TY - JOUR
T1 - From features to benefits
T2 - a data-driven approach for the economic design of adaptive quality control
AU - Xu, Zhaoguang
AU - Minner, Stefan
N1 - Publisher Copyright:
© 2024 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2024
Y1 - 2024
N2 - The current economic design of control charts assumes specific quality distributions, limits parameter choices, and over-relies on historical samples, hindering companies from determining the most economical parameters accurately. Leveraging industrial big data, we propose a data-driven, mixed-integer linear programming model for the economic design of adaptive control charts. Control limits are dynamically designed as a function of features to minimise quality costs. Considering the trade-off between false alarms and penalty costs, we develop three models: a basic model incorporating big data, a model with cost-penalised features, and a model that uses regularisation to manage overfitting. We simulate the model using new performance measures. Our findings demonstrate the economic value of adaptive control limits strategies incorporating feature data compared to benchmarks. We expanded the model to an endogenous sample size and sampling interval framework, further demonstrating the superiority of our approach. We undertook a case study using real-world data from a casting company and revealed that employing our approach culminates in a 24.6% reduction in costs relative to the company's existing quality control protocols. Our approach enables manufacturers to make strategic decisions about quality control by operationalising big data, thereby proving advantageous in reducing quality costs.
AB - The current economic design of control charts assumes specific quality distributions, limits parameter choices, and over-relies on historical samples, hindering companies from determining the most economical parameters accurately. Leveraging industrial big data, we propose a data-driven, mixed-integer linear programming model for the economic design of adaptive control charts. Control limits are dynamically designed as a function of features to minimise quality costs. Considering the trade-off between false alarms and penalty costs, we develop three models: a basic model incorporating big data, a model with cost-penalised features, and a model that uses regularisation to manage overfitting. We simulate the model using new performance measures. Our findings demonstrate the economic value of adaptive control limits strategies incorporating feature data compared to benchmarks. We expanded the model to an endogenous sample size and sampling interval framework, further demonstrating the superiority of our approach. We undertook a case study using real-world data from a casting company and revealed that employing our approach culminates in a 24.6% reduction in costs relative to the company's existing quality control protocols. Our approach enables manufacturers to make strategic decisions about quality control by operationalising big data, thereby proving advantageous in reducing quality costs.
KW - data-driven optimisation
KW - economic design of control charts
KW - industrial big data
KW - machine learning
KW - Quality control
UR - http://www.scopus.com/inward/record.url?scp=85212229895&partnerID=8YFLogxK
U2 - 10.1080/00207543.2024.2441449
DO - 10.1080/00207543.2024.2441449
M3 - Article
AN - SCOPUS:85212229895
SN - 0020-7543
JO - International Journal of Production Research
JF - International Journal of Production Research
ER -