Construction of Regression Models Predicting Lead Times and Classification Models

Pawel Olszewski, Leszek Gil, Natalia Rak, Tomasz Wolowiec and Michal Jasienski

European Research Studies Journal, 2024, vol. XXVII, issue Special A, 179-189

Abstract: Purpose: This article presents the process of building and applying regression models to predict lead time and classification models in supply chain management. Design/Methodology/Approach: The article presents the construction of regression models predicting lead times and classification models for partial orders and complete orders Findings: Using classification and regression models in the furniture industry increases customer satisfaction through timely order fulfillment, reduced costs associated with delays, and effective management of company resources. Practical Implications: Using regression models to determine forecast delivery times for delayed orders allows you to manage customer expectations better and minimize delays' impact on the entire supply chain. With accurate lead time forecasts, the company can make informed decisions about resource allocation, production planning, and logistics, contributing to operational efficiency. Originality/Value: Using predictive models in the procurement management process allows for continuous improvement of logistics processes by analyzing historical data and identifying trends.

Keywords: Regression; classification; XGBoost; knn. (search for similar items in EconPapers)
JEL-codes: C01 C45 C53 L74 M11 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:ers:journl:v:xxvii:y:2024:i:speciala:p:179-189

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