In a demand context of mass customization, shifting towards the mass personalization of products, assembly operations face the trade-off between highly productive automated systems and flexible manual operators. Novel digital technologies—conceptualized as Industry 4.0—suggest the possibility of simultaneously achieving superior productivity and flexibility. This article aims to address how Industry 4.0 technologies could improve the productivity, flexibility and quality of assembly operations. A systematic literature review was carried out, including 234 peer-reviewed articles from 2010–2020. As a result, the analysis was structured addressing four sets of research questions regarding (1) assembly for mass customization; (2) Industry 4.0 and performance evaluation; (3) Lean production as a starting point for smart factories, and (4) the implications of Industry 4.0 for people in assembly operations. It was found that mass customization brings great complexity that needs to be addressed at different levels from a holistic point of view; that Industry 4.0 offers powerful tools to achieve superior productivity and flexibility in assembly; that Lean is a great starting point for implementing such changes; and that people need to be considered central to Assembly 4.0. Developing methodologies for implementing Industry 4.0 to achieve specific business goals remains an open research topic.

Mass customisation demand requires increasingly flexible assembly operations. For the in-plant logistics of such systems, milkrun trains could present advantages under high variability conditions. This article uses an industrial study case from a global white-goods manufacturing company. A discrete events simulation model was developed to explore the performance of multi-model assembly lines using a set of operational and logistics Key Performance Indicators. Four simulation scenarios analyse the separate effects of an increased number of product models and three different sources of variability. The results show that milkruns can protect the assembly lines from upstream process disturbances.

Manual assembly lines productivity is threatened by the increased complexity brought by mass customisation demand trends. Industry 4.0 offers potential solutions to address this situation, but the methodology to implement it is still a subject of study. As a preliminary step, this article aims to identify the dominant factors affecting the Key Performance Indicators of mixed-model assembly lines. To do so, parametric and discrete-events simulation models were developed, and Design of Experiments techniques were used. The results show that the key drivers for assembly line performance are number of work stations and batch size, and that increasing the work content ratio of the products assembled does not interact negatively with other factors. The results presented here pave the way for developing Industry 4.0 projects that address specifically the most relevant factors that affect assembly lines performance.

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Demand trends towards mass customization drive the need for increasingly productive and flexible assembly operations. Walking-worker assembly lines can present advantages over fixed-worker systems. This article presents a multiproduct parallel walking-worker assembly line with shared automated stations, and evaluates its operational performance compared to semiautomated and manual fixed-worker lines. Simulation models were used to set up increasingly challenging scenarios based on an industrial case study. The results revealed that semiautomated parallel walking-worker lines could achieve greater productivity (+30%) than fixed-worker lines under high-mix low-volume demand conditions.

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In a demand context of mass customisation, Industry 4.0 technologies open new possibilities of increased productivity and flexibility for mixed-model assembly lines. Analytical and parametric analysis is used to better understand the productivity losses of model changeovers as a preliminary step to develop an Assembly 4.0 implementation methodology.