Millerknoll (MLKN) Operating Expenses (2009 - 2026)
Millerknoll's Operating Expenses was $344.2 million in fiscal Q4 2026 (quarter ended May 30, 2026), up 6.9% from $321.9 million a year earlier and up 11.8% from the prior quarter.
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Analysis
Millerknoll (MLKN) Operating Expenses (2009 - 2026) Analysis & Trends
For FY2026 (ended May 30, 2026), Operating Expenses at Millerknoll came in at $1.29 billion, down 5.9% from FY2025.
- Operating Expenses shows a five-year compound annual growth rate of 12.4% (FY2021 to FY2026).
- In earlier fiscal years, Operating Expenses was $1.37 billion in FY2025 (+9.6%), $1.25 billion in FY2024 (-4.2%), $1.31 billion in FY2023 (-0.4%) and $1.31 billion in FY2022 (+82.7%).
- The fiscal Q4 2026 figure marks the highest quarterly Operating Expenses since fiscal Q3 2025.
- Compared with a year earlier, Operating Expenses was higher in five of the last eight quarters, with growth averaging 2.9%.
- The best year-over-year quarter for Operating Expenses over five years was fiscal Q1 2022 (growth of 113.6%); the worst was fiscal Q3 2026 (a decline of 25.7%).
- Per Business Quant data, MLKN's Operating Expenses in the three fiscal quarters before Q4 2026 was $308 million (Q3 2026), $323.7 million (Q2 2026) and $314.6 million (Q1 2026).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | SharkNinja | 25.65 Bn | 23.31 Bn | 860.34 Mn | 680.96 Mn |
| 2 | Somnigroup International | 13.10 Bn | 12.65 Bn | 817.20 Mn | - |
| 3 | Hni | 3.38 Bn | 2.95 Bn | 647.20 Mn | 550.90 Mn |
| 4 | Newell Brands | 2.32 Bn | 1.47 Bn | 812.00 Mn | 529.00 Mn |
| 5 | Sonos | 2.13 Bn | 1.02 Bn | 189.31 Mn | 157.78 Mn |
| 6 | Whirlpool | 2.04 Bn | -1.44 Bn | 442.00 Mn | 412.00 Mn |
| 7 | Corsair Gaming | 1.46 Bn | 1.00 Bn | 104.29 Mn | 96.68 Mn |
| 8 | Millerknoll | 1.38 Bn | 756.09 Mn | 395.60 Mn | 344.20 Mn |
| 9 | Arhaus | 1.36 Bn | 443.23 Mn | 172.07 Mn | 117.81 Mn |
| 10 | Cricut | 1.33 Bn | 300.62 Mn | 116.41 Mn | 68.99 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| May 30, 2026 | 344.20 Mn |
| Feb 28, 2026 | 308.00 Mn |
| Nov 29, 2025 | 323.70 Mn |
| Aug 30, 2025 | 314.60 Mn |
| May 31, 2025 | 321.90 Mn |
| Mar 1, 2025 | 414.60 Mn |
| Nov 30, 2024 | 314.50 Mn |
| Aug 31, 2024 | 321.10 Mn |
| Jun 1, 2024 | 328.70 Mn |
| Mar 2, 2024 | 294.20 Mn |
| Dec 2, 2023 | 311.60 Mn |
| Sep 2, 2023 | 317.80 Mn |
| Jun 3, 2023 | 343.10 Mn |
| Mar 4, 2023 | 314.40 Mn |
| Dec 3, 2022 | 328.90 Mn |
| Sep 3, 2022 | 321.30 Mn |
| May 28, 2022 | 325.50 Mn |
| Feb 26, 2022 | 310.30 Mn |
| Nov 27, 2021 | 346.80 Mn |
| Aug 28, 2021 | 330.30 Mn |
API Access
Millerknoll 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=MLKN&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "MLKN", "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=MLKN&period=max&api_key=YOUR_API_KEY");
const data = await res.json();