Yesterday, I received by e-mail the publication details of my last collaboration with my chemical engineers colleagues. The article is entitled “Is methanol synthesis from co-gasification of olive pomace and petcoke economically feasible?” and during 50 days the access will be free (until July 27th.).

This work deals with a problem very related to a circular economy application, where residuals of the olive industry can be used to generate methanol. It shows the techno-economic analysis of the process involved. However, there is a part referring to forecasting that I consider interesting and somehow overlooked. Essentially, I’ve read many papers that deals with techno-economic analysis and, in the best cases, a Monte Carlo simulation is run to estimate potential risks of the investment. However, and this is the key part, only few (if any) have tried to forecast important variables that are subject to uncertainty. In my experience, most of the works have assumed those variables to be iid (independent, identically distributed), that in christian words it means that the mean and variance are assumed to be constant and, therefore, no forecasting is needed. Although as you can imagine that assumption is frequently not fulfilled.

In this article, we cope with the methanol price forecasting problem, which is not iid by far. That problem is a very difficult one, because the forecasting horizon is also quite a big one. Here, we used a damped trend exponential smoothing and, not only, we provide a point forecast for the methanol price but, we also include a density forecast (probabilistic forecast) that fed the Monte Carlo simulation and that allows us to estimate the investment risks by means of metrics as the Value at Risk.

I think that, currently, many forecasting models that offer a probabilistic forecast are readily available by many free software and, thus, techno-economic analysis should (must) incorporate forecasts of key variables subject to uncertainty, overall, when past information of those variables are available. In my view, that exercise will help considerably the decision makers to optimize their investment strategies by getting a better idea of the future investment risks.

Leave a Reply

Your email address will not be published.


1 2
August 14th, 2020

Sistemas de predicción locales asociados a hospitales

August 13th, 2020

Supply chain forecasting special issue

June 7th, 2020

Forecasting in techno-economic analysis.

Yesterday, I received by e-mail the publication details of my last collaboration with my chemical engineers colleagues. The article is […]

January 26th, 2020

Previsiones macroeconómicas

Aquellos que nos dedicamos a realizar previsiones sabemos la dificultad que entrañan. Uno de los numerosos campos del forecasting es […]

January 8th, 2020

New publication: “Optimising forecasting models for inventory planning”

Inaccurate forecasts can be costly for company operations, in terms of stock-outs and lost sales, or over-stocking, while not meeting […]

October 4th, 2019

Presentation at ISF 2019 (Greece)

One of the most important conferences, possibly the most important, about forecasting is The International Symposium on Forecasting. This year […]

January 26th, 2017

Workshop: Forecasting and inventory control.

It is a pleasure to inform that two members of the INTERCOL Project (Eugenia and Ester) are organising a very interesting event […]

October 21st, 2016

A data-driven approach to compute safety stocks.

Supply chain risk management is drawing the attention of practitioners and academics. A source of risk is demand uncertainty. To […]

October 21st, 2016

Real-time bullwhip metric

Bullwhip effect is a problem of paramount importance that reduces competitiveness of supply chains around the world. A significant effort […]

June 22nd, 2016

Optimal combination of volatility forecasts to enhance solar irradiation prediction intervals estimation

Please find my presentation at the ISF 2016 celebrated in Santander . As usual, it was a pleasant experience from […]