Oxford Industries (OXM) Inventory (2010 - 2026)
Oxford Industries (OXM) reported Inventory of $147.14 million for fiscal Q2 2027 (quarter ended Aug 1, 2026), down 11.7% from $166.67 million a year earlier and down 0.2% from the prior quarter.
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Analysis
Oxford Industries (OXM) Inventory (2010 - 2026) Analysis & Trends
At the end of FY2026 (ended Jan 31, 2026), Oxford Industries posted Inventory of $165.28 million, down 1.2% from FY2025.
- Inventory has a five-year compound annual growth rate of 6.0% (FY2021 to FY2026).
- By fiscal year, Inventory came in at $167.29 million in FY2025 (+4.8%), $159.57 million in FY2024 (-27.5%), $220.14 million in FY2023 (+87.0%) and $117.71 million in FY2022 (-4.7%).
- The fiscal Q2 2027 figure ranks as the lowest quarterly Inventory since fiscal Q2 2025.
- Year over year, Inventory has now declined in each of the last three quarters, with growth averaging 1.7% over the last eight quarters.
- The high point for year-over-year Inventory in five years was fiscal Q3 2023 (growth of 88.7%); the low point was fiscal Q3 2022 (a decline of 38.8%).
- Per Business Quant data, the three fiscal quarters before Q2 2027 came in at $147.49 million (Q1 2027), $165.28 million (Q4 2026) and $155.4 million (Q3 2026).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Inventory (Qtr) |
|---|---|---|---|---|---|
| 1 | Nike | 53.14 Bn | 19.13 Bn | 5.39 Bn | - |
| 2 | Tapestry | 22.87 Bn | 18.83 Bn | 1.56 Bn | 826.20 Mn |
| 3 | Ralph Lauren | 21.39 Bn | 13.49 Bn | 1.44 Bn | 1.16 Bn |
| 4 | Deckers Outdoor | 11.06 Bn | 3.93 Bn | - | 487.02 Mn |
| 5 | Lululemon Athletica | 10.32 Bn | 4.58 Bn | 1.46 Bn | 1.71 Bn |
| 6 | Levi Strauss | 7.62 Bn | 4.28 Bn | 979.10 Mn | 1.16 Bn |
| 7 | Gildan Activewear | 6.55 Bn | 5.54 Bn | 459.76 Mn | 2.17 Bn |
| 8 | Birkenstock Holding | 6.07 Bn | 4.38 Bn | 493.89 Mn | 980.27 Mn |
| 9 | Crocs | 5.90 Bn | 5.31 Bn | 700.71 Mn | 389.21 Mn |
| 10 | Oxford Industries | 374.46 Mn | 339.97 Mn | 291.13 Mn | 147.14 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Aug 1, 2026 | 147.14 Mn |
| May 2, 2026 | 147.49 Mn |
| Jan 31, 2026 | 165.28 Mn |
| Nov 1, 2025 | 155.40 Mn |
| Aug 2, 2025 | 166.67 Mn |
| May 3, 2025 | 162.33 Mn |
| Feb 1, 2025 | 167.29 Mn |
| Nov 2, 2024 | 154.26 Mn |
| Aug 3, 2024 | 139.58 Mn |
| May 4, 2024 | 144.37 Mn |
| Feb 3, 2024 | 159.57 Mn |
| Oct 28, 2023 | 157.52 Mn |
| Jul 29, 2023 | 161.87 Mn |
| Apr 29, 2023 | 179.61 Mn |
| Jan 28, 2023 | 220.14 Mn |
| Oct 29, 2022 | 171.64 Mn |
| Jul 30, 2022 | 135.48 Mn |
| Apr 30, 2022 | 122.76 Mn |
| Jan 29, 2022 | 117.71 Mn |
| Oct 30, 2021 | 90.98 Mn |
API Access
Oxford Industries Inventory 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=inventory&ticker=OXM&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "inventory", "ticker": "OXM", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=inventory&ticker=OXM&period=max&api_key=YOUR_API_KEY");
const data = await res.json();