AI Analysis: Wood chips production, shipments and stocks, monthly

Executive Summary

Canada's wood chips industry, tracked monthly from 2012 to 2025 across 63 data series, is heavily dominated by softwood chips, which average 398.7 thousand oven-dry metric tonnes (ODT) in production — more than 33 times the hardwood average of 11.9 thousand ODT. Production and shipments move in near-perfect lockstep (r=0.984), indicating a tightly coupled, just-in-time supply chain with minimal inventory buildup. The dataset is remarkably stable over 13 years, with no statistical outliers detected even during potentially disruptive periods such as the COVID-19 pandemic.

Key Findings

• Softwood chips dominate Canada's wood chips industry, with average monthly production of 398.7 thousand ODT compared to just 11.9 thousand ODT for hardwood chips, which frequently report zero values.
• Total wood chips production and shipments are almost perfectly correlated (r=0.984), indicating that chips are shipped very quickly after production with little accumulation in inventory.
• The overall value distribution is heavily right-skewed, with a mean of 266.4 thousand ODT versus a median of only 124.8 thousand ODT and a wide standard deviation of 355.6, driven by a peak single value of 1,597.4 thousand ODT in total shipments.
• Stocks levels remain comparatively low and stable, with softwood and total stocks medians of just 26.7–31.7 thousand ODT, reinforcing that wood chips are a high-throughput, low-inventory commodity.
• Geographic variation is significant — the same product category can rank in both the top 10 and bottom 10 average values depending on the province or region, with British Columbia sub-regions and Other Provinces showing notably different volumes.
• No statistical outliers were detected across all 9 categories using the IQR method at a 2.5x threshold, suggesting consistent and stable industry operations throughout the entire 2012–2025 period.
• The dataset achieves approximately 84% data completeness (5,979 of 7,098 values non-null), with missing data concentrated in specific regional and period combinations rather than being spread uniformly across the dataset.