AI Analysis: Historical (real-time) releases manufacturing capacity utilization rates, inactive

Executive Summary

Canadian manufacturing capacity utilization averaged 77.1% from 2017 to 2025, demonstrating overall stability with the most significant disruption being a sharp drop to 54.6–57.6% in April 2020 due to COVID-19 shutdowns, followed by a full recovery to 77.2% by February 2025. The dataset, drawn from Statistics Canada Table 16100015, covers 28 NAICS industries across 159,118 data points and employs a real-time vintage structure that enables analysis of both sector performance and data revision patterns. While most industries move in moderate positive correlation (r=0.243), notable divergences exist across sub-sectors, with utilization rates ranging from an extreme low of -18.3% to a high of 107.5%.

Key Findings

• The overall average manufacturing capacity utilization rate from 2017 to 2025 was 77.1%, with a median of 77.7%, indicating a roughly symmetric and stable distribution centered in the high-70s.
• The most severe anomaly in the dataset was the April 2020 utilization drop to approximately 54.6–57.6% — nearly 6 standard deviations below the mean — directly attributable to COVID-19 pandemic shutdowns.
• Peak utilization of 81.1–84.2% was recorded in late 2017 to early 2018, representing the strongest period of manufacturing activity in the dataset's coverage window.
• By February 2025, utilization had fully rebounded to 77.2%, closely matching the long-term historical average and signaling a complete post-pandemic recovery.
• The middle 50% of utilization values fall within a tight interquartile range of 73.9% to 81.0%, reflecting consistent industrial activity across most periods, with extreme values (-18.3% and 107.5%) representing notable sector-specific outliers.
• Durable goods industries and total Manufacturing are very strongly correlated (r=0.951), while some sector pairs such as Beverage manufacturing vs. Non-durable goods (r=-0.346) show slight negative correlations, highlighting divergent sector dynamics.
• The dataset's real-time vintage structure — with the same reference quarter appearing across up to 92 different release dates — makes it uniquely valuable for studying how Statistics Canada's manufacturing estimates are revised over time.