摘要
Providing a sustainable and reliable supply of raw materials at economic prices has become essential to industrialized economies. Therefore, the need for both economical and sustainable methods and strategies for the management of raw materials has been postulated to enable companies and economies to counteract dramatic effects of supply disruptions, or at least to provide early warnings. The relevant studies assign generic weights to different driving factors and therefrom derive criticality indexes. However, it often remains open how to interpret the resulting measures and how to apply them practically. Here we show that based on current commodity key figures, it is possible to empirically determine the risk for future price increases and fluctuations. Thus, we can identify future supply risks and incorporate their patterns into an empirically calibrated criticality measurement. To this end, we apply the well-known compounding framework used by many companies for their financial planning, calculating net present values and volatility from the predicted future price development. To calibrate each resource specific model, we perform extended regression analyses on our compounded criticality index from time series of 42 (out of about 60 industrially relevant) chemical elements. The analysis thereby covers 9 driving factors for criticality and a 40-year time span. Our results suggest a fundamental modification of current practices for criticality assessment, in particular by scaling the criticality measure to correspond with the net present value of future commodity expenses and future volatility.
Providing a sustainable and reliable supply of raw materials at economic prices has become essential to industrialized economies. Therefore, the need for both economical and sustainable methods and strategies for the management of raw materials has been postulated to enable companies and economies to counteract dramatic effects of supply disruptions, or at least to provide early warnings. The relevant studies assign generic weights to different driving factors and therefrom derive criticality indexes. However, it often remains open how to interpret the resulting measures and how to apply them practically. Here we show that based on current commodity key figures, it is possible to empirically determine the risk for future price increases and fluctuations. Thus, we can identify future supply risks and incorporate their patterns into an empirically calibrated criticality measurement. To this end, we apply the well-known compounding framework used by many companies for their financial planning, calculating net present values and volatility from the predicted future price development. To calibrate each resource specific model, we perform extended regression analyses on our compounded criticality index from time series of 42 (out of about 60 industrially relevant) chemical elements. The analysis thereby covers 9 driving factors for criticality and a 40-year time span. Our results suggest a fundamental modification of current practices for criticality assessment, in particular by scaling the criticality measure to correspond with the net present value of future commodity expenses and future volatility.
