Agriculture
Risk Management Example
Chapter 7: Agricultural Planning Risk
In Book 1: Novel Six Sigma Approaches to Risk Assessment and Management
ISBN: 9781522527039
Author: Vojo Bubevski (Independent Researcher)
Abstract
The method is applied to Farm Planning to manage the associated risk. DMAIC applies proven stochastic techniques: i) Define: Stochastic optimisation determines the optimal land allocation strategies to maximise profit; ii) Measure: Once the optimal strategies are resolved, every determined strategy is simulated. So, the profit and associated risk factors are stochastically calculated. Six Sigma process capability metrics are also calculated to measure the process performance of the mean profit distribution; iii) Analyse: Simulation results are analysed and sensitivity analysis is used to identify and quantify the main contributors to the profit variability; iv) Improve: The optimal strategies are ranked and prioritised for management’s attention based on their gain and associated risk factors. This will facilitate the management decision-making to select the best strategy for implementation; v) Control: The execution of the optimal plan is considered as an individually implemented project. Therefore, the Project Management approach is used to control the plan execution.
Keywords: Agriculture; Agribusiness; Farm Management; Six Sigma; DMAIC; Stochastic Optimisation; Monte Carlo simulation.
The Results
In the Improve stage, the profit and risk factors of the two optimal plans are compared, and the riskier optimal plan is acknowledged (Table 5).
Table 5: The Optimal Plans Profit and Risk Factors
| Profit µ | σ | Cp(µ) | Cpk(µ) | σ-L(µ) | RMV | SR | |
| Plan 1 | $78,639.71 | $22,625.27 | 0.6964 | 0.6015 | 2.0939 | $22.743.91 | 3.4521 |
| Plan 2 | $79,310.14 | $23,116.02 | 0.6888 | 0.5926 | 2.0707 | $23,167.19 | 3.4078 |
Identifying the riskier optimal plan is very important for decision support as it provides for substantial facilitation of the management decision-making for project control. Risk factors of the two optimal plans are compared considering Mean Profit (µ), Standard Deviation (σ), Process Capability (Cp), Process Capability Index (Cpk), and Sigma Level (σ-L); as well as the profit Regression Mapped Value (RMV) and Sharpe Ratio (SR). The comparison shows that Optimal Plan 2 is riskier than Optimal Plan 1. Even though, Optimal Plan 2 gains more profit, it has a smaller Sharpe Ratio. Therefore, Optimal Plan 1 is technically better than Optimal Plan 2 as it has less risk and a greater Sharpe Ratio, i.e., the return on investment per unit of risk related to the Risk-Free Rate.