Causal Inference to Predict Delayed Arrival of Ordered Production Materials at PT. XYZ

Muhammad Ridzky Hanura(1*), Yusuf Priyandari(2), Muhammad Hisjam(3),

(1) Sebelas Maret University
(2) Sebelas Maret University
(3) Sebelas Maret University
(*) Corresponding Author


PT XYZ has a problem with the delayed arrival of ordered production materials. Although the company is aware of the delays based on data, the company does not yet know the causes or sources of problems that cause delays. On the other hand, not all factors can be controlled to reduce the delay in the arrival of production materials. The company intends to predict the change in delay time if control or intervention is carried out on certain factors by utilising data availability. The factor to be treated is requisition-to-order lead time A causal inference model is used using the Dowhy library (a Python library for causal inference by graphing the model, quantitatively evaluating causal effects, and validating the causal assumptions) to estimate the quantitative causal effect between requisition-to-order lead time and the arrival time of the ordered material by considering other factors that also affect the delay. The results of the causal effect estimation are that by intervening or controlling the requisition-to-order lead time factor by one day, there is a decrease in the average delay in material arrival time by one day


causal effects; causal inference; supply chain management

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Arif, S., & MacNeil, M. A. (2023). Applying the structural causal model framework for observational causal inference in ecology. Ecological Monographs, 93(1). doi:10.1002/ecm.1554

Chen, X., Abualdenien, J., Singh, M. M., Borrmann, A., & Geyer, P. (2022). Introducing causal inference in the energy-efficient building design. Energy and Buildings, 277, 1-14. doi:10.1016/j.enbuild.2022.112583

Colicchia, C., Dallari, F., & Melacini, M. (2010). Increasing supply chain resilience in a global sourcing context. Production Planning and Control, 21(7), 680-694. doi:10.1080/09537280903551969

Dolgui, A., Ben-Ammar, O., Hnainen, F., & Louly, M. A. (2013). A State of the Art on Supply Planning and Inventory Control under Lead Time Uncertainty. Studies in Informatics and Control, 2, 255-268.

Feng, Y., Wang, Y., Wu, L., Wu, L., Shu, Q., Li, H., & Yang, X. (2023). Causal relationship between outdoor atmospheric quality and pediatric asthma visits in hangzhou. Heliyon, 9(3).

Heizer, J., Render, B., & Munson, C. (2020). Operations Management: Sustainability and Supply Chain Management. Pearson Education Limited.

Kaban, M. G., & Wicaksono, P. A. (2020). Analisis Dan Mitigasi Risiko Rantai Pasok Pada Pengadaan Material Produksi Dengan Model House Of Risk (HOR) Pada Industri Mebel (Studi Kasus PT. XYZ). Industrial Engineering Online Journal, 9(1). Retrieved from

Oprescu, M., Syrgkanis, V., Battocchi, K., Hei, M., & Lewis, G. (2019). EconML: A Machine Learning Library for Estimating Heterogeneous Treatment Effects.

Pearl, J. (2009). Causality: Models, Reasoning and Inference (2nd ed.). Cambridge: Cambridge University Press.

Pujawan, I. N., & Mahendrawati, E. (2017). Supply Chain Management. Yogyakarta: Andi.

Putra, R. R., & Vikaliana, R. (2022). Pengaruh Defect dan Lead Time pada Lini Distribusi di PT Lasindo Jaya Bersama. Jurnal Abiwara, 116-124.

Sharma, A., & Kiciman, E. (2020). . DoWhy: An End-to-End Library for Causal Inference. doi:10.48550/arXiv.2011.04216

Sprenger, J., & Weinberger, N. (2021). Simpson’s Paradox. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy (Summer 2021 Edition). Metaphysics Research Lab, Stanford University. Retrieved from

Sun, Q., Zheng, T., Zheng, X., Cao, M., Zhang, B., & Jiang, S. (2023). Causal interpretation for groundwater exploitation strategy in a coastal aquifer. Science of the Total Environment, 867. doi:10.1016/j.scitotenv.2023.161443

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