Cognitive assistance systems help people with learning disabilities to increase their skills and consequently their employment opportunities in the regular labour market. Research on advanced work instructions has encouraged training disabled workers in cognitively demanding production tasks, especially manual assembly. However, studies lack evidence on the effect of repetition or work cycle alongside the form of instruction and type of disability. This paper addresses this gap and reports on an experiment conducted at a sheltered workplace. Four forms of instruction (paper-based, animations, projection, adaptive projection) were tested to assist operators with three types of disability (illiterate, psychosocial, cognitive) with a manual assembly task. The results show that projection enhances the first assembly cycle. Challenging operators by filtering the content of the instruction with increased experience leads to greater independence and a better understanding of their tasks. However, adaptive instructions can form a barrier for those operators who are most dependent on mentor support. The form of instruction should thus be considered carefully for each operator as their adaptation to changes and cognitive assistance systems varies. The results are discussed in light of the Industry 5.0 human-centric and socially sustainable production agenda with managerial and research implications and future research priorities.

Industry 4.0 provides a tremendous potential of data from the work floor. For manufacturing companies, these data can be very useful in order to support assembly operators. In literature, a lot of contributions can be found that present models to describe both the learning and forgetting effect of manual assembly operations. In this study, different existing models were compared in order to predict the cycle time after a break. As these models are not created for a real time prediction purpose, some adaptations are presented in order to improve the robustness and efficiency of the models. Results show that the MLFCM (modified learn-forget curve model) and the PID (power integration diffusion) model have the greatest potential. Further research will be performed to test both models and implement contextual factors. In addition, since these models only consider one fixed repetitive task, they don’t target mixed-model assembly operations. The learning and forgetting effect that executing each assembly task has on the other task executions differs based on the job similarity between tasks. Further research opportunities to implement this job similarity are listed.

As assembly systems move into the era of mass customisation, the complexity of design processes, (re)configurations and operations rises. Well-structured data are key in keeping this complexity manageable. Here to, this paper presents a multidimensional formal skill model designed to deliver generic descriptions of needs and capacities with skills as the connector between products, processes and resources. The model formalises resource structures in relation to the processes they master and products they can produce. This paper discusses the case-based evaluation in a reconfigurable assembly system and highlights the added-value of a skill-based modelling approach. The presented formal model combines concepts coming from both offline and online modelling perspectives and allows for various applications and levels of detail. The resource structures embedded in the prerequisites of a skill enable matchmaking of resources for workspace design and reconfigurations. The mapping of the model to standardised ISA-95 models couples production needs to the resources allowing for more optimal production planning, control and a structured interface between enterprise and control systems. The possibility to couple states to the assembly environment allows for optimal runtime orchestration.