Rating and ranking firms with fuzzy expert systems: the case of Camuzzi

In this paper we present a real-life application of a fuzzy expert system aimed at rating and ranking firms. Unlike standard DCF models, it integrates financial, strategic and business determinants and processes both quantitative and qualitative variables. Twenty-one value drivers are defined, concerning the target firm (strategic assets in place and expected financial performance), the acquisition (synergies, quality of management) and the sector (intensity of competition, entry barriers). Their combination via “if-then” rules leads to the definition of an output represented by a real number in the interval [0,1]. Such a number expresses the value-generating power of the target firm inclusive of synergies with the bidder (Strategic Enterprise Value). The system may be used for rating and ranking firms operating in the same sector. A regression analysis using hostile takeovers multiples may be employed to translate the score into price. The real-life case refers to Camuzzi (a natural gas distributor), acquired by Enel, the Italian ex monopolist of electric energy.Corporate finance, firm, rating, ranking, expert system, fuzzy, evaluation

Rating and ranking firms with fuzzy expert systems: the case of Camuzzi

In this paper we present a real-life application of a fuzzy expert system aimed at rating and ranking firms. Unlike standard DCF models, it integrates financial, strategic and business determinants and processes both quantitative and qualitative variables. Twenty-one value drivers are defined, concerning the target firm (strategic assets in place and expected financial performance), the acquisition (synergies, quality of management) and the sector (intensity of competition, entry barriers). Their combination via “if-then” rules leads to the definition of an output represented by a real number in the interval [0,1]. Such a number expresses the valuegenerating power of the target firm inclusive of synergies with the bidder (Strategic Enterprise Value). The system may be used for rating and ranking firms operating in the same sector. A regression analysis using hostile takeovers multiples may be employed to translate the score into price. The real-life case refers to Camuzzi (a natural gas distributor), acquired by Enel, the Italian ex monopolist of electric energy.Corporate finance, firm, rating, ranking, expert system, fuzzy logic, evaluation

Downtime Estimation of Buildings and Infrastructures Using Fuzzy Logic

Extreme natural events (e.g. earthquakes, floods, fire) are the major sources of threat to society and infrastructure. Communities that are able to absorb the impacts, recover quickly after disasters, and adapt to adverse events are fairly resilient communities. Economic and public health consequences from natural disasters have increased over time and motivated discussion of a new resilience management worldwide. The key parameter to estimate the resilience of buildings and infrastructures is the downtime (DT). Several strategies have been investigated to reduce disaster risk and evaluate the recovery time of buildings and infrastructures following dangerous events. However, the estimation of the DT is still challenging due to the uncertainty and vagueness of the data available. This paper introduces a method to predict the DT of buildings and infrastructures following earthquakes through a Fuzzy Logic hierarchical scheme. The use of expert-based systems can be helpful to deal with uncertainties, randomness, and limited data availability in the context of risk analysis and management. Fuzzy theory describes the behavior of a complex system through linguistic variables and it is based on deterministic functions. Two different DT models are introduced in this work for residential buildings and infrastructures, since different are the input parameters involved in the estimation process. In the first model, the DT can be divided into three main components: downtime due to the actual damage (DT1); downtime caused by irrational delays (DT2); and downtime due to utilities disruption (DT3). DT1 is evaluated by relating the building damageability to given repair times of the building’s components. A rapid visual screening survey is filled out by an expert to acquire information about the analyzed building. Then, fuzzy logic is implemented to determine the building vulnerability, which is combined with a given earthquake intensity to obtain the building damageability. DT2 and DT3 are estimated using the REDITM Guidelines. DT2 considers irrational components through a specific sequence, which defines the order of components repair, while DT3 depends on the site seismic hazard and on the infrastructure vulnerability. The downtime of the building is finally estimated by combining the three components above, identifying three recovery states: re-occupancy, functional recovery, and full recovery. For estimating the recovery time of buried infrastructures, 31 indicators have been selected from previous publications and studies referring to programs and policies intending to reduce risk and increase recovery. The DT model is designed by aggregating four downtime indices: exposed infrastructure, earthquake intensity, human resources, and infrastructure type. The collected information on the potentially damaged lifelines are aggregated into a fuzzy hierarchical scheme and combined to obtain the DT. The methodology can be used to effectively support decision-makers in managing and minimizing the impacts of earthquakes and to recover damaged infrastructure promptly

Architecture value mapping: using fuzzy cognitive maps as a reasoning mechanism for multi-criteria conceptual design evaluation

The conceptual design phase is the most critical phase in the systems engineering life cycle. The design concept chosen during this phase determines the structure and behavior of the system, and consequently, its ability to fulfill its intended function. A good conceptual design is the first step in the development of a successful artifact. However, decision-making during conceptual design is inherently challenging and often unreliable. The conceptual design phase is marked by an ambiguous and imprecise set of requirements, and ill-defined system boundaries. A lack of usable data for design evaluation makes the problem worse. In order to assess a system accurately, it is necessary to capture the relationships between its physical attributes and the stakeholders\u27 value objectives. This research presents a novel conceptual architecture evaluation approach that utilizes attribute-value networks, designated as \u27Architecture Value Maps\u27, to replicate the decision makers\u27 cogitative processes. Ambiguity in the system\u27s overall objectives is reduced hierarchically to reveal a network of criteria that range from the abstract value measures to the design-specific performance measures. A symbolic representation scheme, the 2-Tuple Linguistic Representation is used to integrate different types of information into a common computational format, and Fuzzy Cognitive Maps are utilized as the reasoning engine to quantitatively evaluate potential design concepts. A Linguistic Ordered Weighted Average aggregation operator is used to rank the final alternatives based on the decision makers\u27 risk preferences. The proposed methodology provides systems architects with the capability to exploit the interrelationships between a system\u27s design attributes and the value that stakeholders associate with these attributes, in order to design robust, flexible, and affordable systems --Abstract, page iii

A Review on Fuzzy - AHP technique in Environmental Impact Assessment of Construction Projects, India

There are several countries today using procedures for Environmental impact assessment (EIA) is based on a series of mathematical techniques which attempt to localize, describe and assess the positive and negative effects that any human activity has on our environment, generally causing it to deteriorate. The environmental impact assessment (EIA) of projects requires the evaluation of the effects of very diverse actions on a number of different environmental factors, the uncertainty and inaccuracy being inherent in the process of allocating values to environmental impacts carried out by a panel of experts, stakeholders and affected population. The application of the fuzzy Logic and AHP technique can be helpful in identification of the risk associated with construction or developing project and improves the study of EIA. Fuzzy is one of the characteristics of human thoughts for which fuzzy sets theory is an effective tool for fuzziness. A fuzzy logic knowledge-based approach can be used for the environmental impact assessment study of the different construction projects. The review article highlights the role of Fuzzy AHP logic method in EIA of different construction projects, fuzzy logic modeling - software for fuzzy EIA, fuzzy numbers and steps of fuzzy methods as well as reveals that how fuzziness can be determined by applying fuzzy logic method in construction projects

A fuzzy-based evaluation of financial risks in build-own-operate-transfer water supply projects

The build–own–operate–transfer (BOOT) scheme is widely used for the provision of new bulk water supply. However, this scheme is complex and carries significant financial risks because of the characteristics of the water sector and the involvement of public-private stakeholders with new and extended responsibilities, large private capital, and long contract duration. Drawing on the Nungua Seawater Desalination Plant (NSDP) in Ghana, this study seeks to identify and assess the critical financial risks associated with BOOT water supply projects and evaluate the financial risk level of the NSDP project. The risks and their relative criticality on the NSDP project are investigated by using a questionnaire survey method. The questionnaire was formulated with a set of 18 risks derived from extant literature and project documentation. Perceived critical financial risks affecting the NSDP project were assessed by a team of experts who had direct involvement in the project. A fuzzy synthetic evaluation suggests that the project is financially risky and that all the risks are critical to the project. Bankruptcy of consortium members, unfavorable economy of the host country, uncertainty in tariff adjustment of water products, rate of return restrictions, and availability problem of private capital are the five most highly-ranked risks. The fuzzy technique is used to represent and model experiential knowledge of the survey participants and to address the fuzziness of their expert judgments. The study’s results facilitate prioritization of risks and a comprehensive risk management program during the lifecycle of the case project and future projects. The fuzzy technique is suitable for early phases of BOOT projects to prioritize the risks that require a detailed analysis and to predict the risk level of a project

New Approaches for Modeling and Evaluating Agility in Integrated Supply Chains

International audienc