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This paper examines the strategic importance of fostering a multigenerational workforce to enhance the competitiveness of Singapore’s manufacturing industry in the face of the Fourth Industrial Revolution. Addressing common myths about age and technological adoption, our mixed-methods study reveals that the integration of the diverse strengths of an age-diverse workforce is not only beneficial but also necessary for innovation and sustainable growth. With Singapore’s manufacturing workforce rapidly ageing and fresh talent dwindling, the industry faces challenges like knowledge transfer, automation, and the risk of skills dilution. We argue that a multigenerational approach that combines the expertise of older workers with the digital fluency of the younger generation can create a resilient talent pool essential for continuous expertise and complex problem-solving. The aim of this paper is thus to debunk several myths around an ageing workforce that currently stand in the way of fostering a multigenerational workforce in Singapore’s manufacturing industry. We end the paper by arguing that such a workforce will become increasingly important for other industries as well.

This study investigates the use of linear-regression-based identification in rigid multibody system applications. A multibody system model, originally described with differential-algebraic equations, is transformed into a set of ordinary differential equations using coordinate partitioning. This allows the identification framework (where the system is described with ordinary differential equations) to be applied to rigid multibody systems described with non-minimal coordinates. The methodology is demonstrated via numerical and experimental validation on a slider-crank mechanism. The results show that the presented methodology is capable of accurately identifying the system's inertial parameters even with a short motion trajectory used for training. The presented linear-regression-based identification approach opens new opportunities to develop more accurate multibody models. The resulting updated multibody models can be considered especially useful for state-estimation and the control of multibody systems.