Antonio Capodieci

The Aircraft uptime is getting increasingly important as the transport solutions become more complex and the transport industry seeks new ways of being competitive. To reach this objective, traditional Fleet Management systems are gradually extended with new features to improve reliability and then provide better maintenance planning. Main goal of this work is the development of iterative algorithms based on Artificial Intelligence to define the engine removal plan and its maintenance work, optimizing engine availability at the customer and maintenance costs, as well as obtaining a procurement plan of integrated parts with planning of interventions and implementation of a maintenance strategy. In order to reach this goal, Machine Learning has been applied on a workshop dataset with the aim to optimize warehouse spare parts number, costs and lead-time. This dataset consists of the repair history of a specific engine type, from several years and several fleets, and contains information like repair claim, engine working time, forensic evidences and general information about processed spare parts. Using these data as input, several Machine Learning models have been built in order to predict the repair state of each spare part for a better warehouse handling. A multi-label classification approach has been used in order to build and train, for each spare part, a Machine Learning model that predicts the part repair state as a multiclass classifier does. Mainly, each classifier is requested to predict the repair state (classified as “Efficient”, “Repaired” or “Replaced”) of the corresponding part, starting from two variables: the repairing claim and the engine working time. Then, global results have been evaluated using the Confusion Matrix, from which Accuracy, Precision, Recall and F1-Score metrics are retrieved, in order to analyse the cost of incorrect prediction. These metrics are calculated for each spare part related model on test sets and, then, a final single performance value is obtained by averaging results. In this way, three Machine Learning models (Naïve Bayes, Logistic Regression and Random Forest classifiers) are applied and results are compared. Naïve Bayes and Logistic Regression, that are fully probabilistic methods, have best global performances with an accuracy value of almost 80%, making the models being correct most of the times.

The problem of data flooding is becoming more and more important, and it is necessary to find a way to access relevant information and to collect information and data from different data sources. In the field of cultural heritage, users need to be able to find and extract information from databases of specific contexts (paintings, inscriptions, churches, old coins, etc.). The problem is that each of these databases has a different data model. A project created to solve this problem is Europeana, which aims to bring together data from different providers (museums, libraries or other entities) in a single dataset. This paper aims to propose an architecture for a heterogeneous data sources system for the integration of structured/semi-structured data sources, enabling the information content to be more accessible and searchable for users. The data integration model proposed in this paper aims to exploit the method of "schema matching", using ontology-based data integration techniques by automating the integration process.

Nowadays companies are rapidly moving towards transforming their business into digital businesses. Being digital often means exploiting emerging technologies to enable new ways of serving customers. This paper presents two digital tools that have been designed to drive innovation in maintenance operations. A remote assistance collaboration tool combining wearable technologies and augmented reality is presented to allow a specialist from an engineering services company to remotely assist in real time a maintenance operator located at an operation site. A visible and open collaborative business model built to model the B2B process between the engineering services and the operation companies is presented to monitor compliance to the service level agreement contract and provide a basis for improving customer facing interactions. This paper provides an example of digital innovation in maintenance operations that integrates new technologies with business process models.

In the last years, the use of Web 2.0 has modified common organisational and operative practices. This has led "knowledge workers" to change their working practices through the use of Web 2.0 communication tools. Unfortunately, these tools have not been integrated with existing enterprise information systems. This is an important problem in an organisational context, because knowledge of information exchanged within the organisation is needed to increase the efficiency and competitiveness of the organisation. In previous works we demonstrate that it is possible to capture this knowledge using collaboration processes, which are processes of abstraction created in accordance with design patterns and applied to new organisational operative practices. In this article, we want to present the experience of the adoption of the methodology and the catalog of patterns from some pattern designers of a company operating in Public Administration and Finance, with the aim of shaping an information system Enterprise 2.0, which takes into account the collaborative processes.

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