Dairy Business Sustainability, Market Risk Management Resilience and Stability Strategies
- 1 Canadian Marketing and Business Consultant, Calgary, Canada
Abstract
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 recorded a CE of RO 707,490 for risk neutral decision makers and reduced to RO 443,459 for extreme risk averse decision makers. The Factory Area market risk premium value for year 2020 and 2023 record CE of RO 258,785 for risk averse decision maker and increased to 396,253 for extreme risk averse decision maker and confirmed Factory Area as a market system resilience. Stop Light chart for lower and upper target net profit value constructed to rank dairy market efficiency. The total country business achieves 98% profit above upper target profit RO 100 thousand after supply chain disruption settlement compared to 29% during COVID-19 period. The analysis showed total country sales achieved consistent and reliable financial sustainability and market recovery efficiency and system resilience capacities. In addition to finance and operations risk management, the corporate should include resilience in the central of organization strategic process and move from risk reporting to more advanced foresight capabilities and adopt comprehensive strategic prospective to meet challenges of next disruption over the horizon. Understanding risk statistical measures is essential for making informed decisions in dairy business and risk management, as they provide insights into the behavior of profit distributions and the associated risks.
- MAFWR, Ministry of Agriculture and Fisheries and Water Resources (2023) Agriculture Statistic Report 2023.
- 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. Sustainable Food Technology , 3, 1556-1568. https://doi.org/10.1039/d5fb00156k
- Ukaoha, C. (2023) Economic Impact of Poultry Supply Chain Disruptions on Food Security: Evidence from Post-Pandemic Market Volatility in West Africa-2023. World Journal of Advanced Research and Reviews , 20, 2380-2394. https://doi.org/10.30574/wjarr.2023.20.3.2507
- Ishag, K.H.M. (2024) Animal Feed Business Risk Assessment Quantification COVID-19 and Supply Chains Disruptions Losses. Journal of Mathematical Finance , 14, 337-355. https://doi.org/10.4236/jmf.2024.143019
- Ben Hassen, T., Daher, B., Burkart, S. and El Bilali, H. (2025) Sustainable and Resilient Food Systems in Times of Crises. Frontiers Media SA.
- 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. Ciência e Agrotecnologia , 39, 240-247. https://doi.org/10.1590/s1413-70542015000300005
- Ishag, K.H.M. (2020) Economics of Dairy Cow Feed Management Strategies and Policy Analysis. IOSR Journal of Agriculture and Veterinary Science , 13, 9-18.
- 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. Journal of Dairy Science , 107, 3207-3218. https://doi.org/10.3168/jds.2023-24059
- Albright, S.C. and Winston, W.L. (2019) Business Analytics Data Analysis and Decision Making. 7th Edition, Cengage.
- Lehman, D.E. and Groenendaal, H. (2020) Practical Spreadsheet Modeling Using @Risk. CRC Press.
- 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
- Chavas, J.P. (2004) Risk Analysis in Theory and Practice. Business & Economics Book.
- Rodrigues-da-Silva, L.H. and Crispim, J.A. (2014) The Project Risk Management Process, a Preliminary Study. Procedia Technology , 16, 943-949. https://doi.org/10.1016/j.protcy.2014.10.047