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Prediction Control Charts: A New and Flexible Artificial Intelligence-Based Statistical Process Control Approach

Author

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  • Laion L. Boaventura

    (Federal University of Bahia)

  • Rosemeire L. Fiaccone

    (Federal University of Bahia)

  • Paulo H. Ferreira

    (Federal University of Bahia)

Abstract

Statistical techniques allow assertive and controlled studies of projects, processes and products, aiding in management decision-making. Statistical Process Control (SPC) is one of the most important and powerful statistical tools for measuring, monitoring and improving the quality of processes and products. Adopting Artificial Intelligence (AI) has recently gained increasing attention in the SPC literature. This paper presents a combined use of SPC and AI techniques, which results in a novel and efficient process monitoring tool. The proposed prediction control chart, which we call pred-chart, may be regarded as a more robust and flexible alternative (given that it adopts the median behavior of the process) to traditional SPC tools. Besides its ability to recognize patterns and diagnose anomalies in the data, regardless of the sample scenario, this innovative approach is capable of performing its monitoring functions also on a large scale, predicting market scenarios and processes on massive amounts of data. The performance of the pred-chart is evaluated by the average run length (ARL) computed through Monte Carlo simulation studies. Two real data sets (small and medium sets) are also used to illustrate the applicability and usefulness of the proposed control chart for prediction of continuous outcomes.

Suggested Citation

  • Laion L. Boaventura & Rosemeire L. Fiaccone & Paulo H. Ferreira, 2024. "Prediction Control Charts: A New and Flexible Artificial Intelligence-Based Statistical Process Control Approach," Annals of Data Science, Springer, vol. 11(1), pages 273-306, February.
    • Handle: RePEc:spr:aodasc:v:11:y:2024:i:1:d:10.1007_s40745-022-00441-5
    DOI: 10.1007/s40745-022-00441-5
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    References listed on IDEAS

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    1. Dan Zhou & Liu Liu & Xin Lai, 2018. "The Improved EWMA Chart for Heteroscedasticity Process," Annals of Data Science, Springer, vol. 5(1), pages 21-27, March.
    2. Furrer, Reinhard & Sain, Stephan R., 2010. "spam: A Sparse Matrix R Package with Emphasis on MCMC Methods for Gaussian Markov Random Fields," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i10).
    3. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
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    1. Pedro Luiz Ramos & Ana Paula Silva Figueiredo & Diego Carvalho do Nascimento & Fernando Moala & Edilson Flores, 2025. "Beyond Regular SPC: Bridging the $$C_{pk}$$ C pk Capability Index for (a)Symmetric Data," Annals of Data Science, Springer, vol. 12(5), pages 1607-1633, October.

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