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

Aug 3, 2026

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 advancements in time series analysis, artificial intelligence, and machine learning, historical project data can drive predictive models that identify key demand drivers, support learning from prior patterns, and enable precise future estimations. The study collects and analyzes historical data, reviews industry estimating practices, and implements forecasting models.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages122

Reading Time5 h 7 min

AccessFree to download

Introduction

Background and Problem Statement

Research Objectives

Expected Contributions

Research Methodology

Research Questions

Thesis Organization

Time Series and Machine Learning Approach for Forecasting the Demand for Small Equipment, Tools, and Consumables for Industrial Construction Projects by Elnaz Jafari A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Construction Engineering and Management Department of Civil and Environmental Engineering University of Alberta © Elnaz Jafari, 2024 ABSTRACT 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. ACKNOWLEDGMENTS First and foremost, my deepest gratitude is extended to my supervisor, Dr. Simaan AbouRizk, whose unwavering guidance and support over the past two years have been invaluable. Dr. AbouRizk has been an exemplary mentor, imparting wisdom that has enriched my professional and personal growth immeasurably. His role in my journey cannot be overstated, and for his mentorship, I am profoundly thankful. I am equally grateful to Dr. Lingzi Wu, her readiness to assist and offer insightful advice has been instrumental in the success of my research. Her contributions have been pivotal, and her support is deeply appreciated. I must extend profound thanks to my friend Mohamed ElMenshawy, whose unparalleled support was crucial throughout my master's journey. His dedication, insightful contributions, and hands-on assistance were instrumental in every phase. Mohamed's involvement was not just as a guide but as a pillar of strength and a constant source of motivation. I am profoundly grateful for his generosity of spirit, expertise, and the countless hours he devoted to my work. My appreciation extends further to Dr. Yasser Mohamed, Maria Al-Hussein, Brenda Penner, and Stephen Hague for their invaluable dedication and support. Each has played a crucial role in my journey, offering their expertise and assistance when most needed. Their collective efforts have significantly contributed to the success of my research, and their support is wholeheartedly appreciated. Alongside Kohlbey Ozipko, whose meticulous efforts in manuscript editing have been essential. Her contributions have significantly enhanced the quality of my work. The financial and resource support provided by the National Science and Engineering Research Council (NSERC) and Alberta Innovates has been fundamental to the accomplishment of this research. Their backin

FAQs

What problem does the research address in industrial construction projects?

It addresses the need for accurate estimation of demand for small equipment, tools, and consumables to support timely project completion and effective procurement.

How does the study build its forecasting approach?

It uses historical data from past projects, combines time series analysis with machine learning, and incorporates findings from current industry estimating practices.

What outputs do the proposed models provide for project managers?

They predict monthly quantities for different consumable types throughout the project, helping managers estimate required amounts and improve procurement decisions.

Aug 3, 2026

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