Aim: In the last years, sustainability has been identified as an enormous problem, with many facets gaining increasing attention. In this broad scenario, the availability of models for environmental sustainability constitutes a conceptual tool to guide industries towards reducing the environmental impact deriving from production. This work aims to contribute to the research on environmental sustainability in manufacturing by proposing a model that leverages the Goal Question Metrics approach and technologies of Industry 4.0. Methods: The Goal Question Metrics approach and technologies of Industry 4.0 are leveraged by proposing a model that contributes to environmental sustainability in manufacturing. Results: A model is proposed that can be used as a conceptual tool to support improvement programs in environmental sustainability. Conclusion: The application of the Goal Question Metrics+ Strategies to a case study of an automotive industry shows how the approach, combined with the implementation of Industry 4.0 technologies, contributes to the efficient use of natural resources and also reduces the emissions in the atmosphere.
This paper presents an innovative methodology, from which an efficient system prototype is derived, for the algorithmic prediction of malfunctions of a generic industrial machine tool. It integrates physical devices and machinery with Text Mining technologies and allows the identification of anomalous behaviors, even of minimal entity, rarely perceived by other strategies in a machine tool. The system works without waiting for the end of the shift or the planned stop of the machine. Operationally, the system analyzes the log messages emitted by multiple data sources associated with a machine tool (such as different types of sensors and log files produced by part programs running on CNC or PLC) and deduces whether they can be inferred from them future machine malfunctions. In a preliminary offline phase, the system associates an alert level with each message and stores it in a data structure. At runtime, three algorithms guide the system: pre-processing, matching and analysis: Preprocessing, performed only once, builds the data structure; Matching, in which the system issues the alert level associated with the message; Analysis, which identifies possible future criticalities. It can also analyze an entire historical series of stored messages The algorithms have a linear execution time and are independent of the size of the data structure, which does not need to be sorted and therefore can be updated without any computational effort.
The issue of knowledge management in a distributed network is receiving increasing attention from both scientific and industrial organizations. Research efforts in this field are motivated by the awareness that knowledge is more and more perceived as a primary economic resource and that, in the context of organization of organizations, the augmented management complexity of its whole life cycle requires new reference models. In this paper, we build on recent research work to propose a distributed knowledge management framework that can be used in several application domains. We characterize the dimension of social influences in terms of identity, negotiation and trust modeling them within a framework that can augment learning and cooperation capabilities through knowledge sharing and effective communication. A particular instance of the presented framework, to handle the problem of risk management in enterprise alliance partnership, is discussed as a case study that shows the practical applicability of our approach.
Every enterprise can be affected by risks with potential impact on their single organizational parts or on their organizations as a whole. The awareness of consequences deriving from threats, omissions or adverse events drives enterprises to support risk management programs whose aim is to reduce undesirable consequences. The need to identify, assess, and manage risks has motivated organizations to develop integrated frameworks to improve enterprise risk management. ERM is a framework designed by the Committee of Sponsoring Organizations of Treadway Commission (COSO, 2004) that helps business to assess and enhance their internal control systems. COSO defines ERM as “... a process, effected by an entity’s board of directors, management and other personnel, applied in strategy setting and across the enterprise, designed to identify potential events that may affect the entity, and manage risk to be within its risk appetite, to provide reasonable assurance regard in the achievement of entity objectives”. The literature about risk proposes various techniques to identify and classify risks in different fields of knowledge or descriptions of various innovative approaches for managing risks. For example, in (Alberts&Dorofee, 2009) two approaches for managing risks are compared: tactical risk management and systemic risk management. Tactical risk is traditional, bottom-up analysis defined as a measure of the likelihood that an individual potential event will lead to a loss coupled with the magnitude of loss. This approach has the limit that does not readily scale to distributed environments. In contrast to the bottom-up analyses employed in tactical risk management, systemic risk management approach starts at the top with the identification of a program’s key objectives. Once the key objectives are known, the next step is to identify a set of critical factors, called drivers that influence whether or not the key objectives will be achieved. In order to minimize the impact of risks Enterprise Risk management frameworks typically includes four major areas corresponding to the achievement of enterprise objectives:
Relation grammars (RGs) are introduced as a possible general framework for specifying the syntax of visual languages and, more generally, of multi-dimensional languages. A formal definition of relation grammars is given. Two examples of applications on graphs are shown. RG formalism is compared to conventional context-free grammars. RGs are used to describe the syntax of horizontal lines and statechart graphs using picture processing grammars and picture layout grammars, respectively.< >
This study proposes a knowledge management network framework of local governments, investigating how it can affect transparency and improve services provided to citizens. Adopting an ‘action research’ approach, the study provides a framework for implementing a knowledge management network, able to emphasise the role of transparency in the public sector context. The findings from its implementation show that it is now no longer possible to focus only on administrative processes or intra-organisational management, which are the central preoccupations of the new public management paradigm. Institutional theory suggests that IT innovations do not often change existing routines and organisational structures. Considering the potentiality of the network to increase transparency and provide new integrated services, our findings highlight that the main challenge is on modifying inter-organisational routines, considering citizens as components of the network. The paper combines managerial and technological perspectives on transparency and public service delivery.
The problem of performance evaluation of business processes supported by Workflow Management Systems is a recent research issue. In this paper, we propose an approach to the performance evaluation of automated business processes based on the measurement language WPQL (Workflow Performance Query Language). The paper first describes the WPQL architecture together with a selection mechanism by means of which the workflow entities to measure are isolated. Then, the main constructs of WPQL for measure definition and measure application are presented and exemplified. Finally, we show a working session of the support tool and discuss some guideline for further research.
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