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Return-to-Log (RTL)
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Return-to-log is the standard forest-industry term for the value of the products that will develop from a log when it is converted to products, less the conversion cost.
- Return-to-log (RTL) values are obviously useful as a measure of value and as a guide to purchasing and selling prices, so there is often a need to calculate them. For this purpose it is not unreasonable to use average prices and costs, depending on how the RTL values will be used.
- RTL values are often determined through execution of a series of detailed log tests. This is problematic, for a number of reasons:
- Executing the tests is very costly;
- Mill tests are notoriously inaccurate. One reason for this is the “mill test effect”, where the mill performs better during testing. Unfortunately, for the purposes of RTL calculation, one cannot be certain that the extent of the “mill test effect” will be equal for different log classes, or on different days.
- The results will be valid only for the sawing practices in place on the day of the test. Once any variable is changed – even something as common as a target size or saw kerf change – the results are no longer valid.
- As an alternative to using mill tests to calculate RTL values, a calibrated SAWSIM® model can be used. SAWSIM® has an option to calculate RTL values for sawlogs, peelers and pulplogs, given lumber, veneer and pulp values, and the costs of mill time. One clear advantage of using SAWSIM® to calculate RTL values is that the values can be easily updated when mill specifications or sawing practices change.
The Trouble with RTL
- It is inherent in the RTL calculation procedure that only one value for each product and one cost for each machine can be used. Constraints that will change the values for products and costs for machine time as volumes change are not taken into account.
- RTL values are often used as a basis for log purchase decisions and for allocating logs to alternative conversion facilities. However there are problems with this that lead to incorrect allocations. WOODMAN™ has an option to use these values for log allocation, but this is not recommended except for testing.
- The problem is that for allocations it is the marginal, not average, values that are needed. And marginal values can change rapidly with the amount allocated, as constraints are encountered. The same is true for any optimization procedure. So WOODMAN™, which uses linear programming, has a built-in procedure for changing the product values and conversion costs as constraints are met and allocations are changed in an iterative procedure. Starting with a fixed and unchangeable set of RTL values leads to an incorrect allocation.
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