The Digital Business Ecosystem (DBE) is an organizational approach which allows even competitive enterprises to cooperate each other. The industrial system is interested in the realization of such new approach and European policies promote such realizations. In fact there are already new DBE realizations in different production areas. Now it is necessary to put aside the operating realizations to formalize this new approach in order to build up a theory to ground the empirical experimentation on, and obtaining those evidences which can promote the acquisition of this approach in the current state and as a good practice for the enterprises of all sizes and of all productive areas. This work is a contribute to the formalization with the goal to understand how competitive enterprises with heterogeneous organisations and belonging to different areas, can coexist in a same DBE. The formalization proposed has a logical reading based on processes models. Thanks to the use of the process models it is possible to organise some tools which allow to carry out quickly platforms for DBE management and governance and to make this flexible to comply with the different operating modes foreseen by the management of the enterprises which participate to the DBE.

Cloud computing is becoming more and more adopted as infrastructure for providing service oriented solutions. Such a solution is especially critical when software and hardware resources are remotely distributed. In this paper we illustrate our experience in designing the architecture of a community cloud infrastructure in an industrial project related to integrated logistics (LOGIN) for made in Italy brand products. The cloud infrastructure has been designed with particular attention towards aspects such as virtualization, server consolidation and business continuity. Copyright © 2015 SCITEPRESS - Science and Technology Publications.

A major challenge faced by organizations is to better capture business strategies into products and services at an ever-increasing pace as the business environment constantly evolves. We propose a novel methodology base on a Business Process Line (BPL) engineering approach to inject flexibility into process modeling phase and promote reuse and flexibility by selection. Moreover we suggest a decision-table (DT) formalism for eliciting, tracking and managing the relationships among business needs, environmental changes and process tasks. In a real case study we practiced the proposed methodology by leveraging the synergy of feature models, variability mechanisms and decision tables. The application of DT-based BPL engineering approach proves that the Business Process Line benefits from fundamental concepts like composition, reusability and adaptability and satisfies the requirements for process definition flexibility

The easy access to training and content in the digital age has greatly accelerated the level of competitiveness of individuals and organizations. The e-learning space is outside the context of academic and corporate. It supports lifelong learning which has become central to the development of competitive advantage and for the affirmation individual. This implies that training is paid to individuals are characterized by requirements training extremely varied. The designers of e-learning systems have, in this context, the task of developing systems with adaptive and flexible functions in order to adapt the path training, the pedagogical models and the interactions between attending to their needs and preferences. In literature, these systems are called adaptive e-learning systems. Adaptive e-learning systems adopt a system of rules with complex logic. This may involve a high number of anomalies which correct can be difficult and expensive. In this study presents the conceptual models and technology of adaptive elearning more accredited in the literature and a framework based on the technique of decision table for the representation and validation of complex business logic in the rules

Today's organizations are increasingly pushed to be distributed by space, time and capabilities and are involved to leverage synergies by integrating their business processes in order to produce new value-added products and services. Here the importance of integrating whole processes rather than simply integrate databases or software applications. Seeing the duality between products and processes, we propose to exploit flexibility provided by the product-line engineering approach for modeling business processes as a Business Process Line (BPL) in order to capture process variability, promote reuse and integration and provide the capacity to anticipate process changes. To support process evolution and consistency, we suggest the use of decision tables to elicit, track and manage all the emerging decision points during business process modeling, with the purpose of maintaining the relationships among business needs, environmental changes and process tasks. In a real case study we practiced the proposed methodology by leveraging the synergy of feature models, variability mechanisms and decision tables. The results prove that the BPL satisfies the requirements for business process flexibility.

This paper presents a Knowledge Management System (KMS), called PROMETHEUS, which consists of a set of processes that constitute the Experience Factory (EF) and a platform that is the Knowledge Experience Base (KEB), which collects Knowledge Experience Packages (KEP). The KMS thus formed supports the formalization and packaging of knowledge and experience of producers and innovation transferors encouraging gradual explanation of tacit information of bearers of knowledge to facilitate the transfer. The KMS enables the cooperative production of KEP between different authors contributing to the production of KEP and users of the latter. The paper describes the approach outlined in the PROMETHEUS Project and the precautions taken in the design of KEP to ensure that: the experience contained in it, even when collected through projects executed by many person-years, can be quickly acquired by the user, contains the tools to facilitate the acquisition of knowledge innovation support to transfer.

The competitive pressure for organizations and countries has moved the focus of economy from material to immaterial assets. The recognition that knowledge is the fundamental driver of sustainable competitive and collaborative advantage has been a major breakthrough in management thinking. The movement from internal R&D to external connect and develop opens the door for large and small companies to reach beyond their core competencies to remain competitive in an increasingly complex, uncertain and changing environment. This phenomenon, called Open Innovation, alone has opened completely new perspectives about how to manage human, physical and financial resources. It has also influenced organisations to start recognising that to thrive they need to find new ways of accessing the knowledge they need exactly when they need it, in order to adapt to an ever-changing and increasingly complex and uncertain environment. To this end we propose a platform that is the Knowledge Base Experience (KEB), which collects Knowledge Experience Packages (KEP). This framework is able to support a cooperative “innovation chain” from an Open Innovation (OI) perspective because it contains the tools needed to facilitate the acquisition of knowledge that support the innovation to transfer with particular emphasis on supporting, in particular, Small and Medium companies to survive the current turbulence of the markets

Practitioners must continually update their skills to align their professional profile to market needs and social organizations in which they live, both characterized by extreme variability and volatility. In this scenario, Universities, the traditional Institution for the knowledge transferring, assume the role of an institution dedicated to lifelong learning. However the lifelong learning highlights several issues that make it unsuitable to the university instructional models. In order to face this problem the authors propose to use a Learning Network model integrating a Knowledge Base Experience (Prometheus) to support distribution of contents and to the enhancement knowledge transferring. The results of an empirical experimentation encourage their adoption in real contexts

All projects involve risk; a zero risk project is not worth pursuing. Furthermore, due to software project uniqueness, uncertainty about final results will always accompany software development. While risks cannot be removed from software development, software engineers instead, should learn to manage them better (Arshad et al., 2009; Batista Webster et al., 2005; Gilliam, 2004). Risk Management and Planning requires organization experience, as it is strongly centred in both experience and knowledge acquired in former projects. The larger experience of the project manager improves his ability in identifying risks, estimating their occurrence likelihood and impact, and defining appropriate risk response plan. Thus risk knowledge cannot remain in an individual dimension, rather it must be made available for the organization that needs it to learn and enhance its performances in facing risks. If this does not occur, project managers can inadvertently repeat past mistakes simply because they do not know or do not remember the mitigation actions successfully applied in the past or they are unable to foresee the risks caused by certain project restrictions and characteristics. Risk knowledge has to be packaged and stored over time throughout project execution for future reuse. Risk management methodologies are usually based on the use of questionnaires for risk identification and templates for investigating critical issues. Such artefacts are not often related each other and thus usually there is no documented cause-effect relation between issues, risks and mitigation actions. Furthermore today methodologies do not explicitly take in to account the need to collect experience systematically in order to reuse it in future projects. To convey these problems, this work proposes a framework based on the Experience Factory Organization (EFO) model (Basili et al., 1994; Basili et al., 2007; Schneider & Hunnius, 2003) and then use of Quality Improvement Paradigm (QIP) (Basili, 1989). The framework is also specialized within one of the largest firms of current Italian Software Market. For privacy reasons, and from here on, we will refer to it as “FIRM”. Finally in order to quantitatively evaluate the proposal, two empirical investigations were carried out: a post-mortem analysis and a case study. Both empirical investigations were carried out in the FIRM context and involve legacy systems transformation projects. The first empirical investigation involved 7 already executed projects while the second one 5 in itinere projects. The research questions we ask are: Does the proposed knowledge based framework lead to a more effective risk management than the one obtained without using it? Does the proposed knowledge based framework lead to a more precise risk management than the one obtained without using it? The rest of the paper is organized as follows: section 2 provides a brief overview of the main research activities presented in literature dealing with the same topics; section 3 presents the proposed framework, while section 4 its specialization in the FIRM context; section 5 describes empirical studies we executed, results and discussions are presented in section 6. Finally, conclusions are drawn in section 7.

According to the Project Management Institute (PMI) project management consists of planning, organizing, motivating and controlling resources such as time and cost in order to produce products with acceptable quality levels. As so, project managers must monitor and control project execution, i.e. verify actual progress and performance of a project with respect to the project plan and timely identify where changes must be made on both process and product. Earned Value Management (EVM) is a valuable technique for determining and monitoring the progress of a project as it indicates performance variances based on measures related to work progress, schedule and cost information. This technique requires that a set of metrics be systematically collected throughout the entire project. A consequence is that, for large and long projects, managers may encounter difficulties in interpreting all the information collected and using it for decision-making. To assist managers in this tedious task, in this paper we classify the EVM metrics distinguishing them into five conceptual classes and present an interpretation model that managers can adopt as checklist for monitoring EVM values and tracking the project's progress. At this point of our research the decision model has been applied during an industrial project to monitor project progress and guide project manager decisions. Copyright © 2015 SCITEPRESS - Science and Technology Publications.