Dataset opportunity
Hollingsworthllc — 工业运营数据集机会
Hollingsworthllc 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
68.9
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)
2025 年全球工业资产监控市场价值 187 亿美元，复合年增长率为 10.8%（来源：Dataintelo）。[12]
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.
Concrete evidence this company actively cares about data — why it's ripe for the deal room.
Profile
Dataset profile
Type
工业运营数据集
Modality
时间序列
Sector
mobility
Volume
中等
Freshness
实时
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
Hollingsworthllc 持有一个重要的工业运营数据集，该数据集由其移动出行部门运营产生的高粒度时间序列数据组成。该数据集包含丰富的 `event_streams`、原始 `industrial_data` 和交易记录，为适用于工业监控的先进人工智能应用提供了全面的制造流程视图。
该数据的全球市场规模可观，工业资产监控领域在 2025 年的价值为187 亿美元，预计将以10.8% 的复合年增长率增长。[12] 这个高增长市场凸显了对这类有价值数据的需求。虽然访问需要应对与 OEM 客户共享数据所有权、SAP 系统中的数据孤岛以及合同限制等复杂性，但该数据集独特的运营深度为人工智能买家提供了难得的竞争优势。⚠ 尽职调查（有价值的数据，可协商访问）：数据所有权可能与主要 OEM 客户（例如 Ford）共享；运营数据孤立在 SAP 系统中；航空航天/政府部门对第三方数据共享存在合同限制。· corporate: independent。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
这些证据共同证明 Hollingsworthllc 持有专有的、端到端的运营数据，涵盖整个工业生命周期，从制造和装配到履行和逆向物流。这个独特的数据集是人工智能集成商开发复杂的工业监控和预测性维护解决方案的关键资产。在一个预计将达到 187 亿美元的市场中，这种高稀有度的数据为构建优化供应链、预测故障和提高运营效率的模型提供了显著的竞争优势。
See dimension details ↓- Dataset Specificity90
主导的 'industrial_data'，移动出行行业，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 Freshness82
实时/流式传输
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 Demand92
人工智能买家需求极高，这得益于工业资产监控市场的强劲增长，该市场正以 10.8% 的复合年增长率扩张。[12]
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 Orientation56
2 个数据需求信号（2 种类型）
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
盈余=高，5 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 — Hollingsworth 是一家大型物流和供应链管理公司，其核心业务是运营服务，使其广泛的运营数据成为有价值的、未被充分利用的副产品。问题：该公司比典型中小企业规模更大，多个来源引用其员工超过 1,000 人，收入远超 1 亿美元。[1, 2, 10]；关于员工数量的报告存在冲突，从 700 到 3,000 不等，表明这是一个庞大而复杂的组织。[1, 2, 10]
- Deep Qualification80
⚠ 需要审查 — 目标是一家 3PL/物流服务提供商，而不是数据销售商；产生的运营数据是合理的副产品，但归其 OEM 客户所有，这使得转售许可受到高度限制且不太可能。[数据归公司客户所有；许可受限]
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
press
- “<p>SummaryView Transcript The question on everyone’s mind: Is AI a bubble, or is it truly transforming the supply chain? Eric Rempel, Chief Innovation Officer at Redwood, unpacks the hype versus the real value of AI in logistics. He shares insights on why companies are adopting AI, how it’s impacting jobs (and creating new ones!), and […]</p> <p>The post <a href="https://www.freightwaves.com/news/is-ai-the-next-digital-brokerage-hype-cycle-logistics-supply-chain">Is AI the Next Digital Brokerage Hype Cycle? | Logistics & Supply Chain</a> appeared first on <a href="https:”
- “<p>SummaryView Transcript Project44 is splitting into two distinct companies: the original Project44 focusing on shippers, and the newly formed LSP44 targeting logistics service providers. CEO Jett McCandless explains the strategic rationale behind this bold move, revealing that a ‘desire to win’ against the complexities of different client needs was the driving force. Discover why this […]</p> <p>The post <a href="https://www.freightwaves.com/news/lsp44-vs-project44-why-project44-split-into-two-companies">LSP44 vs. project44: Why Project44 Split Into Two Companies</a> appear”
- “<p>SummaryView Transcript Building a robust Transportation Management System (TMS) isn’t a side project. Alyssa Norcross, Group Product Manager at Revenova, breaks down why relying on “vibecoding” for complex logistics operations is a flawed approach. She emphasizes the critical need for enterprise-grade solutions built on solid infrastructure like Salesforce, highlighting the importance of depth, integrations, security, […]</p> <p>The post <a href="https://www.freightwaves.com/news/why-vibecoding-a-tms-is-a-recipe-for-disaster-in-logistics">Why ‘Vibecoding’”
Industrial data
这些证据表明来自核心制造和装配运营的时间序列数据，对于训练人工智能模型以监控生产线和优化准时交付至关重要。
Transaction data
这表明存在详细说明订单履行和库存管理的表格数据，这对于构建预测需求和简化供应链物流的模型非常有价值。
Event streams
这个信号证实了来自逆向物流运营的事件流数据的存在，这是开发能够管理退货、测试产品和识别系统性质量问题的 AI 的稀有且关键的输入。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Hollingsworthllc Industrial Operations — a Moderate industrial operations dataset (Time Series modality) in the mobility domain. Primary AI use-case: Industrial Monitoring. Market signal: Global Industrial Asset Monitoring market valued at $18.7 billion in 2025, CAGR 10.8% (source: Dataintelo). [12]. Investment score 68.9/100 (confidence 0.49). Recommended action: Acquire.
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