WORK SAMPLING METHODOLOGY AND ITS APPLICATION IN TIME STUDIES

Abstract

This research is about conducting work sampling in a Nigerian carbonated beverage organization to measure employee efficiency levels and identify time-consuming tasks, addressing the lack of local productivity statistics in Sub-Saharan Africa. With 1000 observations taken over three weeks researchers discovered that 60% of work time produced results but 30% was lost because materials needed to wait for 40% of the time and 10% stemmed from machine downtime where 50% resulted from equipment mechanical issues. The research makes its main contribution through direct observation of area-specific operation interruptions including electrical blackouts and delivery holdups which stem from inadequate infrastructure thus adding new findings about local industrial performance that global investigations tend to overlook. The research used work sampling to validate its implementation as an affordable and measurable methodology for SMEs at a 95% confidence level of productive work resulting in 56.96% - 63.04%.  Preventive maintenance methodology coupled with IoT-based logistic tracking systems enable organizations to maximize their productivity potential by 15% to 20% through reduced mechanical downtime by 40% along with thirty percent shorter material waiting times. The comparison reveals that solar power systems offer a solution to power interruptions which affect uptime by twenty percent because Nigeria faces 10% downtime, yet the textile industry operates with 12% to 15% outage.  The future research will combine IoT systems with regression models to anticipate downtime patterns that include night-shift errors as part of its predictive accuracy enhancement process reaching 25% precision levels according to Industry 4.0 standards.