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

Abstract
Machine Learning problems are significantly growing in complexity, either due to an increase in the volume of data, to new forms of data, or due to the change of data over time. This poses new challenges that are both technical and scientific. In this paper we propose a Distributed Learning System that runs on top of a Hadoop cluster, leveraging its native functionalities. It is guided by the principle of data locality. Data are distributed across the cluster, so models are also distributed and trained in parallel. Models are thus seen as Ensembles of base models, and predictions are made by combining the predictions of the base models. Moreover, models are replicated and distributed across the cluster, so that multiple nodes can answer requests. This results in a system that is both resilient and with high availability.

Abstract
This paper introduces RVAF, a runtime verification (RV) extension of the Arrowhead Framework (AF) with container-based service-deployment and runtime-enforcement of a desired quality of service (QoS). AF is a service-oriented middleware architecture for IoT-applications, consisting of a set of core and auxiliary services and systems, respectively. The QoS manager (QoSM) is one AF's most important auxiliary systems, which can be used to guarantee the application's QoS for a wide set of parameters. In RVAF the QoS offered to a particular IoT-application is specified in signal temporal logic, and is continuously monitored by the RVAF-QoSM. In case of an imminent violation, RVAF automatically initiates a container-based reconfiguration, which is ensured to maintain the desired QoS. RVAF is beneficial to large IoT-applications, where the use of continuous-integration and continuous-deployment tools, is not only a recommended practice but also a necessity. Moreover, the use of RVAF is advantageous both during the development of an IoT application, and after its deployment. We describe the architecture of RVAF, provide its formal underpinning, and demonstrate the usefulness of RVAF supported by an industrial IoT application. The main contribution of this work is to show what it takes to incorporate RV concepts into modern SOA frameworks supporting the development of IoT applications.