Dataset opportunity
Broekmanlogistics — 工业运营数据集机会
由 Broekmanlogistics 持有的中等规模工业运营数据集，可用于工业监控和预测。
Score
64.9
Score (0–100) blends weighted dimensions — dataset rarity, training value, buyer demand, evidence strength and right-to-license. 70+ is deal-ready. See the scored dimensions below for the breakdown.Confidence
49%
Action
收购
The recommended deal structure for this dataset: Acquire (full buyout), License (paid usage rights), Data Sharing Agreement (controlled access, no transfer of ownership), Partnership (co-development) or Annotation Program (labeling). Chosen from data ownership, licensing complexity and accessibility.Market size (indicative estimate)
全球物流和供应链人工智能市场 = 2024 年为 201 亿美元，复合年增长率为 25.9%（2025-2034 年）（来源：AI in Logistics and Supply Chain Market Size & Share 2025 - 2034）
Lineage
How this lead was derived
The signal-first chain, end to end: recent external signals → qualified niche → resolved data-holder → site verification → scored opportunity. Every lead is explainable.
Profile
Dataset profile
Type
工业运营数据集
Modality
时间序列
Sector
mobility
Volume
中等
Freshness
定期
Rarity
高（专有）
Accessibility
受限
Legal
混合所有权 — 需明确许可权 · PII/受监管
Buyer persona
工业人工智能集成商
Broekman Logistics 持有一个全面的时间序列工业运营数据集，结合了精细的地理数据、专有的工业数据和高容量的交易数据。这些数据模态的独特融合提供了港口和物流运营的全景图，使其特别适合高级工业监控用例，例如资产跟踪、性能优化以及物流车队和码头设备的预测性维护。
该数据集的价值体现在快速增长的物流和供应链人工智能市场中，该市场在 2024 年的估值为201 亿美元，预计复合年增长率为25.9%。[2] 虽然由于其涵盖国际海关、化学品安全法规和第三方客户数据，访问权限需经协商，但这种复杂性也确保了数据的稀有性。包含来自专有港口码头的运营数据使其成为寻求决定性竞争优势的 AI 买家的一项独有高价值资产，这证明了访问的审慎性。⚠ 审慎性（有价值的数据，可协商的访问权限）：数据涉及国际海关和化学品安全法规；很大一部分数据与第三方货主（客户相关）相关；来自专有港口码头的运营数据是一项高价值的独有资产。· 公司：独立。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
这些证据共同证明 Broekman Logistics 拥有专有的、多模态的数据集，捕获了复杂工业物流的完整生命周期，从特种化学品处理到最终交付。它将时间序列运营数据与来自其自有码头的详细记录以及端到端的货运模式相结合。对于工业人工智能集成商而言，这是一个稀有、高价值的资产，可用于构建下一代工业监控和预测优化模型。在全球物流人工智能市场预计将在 2024 年超过 200 亿美元的情况下，这些真实数据为训练算法提供了独特的机会，使其能够了解高风险供应链的实际情况。
See dimension details ↓- Deep Qualification90
⚠ 需要审查 — Broekman Logistics 是一家物流服务提供商，而非数据经纪商。它作为其核心活动的副产品持有有价值的运营数据，但这些数据与客户资产交织在一起，并受复杂的国际法规约束，因此访问权限需要仔细协商。[许可受限]
- Dataset Specificity90
主导的“工业数据”，行业 mobility，3 种特定类型
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity82
专有领域数据
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume52
3 个证据命中
Apparent scale of the data, inferred from the number of evidence hits and any explicit volume mentions. - Dataset Freshness46
定期
How current the data stays — real-time/streaming scores highest, periodic dumps lower. - Training Value84
适用于工业监控
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand90
买家需求极高，这得益于 201 亿美元的物流人工智能市场的快速扩张，该市场正以 25.9% 的复合年增长率增长，因为公司竞相采用人工智能来提高运营效率。[2]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility0
PII/受监管
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility0
中等难度，独立
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength62
3 种证据类型，3 次命中
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License36
所有权=混合，许可=权利不明确
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence90
独立
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation22
0 数据需求信号（0 类型）
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
盈余=高，2 个近期外部信号 — 专有数据超出已货币化的部分
Volume and value of proprietary data this company holds BEYOND what it already monetises — the dormant surplus we can unlock. A company can sell some insights AND still sit on a far larger dormant asset. - ICP Audit75
✓ 良好目标 — 一家大型家族式物流公司，拥有广泛的运营资产，可能在其核心业务的副产品中产生大量未被充分利用的专有数据。问题：该公司约有 1,000 名员工，营业额约为 2.5-2.9 亿欧元，这将其归类为大型企业，而非中小企业。[1, 12]；虽然他们提到了“智能数字化”并使用运输管理平台，但没有证据表明他们目前正在销售数据或高级分析作为
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
press
- “<p>Manufacturers and retailers that moved early on their digital transformations and automation strategies are starting to reap the benefits, experts said at Supply Chain Dive’s outlook event.</p>”
- “<figure><div><img src="https://imgproxy.divecdn.com/pJ96NFhyD-7ZO-Ue-rOi9qTrzaYpB2XAjRWRHeCr9UY/g:ce/rs:fill:1600:900:1/Z3M6Ly9kaXZlc2l0ZS1zdG9yYWdlL2RpdmVpbWFnZS9sb2dpc3RpY3Nfc2Vzc2lvbl8yYi5qcGc=.webp" /></div></figure><p>“It is increasingly difficult to really predict where that next disruption is going to come from,” Ranole Beng, Maersk's head of Transpacific market for ocean, said in a virtual panel.</p>”
Industrial data
这些证据表明存在关于特种化学品处理和储存的时间序列数据，这是预测高风险物流中安全性和效率的人工智能模型的关键输入。
Geospatial data
这表明来自公司自有集装箱码头的专有表格数据，为训练优化港口吞吐量和缓解拥堵的人工智能提供了真实来源。
Transaction data
这些证据证实了端到端、多模态物流记录的存在，非常适合开发预测运输时间并自动化全球供应链复杂海关文件的人工智能。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Broekmanlogistics Industrial Operations — a Moderate industrial operations dataset (Time Series modality) in the mobility domain. Primary AI use-case: Industrial Monitoring. Market signal: Global AI in logistics and supply chain market = $20.1 billion in 2024, CAGR 25.9% (2025-2034) (source: AI in Logistics and Supply Chain Market Size & Share 2025 - 2034). Investment score 64.9/100 (confidence 0.49). Recommended action: Acquire.
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