Massimo de Falco

Financial derivatives for risk management in shipping operations: A simulation model applied to the oil and gas sector

The objective of the study is to demonstrate the utility of financial derivatives instruments in risk protection for logistics. In the specific case, we model the application of financial instruments to shipping operations in the oil and gas sector. We first identify all risks confronting actors in international shipping operations: exchange and interest rate fluctuations, and variations in freight and commodity prices. Next we provide a brief summary of three major types of financial derivatives contracts: futures, swaps and options. Finally, we apply numerical simulations to identify appropriate cover against financial risks through application of the derivatives instruments. The methodology serves as a guide to efficient and effective advance planning and management of risk. Companies can: i) measure risk parameters; ii) compare the projected performance of alternate financial tools for their reduction; iii) select the appropriate instruments on the basis of cost and benefit standards defined by the company.

Improving Project Control and Guidance Through a Time-Frame Simulation Approach

Nowadays projects dynamicity and complexity make the control process highly critical. The existing planning and control techniques have frequently proved inadequacy to manage the present challenge. The paper proposes a simulative approach to managing with more effectiveness projects life cycle. The appositely developed simulation model is populated with both deterministic and stochastic elements: the formers come from the project plan; the stochastic elements have been introduced in order to consider the probabilistic nature of activities duration. In the planning phase the model generates a “baseline pencil” that gives a more confident estimation of the time to complete the project. During the execution phase the model is able to store the data related to the ongoing activities and updates in real-time the estimation of the project completion. Concurrently, it allows the calculation of specific performance indexes which permit to identify in real-time possible occurring “warnings” to users and suggest potential solutions.

A structured approach to managing technical process reengineering projects

The emerging knowledge-based economy has pointed out the importance of competence and its interface in the conception of any economic process. There has been a natural evolution in industrial innovation processes: from a sequence of separated phases (sequential approach) to a multidisciplinary network of competences (interactive approach). These changes have brought about the increase in the information to be managed and coordinated especially if the project regards processes such as reengineering ones in which the technical aspects are predominant and critical. The aim of this paper is to propose an integrated methodology based on a logical framework which guarantees effective analysis and management of data generated during a project of Product/Process Reengineering (PR). The framework is able to coordinate these information flows whose complexity is related to the different nature of the data involved, by supporting design and operation processes during the evaluation of all the feasible alternatives.

A Queueing Networks-Based Model for Supply Systems

In this paper, a stochastic approach, based on queueing networks, is analyzed in order to model a supply system, whose nodes are working stations. Unfinished goods and control electrical signals arrive, following Poisson processes, at the nodes. When the working processes at nodes end, according to fixed probabilities, goods can leave the network or move to other nodes as either parts to process or control signals. On the other hand, control signals are activated during a random exponentially distributed time and they act on unfinished parts: precisely, with assigned probabilities, control impulses can move goods between nodes, or destroy them. For the just described queueing network, the stationary state probabilities are found in product form. A numerical algorithm allows to study the steady state probabilities, the mean number of unfinished goods and the stability of the whole network.

An Analytical Model for Optimizing the Combination of Energy Sources in a Single Power Transmission Network

The increasing amount of renewable energy currently being added to distribution networks presents new challenges and opportunities to system operators. This situation further complicates the operators’ tasks in dealing with changing net loads and balancing. The current work provides an analytical model to assist systems operators in stabilizing power generation and lowering total costs, through optimization of choices in the combination of programmable fossil sources and nonprogrammable renewable sources. The study first examines the various programmable and renewable energy sources that appear broadly suitable and economically appealing for combination. Next we identify the most important factors determining the potential integration of the sources in the system. Based on this introductory information we then develop the model for the selection of the appropriate mix of sources to achieve stable production. In developing the model we define indicators to evaluate and select the best configurations of the sources included in a particular combination. Next we apply the model to a specific case study and finally reexamine the interdependencies among all the variables of the model, to provide a better understanding of its dynamics and results.

Analytic Model of Energy Sources Combination to Reduce Power Generation Variability

The increasing amount of renewables generation feeding to the grids is a new challenge to system operators who have to deal with higher unpredictability of net load variability and balance fluctuations. This work focuses on how to stabilize power generation through energy combination of fossil and renewable sources. To avoid an excessive combinatory analysis, the first part of the work explores current technology options to find out what energy sources are the most suitable for the purpose. The second part of the work identifies the most important “factors” that characterize an energy source and its combination. The third part of the work shows the proposed combination model that explains how to combine, evaluate and find the best combination. The application of the model with a specific case study is in the fourth part of the work. The last part analyzes the interdependencies among all the variables of the model to give a better understanding of the dynamics of the model and its results.