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Recent advances in the field of web technologies, including the increasing support of virtual reality hardware, have allowed for shared virtual environments, reachable by just entering a URL in a browser. One contemporary solution that provides such a shared virtual reality is LIRKIS Global Collaborative Virtual Environments (LIRKIS G-CVE). It is a web-based software system, built on top of the A-Frame and Networked-Aframe frameworks. This paper describes LIRKIS G-CVE and introduces its two original components. The first one is the Smart-Client Interface, which turns smart devices, such as smartphones and tablets, into input devices. The advantage of this component over the standard way of user input is demonstrated by a series of experiments. The second component is the Enhanced Client Access layer, which provides access to positions and orientations of clients that share a virtual environment. The layer also stores a history of connected clients and provides limited control over the clients. The paper also outlines an ongoing experiment aimed at an evaluation of LIRKIS G-CVE in the area of virtual prototype testing.

The complexity of medical devices and its user interactions increases. A growing number of incident reports are assumed to be associated primarily with user errors. This development is tackled through current modifications in standards, such as ISO 13485:2016 and legislations, such as the Medical Device Regulation. Both intensify the focus on use errors significantly. The aim of this paper was the development of a process orientated approach integrating usability engineering into a consisting risk management based on a classic V-model. An appropriate procedure was worked out. For each development step, risk and usability activities were cumulated. Thus, the present paper might help medical device manufacturers to reflect their risk management and usability management processes to find synergies. Prospectively, a step-by-step guide for the integration of risk management and usability engineering based on this approach should be developed.

Organisations are computing systems. The university’s sports centre is a computing system for managing sports teams and facilities. The tenure committee is a computing system for assigning tenure status. Despite an increasing number of publications in group ontology, the computational nature of organisations has not been recognised. The present paper is the first in this debate to propose a theory of organisations as groups structured for computing. I begin by describing the current situation in group ontology and by spelling out the thesis in more detail. I then present the example of a sports centre to illustrate why one might intuitively think of organisations as computing systems. To substantiate the thesis, I introduce Piccinini’s restrictive analysis of physical computation. As I show, organisations meet all criteria for being computing systems. Organisations are structured groups with the function of manipulating medium-independent vehicles according to rules. Furthermore, I argue for the modal claim that this is a necessary feature of organisations. Having sketched the computational account of organisations, I compare it to other proposals in the literature.

In the present context, companies to be competitive must develop high-performance innovation capabilities that enable them to respond quickly to market needs. However, the lack of tools and methodologies to assess the performance of innovation projects in an integrated way remains an obstacle. The paper begins by discussing the principles of Six Sigma and the Balanced Scorecard for performance evaluation. Next, the advantages of the Six Sigma Business Scorecard are discussed as a tool to support the evaluation of performance in innovation projects. Finally, the advantages of their application in the context of a collaborative ecosystem are discussed. It is illustrated that the BSC ensures that top management pays attention at any time to the specific elements of the Six Sigma implementation that are not working as planned, providing a link between strategy and quality initiatives assuring customers satisfaction in innovation projects.

Today’s mankind and all human activities are constantly changing and evolving in response to changes in technology, social and economic environments and climate. Those changes drive a “new” way of manufacturing industry. That novelty could be described as the organization of production processes based on technology and devices autonomously communicating with each other along the value chain. Decision-makers have to address this novelty (usually named as Industry 4.0) and try to develop appropriate information systems, physical facilities, and different kind of technologies capable of meeting the future needs of economy. As a consequence, there is a need for new paradigms of the way freight is move, store, realize, and supply through the world (logistics system). One of the proposed solutions is the Physical Internet, concept of open global logistics system which completely redefines current supply chain configuration, business models, and value-creation patterns.However, further detailed research on this topic is much needed. This paper aims to provide a balanced review of the variety of views considered among professionals in the field of Physical Internet with the final aim to identify the biggest challenges (technological, societal, business paradigm) of proposed new logistics paradigm as a practical solution in supporting Industry 4.0.

The successful completion of a software development process depends on the analytical capability and foresightedness of the project manager. For the project manager, the main intriguing task is to manage the risk factors as they adversely influence the completion deadline. One such key risk factor is staff training. The risk of this factor can be avoided by pre-judging the amount of training required by the staff. So, a procedure is required to help the project manager make this decision. This paper presents a system that uses influence diagrams to implement the risk model to aid decision making. The system also considers the cost of conducting the training, based on various risk factors such as, (i) Lack of experience with project software; (ii) Newly appointed staff; (iii) Staff not well versed with the required quality standards; and (iv) Lack of experience with project environment. The system provides estimated requirement details for staff training at the beginning of a software development project.