ECONOMIC ORDER QUANTITY - Predictive Model - Using Supervised Machine Learning for Inventory Management of Fast-Moving Consumer Goods Distributors

Aug 4, 2026

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The thesis develops an Economic Order Quantity predictive model for inventory management in fast-moving consumer goods distribution networks. It applies supervised machine learning approaches to forecast demand patterns and support ordering decisions, aiming to improve planning accuracy and reduce inefficiencies in stock control. The study focuses on practical modeling for distributor operations, using academic rigor and structured research procedures. Findings are intended to help companies balance service levels and inventory costs in dynamic retail environments.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages254

Reading Time10 h 40 min

AccessFree to download

Acknowledgments

Author’s Declaration

Plymouth Business School Theses Faculty of Arts, Humanities and Business Theses 2023-01-01 ECONOMIC ORDER QUANTITY PREDICTIVE MODEL USING SUPERVISED MACHINE LEARNING FOR INVENTORY MANAGEMENT OF THE FAST-MOVING CONSUMER GOODS DISTRIBUTORS Nancy Deraz Let us know how access to this document benefits you General rights All content in PEARL is protected by copyright law. Author manuscripts are made available in accordance with publisher policies. Please cite only the published version using the details provided on the item record or document. In the absence of an open licence (e.g. Creative Commons), permissions for further reuse of content should be sought from the publisher or author. Take down policy If you believe that this document breaches copyright please contact the library providing details, and we will remove access to the work immediately and investigate your claim. Follow this and additional works at: [https://pearl.plymouth.ac.uk/pbs-theses](https://pearl.plymouth.ac.uk/pbs-theses) Recommended Citation Deraz, N. (2023) ECONOMIC ORDER QUANTITY PREDICTIVE MODEL USING SUPERVISED MACHINE LEARNING FOR INVENTORY MANAGEMENT OF THE FAST-MOVING CONSUMER GOODS DISTRIBUTORS. Thesis. University of Plymouth. Available at: [https://doi.org/10.24382/2668](https://doi.org/10.24382/2668) This Thesis is brought to you for free and open access by the Faculty of Arts, Humanities and Business Theses at PEARL. It has been accepted for inclusion in Plymouth Business School Theses by an authorized administrator of PEARL. For more information, please [contact openresearch@plymouth.ac.uk](contact openresearch@plymouth.ac.uk). Copyright Statement This copy of the thesis has been supplied on condition that anyone who consults it is understood to recognise that its copyright rests with its author and that no quotation from the thesis and no information derived from it maybe published without the author's prior consent. ECONOMIC ORDER QUANTITY PREDICTIVE MODEL USING SUPERVISED MACHINE LEARNING FOR INVENTORY MANAGEMENT OF THE FASTMOVING CONSUMER GOODS DISTRIBUTORS By NANCY DERAZ A thesis submitted to the University of Plymouth in partial fulfilment for the degree of DOCTOR OF PHILOSOPHY Plymouth Business School February 2023 ACKNOWLEDGMENTS First and above all, I praise Allah, the Almighty for providing me the strength and courage to proceed successfully and fulfil my dreams. I would like to express my extreme gratitude to my supervisors, Dr Yi Wang and Dr Jonathan Moizer, for their constant support, guidance, and encouragement throughout the course of this study. Dr Yi Wang, you were a true example of a supervisor that can get the best out of his student. I am truly thankful to you; without you this work could not have been done. My sincere gratefulness to Dr Jonathan Moizer, who offered me valuable advice, comments, and helped me, patiently, to improve my writing skills throughout the preparation of this thesis. Although Dr Irina only lately joined my supervisory team, her friendly support was very helpful to me. I would like to deeply thank my home university, the Arab Academy for Science, Technology, and Maritime Transport – College of International Transport and Logistics– Alexandria, for providing me an educational funding abroad. Many thanks are due to Professor Sara el Gazzar, Dean of College of International Transport and Logistics, whose sincerely support I will never forget. Also, I would like to thank Dr. Sara ElGamal, Head of Logistics & Supply Chain Management department, who had kind concern and consideration regarding my research. Exceptional thanks and appreciation are dedicated to the case study company involved for the help given during the research. My deep gratitude goes to Dr Ahmed Hamido who made this work possible. I would also like to thank all my friends for their encouragement, assistance, and friendship. You are part of this success, which would have been impossib

FAQs

What problem does the thesis address?

The thesis targets inventory management for fast-moving consumer goods distributors by building a predictive approach grounded in Economic Order Quantity decision-making.

What modeling approach is used?

The work uses supervised machine learning to create a predictive model that supports inventory and ordering decisions based on forecasted patterns.

What is the thesis degree and institutional context?

It is submitted as a Doctor of Philosophy thesis to the University of Plymouth under Plymouth Business School, dated February 2023.

Aug 4, 2026

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