Ufp Technologies (UFPT) Cash from Operations (2010 - 2026)
Ufp Technologies' Cash from Operations came in at $15.61 million for Q2 2026, down 38.4% from $25.33 million a year earlier but up 387.3% from the prior quarter.
Ufp Technologies (UFPT) Cash from Operations (2010 - 2026) Analysis & Trends
Over the trailing twelve months to Jun 30, 2026, Ufp Technologies reported Cash from Operations of $71.58 million, down 14.1% year-over-year; for FY2025, it came in at $91.91 million, up 38.0% from FY2024.
- Cash from Operations has increased in each of the last four years, with a five-year compound annual growth rate of 29.7% (FY2020 to FY2025).
- Going back by year, Cash from Operations was $66.59 million in FY2024 (+61.1%), $41.33 million in FY2023 (+132.9%), $17.74 million in FY2022 (+24.1%) and $14.29 million in FY2021 (-42.9%).
- The five-year range for quarterly Cash from Operations is -$3.22 million (Q1 2022) to $35.93 million (Q3 2025).
- Year-over-year, Cash from Operations has declined for three consecutive quarters, with growth averaging 22.9% over the last eight quarters.
- The fastest year-over-year change in Cash from Operations over five years came in Q1 2024 (growth of 518.3%), and the weakest in Q4 2021 (a decline of 92.6%).
- Business Quant data shows UFPT's Cash from Operations at $3.2 million (Q1 2026), $16.84 million (Q4 2025) and $35.93 million (Q3 2025) in the three quarters before Q2 2026.
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Cash from Ops. (Qtr) |
|---|---|---|---|---|---|
| 1 | Thermo Fisher Scientific | 249.71 Bn | 228.86 Bn | 4.88 Bn | 2.13 Bn |
| 2 | Abbott Laboratories | 171.00 Bn | 142.08 Bn | 7.27 Bn | 2.49 Bn |
| 3 | Danaher | 155.79 Bn | 139.61 Bn | 3.61 Bn | 1.53 Bn |
| 4 | Intuitive Surgical | 143.91 Bn | 123.46 Bn | 1.96 Bn | 1.06 Bn |
| 5 | Medtronic | 110.42 Bn | 76.29 Bn | 6.34 Bn | 1.79 Bn |
| 6 | Stryker | 105.71 Bn | 91.82 Bn | 4.50 Bn | 1.26 Bn |
| 7 | Boston Scientific | 63.26 Bn | 58.27 Bn | 3.85 Bn | 1.47 Bn |
| 8 | Edwards Lifesciences | 49.80 Bn | 33.91 Bn | 1.35 Bn | 651.90 Mn |
| 9 | Becton Dickinson | 48.78 Bn | 45.93 Bn | 2.32 Bn | 776.00 Mn |
| 10 | Ufp Technologies | 2.30 Bn | 2.23 Bn | 50.92 Mn | 15.61 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 15.61 Mn |
| Mar 31, 2026 | 3.20 Mn |
| Dec 31, 2025 | 16.84 Mn |
| Sep 30, 2025 | 35.93 Mn |
| Jun 30, 2025 | 25.33 Mn |
| Mar 31, 2025 | 13.81 Mn |
| Dec 31, 2024 | 24.42 Mn |
| Sep 30, 2024 | 19.80 Mn |
| Jun 30, 2024 | 11.71 Mn |
| Mar 31, 2024 | 10.65 Mn |
| Dec 31, 2023 | 12.70 Mn |
| Sep 30, 2023 | 18.03 Mn |
| Jun 30, 2023 | 8.88 Mn |
| Mar 31, 2023 | 1.72 Mn |
| Dec 31, 2022 | 17.89 Mn |
| Sep 30, 2022 | -109,000.00 |
| Jun 30, 2022 | 3.18 Mn |
| Mar 31, 2022 | -3.22 Mn |
| Dec 31, 2021 | 616,000.00 |
| Sep 30, 2021 | 3.79 Mn |
Ufp Technologies Cash from Operations 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=cash-from-operations&ticker=UFPT&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "cash-from-operations", "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=cash-from-operations&ticker=UFPT&period=max&api_key=YOUR_API_KEY");
const data = await res.json();