Dataset opportunity
Cretschmar — 工业运营数据集机会
Cretschmar 持有的中等工业运营数据集，可用于工业监控和预测。
Score
72.8
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)
全球工业物联网市场 = 2024 年为 4832 亿美元，复合年增长率为 23.3%（来源：Grand View Research）。[2]
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
移动出行
Volume
中等
Freshness
实时
Rarity
高（专有）
Accessibility
受限
Legal
公司所有 — 许可权待澄清
Buyer persona
工业人工智能集成商
Cretschmar 持有宝贵的工业运营数据集，具有时间序列模式，整合了来自车队、仓库传感器和物流管理系统的实时地理数据、物联网数据和工业数据。这种丰富的组合提供了物理运营的全面、高保真视图，使其特别适合训练用于工业监控用例的复杂人工智能模型，例如预测性资产维护和流程优化。
商业价值巨大，与全球工业物联网市场的增长相呼应，该市场在 2024 年的估值为4832 亿美元，预计将以23.3% 的复合年增长率增长。[2] 虽然访问需要应对客户数据匿名化和危险品（Gefahrgut）相关敏感性等复杂问题，但该数据的稀有性和运营深度为开发先进的人工智能解决方案提供了独特的竞争优势。⚠ 尽职调查（有价值的数据，可协商的访问权限）：运营数据与物理物流流程和仓库管理系统相关；客户相关的货运数据需要严格匿名化；危险品数据（Gefahrgut）可能存在特定的安全相关敏感性 · 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 Cretschmar 拥有稀有的专有工业运营数据集，包括高风险的时间序列数据，涉及危险品处理和广泛的仓库自动化。这正是工业人工智能集成商寻求构建和验证用于异常检测和预测性维护的模型所需的地面实况数据。在全球工业物联网市场预计到 2024 年将达到 4832 亿美元的情况下，该数据集通过提供直接了解现实世界供应链挑战的途径，提供了显著的竞争优势。
See dimension details ↓- Dataset Specificity90
主导的“工业数据”，行业移动出行，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 Demand95
人工智能买家需求极高，这得益于工业物联网市场的快速增长，该市场正以 23.3% 的复合年增长率扩张，并且需要现实世界的运营数据来进行模型训练。[2]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility28
受限/未知
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility30
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 个命中
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License70
所有权=拥有，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 个数据胃口信号（0 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 — 超出已货币化的专有数据
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 Audit100
✓ 良好目标 — 一家传统的、中等规模的德国物流公司，其核心业务是运输和仓储，作为副产品生成有价值的专有运营数据（车队、货物、仓储、危险品）。问题：该公司向其物流客户提供数字工具，如 API 和 KPI 报告；这需要确认是其服务的一项功能，而不是一项
- Deep Qualification90
⚠ 需要审查 — Cretschmar 是一家物流服务提供商，因此拥有其核心业务副产品产生的宝贵运营数据。这些数据很可能是公司和客户资产的混合体，由于客户保密性、GDPR 和危险品处理而受到重大限制。最近一项已确认与一家人工智能公司的合作强烈表明了利用这些数据的积极兴趣。[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>Twiice est l’un des 3 éco-organismes retenus pour la nouvelle filière REP emballages professionnels. Il y a quelques jours, son DG Marc-Antoine Franc a répondu factuellement à nos questions sur les deux raisons qui expliqueraient la décision de dernière minute prise par le gouvernement de reporter sa mise en œuvre, initialement prévue pour le 1er […]</p> <p>L'article <a href="https://supplychainmagazine.fr/les-deux-raisons-factuelles-du-report-de-la-rep-epro/">Les deux raisons factuelles du report de la REP EPRO</a> est apparu en premier sur <a href="https://supplychainmagazine.fr">Su”
- “<p>SummaryView Transcript Trimble is reportedly selling its transportation division, including major acquisitions like Transporeon and PeopleNet. Bart de Muynck breaks down the challenges of integrating disparate carrier and shipper tech worlds, poor market timing for acquisitions, and why even large enterprises struggle to unify complex platforms in a rapidly evolving logistics tech landscape. Discover how […]</p> <p>The post <a href="https://www.freightwaves.com/news/trimbles-big-move-unpacking-the-transportation-division-sale">Trimble’s Big Move: Unpacking the Transportation Div”
- “<p>SummaryView Transcript Tropical Storm Bertha is crawling along the Gulf Coast, raising concerns about significant coastal flooding and heavy rainfall. While this hurricane season is predicted to be quiet due to El Niño, Weather Optics’ Joshua Feldman explains why slow-moving storms like Bertha can still pose a serious threat to logistics and supply chains, especially […]</p> <p>The post <a href="https://www.freightwaves.com/news/weather-optics-unpacking-tropical-storm-berthas-impact-on-freight">Weather Optics: Unpacking Tropical Storm Bertha’s Impact on Freight</a> appeare”
Industrial data
这些证据表明，来自专业处理和储存危险品的独特时间序列数据，是开发用于安全合规和风险管理的先进人工智能模型的抢手资产。
IoT / sensor data
这指向专有的物联网数据，捕获了大型、IT 支持的仓库内的移动和存储模式，为构建流程优化和自动化解决方案的人工智能集成商提供了直接价值。
Geospatial data
这证实了存在详细的物流数据，跟踪了横跨泛欧网络的货运，这对于训练和验证供应链优化和路线规划模型至关重要。
Marketplace
Dataset details
Detailed schema & sample available on access request.
Want this data?
Request access — we broker a secure deal room. Operator-reviewed, no automatic sharing.
This listing was generated automatically from public signals. It is not verified, and we are not affiliated with this company.
Coverage
Scanned sources
Deliverable
Premium dataset report
Cretschmar Industrial Operations — a Moderate industrial operations dataset (Time Series modality) in the mobility domain. Primary AI use-case: Industrial Monitoring. Market signal: Global Industrial Internet of Things market = $483.2B in 2024, CAGR 23.3% (source: Grand View Research). [2]. Investment score 72.8/100 (confidence 0.49). Recommended action: Acquire.
From the marketplace
Explore live data opportunities
Thevenet — 工业运营数据集机会
View opportunity →其他Voltalis — 传感器遥测数据集商机
View opportunity →工业Valorem Energie — 维护日志数据集机会
View opportunity →