The challenge proposed by EDP consists in simulating electricity prices not only for risk measures purposes but also for scenario analysis in terms of pricing and strategy. Data concerning hourly electricity prices from 2008 to 2016 was provided.

Numerous methods to deal with Electricity Price Forecasting (EPF) have been proposed and can be classified as: (i) multi-agent models, (ii) fundamental models, (iii) reduced-form models, (iv) statistical models and (v) computational intelligence models. A recent an exaustive review is presentes in [13].

During this study group different promising Statistical techniques were propose by the study group contributors: ARIMA, sARIMA, Longitudinal Models, Generalized Linear Models and Vector Autoregressive Models. In this report a GLM and a vector autoregressive model are presented and their predictive power is discussed.

In the GLM framework two different transformations were consider and for both the season of the year, month or winter/summer period revealed significant explanatory variables in the different estimated models.

On the other hand, the multivariate approach using VAR considering as exogenous variables the meteorologic season and the type of day yield a multivariate model that explains the intra-day and intra-hour dynamics of the hourly prices. Although the forecast do not exactly replicate the real price they are quite similar.

In both of the approaches here reported a more extensive work would certainly improve the proposed models.

In conclusions, EPF is a growing area that groups multiple different approaches that can be applied. In fact, other approaches from multi-agent models, fundamental models, reduced-form models and computational intelligence models, also present a great space for EPF.

Due to a frequent introduction of new models in the production environ- ment, the box sizes are initially set manually in an experimental procedure (testing), which is often time consuming. Savana challenged ESGI’s par- ticipants to study their packing process, in order to reduce the variety of box sizes, the empty space inside the boxes and to eliminate the need to perform testing, thereby reducing the time and increasing the e�ciency of the packing process.

Furthermore, the footwear ordered by each customer is packed into large boxes, which will henceforth be referred to as containers. With regard to these large boxes, various designs and sizes can be delivered to a single client. The dimensions, weight and forms of these are subject to the customer’s specifications. In this context, Savana intends to determine automatically the sizes of the containers to be sent to each customer and how to arrange the individual boxes for each client’s order.

This report tells how to automatize and speed up the overall process. It describes how to automatically assign shoes to boxes, and gives a manner to pack the shoe boxes, in such way that permit to reduce the size of the card box.

Savana should be aware that this is not yet a ready to use solution, because more data analysis need to be done, in order to improve and make the method reliable. Furthermore, during implementation it may appear new important challenges.