Facing the multi-stage assembly and inspection problems in automobile production, the actual production and management process often involves multiple factors such as unavailable quality information, machine measurement errors, cycle time correlation between stations, and limited rework capabilities. This leads to typical decision-making characteristics at the workshop level: "experience-triggered thresholds, overly cautious repetitive inspections, and a bias towards short-term output." This paper uses incomplete information as the main constraint and defines a partially observable process decision-making Markov process (POMDP) model, integrating latent quality variables, observed signals, and executed operations within the same model. Furthermore, risk quantification and robustness constraints considering rework and pass/fail indicators are introduced to construct an achievable and executable "confidence state-plan-feedback" loop system. The paper also presents a hybrid computational method of "offline approximation iteration and online rolling optimization" to address the combinatorial explosion of state space and the requirements of real-time computation, and proposes an analytically sound threshold decision extraction scheme. In simulation experiments of typical automotive body component welding operation chains, compared to responsive processing methods and fixed periodic inspection methods, the proposed method demonstrates lower expected cumulative costs and more reliable quality risk control at different detection error levels. The results can be used to guide automotive production lines in understanding and supporting autonomous process decisions under incomplete information during their transformation and upgrading towards digital and intelligent factories.

Xiao Han. Research on Process Decision-Making Behavior under Incomplete Information Conditions in Automobile Manufacturing Systems. International Journal of Multimedia Computing (2026), Vol. 7, Issue 1: 33-41. https://doi.org/10.38007/IJMC.2026.070105.

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