Abstract
The advance of new generation of IT and sensor technologies results in data enriched production environment. However, there is a lack of an effective utilization of the data to improve productivity while reducing quality management cost. Therefore, this paper proposes a systematic method to analyze the production dynamics, and presents an event-based method to quantitatively evaluate the impact of various disruptions on system throughput, including machine breakdown and quality failure. It is proved that the impact of the events can be measured with system loss which is the summation of the production loss of the slowest machine and the overall number of defective parts produced in the subsystem where the slowest machine locates in. The data-driven method is integrated into an optimization method to exploit the optimal quality inspection allocations. In the method, a non-linear optimization problem is formulated and solved with an adaptive genetic algorithm to trade off the penalty cost of production loss and the investment cost of quality inspection. The research results in a comprehensive understanding of production dynamics subject to quality inspection and rework. It is of critical importance to boost productivity with better quality inspection allocations. Simulation studies are performed to validate the proposed methods.
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09 September 2021
A Correction to this paper has been published: https://doi.org/10.1007/s10845-021-01833-9
Abbreviations
- \(L_{h}\) :
-
The \(h{\text{th}}\) subsystem in the production system, \(1 \le h \le N\)
- \(n_{h} \) :
-
The number of machines in the \(h{\text{th}}\) subsystem, \(1 \le h \le N\)
- \(m_{j}^{h} \) :
-
The \(j{\text{th}}\) machine in the \(h{\text{th}}\) subsystem, \(1 \le h \le N, 1 \le j \le n_{h}\)
- \(v_{j}^{h} \) :
-
The rated speed of machine \(m_{j}^{h}\)
- \(v_{j}^{h} \left( t \right)\) :
-
The instantaneous speed of machine \(m_{j}^{h}\) at time \(t\)
- \({\Lambda }_{j}^{h} \left( t \right) \) :
-
The state of machine \(m_{j}^{h}\) at time \(t\)
- \(X_{j}^{h} \left( t \right)\) :
-
The accumulated production count of machine \(m_{j}^{h}\) during \(\left( {0,t} \right]\)
- \(B_{i}^{h}\) :
-
The ith buffer in the hth subsystem, \(1 \le h \le N, 0 \le i \le n_{h}\)
- \(\psi_{i}^{h} \) :
-
The capacity of buffer \(B_{i}^{h}\)
- \(\omega_{i}^{h} \left( t \right)\) :
-
The buffer level of buffer \(B_{i}^{h}\). It is the number of parts in buffer \(B_{i}^{h}\) at time \(t\)
- \(\vec{\vartheta }_{k} = \left( {m_{j}^{h} ,t_{k} ,d_{k} } \right)\) :
-
The kth machine breakdown event
- \(\vec{\Theta } = \left[ {\vec{\vartheta }_{1} , \ldots ,\vec{\vartheta }_{K} } \right] \) :
-
A sequence of breakdown events, \( 0 < t_{1} \le t_{2} \le \ldots \le t_{K}\)
- \(\vec{\varsigma }_{r} = \left( {L_{h} ,t_{r} ,d_{r} } \right)\) :
-
The rth quality failure event
- \(\vec{\sigma } = \left[ { \vec{\varsigma }_{1} , \ldots , \vec{\varsigma }_{R} } \right] \) :
-
A sequence of quality failure events, \( 0 < t_{1} \le t_{2} \le \ldots \le t_{R}\)
- \(\vec{\sigma }^{{h^{*} }} = \left[ {\vec{\varsigma }_{1}^{{h^{*} }} , \ldots , \vec{\varsigma }_{w}^{{h^{*} }} } \right] \) :
-
A set of quality failure events that happen at subsystem \({ }L_{{h^{*} }}\), \(1 \le w \le R\)
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Acknowledgements
The authors are grateful to the editors and anonymous reviewers for their valuable comments, which have significantly improved the quality of this work.
Funding
This work was supported by the National Key R&D Program of China (No. 2019YFB1703800); the National Natural Science Foundation of China (Nos. 52075453 and 71931007); and the Aeronautical Science Foundation of China (No. 2019ZG053001).
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Wang, JQ., Song, YL., Cui, PH. et al. A data-driven method for performance analysis and improvement in production systems with quality inspection. J Intell Manuf 34, 455–469 (2023). https://doi.org/10.1007/s10845-021-01780-5
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DOI: https://doi.org/10.1007/s10845-021-01780-5