Forecasting spare parts requirements is a highly debated issue that has attracted considerable attention from both academics and industry professionals over the years. It is observed that a gap still exists between research findings and their practical application in the industry. This systematic literature review (SLR) aims to explore different techniques used for forecasting spare parts requirements in the industrial sector and improvements proposed by researchers. The review follows the “Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA)” guidelines and examines 62 research publications related to demand forecasting for spare parts from 2015 to 2023, sourced from seven prominent databases. It categorizes demand forecasting techniques into parametric, non-parametric, and contextual methods, highlighting the contributions of researchers in each category. It also discusses enhancements to various techniques and compares them as suggested by scholars. The findings are organized by industry type, demand type within each industry, proposed techniques, key research contributions, and limitations. These efforts facilitate easy understanding and comparison, helping to evaluate the effectiveness and suitability of techniques in different contexts. Finally, the review outlines directions for future research.