IDEAS home Printed from https://ideas.repec.org/a/ers/journl/vxxviiy2024ispecialap179-189.html
   My bibliography  Save this article

Construction of Regression Models Predicting Lead Times and Classification Models

Author

Listed:
  • Pawel Olszewski
  • Leszek Gil
  • Natalia Rak
  • Tomasz Wolowiec
  • Michal Jasienski

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.

Suggested Citation

  • Pawel Olszewski & Leszek Gil & Natalia Rak & Tomasz Wolowiec & Michal Jasienski, 2024. "Construction of Regression Models Predicting Lead Times and Classification Models," European Research Studies Journal, European Research Studies Journal, vol. 0(Special A), pages 179-189.
    • Handle: RePEc:ers:journl:v:xxvii:y:2024:i:speciala:p:179-189
    as

    Download full text from publisher

    File URL: https://ersj.eu/journal/3398/download
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Matthias Schonlau & Rosie Yuyan Zou, 2020. "The random forest algorithm for statistical learning," Stata Journal, StataCorp LP, vol. 20(1), pages 3-29, March.
    2. Marta Kadłubek & Eleftherios Thalassinos & Joanna Domagała & Sandra Grabowska & Sebastian Saniuk, 2022. "Intelligent Transportation System Applications and Logistics Resources for Logistics Customer Service in Road Freight Transport Enterprises," Energies, MDPI, vol. 15(13), pages 1-27, June.
    3. Vicky Zampeta & Gregory Chondrokoukis, 2023. "A Comprehensive Approach through Robust Regression and Gaussian/Mixed-Markov Graphical Models on the Example of Maritime Transportation Accidents: Evidence from a Listed-in-NYSE Shipping Company," JRFM, MDPI, vol. 16(3), pages 1-30, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Edmund Wasik & Tomasz Sidor & Tomasz Wolowiec & Jacek Piwkowski & Michal Jasienski, 2024. "Supporting Supply Chain Risk Management: An Innovative Approach Using Graph Theory and Forecasting Algorithms," European Research Studies Journal, European Research Studies Journal, vol. 0(Special A), pages 25-37.
    2. Sascha O. Becker, Sascha O & Voth, Hans-Joachim, 2023. "From the Death of God to the Rise of Hitler," The Warwick Economics Research Paper Series (TWERPS) 1478, University of Warwick, Department of Economics.
    3. Malgorzata Gorzalczynska-Koczkodaj, 2023. "Intelligent Specializations as an Opportunity for Regional Development on the Example of the West Pomeranian Voivodeship," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 446-455.
    4. Bartosz Przysucha & Pawel Kaleta & Artur Dmowski & Jacek Piwkowski & Piotr Czarnecki & Tomasz Cieplak, 2024. "Product Knowledge Graphs - Creating a Knowledge System for Customer Support," European Research Studies Journal, European Research Studies Journal, vol. 0(Special A), pages 150-159.
    5. Czeslawa Christowa, 2023. "Safety Management in Polish Seaports: Identification and Analysis of Threats," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 615-631.
    6. Tomasz Rokicki & Piotr Borawski & Aneta Beldycka-Borawska & Andras Szeberenyi & Luiza Ochnio & Bogdan Klepacki, 2024. "Resilience of Supply Chains in the Automotive Industry during the COVID-19 Pandemic on the Example of Polish Enterprises," European Research Studies Journal, European Research Studies Journal, vol. 0(1), pages 238-252.
    7. Bartosz Przysucha & Magdalena Halas & Cezary Figura & Natalia Rak & Pawel Barwiak & Adam Hernas, 2024. "Exploring and Analyzing YouTube Communities through Data Mining and Knowledge Graphs," European Research Studies Journal, European Research Studies Journal, vol. 0(Special A), pages 94-102.
    8. Wang, Feipeng & Wong, Wing-Keung & Wang, Zheng & Albasher, Gadah & Alsultan, Nouf & Fatemah, Ambreen, 2023. "Emerging pathways to sustainable economic development: An interdisciplinary exploration of resource efficiency, technological innovation, and ecosystem resilience in resource-rich regions," Resources Policy, Elsevier, vol. 85(PA).
    9. Pawel Rymarczyk & Cezary Figura & Lukasz Wojciechowski & Kamila Cwik & Piotr Stalinski, 2024. "Evaluating the Effectiveness of Advertising Campaigns in the Fast-Food Industry Using an Analytical Engine," European Research Studies Journal, European Research Studies Journal, vol. 0(Special A), pages 126-136.
    10. Xiaxuan He & Qifeng Yuan & Yinghong Qin & Junwen Lu & Gang Li, 2024. "Analysis of Surface Urban Heat Island in the Guangzhou-Foshan Metropolitan Area Based on Local Climate Zones," Land, MDPI, vol. 13(10), pages 1-34, October.
    11. Becker, Sascha O. & Voth, Hans-Joachim, 2023. "From the Death of God to the Rise of Hitler," CEPR Discussion Papers 18543, C.E.P.R. Discussion Papers.
    12. Tomasz Smutek & Jan Sikora & Sylwester Bogacki & Marek Rutkowski & Dariusz Wozniak, 2024. "Use of Autoencoder and One-Hot Encoding for Customer Segmentation," European Research Studies Journal, European Research Studies Journal, vol. 0(Special A), pages 72-82.
    13. Diellza Kukaj, 2023. "Nominal and Real Convergence of European Union and Western Balkan Countries: A Panel Data Analysis," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(2), pages 69-84.
    14. Ahmet Faruk Aysan & Bekir Sait Ciftler & Ibrahim Musa Unal, 2024. "Predictive Power of Random Forests in Analyzing Risk Management in Islamic Banking," JRFM, MDPI, vol. 17(3), pages 1-19, March.
    15. Sakiru Adebola Solarin & Muhammed Sehid Gorus & Onder Ozgur, 2024. "Modelling the economic effect of inbound birth tourism: a random forest algorithm approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(5), pages 4223-4240, October.
    16. Zhu, Xinyi & Shen, Xiaoyan & Chen, Kailiang & Zhang, Zeqing, 2024. "Research on the prediction and influencing factors of heavy duty truck fuel consumption based on LightGBM," Energy, Elsevier, vol. 296(C).
    17. Ilona Jacyna-Gołda & Nadiia Shmygol & Nataliia Gavkalova & Mariusz Salwin, 2023. "Sustainable Development of Intermodal Freight Transportation—Through the Integration of Logistics Flows in Ukraine and Poland," Sustainability, MDPI, vol. 16(1), pages 1-13, December.
    18. Murat Aslan & Onder Ozgur, 2024. "Financial dollarization and its effects on inflation and output in Turkey: a machine learning approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(6), pages 5777-5804, December.
    19. Maria A. F. Silva Dias & Yania Molina Souto & Bruno Biazeto & Enzo Todesco & Jose A. Zuñiga Mora & Dylana Vargas Navarro & Melvin Pérez Chinchilla & Carlos Madrigal Araya & Dayanna Arce Fernández & Be, 2024. "Reduction of Wind Speed Forecast Error in Costa Rica Tejona Wind Farm with Artificial Intelligence," Energies, MDPI, vol. 17(22), pages 1-12, November.
    20. Özer Depren & Mustafa Tevfik Kartal & Serpil Kılıç Depren, 2021. "Recent innovation in benchmark rates (BMR): evidence from influential factors on Turkish Lira Overnight Reference Interest Rate with machine learning algorithms," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-20, December.

    More about this item

    Keywords

    Regression; classification; XGBoost; knn.;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • L74 - Industrial Organization - - Industry Studies: Primary Products and Construction - - - Construction
    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ers:journl:v:xxvii:y:2024:i:speciala:p:179-189. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Marios Agiomavritis (email available below). General contact details of provider: https://ersj.eu/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.