Using Machine Learning Methods to Predict Order Lead Times

  • Farhana Sethi Global Data & Analytics Business Intelligence - Quality & Governance Manager with Schlumberger Oilfield, Texas, Houston
Keywords: Machine Learning, Lead time, Random Forest, Logistic, Production


The precise prediction of end-to-end Parts shipments delivery lead time (LT) considerably influences the efficiency and quality of manufacture planning and job scheduling in Oil and gas industry. Lead time transparency and predicting precise lead times allow Oil and gas industry to reduce operating expenses, enhance capital, upturn revenues, and improve their viable advantages. Clients will be able to better assign resources well and reduce the risk through conviction in their product and services allocation. The lead time prediction using machine learning algorithm can overall improve the job scheduling, improve service levels, gain efficiency, lead to reduce cost, and improves customer satisfaction. This paper describes the algorithm and techniques to execute the research using Machine Learning Methods to Predict Order Lead times. We are going to share the prediction accuracy results using different algorithm and share the sensitivity analysis results. The algorithm used to train the model consumed historical data from organization data using logistic application and various variables which are specific to the Supply chain in the Oil and gas industry.


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