High Performance Organization (HPO) characteristics indicate why an organization is able to achieve significantly better results than other organizations and these characteristics can facilitate associations to optimize employees’ work outcomes. The independent professional (IP) is an increasingly occurring phenomenon in the labor market that fulfils an organizations’ need for flexibility in knowledge productivity. This study focuses on the contribution of HPO characteristics to the knowledge productivity of IP's. It was conducted among managers and HRM professionals in various Dutch knowledge-intensive organizations that frequently enlist the services of IPs. This study found a number of HPO attributes that appeared to contribute to the IPs' knowledge productivity, namely the quality of management, an open and actionfocused organizational culture, and continual improvement and innovation. We will use these results to look ahead and consider the future consequences for professional practice. Managers and HRM professionals should strive to contribute to the incorporation of these characteristics within the organization in order to safeguard and enhance knowledge productivity of independent professionals.
Estimating the remaining useful life (RUL) of an asset lies at the heart of prognostics and health management (PHM) of many operations-critical industries such as aviation. Mod- ern methods of RUL estimation adopt techniques from deep learning (DL). However, most of these contemporary tech- niques deliver only single-point estimates for the RUL without reporting on the confidence of the prediction. This practice usually provides overly confident predictions that can have severe consequences in operational disruptions or even safety. To address this issue, we propose a technique for uncertainty quantification (UQ) based on Bayesian deep learning (BDL). The hyperparameters of the framework are tuned using a novel bi-objective Bayesian optimization method with objectives the predictive performance and predictive uncertainty. The method also integrates the data pre-processing steps into the hyperparameter optimization (HPO) stage, models the RUL as a Weibull distribution, and returns the survival curves of the monitored assets to allow informed decision-making. We vali- date this method on the widely used C-MAPSS dataset against a single-objective HPO baseline that aggregates the two ob- jectives through the harmonic mean (HM). We demonstrate the existence of trade-offs between the predictive performance and the predictive uncertainty and observe that the bi-objective HPO returns a larger number of hyperparameter configurations compared to the single-objective baseline. Furthermore, we see that with the proposed approach, it is possible to configure models for RUL estimation that exhibit better or comparable performance to the single-objective baseline when validated on the test sets.
KLM Royal Dutch Airlines has been a forerunner of the airline industry since 1919. As the oldest operating airline to date, the company aims to become innovators of today. This paper proposes an addition to the KLM transformation projects: Moving Your World, The Digital Transformation, and The KLM Real Estate Vision. This addition is a concept for ‘The Winning Way of Working,’ which aims to create a holistic workplace design; one where KLM employees are able to experience flexible and customizable environments, disconnection between colleagues and locations is reduced, and health benefits of vegetation in work environments are promoted.
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