3 steps to apply Monte-Carlo simulations to any investment project decision

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 3^rdparty 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.