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The paper is aimed at the development of novel model reduction techniques for nonlinear systems.

The analysis is based on the bilinear and polynomial representation of nonlinear systems and the exact solution of the bilinear system in terms of Volterra series. Two sets of Krylov subspaces are identified which capture the most essential part of the input‐output behaviour of the system.

The paper proposes two novel model‐reduction strategies for nonlinear systems. The first involves the development, in a novel manner compared with previous approaches, of a reduced‐order model from a bilinear representation of the system, while the second involves reducing a polynomial approximation using Krylov subspaces derived from a related bilinear representation. Both techniques are shown to be effective through the evidence of a standard test example.

The proposed methodology is applicable to so‐called weakly nonlinear systems, where both the bilinear and polynomial representations are valid.

The suggested methods lead to an improvement in the accuracy of nonlinear model reduction, which is of paramount importance for the efficient simulation of state‐of‐the‐art dynamical systems arising in all aspects of engineering.

The proposed novel approaches for model reduction are particularly beneficial for the design of controllers for nonlinear systems and for the design and analysis of radio‐frequency integrated circuits.

This paper presents the application in circuit simulation of a method for model reduction of nonlinear systems that has recently been developed for chemical systems. The technique is an extension of the well‐known balanced truncation method that has been applied extensively in the reduction of linear systems. The technique involves the formation of controllability and observability gramians either by simulated results or by measurement data. The empirical gramians are subsequently employed to determine a subspace of the full state‐space that contains the most significant dynamics of the system. A Galerkin projection is used to project the system onto the subspace to form a lower‐dimensional nonlinear model. The method is applied to a nonlinear resistor network which is a standard example for exemplifying the effectiveness of a nonlinear reduction strategy.

Nonlinear dynamical systems may, under certain conditions, be represented by a bilinear system. The paper is concerned with the construction of the controllability and observability gramians for the corresponding bilinear system. Such gramians form the core of model reduction schemes involving balancing.

The paper examines certain properties of the bilinear system and identifies parameters that capture important information relating to the behaviour of the system.

Novel approaches for the determination of approximate constant gramians for use in balancing‐type model reduction techniques are presented. Numerical examples are given which indicate the efficacy of the proposed formulations.

The systems under consideration are restricted to the so‐called weakly nonlinear systems, i.e. those without strong nonlinearities where the essential type of behaviour of the system is determined by its linear part.

The suggested methods lead to an improvement in the accuracy of model reduction. Model reduction is a vital aspect of modern system simulation.

The proposed novel approaches for model reduction are particularly beneficial for the design of controllers for nonlinear systems and for the design of radio‐frequency integrated circuits.

This paper adopts a multi-tier perspective and aims to explore challenges of small and medium-sized enterprises (SMEs) in collaborative manufacturing amid the emergence of dedicated B2B platforms. Original equipment manufacturers welcome formation of demand-driven collaborations between SME suppliers to facilitate ramp-up of production capacity. While being potentially beneficial to suppliers, such collaborations face various barriers.

An exploratory study of 17 suppliers within the European Union’s aerospace industry was undertaken. The study comprised two stages. In the first stage, suppliers’ answers to self-administered interviews were analysed using thematic analysis. In the second stage, interactions between the barriers were determined through interviews with experienced SME collaboration facilitators. The authors apply system dynamics modelling to analyse the links between barriers and identify re-enforcing and balancing loops of other factors.

The authors establish five major groups of barriers to collaboration impeding: market transparency, access to orders, partner trust, contracting and (e) data sharing and coordination. The authors model application of four enablers that facilitate barrier removal for technology-enabled supply chains: digital platforms, supplier development, smart contracts and Industry 4.0.

The study is limited by the data collection from the aerospace industry; validation of the models in other low-volume high-variability manufacturing sectors is needed.

The reader will learn about the barriers which impede demand-driven SME collaboration within manufacturing supply chains, interrelationships between these barriers and suggestions about how to remove them. SME cluster managers will find managerial implications particularly interesting as they will help them to overcome collaboration concerns and better prepare cluster members for Industry 4.0.

The models developed within this study can be used to explore the effects of intervening at critical points in the model to create virtuous improvement cycles between key barriers and related variables in the model. This can help decision-making and policymaking in the area of supply chain integration.

There is currently a lack of studies about how the existing barriers amplify and de-amplify themselves and what the managerial approaches to tackle the barriers are. It is unclear how far companies will go in terms of information sharing, given the trust levels, power dynamics and governance structures evident in supply chains. This study contributes by explaining the reinforcing interaction between the barriers and showing ways to overcome these using enablers.

What is it about academia anyway? We profess to hate it, spend endless amounts of time complaining about it, and yet we in academia will do practically anything to stay. The pay may be low, job security elusive, and in the end, it's not the glamorous work we envisioned it would be. Yet, it still holds fascination and interest for us. This is an article about American academic fiction. By academic fiction, I mean novels whosemain characters are professors, college students, and those individuals associated with academia. These works reveal many truths about the higher education experience not readily available elsewhere. We learn about ourselves and the university community in which we work.