It is a pleasure to inform that two members of the INTERCOL Project (Eugenia and Ester) are organising a very interesting event about Forecasting and inventory control within the 11th. International Conference on Industrial Engineering and Industrial Management that will take place in Valencia between the 5th. and 6th. of July. Note that these disciplines are typically studied as different areas, although both of them have many links that often are overlooked. Therefore this workshop can be very useful to bridge that gap!.
Supply chain risk management is drawing the attention of practitioners and academics. A source of risk is demand uncertainty. To deal with it demand forecasting and safety stocks are employed. Most of the work has focused on point demand forecasting, assuming that forecast errors follow the typical normal i.i.d. assumption. The variability of the forecast errors is used to compute the safety stock, in order to reduce the risk of stockouts with a reasonable inventory investment. Nevertheless, real products’ demand is very hard to forecast and that means that at minimum the normally i.i.d. assumption should be questioned. This work analyses the effects of possible deviations from these assumptions and it proposes empirical methods based on Kernel density estimators (non-parametric) and GARCH models (parametric) in order to compute the safety stock.
Bullwhip effect is a problem of paramount importance that reduces competitiveness of supply chains around the world. A significant effort is being devoted by both practitioners and academics to understand its causes and to reduce its pernicious consequences. Nevertheless, limited research has been carried out to analyse potential metrics to measure it, that typically are summarized in the coefficient of variation ratio of different echelons demand. This work proposes a new metric based on a time-varying extension of the aforementioned bullwhip effect metric by employing recursive estimation algorithms expressed in the State Space framework to provide at each single time period a real-time bullwhip effect estimate. Continue reading
Please find my presentation at the ISF 2016 celebrated in Santander . As usual, it was a pleasant experience from both perspectives, personal and professional.
Currently, I am teaching a subject about operations management and I have to introduce to my students the importance of safety stocks and the different ways to determine it. At this point, I was analyzing how this issue is explained in operations management books, and I realized that some of them compute the safety stock on the basis of the lead time demand distribution (Heizer and Render, 2008), whereas books more specialized in inventory management (Silver et al, 1998) and (Nahmias, 2004), they suggest to use the lead time forecast demand distribution. To be more precise, if we compute the safety stock for a certain service level, the safety stock (SS=k*standard deviation of lead time demand), where k can be obtained given the service level. The problem relies on the standard deviation, shall I use the standard deviation of the lead time demand distribution or shall I use the standard deviation of the lead time forecast demand error?
Energy modelling and forecasting has become essential to optimize the generation, control and distribution processes of countries’ energy systems. This special session of the EURO confererence (12-15 July, Glasgow) calls for abstracts analyzing concepts, models, methodologies, case studies that contribute to strengthen our knowledge of such an important area. Topics of interests (but not limited to) are forecasting: electricity load and price; renewables including wind energy, solar energy, wave energy and biomass; coal and gas demand, oil products and its derivatives; as well as model for predicting the energy mix.
Abstract Submission Deadline: Monday 16th March 2015
The abstract submission code for this energy forecasting session is: 4e0e6c1f
We’re looking forward to meeting you
I am pleased to devote a few words to the new working paper that we (Alberto Martín, Nikos Kourentzes and myself) have elaborated with the collaboration of the ISFOC that kindly provided the research data.
The paper reports the Dynamic Harmonic Regression applied to forecast the solar irradiance at short-term, but, why is this important? Generally speaking, solar power generation is a crucial research area for countries that suffers from high dependency on external energy sources and is gaining prominence with the current shift to renewable sources of energy (This is particularly true for Spain) . In order to integrate this generated energy into the grid, solar irradiation must be forecasted, where deviations of the forecasted value involve significant costs. It should be noted that the need for these forecasts are also required for other renewable resources as the wind.
Ejemplo de predicción con el método DHR (línea discontinua) frente a los datos reales de irradiación solar directa o DNI (línea continua)
In this working-paper we propose a univarivate Dynamic Harmonic Regression model set up in a State Space framework for short-term (1 to 24 hours) solar irradiation forecasting (take a look at the other blog post that we use the same technique to forecast electricity load and prices). Time series hourly aggregated as the Global Horizontal Irradiation and the Direct Normal Irradiation will be used to illustrate the proposed approach. Continue reading
Bullwhip effect is a problem of paramount importance that reduces competitiveness of supply chains around the world. A significant effort is being devoted by both practitioners and academics to understand its causes and to reduce its pernicious consequences. Nevertheless, limited research has been carried out to analyze potential metrics to measure it, that typically are summarized in the coefficient of variation ratio of different echelons demand.
This work proposes a new metric based on a time-varying extension of the aforementioned bullwhip effect metric by employing recursive estimation algorithms expressed in the State Space framework to provide at each single time period a real-time bullwhip effect estimate. In order to illustrate the results, a case study based on a serially-linked supply chain of two echelons from the chemical industry is analyzed. Particularly, this metric is employed to analyze the effect of promotional campaigns on the bullwhip effect on a real-time fashion. The results show that, effectively, the bullwhip effect is not constant along time, but interestingly, it is reduced during the promotional periods and it is bigger before and after the promotion takes place.