Columbus Mckinnon (CMCO) Operating Expenses (2010 - 2026)
Columbus Mckinnon's Operating Expenses came in at $163.97 million for fiscal Q1 2027 (quarter ended Jun 30, 2026), up 128.6% from $71.73 million a year earlier and up 89.7% from the prior quarter.
Columbus Mckinnon (CMCO) Operating Expenses (2010 - 2026) Analysis & Trends
Over the trailing twelve months to Jun 30, 2026, Columbus Mckinnon reported Operating Expenses of $401.34 million, up 67.5% year-over-year; for FY2026 (ended Mar 31, 2026), it was $199.73 million, up 52.3% from FY2025.
- Operating Expenses carries a five-year compound annual growth rate of 17.7% (FY2021 to FY2026).
- Going back by fiscal year, Operating Expenses was $131.12 million in FY2025 (-1.4%), $132.95 million in FY2024 (+14.9%), $115.73 million in FY2023 (-1.5%) and $117.48 million in FY2022 (+32.8%).
- The fiscal Q1 2027 figure represents the highest quarterly Operating Expenses in data going back to fiscal Q1 2011.
- Year-over-year, Operating Expenses has increased for six consecutive quarters, with growth averaging 36.9% over the last eight quarters.
- The fastest year-over-year change in Operating Expenses over five years came in fiscal Q1 2027 (growth of 128.6%), and the weakest in fiscal Q1 2023 (a decline of 5.7%).
- Business Quant data shows CMCO's Operating Expenses at $86.41 million (Q4 2026), $72.99 million (Q3 2026) and $77.97 million (Q2 2026) in the three fiscal quarters before Q1 2027.
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Caterpillar | 372.79 Bn | 344.49 Bn | 7.76 Bn | 16.25 Bn |
| 2 | Amphenol | 207.98 Bn | 202.68 Bn | 3.55 Bn | 963.40 Mn |
| 3 | Deere | 181.02 Bn | 189.78 Bn | 4.66 Bn | 10.73 Bn |
| 4 | Eaton | 166.86 Bn | 164.09 Bn | 2.86 Bn | 1.46 Bn |
| 5 | Parker-Hannifin | 120.45 Bn | 118.58 Bn | 2.25 Bn | 874.00 Mn |
| 6 | Vertiv Holdings | 92.94 Bn | 83.56 Bn | 1.23 Bn | 490.50 Mn |
| 7 | Emerson Electric | 86.56 Bn | 79.32 Bn | 2.66 Bn | 1.34 Bn |
| 8 | 3M | 84.72 Bn | 66.18 Bn | 2.68 Bn | 5.52 Bn |
| 9 | Illinois Tool Works | 73.32 Bn | 69.88 Bn | 1.90 Bn | - |
| 10 | Columbus Mckinnon | 459.32 Mn | 200.43 Mn | 146.27 Mn | 163.97 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 163.97 Mn |
| Mar 31, 2026 | 86.41 Mn |
| Dec 31, 2025 | 72.99 Mn |
| Sep 30, 2025 | 77.97 Mn |
| Jun 30, 2025 | 71.73 Mn |
| Mar 31, 2025 | 39.48 Mn |
| Dec 31, 2024 | 64.41 Mn |
| Sep 30, 2024 | 63.94 Mn |
| Jun 30, 2024 | 67.88 Mn |
| Mar 31, 2024 | 34.41 Mn |
| Dec 31, 2023 | 66.99 Mn |
| Sep 30, 2023 | 66.63 Mn |
| Jun 30, 2023 | 65.20 Mn |
| Mar 31, 2023 | 31.86 Mn |
| Dec 31, 2022 | 61.87 Mn |
| Sep 30, 2022 | 58.94 Mn |
| Jun 30, 2022 | 59.70 Mn |
| Mar 31, 2022 | 27.70 Mn |
| Dec 31, 2021 | 59.74 Mn |
| Sep 30, 2021 | 57.48 Mn |
Columbus Mckinnon Operating Expenses 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=operating-expenses&ticker=CMCO&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "CMCO", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=operating-expenses&ticker=CMCO&period=max&api_key=YOUR_API_KEY");
const data = await res.json();