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inventory-automation-dissertation

This repository contains my Master’s dissertation titled "Automating Inventory Replenishment: A Product Association-Based System for Creating Automated Shopping Lists" completed as part of my MSc in Advanced Computer Science with Business at the University of Exeter.

https://github.com/aditisampathkumar/inventory-automation-dissertation

Science Score: 44.0%

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Repository

This repository contains my Master’s dissertation titled "Automating Inventory Replenishment: A Product Association-Based System for Creating Automated Shopping Lists" completed as part of my MSc in Advanced Computer Science with Business at the University of Exeter.

Basic Info
  • Host: GitHub
  • Owner: aditisampathkumar
  • License: mit
  • Default Branch: main
  • Size: 2.17 MB
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Metadata Files
Readme License Citation

README.md

Automating Inventory Replenishment: A Product Association-Based System for Creating Automated Shopping Lists

This repository hosts the final draft of my Master's dissertation titled "Automating Inventory Replenishment: A Product Association-Based System for Creating Automated Shopping Lists". It explores a data-driven approach for enhancing inventory management in the hospitality industry.

Abstract

This research focuses on creating a product association-based system to generate automated shopping lists by using techniques like DDMRP, association rule mining, and advanced machine learning models. The proposed system aims to optimize stock levels and improve operational efficiency in the hospitality sector.

Keywords:

  • Inventory Management
  • DDMRP
  • Machine Learning
  • Product Association
  • Supply Chain Optimization

Original Submission Date

Submitted: August 28, 2024

Repository Contents

  • Dissertation.pdf: The full text of the dissertation.
  • LICENSE: Terms of use and distribution.
  • Citation Information: Information on how to cite this work (see below).
  • Dissertation PDF: A final draft of my dissertation, available in PDF format.

Citation

If referencing this work, please use the following citation: Sampathkumar, A. (2024). Automating Inventory Replenishment: A Product Association-Based System for Creating Automated Shopping Lists. Department of Computer Science, University of Exeter. Retrieved from https://github.com/aditisampathkumar/inventory-automation-dissertation

Contact

For any questions or feedback, please contact me at as1453@exeter.ac.uk.

Note: This repository is for academic purposes, and the dissertation content is licensed for non-commercial use only.

Owner

  • Name: Aditi Sampathkumar
  • Login: aditisampathkumar
  • Kind: user

Just out here trying my best !

Citation (CITATION.cff) 

cff-version: 1.2.0
title: "Automating Inventory Replenishment: A Product Association-Based System for Creating Automated Shopping Lists"
authors:
  - family-names: "Sampathkumar"
    given-names: "Aditi"
date-released: "2024-11-12"
url: "https://github.com/aditisampathkumar/inventory-automation-dissertation"
abstract: >
  This dissertation investigates the automation of inventory replenishment in the hospitality (food and beverage) industry using a data-driven approach centred around product association and statistical forecasting. Traditional manual inventory management methods are often inefficient, leading to issues such as stockouts or overstocking, which can negatively impact operational efficiency and cost management. This research proposes a product association-based system that leverages frequent pattern mining and demand forecasting, integrating Demand-Driven Material Requirements Planning (DDMRP) principles to automate the generation of dynamic shopping lists. The implemented system utilizes algorithms such as the Apriori algorithm for association rule learning, advanced time-series forecasting models including SARIMA and Prophet, and clustering methods like K-Means to optimize stock levels and minimize waste. Additionally, machine learning models such as Random Forest, Long Short-Term Memory (LSTM) networks, Multi-Layer Perceptron (MLP), and Convolutional Neural Networks (CNN) were tested to predict demand and optimize stock levels. The system’s effectiveness is evaluated through comprehensive analysis, demonstrating its potential to significantly improve inventory management by ensuring timely replenishment and reducing excess stock, thereby enhancing overall operational efficiency and cost-effectiveness in the hospitality sector. The integration of these advanced techniques provides a robust solution that not only meets current needs but also offers scalability for future applications in broader supply chain management contexts.
keywords:
  - Inventory Management
  - DDMRP
  - Machine Learning
  - Product Association
  - Supply Chain Optimization

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