std:iso:22301:ed-1:v2:en.
dc.relation.isbasedonJankelova, N., Masar, D., & Moricova, S. (2017). Risk Factors in the Agriculture Sector. Agricultural Economics, 63(6), 247-258. doi:10.17221/212/2016-AGRICECON.
dc.relation.isbasedonMaboudian, Y., & Rezaie, K. (2017). Applying Data Mining to Investigate Business Continuity in Petrochemical Companies. Energy Sources, Part B: Economics, Planning, and Policy, 12(2), 126-131. doi:10.1080/15567249.2015.1076907.
dc.relation.isbasedonMalachová, H., & Oulehlová, A. (2016). Application of Business Continuity Management System in The Crisis Management Field. Transactions of the VŠB – Technical university of Ostrava, Safety Engineering Series, 6(2), 43-50. doi:10.1515/tvsbses-2016-0016.
dc.relation.isbasedonNIST. (2010). Contingency Planning Guide for Federal Information Systems. Retrieved April 20, 2017, from http://nvlpubs.nist.gov/nistpubs/Legacy/SP/nistspecialpublication800-34r1.pdf.
dc.relation.isbasedonOECD. (2011). Risk Management in Agriculture: What Role for Governments? Retrieved April 20, 2017, from https://www.oecd.org/agriculture/agricultural-policies/49003833.pdf.
dc.relation.isbasedonOkuda, K., Ohashi, M., & Hori, M. (2011). On the Studies of the Disaster Recovery and the Business Continuity Planning for the Private Sector Caused by Great East Japan Earthquake. Communications in Computer and Information Science, 219. 14-21. doi:10.1007/978-3-642-24358-5_2.
dc.relation.isbasedonPlaia, A., & Rugierri, M. (2011). Air Quality Indices – A Review. Reviews in Environmental Science and Bio/Technology, 10(2), 165-179. doi:10.1007/s11157-010-9227-2.
dc.relation.isbasedonStulec, I., Petljak, K., & Bakovic, T. (2016). Effectiveness of weather derivatives as a hedge against the weather risk in agriculture. Agricultural Economics, 62(8), 356-362. doi:10.17221/188/2015-AGRICECON.
dc.relation.isbasedonThompson, R. B., Gallardo, M., Valdez, L. C., & Fernández, M. D. (2007). Using Plant Water Status to Define Threshold Values for Irrigation Management of Vegetable crops Using Soil Moisture Sensors. Agricultural Water Management, 88, 147-158. doi:10.1016/j.agwat.2006.10.007.
dc.relation.isbasedonTorabi, S. A., Giahi, R., & Sahebjamnia, N. (2016). An Enhanced Risk Assessment Framework for Business Continuity Management Systems. Safety Science, 89, 201-218. doi:10.1016/j.ssci.2016.06.015.
dc.relation.isbasedonTorabi, S. A., Rezaei, H., & Sahebjamnia, N. (2014). A New Framework for Business Impact Analysis in Business Continuity Management (with a case study). Safety Science, 68, 309-323. doi:10.1016/j.ssci.2014.04.017.
dc.relation.isbasedonUniversity of Nebraska – Extension Institute of Agriculture and Natural Resources and the Nebraska Department of Environmental Quality. (2016). Using Chemigation Safely and Effectively. Retrieved April 26, 2017, from http://water.unl.edu/Chemigation%20Manual_Revised%202016.pdf.
dc.relation.isbasedonWang, Y., & Hu, H. (2012). Hydropower Computation Using Visual Basic for Application Programming. Physics Procedia, 24, 37-43. doi:10.1016/j.phpro.2012.02.007.
dc.relation.ispartofEkonomie a Managementcs
dc.relation.ispartofEconomics and Managementen
dc.relation.isrefereedtrue
dc.rightsCC BY-NC
dc.subjectrisk managementen
dc.subjectbusiness continuityen
dc.subjectagricultureen
dc.subjectenvironmental hazardsen
dc.subjectavailabilityen
dc.subject.classificationY80
dc.subject.classificationM150
dc.titleRisk based control of the negative effect of discontinued automated processes – a case from the agricultural domainen
dc.typeArticleen
local.accessopen
local.citation.epage261
local.citation.spage251
local.facultyFaculty of Economics
local.fulltextyes
local.relation.abbreviationE+Mcs
local.relation.abbreviationE&Men
local.relation.issue4
local.relation.volume20
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
EM_4_2017_17.pdf
Size:
1010.11 KB
Format:
Adobe Portable Document Format
Description:
Článek
Collections