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Assessment of Smart Transformation in the Manufacturing Process of Aerospace Components Through a Data-Driven Approach

Margherita Bernabei (), Marco Eugeni, Paolo Gaudenzi and Francesco Costantino
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Margherita Bernabei: Sapienza University of Rome
Marco Eugeni: Sapienza University of Rome
Paolo Gaudenzi: Sapienza University of Rome
Francesco Costantino: Sapienza University of Rome

Global Journal of Flexible Systems Management, 2023, vol. 24, issue 1, No 4, 67-86

Abstract: Abstract Smart technologies provide extensive benefits in manufacturing, but many industries, such as aerospace components, are still lagging in their adoption. In such a sector, data-driven digitization initiatives play a significant role as they allow for flexibility, efficiently considering time and resources. The paper presents a framework to assess the smart level of data-driven processes in the aerospace sector since it is not available in the literature. The framework helps companies to draw a roadmap to achieve greater levels of digitization. The design of the framework follows an inductive rationale. An aerospace case study formalizes evidence and needs, identifying features to design the framework. The assessment object, context, timing, and modality are the main originalities. It is based on 7 assessment steps, differentiated into two levels of detail of the process. Firstly, every process activity is evaluated considering 4.0 properties. Then, the overall process is analyzed. Here, the data-driven approach provides added values in terms of performance and level of interconnection . The framework was tested by point-in-time and continuous timing. It leads to management insights, to align the flexibility required by the company strategy to implement a smart transformation. Moreover, the assessment highlights 10 out of 11 digitally enhanced activities, and the company moved from 5 to 22 monitored performance areas.

Keywords: Aerospace sector; Cyber-physical-system; CPS; Data-driven; Industry 4.0; Production flexibility; Smart assessment (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s40171-022-00328-7

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