Dataset opportunity
Mactrans — 工业运营数据集机会
Mactrans 持有的中等工业运营数据集，可用于工业监控和预测。
Score
48
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)
全球供应链分析市场 = 2023 年为 62.7 亿美元，复合年增长率为 17.20%。
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-07-28
UPS shift away from Amazon shows bigger payoff
freightwaves.com ↗ - 📰press2026-07-27
DHL Express to lower import, export fuel surcharge calculations
supplychaindive.com ↗
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
定期
Rarity
高（专有）
Accessibility
受限
Legal
混合所有权 — 许可权待明确 · PII/受监管
Buyer persona
工业人工智能集成商
Mactrans 持有一个丰富的时间序列数据集，该数据集源自其非资产型物流运营，涵盖地理数据、工业数据和交易数据。这种多方面的数据为开发和训练工业监控人工智能模型提供了全面的基础，用于实时跟踪货物状态、预测延误和优化运输路线。
商业价值巨大，目标是全球供应链分析市场，该市场在 2023 年的估值为 62.7 亿美元，预计将以 17.20% 的复合年增长率增长。[6] 虽然访问需要应对承运商产生的数据和客户使用限制等复杂性，但该数据集的独特稀缺性来自于专有的 MACsync 平台，该平台聚合了跨客户航线情报。这提供了独特的竞争优势，尽管访问复杂，但对于人工智能买家来说，该数据极具价值。⚠ 注意（有价值的数据，可协商访问）：非资产型模式意味着部分数据源自承运商；客户发货数据可能存在合同使用限制；专有的 MACsync 平台聚合了跨客户航线情报 · 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 Mactrans 拥有专有的、稀有度高的数据集，详细介绍了北美范围内的承运商绩效和历史运输运营。这些时间序列数据非常适合工业人工智能集成商构建工业监控和供应链优化模型。在全球供应链分析市场预计每年增长超过 17% 的情况下，该数据集提供了现实世界物流情报的独特来源，为下一代人工智能解决方案提供动力。
See dimension details ↓- 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
适合工业监控
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand90
人工智能买家需求极高，这得益于供应链分析市场 17.20% 的复合年增长率，因为公司积极寻求通过实时运营情报获得竞争优势。[6]
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，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
⚠ 审查 — Mactrans 是一家非资产型 3PL/4PL 货运代理，其核心业务是为客户提供运输管理、分析和 TMS 平台，这使其成为一个糟糕的匹配对象，因为它已经销售情报。问题：该公司的核心业务是提供物流情报和运输管理系统 (TMS)，这属于销售排除标准；Mactrans 是一家非资产型 3PL，意味着它不拥有自己的卡车，而是通过数千家承运商的网络安排运输。[3, 9]；他们的“MACsync”4PL 服务明确涉及分析客户的整个供应链、管理 RFP 并提供 TMS 以优化成本和服务，这
- Deep Qualification70
✓ 通过 — Mactrans 是一家非资产型 3PL 提供商，使其成为有价值的物流数据（地理、工业、交易时间序列）的“数据持有者”，这是其服务的副产品。然而，数据所有权是“混合的”（源自客户和承运商），并且由于其法律文件中缺乏具体的数据转售条款，转售许可权“不明确”，这对收购构成了重大障碍。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Transaction data
该公司生成汇总历史运输活动的表格交易数据，包括费率和服务航线，这对于训练成本优化和 RFP 管理模型至关重要。
Industrial data
这些时间序列证据指向一个专有数据集，该数据集跟踪 2,000 多家承运商的持续绩效，为构建预测性工业监控和承运商评估系统提供了关键信号。
Geospatial data
Mactrans 捕获其北美货运网络的表格地理空间数据，详细说明了跨境航线和专项服务，如准时制交付，这对于模拟复杂的供应链物流至关重要。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Mactrans Industrial Operations — a Moderate industrial operations dataset (Time Series modality) in the mobility domain. Primary AI use-case: Industrial Monitoring. Market signal: Global Supply Chain Analytics market = $6.27B in 2023, CAGR 17.20% (source: Zion Market Research). Investment score 48.0/100 (confidence 0.49). Recommended action: Acquire.
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