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11 28 6 1 ; SWORD ; [EN] We present a literature review about organizational resilience, with the goal of identifying how organizational resilience is conceptualized and assessed. The two research questions that drive the review are: (1) how is organizational resilience conceptualized? and (2) how is organizational resilience assessed? We answer the first question by analysing organizational resilience definitions and the attributes or characteristics that contribute to develop resilient organizations. We answer the second question by reviewing articles that focus on tools or methods to measure organizational resilience. Although there are three different ways to define organizational resilience, we found common ideas in the definitions. We also found that organizational resilience is considered a property, ability or capability that can be improved over time. However, we did not find consensus about the elements that contribute to improving the level of organizational resilience and how to assess it. Based on the results of the review, we propose a conceptualization of organizational resilience that integrates the three views found in the literature. We also propose a four-level Maturity Model for Organizational Resilience – MMOR. Using this model, the organization can be in one of the following levels based on its ability and capacity to handle disruptive events: fragile, robust, resilient or antifragile. This research is partially supported by University of Valladolid, Banco Santander and NSERC Ruiz-Martin, C.; López-Paredes, A.; Wainer, G. (2018). What we know and do not know about organizational resilience. International Journal of Production Management and Engineering. 6(1):11-28. doi:10.4995/ijpme.2018.7898

27th IPMA World Congress ; For years, many research studies have focused on programming projects, assuming a deterministic environment and complete task information. However, during the project performance, schedule may be subject to uncertainty which can lead to significant modifications. This fact has led to an increasing scientific literature in the field. In this article we consider the presence of an uncertainty of seasonal type (e.g. meteorological) that affects some of the activities that comprise the project. We discuss how the project risk can be affected by such uncertainty, depending on the start date of the project. By means of Monte Carlo simulation, we compute the statistical distribution functions of project duration at the end of the project. Then, we represent the variability of the project through the so-called Project Risk Baseline. In addition, we examine various sensitivity metrics - Criticality, Cruciality, Schedule Sensitivity Index -. We use them to prioritize each one of the activities of the project depending on its start date. In the last part of the study we demonstrate the relative importance of project tasks must consider a combined version of these three sensitivity measures. ; the project SPPORT: "Computational Models for Strategic Project Portfolio Management", supported by the Regional Government of Castile and Leon (Spain) with grant VA056A12-2

26th IPMA World Congress. 2012, Crete, Greece, ; In this paper, we propose an innovative and simple graphical framework for project control and monitoring, to integrate the dimensions of project cost and schedule with risk management, therefore extending the Earned Value methodology (EVM). EVM allows Project managers to know whether the project has overruns (over-costs and/or delays), but project managers do not know when deviations from planned values are so important that corrective actions should be taken or, in case of good performance, sources of improvement can be detected. From the concept of project planned variability, we build a graphical methodology to know when a project remains "out of control" or "within expected variability" during the project lifecycle. To this aim, we define and represent new control indexes and new cumulative buffers. Five areas in the chart represent five different possible project states. To implement this framework, project managers only need the data provided by EVM traditional analysis and Monte-Carlo simulation. We also explore the sensitivity of the methodology to control variables. ; Project "Computational Models for Strategic Project Portfolio Management", supported by the Regional Government of Castile and Leon (Spain) with grant VA056A12-2.