Ufp Technologies (UFPT) Total Non-Current Liabilities (2014 - 2026)
Ufp Technologies' Total Non-Current Liabilities came in at $212.79 million for Q2 2026, down 12.3% from $242.65 million a year earlier and down 7.1% from the prior quarter.
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Analysis
Ufp Technologies (UFPT) Total Non-Current Liabilities (2014 - 2026) Analysis & Trends
At the end of FY2025, Ufp Technologies' Total Non-Current Liabilities was $224.54 million, down 18.4% from FY2024.
- Total Non-Current Liabilities carries a five-year compound annual growth rate of 58.4% (FY2020 to FY2025).
- Going back by year, Total Non-Current Liabilities was $275.09 million in FY2024 (+167.4%), $102.86 million in FY2023 (-16.0%), $122.43 million in FY2022 (-1.7%) and $124.5 million in FY2021 (+453.3%).
- The Q2 2026 figure represents the lowest quarterly Total Non-Current Liabilities since Q2 2024.
- Year-over-year, Total Non-Current Liabilities has declined for four consecutive quarters, with growth averaging 72.6% over the last eight quarters.
- The fastest year-over-year change in Total Non-Current Liabilities over five years came in Q2 2022 (growth of 519.4%), and the weakest in Q2 2023 (a decline of 26.4%).
- Business Quant data shows UFPT's Total Non-Current Liabilities at $229.07 million (Q1 2026), $224.54 million (Q4 2025) and $242.13 million (Q3 2025) in the three quarters before Q2 2026.
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Total Non-Current Liabilities (Qtr) |
|---|---|---|---|---|---|
| 1 | Thermo Fisher Scientific | 249.71 Bn | 228.86 Bn | 4.88 Bn | 55.95 Bn |
| 2 | Abbott Laboratories | 171.00 Bn | 142.08 Bn | 7.27 Bn | - |
| 3 | Danaher | 155.79 Bn | 139.61 Bn | 3.61 Bn | 33.25 Bn |
| 4 | Intuitive Surgical | 143.91 Bn | 123.46 Bn | 1.96 Bn | - |
| 5 | Medtronic | 110.42 Bn | 76.29 Bn | 6.34 Bn | 40.46 Bn |
| 6 | Stryker | 105.71 Bn | 91.82 Bn | 4.50 Bn | 21.28 Bn |
| 7 | Boston Scientific | 63.26 Bn | 58.27 Bn | 3.85 Bn | 17.56 Bn |
| 8 | Edwards Lifesciences | 49.80 Bn | 33.91 Bn | 1.35 Bn | 2.57 Bn |
| 9 | Becton Dickinson | 48.78 Bn | 45.93 Bn | 2.32 Bn | 23.92 Bn |
| 10 | Ufp Technologies | 2.30 Bn | 2.23 Bn | 50.92 Mn | 212.79 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 212.79 Mn |
| Mar 31, 2026 | 229.07 Mn |
| Dec 31, 2025 | 224.54 Mn |
| Sep 30, 2025 | 242.13 Mn |
| Jun 30, 2025 | 242.65 Mn |
| Mar 31, 2025 | 273.15 Mn |
| Dec 31, 2024 | 275.09 Mn |
| Sep 30, 2024 | 298.42 Mn |
| Jun 30, 2024 | 102.46 Mn |
| Mar 31, 2024 | 98.84 Mn |
| Dec 31, 2023 | 102.86 Mn |
| Sep 30, 2023 | 112.39 Mn |
| Jun 30, 2023 | 123.12 Mn |
| Mar 31, 2023 | 124.82 Mn |
| Dec 31, 2022 | 122.43 Mn |
| Sep 30, 2022 | 142.61 Mn |
| Jun 30, 2022 | 167.18 Mn |
| Mar 31, 2022 | 144.71 Mn |
| Dec 31, 2021 | 124.50 Mn |
| Sep 30, 2021 | 25.88 Mn |
API Access
Ufp Technologies Total Non-Current Liabilities API
Pull this series into your own models, spreadsheets and apps with the Business Quant
Historical Metrics API. The request below matches the chart above — change the
frequency, period or values and it follows. Swap YOUR_API_KEY for your own key.
https://data.businessquant.com/historic?slug=total-non-current-liabilities&ticker=UFPT&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "total-non-current-liabilities", "ticker": "UFPT", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=total-non-current-liabilities&ticker=UFPT&period=max&api_key=YOUR_API_KEY");
const data = await res.json();