Understanding human decision-making during production ramp-up using natural language processing

Ramping up a manufacturing system from being just assembled to full-volume production capacity is a time consuming and error-prone task. The full behaviour of a system is difficult to predict in advance and disruptions that need to be resolved until the required performance targets are reached occur often. Information about the experienced faults and issues might be recorded, but usually, no record of decisions concerning necessary physical and process adjustments are kept. Having these data could help to uncover significant insights into the ramp-up process that could reduce the effort needed to bring the system to its mandatory state. This paper proposes Natural Language Processing (NLP) to interpret human operator comments collected during ramp-up. Recurring patterns in their feedback could be used to gain a deeper understanding of the cause and effect relationship between the system state and the corrective action that an operator applied. A manual dispensing experiment was conducted where human assessments in form of unstructured free-form text were gathered. These data have been used as an input for initial NLP analysis and preliminary results using the NLTK library are presented. Outcomes show first insights into the topics participants considered and lead to valuable knowledge to learn from this experience for the future

A Concept For Data-Driven Decision-Making During The Production Ramp-Up To Increase Resilience In Value Networks

Manufacturing companies are challenged by an increased number of production ramp-ups in shorter intervals due to shorter product life cycles and dynamically changing customer demands. The complexity of new products and the corresponding production systems, particularly in the value network, adds to this challenge. This complexity leads to an increased number of disruptions during the production ramp-up, to which manufacturing companies must be able to adapt flexibly. Although methodological support is available, there is a lack of data-driven approaches for adapting and reacting to potential disruptions during the production ramp-up in the value network. Therefore, this article presents a concept for a data-driven approach in value networks during production ramp-ups to flexibly adapt to disruptions. First, a method for describing and categorizing disruptions and corresponding mitigation decisions is developed based on the ISO/IEC 20000 1. Second, an application method for a generic simulation model is created to generate synthetic disruption production ramp-up data for a given value network configuration and corresponding disruptions. Third and parallel, a method for assessing a manufacturing company's resilience in the value network during the production ramp-up is developed. In the final step, the synthetic data is used to train a data-driven model. This model selects appropriate mitigation decisions for disruptions based on data and can evaluate the impact of disruptions and mitigation decisions on key performance indicators specific to production ramp-up. The possible increase in company-specific resilience is assessed using the developed assessment method