Biomass Supply Chain Risk-- Approach and Analytics v.1 [Autosaved]

Biomass Supply Chain RiskEcostrat Approach and Analytics

Supplier Exposure

Size, longevity Balance sheet / cash flow Capacity utilization Direct vs indirect control of feedstock Equipment Shut downs / interruption history Supply Contract

Term Index Measurement /testing Recourse Liquidated damages

Supply Chain Risk: Supplier Exposures

Risk Minimization

CompetitorExposure

Competitor description: history Competitor location

Short haul risk Feedstock demand

Current Historical fluctuations Future projections of demand

Supply Chain Risk: Competitor Exposures

Risk Minimization

Resource AvailabilityExposure

Understand resource availability Impact of housing market Impact of competition Impact of available infrastructure

Roads Trucking

Impact of policy Understand sensitivity of each component of the supply

chain Stumpage Diesel PPI Labor shortage Weather

Supply Chain Risk: Resources Availability Exposures

Risk Minimization

Risk Analysis Data: Historical Cost of Feedstock

Risk Analysis: Historical Variances in Cost of Stumpage

Impact Analysis Tools: Current and Additional Consumption on Supply

Reynolds Model 2014

Marginal Cost of Supply Replacement in Plant A Woodshed

Impact Analysis Tools: Monte Carlo Simulation Methodology

Questions: What is the vulnerability of the supply chain to a disruption risk? What is the likelihood that feedstock price will exceed $25 per ton over the next 10 years? Which particular variable has the largest impact upon feedstock cost out of all the possible variations? What is the impact of various mitigation strategies on multiple disruption risks?

Risk is all about ‘disruption or disturbance’ in various activities of supply chain. MCS is a computerized mathematical technique that models separate scenarios considering all supply chain factors that have inherent uncertainty:

supplier breach new competition for scarce fiber resources quality issues diesel cost policy risk weather, etc

Ecostrat MCS furnishes a range of over 10,000 possible outcomes and the probabilities they will occur for any choice of action.

Impact Analysis Tools: Monte Carlo Simulation Methodology to Calculate Future Cost

ScenariosSample Results:

After the simulation, we may find an "average" feedstock cost over time will be $20 per ton FOB plant.

But, there exists a relatively high chance - over 30 percent - that a negative outcome where feedstock can reach over $45 per ton.

On the other hand, a "high" positive outcome is also possible: there exists more than a 15-percent likelihood of obtaining feedstock at $15 or less per ton.

Such a curve reveals the relatively wide amplitude of varying possible outcomes. Standard deviation, for example is more than 2.5 times the mean expected outcome– which would denote significant risk in feedstock cost over time.