Optimizing labor management for effective maintenance operations

Abstract

Effective maintenance operations are essential for promoting safety, hygiene, and operational efficiency across various settings. Tasks such as waste management, snow removal, and interior cleaning play a pivotal role in maintaining these standards. However, ensuring effective labor management in these operations presents significant challenges. These include ensuring timely task completion, monitoring employee performance, and maintaining consistent quality standards. The aim of the thesis was to investigate strategies for optimizing labor management systems to enhance the tracking, evaluation, and quality assurance of maintenance activities.

The thesis focused on contemporary best practices, emerging technological solutions, and the application of key performance indicators (KPIs) within labor management frameworks. Particular attention was given to the role of real-time tracking technologies, including mobile applications, and cloud-based management platforms, in driving operational efficiency. The quantitative research method was chosen because it offers a systematic and objective approach to examining labor management systems in maintenance operations.

The findings underscore the significance of transparent reporting mechanisms in fostering accountability and enhancing worker motivation.