<![CDATA[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]]>
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