Forecasting the Life Cycle Assessment in a Cellulose Factory

The purpose of this article is to explain the introduction of simple and adaptive expo-nential smoothing methods in Life Cycle Cycle Assessment (LCA) and their results. In this case, on a cellulose factory wood debarking and cutting line. Based on a case study in which various life cycle models were applied to this asset, the application of expo-nential smoothing methods to forecast operating and maintenance costs for the t+1 pe-riod was explained; therefore, the period subsequent to the known values. The aim was to include a well-established mathematical tool in the calculation of this value, as it gives the equipment manager an idea of what is coming in the near future. Given that all forecasts end up having a Root Mean Square Error (RSME) associated with them, taking into account the actual value obtained in the previous period, this tool ends up being self-correcting and is expected to minimize the absolute error over time and as the sample size increases. Five different exponential smoothing models were applied: one simple exponential smoothing and four with adaptive parameters. As expected, it was observed that there were different results between the various methods applied. Bear-ing in mind the sample used, “Exponential smoothing with adaptive alpha 2” was the method that presented the smallest error in its forecasts, when compared to the actual values. Regardless of this specific result, the aim is for these models to be applied throughout the entire life of the asset, as well as to several different assets. So that even more detailed conclusions can be drawn about the behavior of the models in different contexts. Life cycle models are becoming increasingly reliable. With the application of robust tools to fill previously unresolved gaps, any manager and/or engineer in any field, who knows and can interpret the data collected from the asset for which they are responsible, will be able to obtain excellent results in its management. Optimizing its useful life and maximizing its availability.