Time Series and Machine Learning Approach for Forecasting the Demand for Small Equipment, Tools, and Consumables for Industrial Construction Projects

• Author / Creator

  Jafari, Elnaz

• The high consumption and utilization of demand for small equipment, tools, and consumables in
  construction projects underscores the necessity for effective procurement strategies. Accurate
  estimation of these consumables is crucial for moving toward project completion in a timely
  manner. With recent advancements in time series analysis, artificial intelligence, and machine
  learning, these technologies can be employed to formulate predictive models.
  This research aims to explore the advantages of using time series and machine learning—in
  combination with historical data from past projects—to identify key factors that impact demand
  for these consumables, as well as develop an efficient predictive model that analyzes and learns
  from historical data thereby facilitating precise estimations for future projects. The research
  involves collecting and analyzing historical data, analyzing current industry practices for
  estimating requirements for small equipment, tools, and consumables, and implementing time
  series analysis and machine learning algorithms to forecast demand for various types of
  consumables in construction projects. This study investigates crucial factors that influence these
  items, bridging the gap between literature review and industry practices.
  Finally, this research proposes time series and machine learning models capable of predicting
  quantities in industrial projects using historical data. The proposed models provide an estimation
  of monthly requirements for various types of consumables throughout the project, which assists
  project managers in estimating required quantities, offering them accurate insights to help facilitate
  effective procurement strategies.

• Subjects / Keywords

  • Construction Supply Chain
  • Consumables
  • Machine Learning
  • Time Series
  • Demand Forecasting

• Graduation date

  Spring 2024

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-hfjr-0n55

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.