The dairy business and industry in Oman grapples with complex challenges, from wavering milk prices and escalating input costs to ever-shifting market dynamics; all factors that can significantly impact business profitability and stability. The supply chain disruption due to COVID-19 pandemic and regional market system resistance was also investigated. Dairy business must, therefore, implement potent strategies to enhance cash flow and safeguard financial viability and move to market system resilience. This article delves into key methods designed to analyze dairy business risk assessment and market dynamic channelings and efficiency to boost profits in dairy operations and improve cash flow management. The study applied Stochastic Monte Carlo Simulation Budget and scenarios approach to calculate market risk primum and reform market strategies and align them with acceptable risk tolerance comfort level of the stakeholders’ perceptions. Three production levels, products mix models, were tested under two different scenarios to verify coronavirus impact and to measure market risk and corporate system resilience. The net profit probability distribution skewed to the left for all models in the study and 5% net profit probability distribution of Country sale shift from RO (318k) loss in year 2020 to RO 230k profit in 2023. The analysis shown that Country sale has resilience ecosystem recovery although coronavirus crisis results in a complete lockdown continued for 10 months in 2020 and could achieved stability in year 2023. The cumulated distribution function (CDF) graph was constructed to quantify market risk and indicate the range and shape of profit probabilities distribution for six different production volumes and marketing alternatives. The stochastic efficiency with respect to a function (SERF) analysis used to calculate certain equivalent (CE) figures to rank alternative market’s scenarios under uncertain situations. The analysis showed dairy business net profits in 2023 are sustainable alternatives than COVID-19 year in 2020, and Factory Area are better and stable market region than Capital Area. Tornado sensitivity analysis showed that products unit cost, followed by reginal market demand and market incentives, are highly sensitive to profit in integrated complex system. Quadrant analysis was performed to understand dairy products market shares and market growth potential and accordingly management could develop comprehensive market strategies to achieve business objectives. The risk premium value for total Country sales in 2020 and 2023
References
[1]
MAFWR, Ministry of Agriculture and Fisheries and Water Resources (2023) Agriculture Statistic Report 2023.
[2]
Malik, M., Mor, R.S., Gahlawat, V.K., Hassoun, A. and Jagtap, S. (2025) Drivers of Industry 5.0 Technologies in Dairy Industry: An Exploratory Study. SustainableFoodTechnology, 3, 1556-1568. https://doi.org/10.1039/d5fb00156k
[3]
Ukaoha, C. (2023) Economic Impact of Poultry Supply Chain Disruptions on Food Security: Evidence from Post-Pandemic Market Volatility in West Africa-2023. WorldJournalofAdvancedResearchandReviews, 20, 2380-2394. https://doi.org/10.30574/wjarr.2023.20.3.2507
[4]
Ishag, K.H.M. (2024) Animal Feed Business Risk Assessment Quantification COVID-19 and Supply Chains Disruptions Losses. JournalofMathematicalFinance, 14, 337-355. https://doi.org/10.4236/jmf.2024.143019
[5]
Ben Hassen, T., Daher, B., Burkart, S. and El Bilali, H. (2025) Sustainable and Resilient Food Systems in Times of Crises. Frontiers Media SA.
[6]
Simões, D., Ribeiro, J.P., Gouveia, P.R. and Santos, J.C.d. (2015) Economical and Financial Analysis of Aviaries for the Integration of Broilers under Conditions of Risk. CiênciaeAgrotecnologia, 39, 240-247. https://doi.org/10.1590/s1413-70542015000300005
[7]
Ishag, K.H.M. (2020) Economics of Dairy Cow Feed Management Strategies and Policy Analysis. IOSRJournalofAgricultureandVeterinaryScience, 13, 9-18.
[8]
Chen, L., Thorup, V.M., Kudahl, A.B. and Østergaard, S. (2024) Effects of Heat Stress on Feed Intake, Milk Yield, Milk Composition, and Feed Efficiency in Dairy Cows: A Meta-Analysis. JournalofDairyScience, 107, 3207-3218. https://doi.org/10.3168/jds.2023-24059
[9]
Albright, S.C. and Winston, W.L. (2019) Business Analytics Data Analysis and Decision Making. 7th Edition, Cengage.
[10]
Lehman, D.E. and Groenendaal, H. (2020) Practical Spreadsheet Modeling Using @Risk. CRC Press.
[11]
Lima, R. and Sampaio, R. (2018) Uncertainty Quantification and Cumulative Distribution Function: How Are They Related? In: Polpo, A., Stern, J., Louzada, F., Izbicki, R. and Takada, H., Eds., Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Springer International Publishing, 253-260. https://doi.org/10.1007/978-3-319-91143-4_24
[12]
Chavas, J.P. (2004) Risk Analysis in Theory and Practice. Business & Economics Book.
[13]
Rodrigues-da-Silva, L.H. and Crispim, J.A. (2014) The Project Risk Management Process, a Preliminary Study. ProcediaTechnology, 16, 943-949. https://doi.org/10.1016/j.protcy.2014.10.047
[14]
Machlis, G.E. and Rosa, E.A. (1990) Desired Risk: Broadening the Social Amplification of Risk Framework. RiskAnalysis, 10, 161-168. https://doi.org/10.1111/j.1539-6924.1990.tb01030.x
[15]
Mostaghim, N., Gholamian, M.R. and Arabi, M. (2024) Designing a Resilient-Sustainable Integrated Broiler Supply Chain Network Using Multiple Sourcing and Backup Facility Strategies Dealing with Uncertainties in a Disruptive Network: A Real Case of a Chicken Meat Network. Computers&ChemicalEngineering, 188, Article ID: 108772. https://doi.org/10.1016/j.compchemeng.2024.108772
[16]
Hardaker, J.B., Richardson, J.W., Lien, G. and Schumann, K.D. (2004) Stochastic Efficiency Analysis with Risk Aversion Bounds: A Simplified Approach. Australian JournalofAgriculturalandResourceEconomics, 48, 253-270. https://doi.org/10.1111/j.1467-8489.2004.00239.x
[17]
Regier, G.K., Dalton, T.J. and Williams, J.R. (2012) Impact of Genetically Modified Maize on Smallholder Risk in South Africa. AgBioForum, 15, 328-336.
[18]
Khakbazan, M., Carew, R., Scott, S.L., Chiang, P., Block, H.C., Robins, C., et al. (2014) Economic Analysis and Stochastic Simulation of Alternative Beef Calving and Feeding Systems in Western Canada. CanadianJournalofAnimalScience, 94, 299-311. https://doi.org/10.4141/cjas2013-185
[19]
Ishag, K.H.M. (2019) Broiler Production Systems Risk Management Sustainability and Feed Subsidy Policy Analysis. IOSRJournalofAgricultureandVeterinaryScience, 12, 33-44.
[20]
Hardaker, J.B., Richardson, J.W., Lien, G. and Schumann, K.D. (2004) Stochastic Efficiency Analysis with Risk Aversion Bounds: A Simplified Approach. AustralianJournalofAgriculturalandResourceEconomics, 48, 253-270. https://doi.org/10.1111/j.1467-8489.2004.00239.x
[21]
Tzouramani, I., Sintori, A., Liontakis, A., Karanikolas, P. and Alexopoulos, G. (2011) An Assessment of the Economic Performance of Organic Dairy Sheep Farming in Greece. LivestockScience, 141, 136-142. https://doi.org/10.1016/j.livsci.2011.05.010
[22]
Richardson, J.W., Schumann, K. and Feldman, P. (2008) Simulation and Econometrics to Analyze Risk. Simetar, Inc.
[23]
Khakbazan, M., Block, H.C., Huang, J., Colyn, J.J., Baron, V.S., Basarab, J.A., et al. (2022) Effects of Silage-Based Diets and Cattle Efficiency Type on Performance, Profitability, and Predicted CH4 Emission of Backgrounding Steers. Agriculture, 12, Article No. 277. https://doi.org/10.3390/agriculture12020277
[24]
Lien, G., Brian Hardaker, J. and Flaten, O. (2007) Risk and Economic Sustainability of Crop Farming Systems. AgriculturalSystems, 94, 541-552. https://doi.org/10.1016/j.agsy.2007.01.006
[25]
Ascough II, J.C., Fathelrahman, E.M., Vandenberg, B.C., Green, T.R. and Hoag, D.L. (2009) Economic Risk Analysis of Agricultural Tillage Systems Using the SMART Stochastic Efficiency Software Package. 18thWorldIMACS/MODSIMCongress, Cairns, 13-17 July 2009, 463-469. http://mssanz.org.au/modsim09
[26]
Fathelrahman, E.M., Ascough II, J.C., Hoag, D.L., Malone, R.W., Heilman, P., Wiles, L.J., et al. (2011) Continuum of Risk Analysis Methods to Assess Tillage System Sustainability at the Experimental Plot Level. Sustainability, 3, 1035-1063. https://doi.org/10.3390/su3071035
[27]
Stefell, B. (2025) Thriving in Unstable Markets: Essential Risk Management Strategies. Journal of Business Finance & Accounting, 13, 496.
[28]
Liu, Y., Langemeier, M.R., Small, I.M., Joseph, L. and Fry, W.E. (2017) Risk Management Strategies Using Precision Agriculture Technology to Manage Potato Late Blight. AgronomyJournal, 109, 562-575. https://doi.org/10.2134/agronj2016.07.0418
[29]
Miranda, C.N.D.C. (2025) Blockchain-Based Smart Dairy Supply Chain: Catching the Momentum for Digital Transformation. AsianJournalofManagementandCommerce, 6, 502-514. https://doi.org/10.22271/27084515.2025.v6.i1f.493
[30]
Armijal, Marlina, W.A. and Hadiguna, R.A. (2023) The Evaluation of Supply Chain Risk Management on Smallholder Layer Farms. IOPConferenceSeries: EarthandEnvironmentalScience, 1182, Article ID: 012082. https://doi.org/10.1088/1755-1315/1182/1/012082
[31]
Rimhanen, K., Mäkinen, H., Kuisma, M. and Kahiluoto, H. (2023) What Enhances Dairy System Resilience? Empirical Cases in Finland and Russia. AgriculturalandFoodEconomics, 11, Article No. 31. https://doi.org/10.1186/s40100-023-00269-4
[32]
Amare, M., Abay, K.A. and Hatzenbuehler, P.L. (2023) Spatial Market Integration of Food Markets during a Shock: Evidence from Food Markets in Nigeria. The International Food Policy Research Institute.