Deutsche Bank (United Kingdom)

It is often argued that efficient collaboration is the key to success. However, research shows that if collaboration is not properly managed, collaborative risks may emerge, threatening business success. Furthermore, research shows that there is a lack of models to support the management of collaborative initiatives in organizations. To address this lack, presented in this work is a model to manage collaborative risks in organizations that work under the open innovation and the hybrid stage-gate development frameworks (two of the most popular collaborative frameworks in product and process development). The model presented in this work is a novel approach to manage collaborative risks in the open innovation and the hybrid stage-gate frameworks, and was developed based on network graph-theory to be used to identify informal collaborative interactions that may lead to the emergence of three major collaborative risks: (1) partner choice risks, (2) task assignment risks, and (3) behavioral risks. The results of the application of the proposed model in a real organizational collaborative context illustrated in the case study show that such collaborative risks can be identified in a timely manner, enabling an organization to efficiently and preventively act to minimize or eliminate the undesired effects of the mentioned collaborative risks.

Increasing disruption and turmoil continuously challenges organizations regarding the achievement of short- and long-term objectives. Such a hostile environment results from both the natural evolution of the business landscape complexity and the emergence of unpredictable disruptive evets such as the COVID-19 pandemic. More than ever, organizations should continuously develop business strategies that help them to become more agile, adaptative, sustainable, and effectively respond to the countless business risks (threats and opportunities). Innovation, such as the development and implementation of new technology, new ways of thinking and executing work, are just some of the major factors that can help organizations to increase their likelihood of success. In this work, is proposed the incorporation of a heuristic risk model into a typical organizational business intelligence architecture, to identify collaborative critical success factors across the different phases of a project life cycle which can be used to guide, monitor, and increase the success outcome likelihood of ongoing and upcoming projects. Some benefits of the incorporation include: a higher speed regarding the collection and treatment process of project collaborative data, the output of more accurate results with residual bias associated, a timely and efficient 360° view regarding the identification of project collaborative risks, and the impact (positive or/and negative) of these on a project’s outputs and outcomes. Finally, the model capabilities of performing descriptive, predictive, and prescriptive analysis, enables the generation of unique and actionable project’s lessons learned which can be used to make more data-informed decisions, and thus enhances the achievement of sustainable competitive advantages. The development and implementation of the proposed incorporation is illustrated with a with a real case study.

In this paper we consider the diffuse interface generalized antiferromagnetic model with local/nonlocal attractive/repulsive terms in competition studied in [9]. The parameters of the model are denoted by \(\tau \) and \(\varepsilon \): the parameter \(\tau \) represents the relative strength of the local term with respect to the nonlocal one, while the parameter \(\varepsilon \) describes the transition scale in the Modica–Mortola type term. Restricting to a periodic box of size L, with L multiple of the period of the minimal one-dimensional minimizers, in [9] the authors prove that in any dimension \(d\ge 1\) and for small but positive \(\tau \) and \(\varepsilon \) (eventually depending on L), the minimizers are non-constant one-dimensional periodic functions. In this paper we prove that periodicity and one-dimensionality of minimizers occurs also in the zero temperature analogue of the thermodynamic limit, namely as \(L\rightarrow +\infty \).

The construction of an importance density for partially non‐Gaussian state space models is crucial when simulation methods are used for likelihood evaluation, signal extraction, and forecasting. The method of efficient importance sampling is successful in this respect, but we show that it can be implemented in a computationally more efficient manner using standard Kalman filter and smoothing methods. Efficient importance sampling is generally applicable for a wide range of models, but it is typically a custom‐built procedure. For the class of partially non‐Gaussian state space models, we present a general method for efficient importance sampling. Our novel method makes the efficient importance sampling methodology more accessible because it does not require the computation of a (possibly) complicated density kernel that needs to be tracked for each time period. The new method is illustrated for a stochastic volatility model with a Student'stdistribution.