Abstract
This paper discusses the improvement of product management activities in a fast-growing marketing automation software company. Recently, the company experienced an expansion of its international activities, a fast-growing turnover, and a significant enlargement of the software team. The new needs coming from this situation led the company to move the product development team from a traditional functional organization to a holocratic organization, where self-organized teams are entirely responsible for their product development activities. To design effective management practices and tools for this new organizational model, the company decided to analyze its business processes in-depth. At the first stage, high-level process mapping techniques were employed to comprehensively view the product management activities, their embedding organizational structure, and context. Then, detailed process mapping techniques were employed to build up a detailed visualization of the process workflows. Then, it was possible to conduct a root-cause analysis aiming at identifying the opportunities for improvement. These opportunities were then assessed and prioritized based on their expected impact upon business performance, namely productivity and time-to-market. The actions selected for implementation addressed three main issues: standardization of the planning and monitoring procedures; teams misalignments. Through a systematic process analysis and redesign, it was possible to set up a new set of management tools that cope with these issues, reinforce the workforce commitment and involvement, and ultimately improve business performance. © IEOM Society International.

Abstract
Design thinking refers to a creative approach to deal with complex problems in design contexts. Originally harnessed by designers, it is today within everyone's reach, to be learned and employed in their practices. To make it accessible and tangible to those who are not designers, a number of tools began to be developed and systematized. Among those, we highlight the group of card-based tools, which enable individuals to develop their creativity and to generate innovative design concepts. In order to explore these tools and to provide a scenario for creative ideas, we developed a card-based game-Cards GO-and conducted a workshop experiment to evaluate the applicability of the game to conceive other game concepts. We assessed the results of the workshop from three points of view: (1) that of the researchers, through direct observation; (2) that of the participants, by means of a questionnaire about their intrinsic motivation; (3) that of three experts in game design. Overall, Cards GO presented itself as a valuable tool for game design, creative thinking and collaboration. However, it was observed that the developed game concepts needed to be better detailed.

Abstract
The optimisation of forest fuels supply chain involves several entities actors, and particularities. To successfully manage these supply chains, efficient tools must be devised with the ability to deal with stakeholders dynamic interactions and to optimize the supply chain performance as a whole while being stable and robust, even in the presence of uncertainties. This work proposes a framework to coordinate different planning levels and event-based models to manage the forest-based supply chain. In particular, with the new methodology, the resilience and flexibility of the biomass supply chain is increased through a closed-loop system based on the system forecasts provided by a discrete-event model. The developed event-based predictive model will be described in detail, explaining its link with the remaining elements. The implemented models and their links within the proposed framework are presented in a case study in Finland and results are shown to illustrate the advantage of the proposed architecture.

Abstract
In the present-days complex networks modeled on real-world data contain millions of nodes and billions of links. Identifying super spreaders in such an extensive network is a challenging task. Super spreaders are the most important or influential nodes in the network that play the central role during an infection spreading or information diffusion process. Depending on the application, either the most influential node needs to be identified, or a set of initial seed nodes are identified that can maximize the collective influence or the total spread in the network. Many centrality measures have been proposed to rank nodes in a complex network such as 'degree', 'closeness', 'betweenness', 'coreness' or 'k-shell' centrality, among others. All have some kind of inherent limitations. Mixed degree decomposition or m-shell is an improvement over k-shell that yields better ranking. Many researchers have employed single node identification heuristics to select multiple seed nodes by considering top-k nodes from the ranked list. This approach does not results in the optimal seed nodeset due to the considerable overlap in total spreading influence. Influence overlap occurs when multiple nodes from the seed nodeset influence a specific node, and it is counted multiple times during total collective influence computation. In this paper, we exploit the 'node degree', 'closeness' and 'coreness' among the nodes and propose novel heuristic template to rank the super spreaders in a network. We employ k-shell and m-shell as a coreness measure in two variants for a comparative evaluation. We use a geodesic-based constraint (enforcing a minimum distance between seed nodes) to select an initial seed nodeset from that ranked nodes for influence maximization instead of selecting the top-k nodes naively. All models and metrics are updated to avoid overlapping influence during total spread computation. Experimental simulation with the SIR (Susceptible-Infectious-Recovered) spreading model and an evaluation with performance metrics like spreadability, monotonicity of ranking, Kendall's rank correlation on some benchmark real-world networks establish the superiority of the proposed methods and the improved seed node selection technique.

Abstract
E-auditing has been evolving along with the global phenomenon of digitization. Auditors are dealing with new technological challenges and there is the need for more sophisticated tools to support their activities. However, the digital processes used for identifying and validating inconsistencies in the organizations’ financial information are not very efficient. Due to the high number of violations occurrences of tax e-auditing rules, in which many of them turn out to be irrelevant; the auditors’ work is often hindered, which may lead to incomplete data analysis. In this paper, we propose an approach to the e-auditing process based on the SAF-T (PT) files semantics enrichment using a graph-based data structure representation format. Using a graph-based data representation, we can take advantage of another way to perform queries and discovery mechanisms to retrieve information and knowledge, easing the auditing process and consequently enhancing the outcome of the tax e-auditing rules application. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
This paper proposes a scheme to determine the optical dispersion properties of a medium using multiple localized surface plasmon resonances (SPR) in a D-shaped photonic crystal fiber (PCF) whose flat surface is covered by three adjacent gold layers of different thicknesses. Using computational simulations, we show how to customize plasmon resonances at different wavelengths, thus allowing for obtaining the second-order dispersion. The central aspect of this sensing configuration is to balance miniaturization with low coupling between the different localized plasmon modes in adjacent metallic nanostructures. The determination of the optical dispersion over a large spectral range provides information on the concentration of different constituents of a medium, which is of paramount importance when monitoring media with time-varying concentrations, such as fluidic media.