Risk management 2 shouldn’t be difficult. So I started on a quest to come up with a simple methodology for quantitative risk analysis that will actually improve decision making. This is still work in progress, so any comments and suggestions please send them to me or write below the article. Also don’t forget, RAW2020 is coming this October, don’t miss all the amazing workshops.
This is my proposal for a simple methodology to get to know Monte-Carlo simulations and start thinking in distributions and the probability of achieving objectives. This methodology is most suitable for investment project risk analysis.
Step 1 – Key assumptions check
First step involves expert ranking of the common assumptions from the list below again key project or decision assumptions:
- Strategic risks
- Changes in demand, competition, change in product application to different markets
- Sanctions or tariffs, affecting our ability to sell to some markets
- Market risks
- Product price volatility
- FX risks
- Inflation
- Raw materials price volatility
- Raw materials availability
- Logistics cost volatility
- Operational risks
- HSE, incidents affecting capacity, production uptime or fines
- Lack of qualified personnel affecting operating costs
- Increase in processing costs
- Taxes, delay getting government tax and financing benefits
- Maintenance cost volatility
- Construction related risks
- Delayed start-up
- Increase in 3rdparty CAPEX amount
- Increase in own CAPEX amount
- Longer ramp-up
- Specific risks for brownfield projects:
- limitation on productive work periods and use of common infrastructure
- impact on main production volumes or quality
- SIMOPS
Step 2 – Add volatility to assumptions
Depending on the assigned significance or uncertainty (Low, Medium, High) for each key assumption, the corresponding volatility is added to the project financial model (e.g. some assumptions may have low volatility and others high).
Many of the key assumptions are either positively or negatively correlated and may be moving together increasing the overall risk exposure.
We account for uncertainty in the project financial model via simple rules:
| Key assumptions | Low | Medium | High |
| Changes in demand, competition, change in product application to different markets | No effect | Moderate reduction in premiums / volumes sold | Significant reduction in premiums / volumes sold |
| Sanctions or tariffs, affecting our ability to sell to some markets | No effect | Moderate reduction in premiums / volumes sold | Significant reduction in premiums / volumes sold |
| Product price volatility | Delta change from year to year equal to inflation | Delta change from year to year +/- equal to inflation +1.5% | Delta change from year to year +/- equal to inflation +2.5% |
| FX risks | Volatility equal to inflation (both positive and negative) | Delta change from year to year +/- equal to inflation +1.5% | Delta change from year to year +/- equal to inflation +2.5% |
| Inflation | Volatility equal to inflation (both positive and negative) | Delta change from year to year +/- equal to inflation +1.5% | Delta change from year to year +/- equal to inflation +2.5% |
| Raw materials price volatility | Equal to inflation | Inflation +1.5% | Inflation +2.5% |
| Raw materials availability affecting the time of the start-up | Min: 95%
Mode: 100% Max: 105% |
Min: 100%
Mode: 105% Max: 110% |
Min: 100%
Mode: 105% Max: 120% |
| Logistics cost volatility | Equal to inflation | Inflation +1.5% | Inflation +2.5% |
| HSE, incidents affecting capacity, production uptime or fines | Min: 95%
Mode: 98% Max: 100% |
Min: 90%
Mode: 95% Max: 100% |
Min: 80%
Mode: 95% Max: 100% |
| Lack of qualified personnel affecting operating costs | Min: 100%
Mode: 102% Max: 105% |
Min: 100%
Mode: 105% Max: 110% |
Min: 100%
Mode: 105% Max: 120% |
| Increase in processing costs | Min: 95%
Mode: 100% Max: 105% |
Min: 100%
Mode: 105% Max: 110% |
Min: 100%
Mode: 105% Max: 120% |
| Taxes, delay getting government tax and financing benefits | Same as base case | Higher probability to less favourable tax regime | Even higher probability to less favourable tax regime |
| Delayed start-up | Min: 95%
Mode: 100% Max: 105% |
Min: 100%
Mode: 105% Max: 110% |
Min: 100%
Mode: 105% Max: 120% |
| Increase in 3rd party CAPEX amount | Min: 95%
Mode: 100% Max: 105% |
Min: 100%
Mode: 105% Max: 110% |
Min: 100%
Mode: 105% Max: 120% |
| Increase in own CAPEX amount | Min: 95%
Mode: 100% Max: 105% |
Min: 100%
Mode: 105% Max: 110% |
Min: 100%
Mode: 105% Max: 120% |
| Longer ramp-up | Min: 95%
Mode: 100% Max: 105% |
Min: 100%
Mode: 105% Max: 110% |
Min: 100%
Mode: 105% Max: 120% |
| Specific risks for brownfield projects affecting start-up time or CAPEX | Min: 95%
Mode: 100% Max: 105% |
Min: 100%
Mode: 105% Max: 110% |
Min: 100%
Mode: 105% Max: 120% |
The above is just an illustration, we will obviously need to calibrate the estimates and will continue to update the ranges.
Step 3 – Run simulation
The next step is to run simulation using free ModelRisk or free SIPmath. To learn how to use them watch RAW2020.
Do you think this will work? Share your thoughts.
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Alex, thanks for sharing. I feel environment risk, pollution and its awareness need to be considered separately as a class somewhere maybe as a part of Operational risk. In democratic countries, the NGO oppose many developmental projects for environmental concerns leading to delays and stalling them. Also there are public litigations arising out of genuine and not so genuine impacts of the firm.
OK approach. I note that likelihood is 100% for all issues. My guess is, that recognizing some things may not happen/affect results using a likelihood less than 100% is more transparent than using a low impact of zero.
Furthermore, I assume the organisation defines the meabibg og “significanr” which sound awfully qualitative to me.
This is a simple assumptions check, so adding volatility for all key assumptions is necessary. These assumptions are not random, they have been carefully and empirically selected. If one of the assumptions is not relevant it can be switched off. Adding probability to assumptions volatility is superfluous.