Implementation, inclusion strategies

The development and implementation of diversity and inclusion strategies are treated as multiyear change efforts. These efforts are guided by the change management principles we have used in other major organizational change processes. 

Once the vision and strategic direction are agreed, the team-building can begin. Clearly, the team will play a significant part in the development and implementation of the inclusion strategy and policy. However, it needs to have clear strategic direction from the top in order to develop and implement effective practice. 

At the start of implementing our change strategy, our senior leaders underwent a self-assessment of their individual effectiveness as leaders in two areas personal behavior and results in diversity and inclusion. Each leader developed a personal action plan to close any m or gaps identified. Performance improvement plans linking a portion of pay to diversity outcomes were implemented and tracked. 

Redundancy is a term often confused with resilience. In effect, redundancy is a strategy one may choose to implement in pursuit of a resilient system. It can be defined as the Provision of multiple components or mechanisms to achieve the same function such that failure of one or more of the components or mechanisms does not prevent the performance of the function [3]. In other words redundancy represents the inclusion of additional hardware or functionality which might not be critically required during normal operation but is seamlessly available should it be called upon in situations of failure. For example, one might choose to operate a system with not one but two databases hosted in different geographical locations and synchronised in real time. Should one fail the system can be designed to automatically revert to the alternative without any obvious impact on the user. 

Only active variables need be considered for optimization. Inclusion of parameters with noise-level sensitivity into optimization only worsens the character of the objective function—necessarily increasing the dimensionality of the valley structure. If these parameters are to be the subject of optimization, then conditions have to be found where they become active. One does not have to search for a single set of conditions in which all the parameters of interest are active, which may be impossible to find. Instead, a more practical strategy is to perform experiments where different subsets of parameters are active and combine the results into a joint optimization. The SM methodology provides a convenient basis for implementing such a strategy. 

The failure to properly reproduce the particle-particle coalescence asymptotics bears upon the rates of convergence of the computed energies and other observables to their complete-basis-set (CBS) limits [1]. Whereas in practice this convergence is sufficiently rapid for the solutions of the Hartree-Fock equations [2, 3], obtaining accurate approximations to correlated electronic wave-functions is much more difficult [4, 5]. In order to alleviate this problem, two distinct strategies have been developed, namely inclusion of a correlation factor in the trial function [6-8] and extrapolation to the CBS limit [9, 10]. Successful implementations of the latter approach hinge upon understanding how the approximate wavefunction approaches its exact counterpart as the size of the basis set increases. 

Type C-standards apply to special types of machines, e.g. cranes, conveyor belts. These standards are all-inclusive and ensure that a piece of machinery that meets the standard also meets type A and B standards and thus the Machinery Directives. For example, the strategy for implementation of safety measures has to be implemented in type C-standards. 

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